Get real in individual participant data (IPD) meta‐analysis: a review of the methodology

Debray, Thomas P. A.; Moons, Karel G. M.; van Valkenhoef, Gert; Efthimiou, Orestis; Hummel, Noemi; Groenwold, Rolf H. H.; Reitsma, Johannes B.; on behalf of the GetReal methods review group

doi:10.1002/jrsm.1160

Cited by 348Meta-analysis and systematic reviewsStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

Get real in individual participant data (IPD) meta‐analysis: a review of the methodology

Open access

Debray, Thomas P. A., Moons, Karel G. M., van Valkenhoef, Gert, Efthimiou, Orestis, Hummel, Noemi, Groenwold, Rolf H. H., Reitsma, Johannes B., on behalf of the GetReal methods review group·2015·Research Synthesis Methods

DOI 10.1002/jrsm.1160PMC PMC5042043

Open this paper in CoChat — free account Open PDF

Abstract

Individual participant data (IPD) meta‐analysis is an increasingly used approach for synthesizing and investigating treatment effect estimates. Over the past few years, numerous methods for conducting an IPD meta‐analysis (IPD‐MA) have been proposed, often making different assumptions and modeling choices while addressing a similar research question. We conducted a literature review to provide an overview of methods for performing an IPD‐MA using evidence from clinical trials or non‐randomized studies when investigating treatment efficacy. With this review, we aim to assist researchers in choosing the appropriate methods and provide recommendations on their implementation when planning and conducting an IPD‐MA. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1002/jrsm.1160

Cites

119 of 119 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

More advanced (so-called one-stage) approaches exist to directly combine individual participant data with published summary data.
“more advanced (so-called one-stage) approaches exist to directly combine the IPD with published summary data.67”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [67]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701 · see citing works
Multiple imputation: a primer
Schafer, J. L. (1999) · Statistical Methods in Medical Research · doi:10.1177/096228029900800102 · Cited by 3,949 · see citing works
Randomized, Controlled Trials, Observational Studies, and the Hierarchy of Research Designs
Concato, J., Shah, N., & Horwitz, R. I. (2000) · New England Journal of Medicine · doi:10.1056/nejm200006223422507 · Cited by 3,563 · see citing works
Review: a gentle introduction to imputation of missing values.
Donders, A. R. T., van der Heijden, G. J. M. G., Stijnen, T., & Moons, K. G. M. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.01.014 · Cited by 2,582 · see citing works
Self-citation
Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves
Guyot, P., Ades, A. E., Ouwens, M., & Welton, N. J. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-9 · Cited by 2,560 · see citing works
Meta-analysis of individual participant data: rationale, conduct, and reporting
Riley, R. D., Lambert, P. C., & Abo-Zaid, G. (2010) · BMJ · doi:10.1136/bmj.c221 · Cited by 1,701 · see citing works
Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects
Royston, P. J., & Parmar, M. (2002) · Statistics in Medicine · doi:10.1002/sim.1203 · Cited by 1,537 · see citing works
Comparison of Evidence of Treatment Effects in Randomized and Nonrandomized Studies
Ioannidis, J. P. A. (2001) · JAMA · doi:10.1001/jama.286.7.821 · Cited by 919 · see citing works
Zero‐Inflated Poisson and Binomial Regression with Random Effects: A Case Study
Hall, D. B. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.01030.x · Cited by 918 · see citing works
Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data
Stijnen, T., Hamza, T. H., & Özdemir, P. (2010) · Statistics in Medicine · doi:10.1002/sim.4040 · Cited by 757 · see citing works
Summing up evidence: one answer is not always enough
Lau, J., Ioannidis, J. P. A., & Schmid, C. H. (1998) · The Lancet · doi:10.1016/s0140-6736(97)08468-7 · Cited by 646 · see citing works
Extending the simple linear regression model to account for correlated responses: An introduction to generalized estimating equations and multi-level mixed modelling
Burton, P. R., Gurrin, L. C., & Sly, P. D. (1998) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19980615)17:11 · Cited by 629 · see citing works
The Ethics of Childhood Obesity Treatment - from the Childhood Obesity Task Force (COTF) of European Association for the Study of Obesity (EASO)
Holm, J., Nowicka, P., Farpour‐Lambert, N. J., O’Malley, G., Hassapidou, M. N., Weiss, R., & Baker, J. L. (2014) · Obesity Facts · doi:10.1159/000365773 · Cited by 596 · see citing works
Recent developments in meta‐analysis
Sutton, A. J., & Higgins, J. P. T. (2007) · Statistics in Medicine · doi:10.1002/sim.2934 · Cited by 583 · see citing works
To IPD or not to IPD?
Stewart, L. A., & Tierney, J. F. (2002) · Evaluation & the Health Professions · doi:10.1177/0163278702025001006 · Cited by 581 · see citing works
Meta-analysis of individual patient data from randomized trials: a review of methods used in practice
Simmonds, M. C., Higgins, J. P. T., Stewart, L. A., Tierney, J. F., Clarke, M. J., & Thompson, S. G. (2005) · Clinical Trials · doi:10.1191/1740774505cn087oa · Cited by 557 · see citing works
BORROWING STRENGTH FROM EXTERNAL TRIALS IN A META-ANALYSIS
Higgins, J. P. T., & WHITEHEAD, A. (1996) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19961230)15:24 · Cited by 553 · see citing works
The Use of Propensity Scores to Assess the Generalizability of Results from Randomized Trials
Stuart, E. A., Cole, S. R., Bradshaw, C. P., & Leaf, P. J. (2011) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2010.00673.x · Cited by 524 · see citing works
Use of historical control data for assessing treatment effects in clinical trials
Viele, K., Berry, S. M., Neuenschwander, B. E., Amzal, B., Chen, F., Enas, N. H., Hobbs, B. P., Ibrahim, J. G., Kinnersley, N., Lindborg, S. R., et al. (2013) · Pharmaceutical Statistics · doi:10.1002/pst.1589 · Cited by 519 · see citing works
Matching-Adjusted Indirect Comparisons: A New Tool for Timely Comparative Effectiveness Research
Signorovitch, J., Sikirica, V., Erder, M. H., Xie, J., Lü, M., Hodgkins, P., Betts, K. A., & Wu, E. Q. (2012) · Value in Health · doi:10.1016/j.jval.2012.05.004 · Cited by 509 · see citing works
Individual patient‐ versus group‐level data meta‐regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head
Berlin, J. A., Santanna, J., Schmid, C. H., Szczech, L. A. M., & Feldman, H. I. (2002) · Statistics in Medicine · doi:10.1002/sim.1023 · Cited by 468 · see citing works
Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey
Ahmed, I., Sutton, A. J., & Riley, R. D. (2012) · BMJ · doi:10.1136/bmj.d7762 · Cited by 404 · see citing works
GetReal in network meta-analysis: a review of the methodology
Efthimiou, O., Debray, T. P. A., van Valkenhoef, G., Trelle, S., Panayidou, K., Moons, K. G. M., Reitsma, J. B., Shang, A., Salanti, G., & on behalf of GetReal Methods Review Group (2016) · Research Synthesis Methods · doi:10.1002/jrsm.1195 · Cited by 401 · see citing works
Self-citation
A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis
Lambert, P. C., Sutton, A. J., Abrams, K. R., & Jones, D. R. (2002) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00414-0 · Cited by 396 · see citing works
Multilevel modelling of medical data
Goldstein, H., Browne, W. J., & Rasbash, J. (2002) · Statistics in Medicine · doi:10.1002/sim.1264 · Cited by 388 · see citing works
Comparative Effectiveness Without Head-to-Head Trials
Signorovitch, J., Wu, E. Q., Yu, A. P., Gerrits, C. M., Kantor, E., Bao, Y., Gupta, S. R., & Mulani, P. M. (2010) · PharmacoEconomics · doi:10.2165/11538370-000000000-00000 · Cited by 378 · see citing works
Indirect Treatment Comparison/Network Meta-Analysis Study Questionnaire to Assess Relevance and Credibility to Inform Health Care Decision Making: An ISPOR-AMCP-NPC Good Practice Task Force Report
Jansen, J. P., Trikalinos, T., Cappelleri, J. C., Daw, J., Andes, S., Eldessouki, R., & Salanti, G. (2014) · Value in Health · doi:10.1016/j.jval.2014.01.004 · Cited by 319 · see citing works
A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials
Royston, P. J., & Sauerbrei, W. (2004) · Statistics in Medicine · doi:10.1002/sim.1815 · Cited by 281 · see citing works
Meta‐analysis of continuous outcomes combining individual patient data and aggregate data
Riley, R. D., Lambert, P. C., Staessen, J. A., Wang, J., Gueyffier, F., Thijs, L., & Boutitie, F. (2007) · Statistics in Medicine · doi:10.1002/sim.3165 · Cited by 280 · see citing works
Meta-analyses of Adverse Effects Data Derived from Randomised Controlled Trials as Compared to Observational Studies: Methodological Overview
Golder, S., Loke, Y. K., & Bland, M. (2011) · PLoS Medicine · doi:10.1371/journal.pmed.1001026 · Cited by 270 · see citing works
Systematic reviews of trials and other studies.
Sutton, A. J., Abrams, K. R., Jones, D. R., Sheldon, T., & Сонг (1998) · Health Technology Assessment · doi:10.3310/hta2190 · Cited by 268 · see citing works
Meta‐analysis of continuous outcome data from individual patients
Higgins, J. P. T., Whitehead, A., Turner, R., Omar, R., & Thompson, S. G. (2001) · Statistics in Medicine · doi:10.1002/sim.918 · Cited by 265 · see citing works
The strengths and limitations of meta-analyses based on aggregate data
Lyman, G. H., & Kuderer, N. M. (2005) · BMC Medical Research Methodology · doi:10.1186/1471-2288-5-14 · Cited by 250 · see citing works
Meta-regression detected associations between heterogeneous treatment effects and study-level, but not patient-level, factors
Schmid, C. H., Stark, P. C., Berlin, J. A., Landais, P., & Lau, J. (2004) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2003.12.001 · Cited by 245 · see citing works
Statistical Analysis of Individual Participant Data Meta-Analyses: A Comparison of Methods and Recommendations for Practice
Stewart, G., Altman, D., Askie, L., Duley, L., Simmonds, M., & Stewart, L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046042 · Cited by 228 · see citing works
Investigating patient exclusion bias in meta-analysis
Tierney, J. F. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh300 · Cited by 221 · see citing works
Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros
Lee, A. H., Wang, K., Scott, J. A., Yau, K. K. W., & McLachlan, G. J. (2006) · Statistical Methods in Medical Research · doi:10.1191/0962280206sm429oa · Cited by 210 · see citing works
Individual Participant Data Meta-Analysis for a Binary Outcome: One-Stage or Two-Stage?
Debray, T. P. A., Moons, K. G. M., Abo-Zaid, G., Koffijberg, H., & Riley, R. D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060650 · Cited by 201 · see citing works
Self-citation
Evidence synthesis combining individual patient data and aggregate data: a systematic review identified current practice and possible methods
Riley, R. D., Simmonds, M. C., & Look, M. P. (2007) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.09.009 · Cited by 195 · see citing works
Covariate heterogeneity in meta‐analysis: Criteria for deciding between meta‐regression and individual patient data
Simmonds, M. C., & Higgins, J. P. T. (2006) · Statistics in Medicine · doi:10.1002/sim.2768 · Cited by 194 · see citing works
Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE
Jolani, S., Debray, T. P. A., Koffijberg, H., van Buuren, S., & Moons, K. G. M. (2015) · Statistics in Medicine · doi:10.1002/sim.6451 · Cited by 192 · see citing works
Self-citation
Issues relating to study design and risk of bias when including non‐randomized studies in systematic reviews on the effects of interventions
Higgins, J. P., Ramsay, C., Reeves, B. C., Deeks, J. J., Shea, B., Valentine, J. C., Tugwell, P., & Wells, G. (2013) · Research Synthesis Methods · doi:10.1002/jrsm.1056 · Cited by 183 · see citing works
A critical review of methods for the assessment of patient-level interactions in individual participant data meta-analysis of randomized trials, and guidance for practitioners
Fisher, D. J., Copas, A. J., Tierney, J. F., & Parmar, M. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.11.016 · Cited by 178 · see citing works
Zero‐inflated Poisson regression with random effects to evaluate an occupational injury prevention programme
Yau, K. K. W., & Lee, A. H. (2001) · Statistics in Medicine · doi:10.1002/sim.860 · Cited by 175 · see citing works
Investigating heterogeneity in an individual patient data meta-analysis of time to event outcomes
Smith, C. T., Williamson, P. R., & Marson, A. G. (2005) · Statistics in Medicine · doi:10.1002/sim.2050 · Cited by 173 · see citing works
Improving ecological inference using individual‐level data
Jackson, C. H., Best, N., & Richardson, S. T. (2005) · Statistics in Medicine · doi:10.1002/sim.2370 · Cited by 166 · see citing works
Meta‐analysis of individual‐ and aggregate‐level data
Sutton, A. J., Kendrick, D., & Coupland, C. A. C. (2007) · Statistics in Medicine · doi:10.1002/sim.2916 · Cited by 162 · see citing works
Comparison of Effect Estimates from a Meta-Analysis of Summary Data from Published Studies and from a Meta-Analysis Using Individual Patient Data for Ovarian Cancer Studies
STEINBERG, K. K., Smith, S. J., Stroup, D. F., Olkin, I., Lee, N. C., Williamson, G. D., & Thacker, S. B. (1997) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a009051 · Cited by 151 · see citing works
Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes
Li, B., Lingsma, H. F., Steyerberg, E. W., & Lesaffre, E. M. E. H. (2011) · BMC Medical Research Methodology · doi:10.1186/1471-2288-11-77 · Cited by 142 · see citing works
Checklists of methodological issues for review authors to consider when including non‐randomized studies in systematic reviews
Wells, G. A., Shea, B., Higgins, J. P., Sterne, J., Tugwell, P., & Reeves, B. C. (2013) · Research Synthesis Methods · doi:10.1002/jrsm.1077 · Cited by 141 · see citing works
A Statistical Framework for Ecological and Aggregate Studies
Wakefield, J. C., & Salway, R. (2001) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/1467-985x.00191 · Cited by 140 · see citing works
Meta-Analysis Using Multilevel Models with an Application to the Study of Class Size Effects
Goldstein, H., Yang, M., Omar, R., Turner, R., & Thompson, S. G. (2000) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/1467-9876.00200 · Cited by 137 · see citing works
Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials
Jones, A. P., Riley, R. D., Williamson, P. R., & Whitehead, A. (2009) · Clinical Trials · doi:10.1177/1740774508100984 · Cited by 137 · see citing works
Issues in methodological research: perspectives from researchers and commissioners
Lilford, R. J., Richardson, A., Stevens, A., Fitzpatrick, R., Edwards, S., Rock, F., & Hutton, J. L. (2001) · Health Technology Assessment · doi:10.3310/hta5080 · Cited by 132 · cited 1× · see citing works
“This effect is known as theoretical saturation (Lilford et al., 2001).”
Background
Issues relating to confounding and meta‐analysis when including non‐randomized studies in systematic reviews on the effects of interventions
Valentine, J. C., & Thompson, S. G. (2013) · Research Synthesis Methods · doi:10.1002/jrsm.1064 · Cited by 130 · see citing works
Individual participant data meta-analyses should not ignore clustering
Abo-Zaid, G., Guo, B., Deeks, J. J., Debray, T. P. A., Steyerberg, E. W., Moons, K. G. M., & Riley, R. D. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2012.12.017 · Cited by 130 · see citing works
Self-citation
A Questionnaire to Assess the Relevance and Credibility of Observational Studies to Inform Health Care Decision Making: An ISPOR-AMCP-NPC Good Practice Task Force Report
Berger, M. L., Martin, B. C., Husereau, D., Worley, K., Allen, J. D., Yang, W., Quon, N. C., Mullins, C. D., Kahler, K. H., & Crown, W. H. (2014) · Value in Health · doi:10.1016/j.jval.2013.12.011 · Cited by 130 · see citing works
A General Framework for Random Effects Survival Analysis in the Cox Proportional Hazards Setting
Sargent, D. J. (1998) · Biometrics · doi:10.2307/2533673 · Cited by 129 · see citing works
Systematic Reviews: Obtaining data from randomised controlled trials: how much do we need for reliable and informative meta-analyses?
Clarke, M. J., & Stewart, L. A. (1994) · BMJ · doi:10.1136/bmj.309.6960.1007 · Cited by 117 · see citing works
Mixed treatment comparisons using aggregate and individual participant level data
Saramago, P., Sutton, A. J., Cooper, N. J., & Manca, A. (2012) · Statistics in Medicine · doi:10.1002/sim.5442 · Cited by 116 · see citing works
An introduction to methodological issues when including non‐randomised studies in systematic reviews on the effects of interventions
Reeves, B. C., Higgins, J. P. T., Ramsay, C. R., Shea, B., Tugwell, P., & Wells, G. A. (2013) · Research Synthesis Methods · doi:10.1002/jrsm.1068 · Cited by 111 · see citing works
Systematic review of methods for individual patient data meta- analysis with binary outcomes
Thomas, D., Radji, S., & Benedetti, A. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-79 · Cited by 110 · see citing works
Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies
Thompson, S. G., Kaptoge, S., White, I. R., Wood, A., Perry, P., & Danesh, J. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyq063 · Cited by 110 · see citing works
Meta‐analysis of a binary outcome using individual participant data and aggregate data
Riley, R. D., & Steyerberg, E. W. (2010) · Research Synthesis Methods · doi:10.1002/jrsm.4 · Cited by 108 · see citing works
Multiple imputation for handling systematically missing confounders in meta‐analysis of individual participant data
Resche‐Rigon, M., White, I. R., Bartlett, J. W., Peters, S. A. E., Thompson, S. G., & on behalf of the PROG‐IMT Study Group (2013) · Statistics in Medicine · doi:10.1002/sim.5894 · Cited by 106 · see citing works
Self-citation
Combining multiple imputation and meta‐analysis with individual participant data
Burgess, S., White, I. R., Resche‐Rigon, M., & Wood, A. (2013) · Statistics in Medicine · doi:10.1002/sim.5844 · Cited by 105 · see citing works
Individual patient data meta-analysis of survival data using Poisson regression models
Crowther, M. J., Riley, R. D., Staessen, J. A., Wang, J., Gueyffier, F., & Lambert, P. C. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-34 · Cited by 102 · see citing works
Hierarchical Related Regression for Combining Aggregate and Individual Data in Studies of Socio-Economic Disease Risk Factors
Jackson, C. H., Best, A. N., & Richardson, S. T. (2007) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2007.00500.x · Cited by 101 · see citing works
Meta‐analysis of randomised trials with a continuous outcome according to baseline imbalance and availability of individual participant data
Riley, R. D., Kauser, I., Bland, M., Thijs, L., Staessen, J. A., Wang, J., Gueyffier, F., & Deeks, J. J. (2013) · Statistics in Medicine · doi:10.1002/sim.5726 · Cited by 100 · see citing works
Multilevel models for meta-analysis, and their application to absolute risk differences
Thompson, S. G., Turner, R., & Warn, D. E. (2001) · Statistical Methods in Medical Research · doi:10.1177/096228020101000602 · Cited by 99 · see citing works
Generalized linear models with clustered data: Fixed and random effects models
Broström, G., & Holmberg, H. (2011) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2011.06.011 · Cited by 96 · see citing works
Combining individual patient data and aggregate data in mixed treatment comparison meta‐analysis: Individual patient data may be beneficial if only for a subset of trials
Donegan, S., Williamson, P. R., D’Alessandro, U., Garner, P., & Smith, C. T. (2012) · Statistics in Medicine · doi:10.1002/sim.5584 · Cited by 95 · see citing works
Network meta‐analysis of individual and aggregate level data
Jansen, J. P. (2012) · Research Synthesis Methods · doi:10.1002/jrsm.1048 · Cited by 94 · see citing works
Evidence synthesis as the key to more coherent and efficient research
Sutton, A. J., Cooper, N. J., & Jones, D. R. (2009) · BMC Medical Research Methodology · doi:10.1186/1471-2288-9-29 · Cited by 94 · see citing works
Meta‐analysis of ordinal outcomes using individual patient data
Whitehead, A., Omar, R., Higgins, J. P. T., Savaluny, E., Turner, R., & Thompson, S. G. (2001) · Statistics in Medicine · doi:10.1002/sim.919 · Cited by 93 · see citing works
Systematic reviews of trials and other studies.
Sutton, A. J., Abrams, K. R., Jones, D. R. H., Sheldon, T., & Song, F. (1998) · PubMed · Cited by 79 · see citing works
The Use of Two‐Way Linear Mixed Models in Multitreatment Meta‐Analysis
Piepho, H., Williams, E. R., & Madden, L. V. (2012) · Biometrics · doi:10.1111/j.1541-0420.2012.01786.x · Cited by 76 · see citing works
Cross-design Synthesis:A New Form of Meta-analysis for Combining Results from Randomized Clinical Trials and Medical-practice Databases
Droitcour, J. A., Silberman, G., & Chelimsky, E. (1993) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462300004694 · Cited by 76 · see citing works
Random covariances and mixed-effects models for imputing multivariate multilevel continuous data
Yucel, R. M. (2011) · Statistical Modelling · doi:10.1177/1471082x1001100404 · Cited by 72 · see citing works
Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response
Yucel, R. M. (2008) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2008.0038 · Cited by 70 · see citing works
Random Effects Models for Combining Results from Controlled and Uncontrolled Studies in a Meta-Analysis
Li, Z., & Begg, C. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476892 · Cited by 65 · see citing works
Individual patient data meta‐analysis of time‐to‐event outcomes: one‐stage versus two‐stage approaches for estimating the hazard ratio under a random effects model
Bowden, J., Tierney, J. F., Simmonds, M. C., Copas, A. J., & Higgins, J. P. T. (2011) · Research Synthesis Methods · doi:10.1002/jrsm.45 · Cited by 61 · see citing works
Thinking big: large-scale collaborative research in observational epidemiology
Thompson, A. (2009) · European Journal of Epidemiology · doi:10.1007/s10654-009-9412-1 · Cited by 58 · see citing works
Comparative calibration without a gold standard
Lü, Y., Keying Ye, B., Mathur, A. K., Hui, S. L., Fuerst, T. P., & Genant, H. K. (1997) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19970830)16:16 · Cited by 56 · see citing works
Comparison of One‐Step and Two‐Step Meta‐Analysis Models Using Individual Patient Data
Mathew, T., & Nordström, K. (2010) · Biometrical Journal · doi:10.1002/bimj.200900143 · Cited by 55 · see citing works
A comparison of methods for fixed effects meta-analysis of individual patient data with time to event outcomes
Smith, C. T., & Williamson, P. R. (2007) · Clinical Trials · doi:10.1177/1740774507085276 · Cited by 53 · see citing works
An overview of methods and empirical comparison of aggregate data and individual patient data results for investigating heterogeneity in meta‐analysis of time‐to‐event outcomes
Smith, C. T., Williamson, P. R., & Marson, A. G. (2005) · Journal of Evaluation in Clinical Practice · doi:10.1111/j.1365-2753.2005.00559.x · Cited by 51 · see citing works
Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta‐analysis: individual patient‐level covariates versus aggregate trial‐level covariates
Donegan, S., Williamson, P. R., D’Alessandro, U., & Smith, C. T. (2012) · Statistics in Medicine · doi:10.1002/sim.5470 · Cited by 50 · see citing works
Allowing for uncertainty due to missing data in meta‐analysis—Part 2: Hierarchical models
White, I. R., Welton, N. J., Wood, A., Ades, A. E., & Higgins, J. P. T. (2007) · Statistics in Medicine · doi:10.1002/sim.3007 · Cited by 49 · see citing works
Empirical comparison of subgroup effects in conventional and individual patient data meta-analyses
Koopman, L., van der Heijden, G. J. M. G., Hoes, A. W., Grobbee, D. E., & Rovers, M. M. (2008) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462308080471 · Cited by 48 · see citing works
Predictive modeling and heterogeneity of baseline risk in meta-analysis of individual patient data
Trikalinos, T. A., & Ioannidis, J. P. A. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(00)00311-5 · Cited by 47 · see citing works
Investigating trial and treatment heterogeneity in an individual patient data meta‐analysis of survival data by means of the penalized maximum likelihood approach
Rondeau, V., Michiels, S., Liquet, B., & Pignon, J. (2007) · Statistics in Medicine · doi:10.1002/sim.3161 · Cited by 47 · see citing works
Individual Patient- versus Literature-Based Meta-analysis of Survival Data
Duchateau, L., Pignon, J., Bijnens, L., Bertin, S., Bourhis, J., & Sylvester, R. J. (2001) · Controlled Clinical Trials · doi:10.1016/s0197-2456(01)00152-0 · Cited by 44 · see citing works
Comparison of Methods of Handling Missing Data in Individual Patient Data Meta-analyses: An Empirical Example on Antibiotics in Children with Acute Otitis Media
Koopman, L., van der Heijden, G. J. M. G., Grobbee, D. E., & Rovers, M. M. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwm341 · Cited by 41 · see citing works
Individual Patient-level and Study-level Meta-analysis for Investigating Modifiers of Treatment Effect
Teramukai, S. (2004) · Japanese Journal of Clinical Oncology · doi:10.1093/jjco/hyh138 · Cited by 40 · see citing works
Estimating treatment effect via simple cross design synthesis
Kaizar, E. E. (2011) · Statistics in Medicine · doi:10.1002/sim.4339 · Cited by 39 · see citing works
Random effects survival models gave a better understanding of heterogeneity in individual patient data meta-analyses
Michiels, S., BAUJAT, B., Mahé, C., Sargent, D. J., & Pignon, J. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.08.013 · Cited by 37 · see citing works
Proportional hazards models with random effects to examine centre effects in multicentre cancer clinical trials
Yamaguchi, T., Ohashi, Y., & Matsuyama, Y. (2002) · Statistical Methods in Medical Research · doi:10.1191/0962280202sm284ra · Cited by 35 · see citing works
Individual patient meta-analysis—rewards and challenges
van Walraven, C. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.04.001 · Cited by 32 · see citing works
A systematic review of analytical methods used to study subgroups in (individual patient data) meta-analyses
Koopman, L., van der Heijden, G. J. M. G., Glasziou, P., Grobbee, D. E., & Rovers, M. M. (2007) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.01.018 · Cited by 31 · see citing works
Review on evidence-based cancer medicine
Clarke, M. J., & Godwin, J. (1998) · Annals of Oncology · doi:10.1023/a:1008468705492 · Cited by 30 · see citing works
Practical methodology of meta-analysis of individual patient data using a survival outcome
Katsahian, S., Latouche, A., Mary, J., Chevret, S., & Porcher, R. (2007) · Contemporary Clinical Trials · doi:10.1016/j.cct.2007.08.002 · Cited by 29 · see citing works
Bayesian Methods for the Cross-Design Synthesis of Epidemiological and Toxicological Evidence
Peters, J., Rushton, L., Sutton, A. J., Jones, D. R., Abrams, K. R., & Mugglestone, M. A. (2004) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2005.00476.x · Cited by 28 · see citing works
A Bayesian hierarchical model for multi-level repeated ordinal data: analysis of oral practice examinations in a large anaesthesiology training programme
Tan, M. T., Qu, Y., Mascha, E. J., & Schubert, A. (1999) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19990815)18:15 · Cited by 26 · see citing works
One‐stage parametric meta‐analysis of time‐to‐event outcomes
Siannis, F., Barrett, J., Farewell, V. T., & Tierney, J. F. (2010) · Statistics in Medicine · doi:10.1002/sim.4086 · Cited by 24 · see citing works
Exploring the Relationship Between Surrogates and Clinical Outcomes: Analysis of Individual Patient Data vs. Meta-regression on Group-Level Summary Statistics
Li, Z., & Meredith, M. P. (2003) · Journal of Biopharmaceutical Statistics · doi:10.1081/bip-120024209 · Cited by 23 · see citing works
Differences in interaction and subgroup-specific effects were observed between randomized and nonrandomized studies in three empirical examples
Schmidt, A. F., Rovers, M. M., Klungel, O. H., Hoes, A. W., Knol, M. J., Nielen, M., de Boer, A., & Groenwold, R. H. H. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2012.08.008 · Cited by 22 · see citing works
Self-citation
Use of Bayesian Multivariate Meta-Analysis to Estimate the HAQ for Mapping Onto the EQ-5D Questionnaire in Rheumatoid Arthritis
Bujkiewicz, S., Thompson, J. R., Sutton, A. J., Cooper, N. J., Harrison, M. J., Symmons, D. P. M., & Abrams, K. R. (2014) · Value in Health · doi:10.1016/j.jval.2013.11.005 · Cited by 19 · see citing works
Two‐stage meta‐analysis of survival data from individual participants using percentile ratios
Barrett, J., Farewell, V. T., Siannis, F., Tierney, J. F., & Higgins, J. P. T. (2012) · Statistics in Medicine · doi:10.1002/sim.5516 · Cited by 19 · see citing works
The devil is in the details...or not? A primer on individual patient data meta-analysis
Sud, S., & Douketis, J. D. (2009) · BMJ Evidence-Based Medicine · doi:10.1136/ebm.14.4.100 · Cited by 17 · see citing works
Inconsistent results in meta-analyses for the prevention of falls are found between study-level data and patient-level data
Haines, T. P., & Hill, A. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.04.024 · Cited by 16 · see citing works
A linearization approach for the model‐based analysis of combined aggregate and individual patient data
Ravva, P., Karlsson, M. O., & French, J. L. (2014) · Statistics in Medicine · doi:10.1002/sim.6045 · Cited by 13 · see citing works
A comparison of the results of checked versus unchecked individual patient data meta-analyses.
Burdett, S., & Stewart, L. A. (2002) · PubMed · Cited by 10 · see citing works
Understanding differences in results from literature-based and individual patient meta-analyses: An example from meta-analyses of observational data
Poppe, K., Doughty, R. N., Yu, C., Quintana, M., Møller, J. E., Klein, A. L., Gamble, G. D., Dini, F. L., & WHALLEY, G. A. (2009) · International Journal of Cardiology · doi:10.1016/j.ijcard.2009.09.566 · Cited by 10 · see citing works
Bayesian inference for multivariate meta‐analysis Box–Cox transformation models for individual patient data with applications to evaluation of cholesterol‐lowering drugs
Kim, S., Chen, M., Ibrahim, J. G., Shah, A. K., & Lin, J. (2013) · Statistics in Medicine · doi:10.1002/sim.5814 · Cited by 10 · see citing works
Random Effects Models for Combining Results from Controlled and Uncontrolled Studies in a Meta-Analysis
Li, Z., & Begg, C. B. (1994) · Journal of the American Statistical Association · doi:10.2307/2291015 · Cited by 10 · see citing works
Meta-Analysis Of Results And Individual Patient Data In Epidemiologal Studies
Tobı́as, A., Sáez, M., & Kogevinas, M. (2004) · Journal of Modern Applied Statistical Methods · doi:10.22237/jmasm/1083370680 · Cited by 8 · see citing works
The use of individual patient-level data (IPD) to quantify the impact of pretreatment predictors of response to treatment in chronic hepatitis B patients
Ali, S., Mealing, S., Hawkins, N., Lescrauwaet, B., Björk, S., Mantovani, L. G., & Lampertico, P. (2013) · BMJ Open · doi:10.1136/bmjopen-2012-001309 · Cited by 3 · see citing works
Assessing the potential for bias in meta-analysis due to selective reporting of subgroup analyses within studies
Hahn, S., Williamson, P. R., Hutton, J. L., Garner, P., & Flynn, E. V. (2000) · Statistics in Medicine · doi:10.1002/1097-0258(20001230)19:24

Flagged first, then by citations

2 of the 121 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

Turner RM, Omar RZ, Yang M, Goldstein H, Thompson SG 2000 A multilevel model framework for meta‐analysis of clinical trials with binary outcomes. Statistics in Medicine 19: 3417– 3432. DOI: 10.1002/1097-0258(20001230)19:24<3417::AID-SIM614>3.0.CO;2-L. 11122505 · DOI · Paper page
Vaida F, Xu R 2000 Proportional hazards model with random effects. Statistics in Medicine 19: 3309– 3324. DOI: 10.1002/1097-0258(20001230)19:24<3309::AID-SIM825>3.0.CO;2-9. 11122497 · DOI · Paper page

Sorted by citations

Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math
Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., Chambwe, N., Cintrón, D. L., Cooper, J. D., Dunster, G., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1916903117 · Cited by 1,387 · see citing works
Meta-analysis using individual participant data: One-stage and two-stage approaches, and why they may differ
Burke, D. L., Ensor, J., & Riley, R. D. (2017) · Statistics in Medicine · doi:10.1002/sim.7141 · Cited by 538 · see citing works
Risk of acute myocardial infarction with NSAIDs in real world use: bayesian meta-analysis of individual patient data
Bally, M., Dendukuri, N., Rich, B., Nadeau, L., Helin‐Salmivaara, A., Garbe, E., & Brophy, J. M. (2017) · BMJ · doi:10.1136/bmj.j1909 · Cited by 535 · see citing works
GetReal in network meta-analysis: a review of the methodology
Efthimiou, O., Debray, T. P. A., van Valkenhoef, G., Trelle, S., Panayidou, K., Moons, K. G. M., Reitsma, J. B., Shang, A., Salanti, G., & on behalf of GetReal Methods Review Group (2016) · Research Synthesis Methods · doi:10.1002/jrsm.1195 · Cited by 401 · see citing works
Self-citation
A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
Debray, T. P., Damen, J. A., Riley, R. D., Snell, K., Reitsma, J. B., Hooft, L., Collins, G. S., & Moons, K. G. (2019) · Statistical Methods in Medical Research · doi:10.1177/0962280218785504 · Cited by 278 · see citing works
Self-citation
Profile of and risk factors for poststroke cognitive impairment in diverse ethnoregional groups
Lo, J., Crawford, J. D., Desmond, D. W., Godefroy, O., Jokinen, H., Mahinrad, S., Bae, H., Lim, J., Köhler, S., Douven, E., et al. (2019) · Neurology · doi:10.1212/wnl.0000000000008612 · Cited by 262 · see citing works
Paclitaxel-coated balloon angioplasty vs. drug-eluting stenting for the treatment of coronary in-stent restenosis: a comprehensive, collaborative, individual patient data meta-analysis of 10 randomized clinical trials (DAEDALUS study)
Giacoppo, D., Alfonso, F., Xu, B., Claessen, B. E. P. M., Adriaenssens, T., Jensen, C. J., Pérez-Vizcayno, M. J., Kang, D., Degenhardt, R., Pleva, L., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz594 · Cited by 247 · see citing works
Drug-eluting or bare-metal stents for percutaneous coronary intervention: a systematic review and individual patient data meta-analysis of randomised clinical trials
Piccolo, R., Bønaa, K. H., Efthimiou, O., Varenne, O., Baldo, A., Urban, P. M., Kaiser, C. A., Remkes, W. S., Räber, L., de Belder, A., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30474-x · Cited by 234 · see citing works
Self-citation
Audiovisual multisensory integration in individuals with autism spectrum disorder: A systematic review and meta-analysis
Feldman, J. I., Dunham, K., Cassidy, M. M., Wallace, M. T., Liu, Y., & Woynaroski, T. G. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.09.020 · Cited by 195 · see citing works
Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed
White, T., Blok, E., & Calhoun, V. D. (2022) · Human Brain Mapping · doi:10.1002/hbm.25120 · Cited by 176 · see citing works
On weakly informative prior distributions for the heterogeneity parameter in Bayesian random‐effects meta‐analysis
Röver, C., Bender, R., Dias, S., Schmid, C. H., Schmidli, H., Sturtz, S., Weber, S., & Friede, T. (2021) · Research Synthesis Methods · doi:10.1002/jrsm.1475 · Cited by 192 · see citing works
Vitamin D and Risk for Type 2 Diabetes in People With Prediabetes
Pittas, A. G., Kawahara, T., Jorde, R., Dawson‐Hughes, B., Vickery, E. M., Angellotti, E., Nelson, J., Trikalinos, T. A., & Balk, E. M. (2023) · Annals of Internal Medicine · doi:10.7326/m22-3018 · Cited by 186 · see citing works
Prevalence of burnout in medical students: A systematic review and meta-analysis
Almutairi, H., Alsubaiei, A., Abduljawad, S., Alshatti, A., Fekih‐Romdhane, F., Husni, M., & Jahrami, H. (2022) · International Journal of Social Psychiatry · doi:10.1177/00207640221106691 · Cited by 201 · see citing works
Multiple Imputation for Multilevel Data with Continuous and Binary Variables
Audigier, V., White, I. R., Jolani, S., Debray, T. P. A., Quartagno, M., Carpenter, J. R., van Buuren, S., & Resche‐Rigon, M. (2018) · Statistical Science · doi:10.1214/18-sts646 · Cited by 168 · see citing works
Self-citation
For better or worse: An individual patient data meta-analysis of deterioration among participants receiving Internet-based cognitive behavior therapy.
Rozental, A., Magnusson, K., Boettcher, J., Andersson, G., & Carlbring, P. (2017) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000158 · Cited by 137 · see citing works
Global Public Perceptions of Genomic Data Sharing: What Shapes the Willingness to Donate DNA and Health Data?
Middleton, A., Milne, R., Almarri, M. A., Anwer, S., Atutornu, J., Baranova, E. E., Bevan, P., Cerezo, M., Cong, Y., Critchley, C. R., et al. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.08.023 · Cited by 138 · see citing works
The structure of the General Health Questionnaire (GHQ-12): two meta-analytic factor analyses
Gnambs, T., & Staufenbiel, T. (2018) · Health Psychology Review · doi:10.1080/17437199.2018.1426484 · Cited by 137 · see citing works
Psychological interventions to improve self-management of type 1 and type 2 diabetes: a systematic review
Winkley, K., Upsher, R. J., Stahl, D. R., Pollard, D. J., Kasera, A., Brennan, A., Heller, S. R., & Ismail, K. (2020) · Health Technology Assessment · doi:10.3310/hta24280 · Cited by 148 · see citing works
Effects on Mortality and Major Bleeding of Radial Versus Femoral Artery Access for Coronary Angiography or Percutaneous Coronary Intervention: Meta-Analysis of Individual Patient Data From 7 Multicenter Randomized Clinical Trials
Gargiulo, G., Giacoppo, D., Jolly, S. S., Cairns, J. A., May, M. R. L., Bernát, I., Romagnoli, E., Rao, S. V., van Leeuwen, M. A. H., Mehta, S. R., et al. (2022) · Circulation · doi:10.1161/circulationaha.122.061527 · Cited by 136 · see citing works
Pharmacological and dietary-supplement treatments for autism spectrum disorder: a systematic review and network meta-analysis
Siafis, S., Çıray, O., Wu, H., Schneider‐Thoma, J., Bighelli, I., Krause, M., Rodolico, A., Ceraso, A., Deste, G., Huhn, M., et al. (2022) · Molecular Autism · doi:10.1186/s13229-022-00488-4 · Cited by 138 · see citing works
An individual patient data meta-analysis to determine cut-offs for and confounders of NAFLD-fibrosis staging with magnetic resonance elastography
Liang, J., Ampuero, J., Niu, H., Imajo, K., Noureddin, M., Behari, J., Lee, D. H., Ehman, R. L., Rorsman, F., Vessby, J., et al. (2023) · Journal of Hepatology · doi:10.1016/j.jhep.2023.04.025 · Cited by 123 · see citing works
Systematic review and individual participant data meta-analysis of randomized controlled trials assessing mindfulness-based programs for mental health promotion
Galante, J., Friedrich, C., Collaboration of Mindfulness Trials (CoMinT), Aeamla-Or, N., Arts-de Jong, M., Barrett, B., Bögels, S. M., Buitelaar, J. K., Checovich, M. M., Christopher, M. S., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00081-5 · Cited by 113 · see citing works
“We imputed missing data following a prespecified plan 54,100,101 .”
One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information: One-Stage IPD Meta-Analysis Models Must Avoid Ecological Bias
Hua, H., Burke, D. L., Crowther, M. J., Ensor, J., Tudur Smith, C., & Riley, R. D. (2017) · Statistics in Medicine · doi:10.1002/sim.7171 · Cited by 113 · see citing works
Introduction to Meta-Analysis
Evrenoglou, T., Metelli, S., & Chaimani, A. (2021) · Springer eBooks · doi:10.1007/978-3-319-52677-5_287-1 · Cited by 112 · see citing works
The trajectory of cortical GABA across the lifespan, an individual participant data meta-analysis of edited MRS studies
Porges, E. C., Jensen, G., Foster, B. M., Edden, R. A. E., & Puts, N. A. J. (2021) · eLife · doi:10.7554/elife.62575 · Cited by 108 · see citing works
Individual participant data meta‐analysis of intervention studies with time‐to‐event outcomes: A review of the methodology and an applied example
de Jong, V. M. T., Moons, K. G. M., Riley, R. D., Smith, C. T., Marson, A. G., Eijkemans, M. J. C., & Debray, T. P. A. (2019) · Research Synthesis Methods · doi:10.1002/jrsm.1384 · Cited by 104 · see citing works
Self-citation
Antihypertensive treatment and risk of cancer: an individual participant data meta-analysis
Copland, E., Canoy, D., Nazarzadeh, M., Bidel, Z., Ramakrishnan, R., Woodward, M., Chalmers, J. P., Teo, K. K., Pepine, C. J., Davis, B. R., et al. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(21)00033-4 · Cited by 101 · see citing works
A comparison of one‐stage vs two‐stage individual patient data meta‐analysis methods: A simulation study
Kontopantelis, E. (2018) · Research Synthesis Methods · doi:10.1002/jrsm.1303 · Cited by 97 · see citing works
Blood Pressure After Endovascular Thrombectomy and Outcomes in Patients With Acute Ischemic Stroke
Katsanos, A. H., Malhotra, K., Ahmed, N., Seitidis, G., Mistry, E. A., Mavridis, D., Kim, J., Veroniki, A. A., Maier, I., Matusevicius, M., et al. (2021) · Neurology · doi:10.1212/wnl.0000000000013049 · Cited by 97 · see citing works
Exploring changes over time and characteristics associated with data retrieval across individual participant data meta-analyses: systematic review
Nevitt, S. J., Marson, A. G., Davie, B., Reynolds, S., Williams, L., & Smith, C. T. (2017) · BMJ · doi:10.1136/bmj.j1390 · Cited by 94 · see citing works
Moderators of Exercise Effects on Cancer-related Fatigue: A Meta-analysis of Individual Patient Data
van Vulpen, J. K., Sweegers, M. G., Peeters, P. H. M., Courneya, K. S., Newton, R. U., Aaronson, N. K., Jacobsen, P. B., Galvao, D. A., Chinapaw, M. J. M., Steindorf, K., et al. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002154 · Cited by 96 · see citing works
On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis
Röver, C., Bender, R., Dias, S., Schmid, C. H., Schmidli, H., Sturtz, S., Weber, S., & Friede, T. (2021) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 89 · see citing works
Susceptibility to online misinformation: A systematic meta-analysis of demographic and psychological factors
Sultan, M., Tump, A. N., Ehmann, N., Lorenz-Spreen, P., Hertwig, R., Gollwitzer, A., & Kurvers, R. H. J. M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2409329121 · Cited by 112 · see citing works
Short sleep duration and hyperglycemia in pregnancy: Aggregate and individual patient data meta-analysis
Reutrakul, S., Anothaisintawee, T., Herring, S. J., Izci‐Balserak, B., Marc, I., & Thakkinstian, A. (2017) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2017.09.003 · Cited by 89 · see citing works
Factors associated with phosphatidylethanol (PEth) sensitivity for detecting unhealthy alcohol use: An individual patient data meta‐analysis
Hahn, J. A., Murnane, P. M., Vittinghoff, E., Muyindike, W. R., Emenyonu, N. I., Fatch, R., Chamie, G., Haberer, J. E., Francis, J. M., Kapiga, S., et al. (2021) · Alcoholism: Clinical and Experimental Research · doi:10.1111/acer.14611 · Cited by 87 · see citing works
Individual patient data meta-analysis of paclitaxel-coated balloons vs. drug-eluting stents for small-vessel coronary artery disease: the ANDROMEDA study
Fezzi, S., Giacoppo, D., Fahrni, G., Latib, A., Alfonso, F., Colombo, A., Mahfoud, F., Scheller, B., Jeger, R. V., & Cortese, B. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf002 · Cited by 85 · see citing works
Editor's Choice – Risk of Major Amputation Following Application of Paclitaxel Coated Balloons in the Lower Limb Arteries: A Systematic Review and Meta-Analysis of Randomised Controlled Trials
Katsanos, K. H., Spiliοpoulos, S., Teichgräber, U. K.-M., Kitrou, P. M., Giudice, C. D., Björkman, P., Bisdas, T., Boer, S. W. D., Krokidis, M. E., & Karnabatidis, D. (2021) · European Journal of Vascular and Endovascular Surgery · doi:10.1016/j.ejvs.2021.05.027 · Cited by 83 · see citing works
An overview of methods for network meta-analysis using individual participant data: when do benefits arise?
Debray, T. P. A., Schuit, E., Efthimiou, O., Reitsma, J. B., Ioannidis, J. P. A., Salanti, G., Moons, K. G. M., & Workpackage, O. B. O. G. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216660741 · Cited by 82 · see citing works
Self-citation
Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide
Ventresca, M., Schünemann, H. J., Macbeth, F. R., Clarke, M. J., Thabane, L., Griffiths, G. O., Noble, S., Garcia, D. A., Marcucci, M., Iorio, A., et al. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-00964-6 · Cited by 81 · see citing works
Distraction distance and perceived disturbance by noise—An analysis of 21 open-plan offices
Haapakangas, A., Hongisto, V. O., Eerola, M., & Kuusisto, T. (2017) · The Journal of the Acoustical Society of America · doi:10.1121/1.4973690 · Cited by 83 · see citing works
Effectiveness of nutritional interventions in older adults at risk of malnutrition across different health care settings: Pooled analyses of individual participant data from nine randomized controlled trials
Reinders, I., Volkert, D., de Groot, C. P. G. M., Beck, A. M., Feldblum, I., Jobse, I., Neelemaat, F., de van der Schueren, M. A. E., Shahar, D. R., Smeets, E. T. H. C., et al. (2018) · Clinical Nutrition · doi:10.1016/j.clnu.2018.07.023 · Cited by 79 · see citing works
The Effectiveness of Autonomy Support Interventions With Physical Education Teachers and Youth Sport Coaches: A Systematic Review
Raabe, J. J., Schmidt, K., Carl, J., & Höner, O. (2019) · Journal of Sport and Exercise Psychology · doi:10.1123/jsep.2019-0026 · Cited by 77 · see citing works
Tranexamic acid for postpartum bleeding: a systematic review and individual patient data meta-analysis of randomised controlled trials
Ker, K., Sentilhes, L., Shakur‐Still, H., Madar, H., Deneux‐Tharaux, C., Saade, G. R., Pacheco, L. D., Ageron, F., Mansukhani, R., Balogun, E., et al. (2024) · The Lancet · doi:10.1016/s0140-6736(24)02102-0 · Cited by 66 · see citing works
Meta-Analysis of Complex Interventions
Tanner-Smith, E. E., & Grant, S. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-014112 · Cited by 62 · see citing works
A review of methods for addressing components of interventions in meta-analysis
Πετροπούλου, Μ., Efthimiou, O., Rücker, G., Schwarzer, G., Furukawa, T. A., Pompoli, A., Koek, H. L., Giovane, C. D., Rodondi, N., & Mavridis, D. (2021) · PLoS ONE · doi:10.1371/journal.pone.0246631 · Cited by 60 · see citing works
Self-citation
Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration
Debray, T. P. A., Collins, P. G. S., Riley, R. D., Snell, K. I. E., CALSTER, B. V., Reitsma, J. B., & Moons, K. G. M. (2023) · BMJ · doi:10.1136/bmj-2022-071058 · Cited by 62 · see citing works
Self-citation
Applied Mixed Model Analysis
Twisk, J. W. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108635660 · Cited by 59 · see citing works
Zinc Acetate Lozenges May Improve the Recovery Rate of Common Cold Patients: An Individual Patient Data Meta-Analysis
Hemilä, H., Fitzgerald, J. T., Petrus, E. J., & Prasad, A. S. (2017) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofx059 · Cited by 53 · see citing works
Proximity to overhead power lines and childhood leukaemia: an international pooled analysis
Amoon, A. T., Crespi, C. M., Ahlbom, A., Bhatnagar, M., Bray, I., Bunch, K. J., Clavel, J., Feychting, M., Hémon, D., Johansen, C., et al. (2018) · British Journal of Cancer · doi:10.1038/s41416-018-0097-7 · Cited by 53 · see citing works
The effect of spinal manipulative therapy on pain relief and function in patients with chronic low back pain: an individual participant data meta-analysis
de Zoete, A., Rubinstein, S. M., de Boer, M. R., Ostelo, R., Underwood, M. R., Hayden, J. A., Buffart, L. M., van Tulder, M. W., Brønfort, G., Foster, N. E., et al. (2021) · Physiotherapy · doi:10.1016/j.physio.2021.03.006 · Cited by 52 · see citing works
Association of longitudinal alcohol consumption trajectories with coronary heart disease: a meta-analysis of six cohort studies using individual participant data
O’Neill, D., Britton, A. R., Hannah, M., Goldberg, M., Kuh, D. J., Khaw, K., & Bell, S. (2018) · BMC Medicine · doi:10.1186/s12916-018-1123-6 · Cited by 52 · see citing works
P2Y 12 inhibitor or aspirin after percutaneous coronary intervention: individual patient data meta-analysis of randomised clinical trials
Giacoppo, D., Gragnano, F., Watanabe, H., Kimura, T., Kang, J., Park, K. W., Kim, H.-S., Pettersen, A. R., Bhatt, D. L., Pocock, S., et al. (2025) · BMJ · doi:10.1136/bmj-2024-082561 · Cited by 50 · see citing works
Understanding the relation between Zika virus infection during pregnancy and adverse fetal, infant and child outcomes: a protocol for a systematic review and individual participant data meta-analysis of longitudinal studies of pregnant women and their infants and children
Wilder‐Smith, A., Wei, Y., de Araújo, T. V. B., VanKerkhove, M., Martelli, C. M. T., Turchi, M. D., Teixeira, M. M., Tami, A., Souza, J. P., Sousa, P., et al. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-026092 · Cited by 50 · see citing works
Self-citation
Statistical approaches to identify subgroups in meta-analysis of individual participant data: a simulation study
Belias, M., Rovers, M. M., Reitsma, J. B., Debray, T. P. A., & IntHout, J. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0817-6 · Cited by 50 · see citing works
Self-citation
Experimentation for Chatbot Usability Evaluation: A Secondary Study
Ren, R., Zapata, M., Castro, J. W., Dieste, Ó., & Acuña, S. T. (2022) · IEEE Access · doi:10.1109/access.2022.3145323 · Cited by 49 · see citing works
Measuring the performance of prediction models to personalize treatment choice
Efthimiou, O., Hoogland, J., Debray, T. P. A., Seo, M., Furukawa, T. A., Egger, M., & White, I. R. (2023) · Statistics in Medicine · doi:10.1002/sim.9665 · Cited by 47 · see citing works
Self-citation
Putting the Instructional Leadership–Student Achievement Relation in Context: A Meta-Analytical Big Data Study Across Cultures and Time
Pietsch, M., Aydın, B., & Gümüş, S. (2023) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737231197434 · Cited by 71 · see citing works
Problems and pitfalls in subgroup analysis and meta-regression
Spineli, L. M., & Pandis, N. (2020) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2020.09.001 · Cited by 47 · see citing works
Reduced drug use as an alternative valid outcome in individuals with stimulant use disorders: Findings from 13 multisite randomized clinical trials
Amin‐Esmaeili, M., Farokhnia, M., Susukida, R., Leggio, L., Johnson, R. M., Crum, R. M., & Mojtabai, R. (2024) · Addiction · doi:10.1111/add.16409 · Cited by 43 · see citing works
Dismantling, personalising and optimising internet cognitive–behavioural therapy for depression: a study protocol for individual participant data component network meta-analysis
Furukawa, T. A., Karyotaki, E., Suganuma, A. M., Pompoli, A., Ostinelli, E. G., Cipriani, A., Cuijpers, P. J., & Efthimiou, O. (2018) · BMJ Open · doi:10.1136/bmjopen-2018-026137 · Cited by 46 · see citing works
Self-citation
Two‐stage or not two‐stage? That is the question for IPD meta‐analysis projects
Riley, R. D., Ensor, J., Hattle, M., Papadimitropoulou, K., & Morris, T. P. (2023) · Research Synthesis Methods · doi:10.1002/jrsm.1661 · Cited by 45 · see citing works
Predicting response to topical non-steroidal anti-inflammatory drugs in osteoarthritis: an individual patient data meta-analysis of randomized controlled trials
Persson, M. S. M., Stocks, J., Váradi, G., Hashempur, M. H., van Middelkoop, M., Bierma‐Zeinstra, S., Walsh, D. A., Doherty, M., & Zhang, W. (2020) · Lara D. Veeken · doi:10.1093/rheumatology/keaa113 · Cited by 45 · see citing works
Machine learning approaches to evaluate heterogeneous treatment effects in randomized controlled trials: a scoping review
Inoue, K., Adomi, M., Efthimiou, O., Komura, T., Omae, K., Ōnishi, A., Tsutsumi, Y., Fujii, T., Kondo, N., & Furukawa, T. A. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111538 · Cited by 44 · see citing works
Self-citation
Zinc acetate lozenges for treating the common cold: an individual patient data meta‐analysis
Hemilä, H., Petrus, E. J., Fitzgerald, J. T., & Prasad, A. S. (2016) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.13057 · Cited by 43 · see citing works
Pharmacotherapy for smoking cessation: effects by subgroup defined by genetically informed biomarkers
Schuit, E., Panagiotou, O. A., Munafo, M. R., Bennett, D., Bergen, A. W., & David, S. P. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011823.pub2 · Cited by 43 · see citing works
Retracted
Introduction to Meta-Analysis
Evrenoglou, T., Metelli, S., & Chaimani, A. (2022) · Springer eBooks · doi:10.1007/978-3-319-52636-2_287 · Cited by 49 · see citing works
Meta‐analyzing individual participant data from studies with complex survey designs: A tutorial on using the two‐stage approach for data from educational large‐scale assessments
Brunner, M., Keller, L., Stallasch, S. E., Kretschmann, J., Hasl, A., Preckel, F., Lüdtke, O., & Hedges, L. V. (2022) · Research Synthesis Methods · doi:10.1002/jrsm.1584 · Cited by 44 · see citing works
Amiodarone for arrhythmia in patients with Chagas disease: A systematic review and individual patient data meta-analysis
Stein, C., Migliavaca, C. B., Colpani, V., da Rosa, P. R., Sganzerla, D., Giordani, N. E., Miguel, S. R. P. D. S., Cruz, L. N., Polanczyk, C. A., Ribeiro, A. L. P., et al. (2018) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0006742 · Cited by 42 · see citing works
Effects and moderators of exercise on sleep in adults with cancer: Individual patient data and aggregated meta-analyses
Bernard, P., Savard, J., Steindorf, K., Sweegers, M. G., Courneya, K. S., Newton, R. U., Aaronson, N. K., Jacobsen, P. B., May, A. M., Galvao, D. A., et al. (2019) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2019.109746 · Cited by 42 · see citing works
Vaccines to prevent COVID-19: a protocol for a living systematic review with network meta-analysis including individual patient data (The LIVING VACCINE Project)
Korang, S. K., Juul, S., Nielsen, E. E., Feinberg, J. B., Siddiqui, F., Ong, G., Klingenberg, S. L., Veroniki, A. A., Bu, F.-L., Thabane, L., et al. (2020) · Systematic Reviews · doi:10.1186/s13643-020-01516-1 · Cited by 41 · see citing works
An Introduction to Individual Participant Data Meta-analysis
Veroniki, A. A., Seitidis, G., Tsivgoulis, G. K., Katsanos, A. H., & Mavridis, D. (2023) · Neurology · doi:10.1212/wnl.0000000000207078 · Cited by 43 · see citing works
Meta‐analysis of Gaussian individual patient data: Two‐stage or not two‐stage?
Morris, T. P., Fisher, D. J., Kenward, M. G., & Carpenter, J. R. (2018) · Statistics in Medicine · doi:10.1002/sim.7589 · Cited by 41 · see citing works
The Current Landscape in Biostatistics of Real-World Data and Evidence: Clinical Study Design and Analysis
Chen, J., Ho, M., Lee, K., Song, Y., Fang, Y., Goldstein, B. A., He, W., Irony, T., Jiang, Q., van der Laan, M. J., et al. (2021) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2021.1883474 · Cited by 40 · see citing works
Meta-analysis in Sociological Research: Power and Heterogeneity
Tong, G., & Guo, G. (2019) · Sociological Methods & Research · doi:10.1177/0049124119882479 · Cited by 40 · see citing works
Pooled analysis of recent studies of magnetic fields and childhood leukemia
Amoon, A. T., Swanson, J., Magnani, C., Johansen, C., & Kheifets, L. (2021) · Environmental Research · doi:10.1016/j.envres.2021.111993 · Cited by 39 · see citing works
Prevalence and risk factors of self‐reported psychotic experiences among high school and college students: A systematic review, meta‐analysis, and meta‐regression
Fekih‐Romdhane, F., Pandi-Perumal, S. R., Conus, P., Krebs, M., Cheour, M., Seeman, M. V., & Jahrami, H. (2022) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13494 · Cited by 41 · see citing works
Antibiotic treatment of bacterial vaginosis to prevent preterm delivery: Systematic review and individual participant data meta‐analysis
Klebanoff, M. A., Schuit, E., Lamont, R., Larsson, P., Odendaal, H. J., Ugwumadu, A. H. N., Kiss, H., Petričević, L., Andrews, W. W., Hoffman, M., et al. (2023) · Paediatric and Perinatal Epidemiology · doi:10.1111/ppe.12947 · Cited by 41 · see citing works
The methodological quality of individual participant data meta-analysis on intervention effects: systematic review
Wang, H., Chen, Y., Lin, Y., Abesig, J., Wu, X., & Tam, W. (2021) · BMJ · doi:10.1136/bmj.n736 · Cited by 38 · see citing works
A novel approach for identifying and addressing case‐mix heterogeneity in individual participant data meta‐analysis
Vo, T., Porcher, R., Chaimani, A., & Vansteelandt, S. (2019) · Research Synthesis Methods · doi:10.1002/jrsm.1382 · Cited by 37 · see citing works
Individual participant data
Tierney, J. F., Stewart, L. A., Clarke, M. J., & on behalf of the Cochrane Individual Participant Data Meta‐analysis Methods Group (2019) · doi:10.1002/9781119536604.ch26 · Cited by 37 · see citing works
Self-citation
Retrieval of individual patient data depended on study characteristics: a randomized controlled trial
Veroniki, A. A., Ashoor, H. M., Le, S. P. C., Rios, P. D. L., Stewart, L. A., Clarke, M. J., Mavridis, D., Straus, S. E., & Tricco, A. C. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.05.031 · Cited by 36 · see citing works
Modeling multi-level survival data in multi-center epidemiological cohort studies: Applications from the ELAPSE project
Samoli, E., Rodopoulou, S., Hvidtfeldt, U. A., Wolf, K., Stafoggia, M., Brunekreef, B., Strak, M., Chen, J., Andersen, Z. J., Atkinson, R., et al. (2021) · Environment International · doi:10.1016/j.envint.2020.106371 · Cited by 33 · see citing works
An individual participant data analysis of prospective cohort studies on the association between subclinical thyroid dysfunction and depressive symptoms
Wildisen, L., Giovane, C. D., Moutzouri, E., Beglinger, S., Syrogiannouli, L., Collet, T., Cappola, A. R., Åsvold, B. O., Bakker, S. J. L., Yeap, B. B., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-75776-1 · Cited by 33 · see citing works
Adverse events after antipsychotic discontinuation: an individual participant data meta-analysis
Brandt, L., Schneider-Thoma, J., Siafis, S., Efthimiou, O., Bermpohl, F., Loncar, L., Neumann, K., Hasan, A., Heinz, A., Leucht, S., et al. (2022) · The Lancet Psychiatry · doi:10.1016/s2215-0366(22)00014-1 · Cited by 34 · see citing works
Self-citation
How often can meta-analyses of individual-level data individualize treatment? A meta-epidemiologic study
Schuit, E., Li, A. H., & Ioannidis, J. P. A. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyy239 · Cited by 27 · see citing works
The inclusion of real world evidence in clinical development planning
1, O. B. O. G. W., Martina, R., Jenkins, D. A., Bujkiewicz, S., Dequen, P., & Abrams, K. R. (2018) · Trials · doi:10.1186/s13063-018-2769-2 · Cited by 32 · see citing works
Avoiding Methodological Biases in Meta-Analysis: Use of Online Versus Offline Individual Participant Data (IPD) in Educational Psychology
Kaufmann, E., Reips, U.-D., & Maag Merki, K. (2016) · Zeitschrift für Psychologie · doi:10.1027/2151-2604/a000251 · Cited by 31 · see citing works
Cerebellar BOLD signal during the acquisition of a new lexicon predicts its early consolidation
Lesage, E., Nailer, E. L., & Miall, R. C. (2015) · Brain and Language · doi:10.1016/j.bandl.2015.07.005 · Cited by 33 · see citing works
Meta‐analysis of non‐linear exposure‐outcome relationships using individual participant data: A comparison of two methods
White, I. R., Kaptoge, S., Royston, P. J., & Sauerbrei, W. (2018) · Statistics in Medicine · doi:10.1002/sim.7974 · Cited by 31 · see citing works
GetReal: from efficacy in clinical trials to relative effectiveness in the real world
Egger, M., Moons, K. G. M., Fletcher, C., & 4, G. W. (2016) · Research Synthesis Methods · doi:10.1002/jrsm.1207 · Cited by 30 · see citing works
Self-citation
Elevated blood pressure, antihypertensive medications and bone health in the population: revisiting old hypotheses and exploring future research directions
Canoy, D., Harvey, N. C., Prieto‐Alhambra, D., Cooper, C. C., Meyer, H. E., Åsvold, B. O., Nazarzadeh, M., & Rahimi, K. (2021) · Osteoporosis International · doi:10.1007/s00198-021-06190-0 · Cited by 31 · see citing works
Evidence synthesis in prognosis research
Debray, T. P. A., de Jong, V. M. T., Moons, K. G. M., & Riley, R. D. (2019) · Diagnostic and Prognostic Research · doi:10.1186/s41512-019-0059-4 · Cited by 29 · see citing works
Self-citation
Comparing methods for estimating patient‐specific treatment effects in individual patient data meta‐analysis
Seo, M., White, I. R., Furukawa, T. A., Imai, H., Valgimigli, M., Egger, M., Zwahlen, M., & Efthimiou, O. (2020) · Statistics in Medicine · doi:10.1002/sim.8859 · Cited by 29 · see citing works
Self-citation
The development of CHAMP: a checklist for the appraisal of moderators and predictors
van Hoorn, R., Tummers, M., Booth, A., Gerhardus, A., Rehfuess, E. A., Hind, D., Bossuyt, P. M., Welch, V. A., Debray, T. P. A., Underwood, M. R., et al. (2017) · BMC Medical Research Methodology · doi:10.1186/s12874-017-0451-0 · Cited by 28 · see citing works
Self-citation
Which patients benefit specifically from short-term psychodynamic psychotherapy (STPP) for depression? Study protocol of a systematic review and meta-analysis of individual participant data
Driessen, E., Abbass, A. A., Barber, J. P., Gibbons, M. B. C., Dekker, J., Fokkema, M., Fonagy, P., Hollon, S. D., Jansma, E. P., de Maat, S. C. M., et al. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-018900 · Cited by 28 · see citing works
Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models
Riley, R. D., Ensor, J., Jackson, D., & Burke, D. L. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280216688033 · Cited by 28 · see citing works
Does the effect of cognitive behavior therapy for chronic fatigue syndrome (ME/CFS) vary by patient characteristics? A systematic review and individual patient data meta-analysis
Kuut, T. A., Buffart, L. M., Braamse, A. M. J., Csorba, I., Bleijenberg, G., Nieuwkerk, P., Moss-Morris, R., Müller, F., & Knoop, H. (2024) · Psychological Medicine · doi:10.1017/s0033291723003148 · Cited by 27 · see citing works
Awake Prone Positioning in Adults With COVID-19
Luo, J. F., Pavlov, I., Tavernier, E., Perez, Y., Kharat, A., McNicholas, B., Roca, O., Vines, D. L., Ibarra-Estrada, M. Á., Alhazzani, W. A., et al. (2025) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2025.0011 · Cited by 26 · see citing works
Meta‐analysis of continuous outcomes: Using pseudo IPD created from aggregate data to adjust for baseline imbalance and assess treatment‐by‐baseline modification
Papadimitropoulou, K., Stijnen, T., Riley, R. D., Dekkers, O. M., & le Cessie, S. (2020) · Research Synthesis Methods · doi:10.1002/jrsm.1434 · Cited by 26 · see citing works
Multilevel Meta-Analysis of Individual Participant Data of Single-Case Experimental Designs: One-Stage versus Two-Stage Methods
Declercq, L., Jamshidi, L., Fernández‐Castilla, B., Moeyaert, M., Beretvas, S. N., Ferron, J. M., & Noortgate, W. V. D. (2020) · Multivariate Behavioral Research · doi:10.1080/00273171.2020.1822148 · Cited by 26 · see citing works

Methodological Guidance for Individual Participant Data Meta-Analyses: A Systematic ReviewPreprint
Otalike, E. G., Clarke, M. J., Akhter, F., Veroniki, A. A., Kandala, N., & Gagnier, J. J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5554651
Individual Participant Data Meta-Analysis
Stewart, L. A., & Simmonds, M. C. (2020) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315119403-8 · Cited by 6 · see citing works
Research Note: Individual participant data (IPD) meta-analysis
Hayden, J. A., & Riley, R. D. (2021) · Journal of physiotherapy · doi:10.1016/j.jphys.2021.04.001 · Cited by 4 · see citing works
Individual participant data (IPD) meta-analysis: An introduction – Narrative review
Rai, E., Naik, V. M., Williams, A., & Kamath, M. S. (2025) · Indian Journal of Anaesthesia · doi:10.4103/ija.ija_1187_24 · Cited by 5 · see citing works
State of play in individual participant data meta-analyses of randomised trials: Systematic review and consensus-based recommendationsPreprint
Seidler, A. L., Aagerup, J., Nicholson, L., Hunter, K. E., Bajpai, R. C., Hamilton, D. G., Love, T., Marlin, N., Nguyen, D., Riley, R. D., et al. (2026) · medRxiv · doi:10.64898/2026.02.03.26345481

Authors: Debray, T. P. A., Moons, K. G. M., van Valkenhoef, G., Efthimiou, O., Hummel, N., Groenwold, R. H. H., Reitsma, J. B., & on behalf of the GetReal methods review group
Affiliations: University of Groningen (NL)
Department of Epidemiology University of Groningen, University Medical Center Groningen Groningen The Netherlands
University of Ioannina (GR)
University of Bern (CH)
University Medical Center Utrecht (NL)
Corresponding author: Debray, Thomas P. A.
ORCID: Debray, Thomas P. A.
Moons, Karel G. M.
Efthimiou, Orestis
Hummel, Noemi
Groenwold, Rolf H. H.
Reitsma, Johannes B.
Venue: Research Synthesis Methods
Publisher: Wiley
Published: 2015-08-19
Volume: 6
Issue: 4
Pages: 293-309
ISSN: 1759-2879 (ISSN-L)
1759-2887
DOI: 10.1002/jrsm.1160
PMC: PMC5042043
OpenAlex: W1841651683
Cited by: 348
References: 121
Citations by year: 2026: 28 · 2025: 28 · 2024: 24 · 2023: 36 · 2022: 45 · 2021: 47 · 2020: 35 · 2019: 40 · 2018: 33 · 2017: 16 · 2016: 13 · 2015: 2 · 2012: 1
FWCI: 3.53
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Research Synthesis Methods (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Pure Amsterdam UMC (submittedVersion, other-oa)
Data Archiving and Networked Services (DANS) (submittedVersion)
BORIS (University Library Bern) (submittedVersion, cc-by-nc-nd)
Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
Open Access CRIS of the University of Bern
Funders: Innovative Medicines Initiative Joint Undertaking (115546)
European Union's Seventh Framework Programme (FP7/20072013)
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: individual patient data meta-analysis, treatment effect estimation, clinical trials, non-randomized studies, methodological assumptions
MeSH terms: Data Interpretation, Statistical, Humans, Software, Meta-Analysis as Topic, Models, Statistical, Linear Models, Proportional Hazards Models, Randomized Controlled Trials as Topic, Treatment Outcome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Get real in individual participant data (IPD) meta‐analysis: a review of the methodology

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks