The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Meta‐analysis of continuous outcomes combining individual patient data and aggregate data
Open accessAbstract
Meta‐analysis of individual patient data (IPD) is the gold‐standard for synthesizing evidence across clinical studies. However, for some studies IPD may not be available and only aggregate data (AD), such as a treatment effect estimate and its standard error, may be obtained. In this situation, methods for combining IPD and AD are important to utilize all the available evidence. In this paper, we develop and assess a range of statistical methods for combining IPD and AD in meta‐analysis of continuous outcomes from randomized controlled trials. The methods take either a one‐step or a two‐step approach. The latter is simple, with IPD reduced to AD so that standard AD meta‐analysis techniques can be employed. The one‐step approach is more complex but offers a flexible framework to include both patient‐level and trial‐level parameters. It uses a dummy variable to distinguish IPD trials from AD trials and to constrain which parameters the AD trials estimate. We show that this is important when assessing how patient‐level covariates modify treatment effect, as aggregate‐level relationships across trials are subject to ecological bias and confounding. We thus develop models to separate within‐trial and across‐trials treatment–covariate interactions; this ensures that only IPD trials estimate the former, whilst both IPD and AD trials estimate the latter in addition to the pooled treatment effect and any between‐study heterogeneity. Extension to multiple correlated outcomes is also considered. Ten IPD trials in hypertension, with blood pressure the continuous outcome of interest, are used to assess the models and identify the benefits of utilizing AD alongside IPD. Copyright © 2007 John Wiley & Sons, Ltd.
Overview
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
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Meta-analysis in clinical trials
DerSimonian, R., & Laird, N. (1986) · Controlled Clinical Trials · doi:10.1016/0197-2456(86)90046-2 · Cited by 39,552
Littell, R. C. (1996) · Medical Entomology and Zoology · Cited by 10,341
Staessen, J. A., Fagard, R., Thijs, L., Celis, H., Arabidze, G. G., Birkenhäger, W. H., Bulpitt, C. J., de Leeuw, P. W., Dollery, C. T., Fletcher, A. E., et al. (1997) · The Lancet · doi:10.1016/s0140-6736(97)05381-6 · Cited by 2,989
Self-citationAnalysing controlled trials with baseline and follow up measurements
Vickers, A. J., & Altman, D. G. (2001) · BMJ · doi:10.1136/bmj.323.7321.1123 · Cited by 2,116
Advanced methods in meta‐analysis: multivariate approach and meta‐regression
van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002) · Statistics in Medicine · doi:10.1002/sim.1040 · Cited by 1,580
AMERY, A. (1985) · The Lancet · doi:10.1016/s0140-6736(85)91783-0 · Cited by 1,476
Systematic Review: Why sources of heterogeneity in meta-analysis should be investigated
Thompson, S. G. (1994) · BMJ · doi:10.1136/bmj.309.6965.1351 · Cited by 1,017 · cited 2×
“Furthermore, if there is between-study heterogeneity it is important to explain its cause [35] and thus reduce ̂(2) in the above models.”
BackgroundA random‐effects regression model for meta‐analysis
Berkey, C. S., Hoaglin, D. C., Mosteller, F., & Colditz, G. A. (1995) · Statistics in Medicine · doi:10.1002/sim.4780140406 · Cited by 879
Meta‐Analysis Of Controlled Clinical Trials
Whitehead, A. (2002) · doi:10.1002/0470854200 · Cited by 827
Meta-analysis of the literature or of individual patient data: is there a difference?
Stewart, L. A., & Parmar, M. K. B. (1993) · The Lancet · doi:10.1016/0140-6736(93)93004-k · Cited by 769
Liu, L., Wang, J. G., Gong, L., Liu, G., & Staessen, J. A. (1998) · Journal of Hypertension · doi:10.1097/00004872-199816120-00016 · Cited by 730
Self-citationSumming up evidence: one answer is not always enough
Lau, J., Ioannidis, J. P., & Schmid, C. H. (1998) · The Lancet · doi:10.1016/s0140-6736(97)08468-7 · Cited by 655
Letter: Immunotherapy for colorectal cancer.
Hsu, L. Y., David, K., Serotkin, A. V., Godmilov, L., & Hirschhorn, K. (1976) · PubMed · Cited by 652
Stewart, L. A., & Tierney, J. F. (2002) · Evaluation & the Health Professions · doi:10.1177/0163278702025001006 · Cited by 571
Simmonds, M. C., Higginsa, J. P. T., Stewartb, L. A., Tierneyb, J. F., Clarke, M. J., & Thompson, S. G. (2005) · Clinical Trials · doi:10.1191/1740774505cn087oa · Cited by 555
Between- and Within-Cluster Covariate Effects in the Analysis of Clustered Data
Neuhaus, J. M., & Kalbfleisch, J. D. (1998) · Biometrics · doi:10.2307/3109770 · Cited by 525
Pasquali, S., Maqueo, J. D., Vela, J. E., Kyle, R. A., Leong, T. L., Fritz, E., Suciu, S., PALVA, I. P., Oivanen, T. M., Corrado, C., et al. (1998) · Journal of Clinical Oncology · Cited by 470
Berlin, J. A., Santanna, J., Schmid, C. H., Szczech, L. A., & Feldman, H. I. (2002) · Statistics in Medicine · doi:10.1002/sim.1023 · Cited by 468
(1998) · Journal of Clinical Oncology · doi:10.1200/jco.1998.16.12.3832 · Cited by 433
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 394
Self-citationBegg, M. D., & Parides, M. K. (2003) · Statistics in Medicine · doi:10.1002/sim.1524 · Cited by 300
Open Mesh versus non-Mesh for groin hernia repair
Scott, N., Go, P. M. N. Y. H., Graham, P., McCormack, K., Ross, S. J., & Grant, A. M. (2001) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd002197 · Cited by 283
Systolic and Diastolic Blood Pressure Lowering as Determinants of Cardiovascular Outcome
Wang, J.-G., Staessen, J. A., Franklin, S. S., Fagard, R., & Gueyffier, F. (2005) · Hypertension · doi:10.1161/01.hyp.0000165020.14745.79 · Cited by 276
Self-citationMeta‐analysis of continuous outcome data from individual patients
Higgins, J. P. T., Whitehead, A., Turner, R. M., Omar, R. Z., & Thompson, S. G. (2001) · Statistics in Medicine · doi:10.1002/sim.918 · Cited by 265
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
Bivariate random-effects meta-analysis and the estimation of between-study correlation
Riley, R. D., Abrams, K. R., Sutton, A. J., Lambert, P. C., & Thompson, J. R. (2007) · BMC Medical Research Methodology · doi:10.1186/1471-2288-7-3 · Cited by 244
Self-citationRiley, R. D., Simmonds, M. C., & Look, M. P. (2007) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.09.009 · Cited by 195
Self-citationSimmonds, M. C., & Higgins, J. P. T. (2007) · Statistics in Medicine · doi:10.1002/sim.2768 · Cited by 194
Can meta-analysis help target interventions at individuals most likely to benefit?
Thompson, S. G., & Higgins, J. P. (2005) · The Lancet · doi:10.1016/s0140-6736(05)70200-2 · Cited by 174
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
Improving ecological inference using individual‐level data
Jackson, C., Best, N., & Richardson, S. (2006) · Statistics in Medicine · doi:10.1002/sim.2370 · Cited by 170
Riley, R. D., Thompson, J. R., & Abrams, K. R. (2008) · Biostatistics · doi:10.1093/biostatistics/kxm023 · Cited by 167
Self-citationMeta‐analysis of individual‐ and aggregate‐level data
Sutton, A. J., Kendrick, D., & Coupland, C. A. C. (2008) · Statistics in Medicine · doi:10.1002/sim.2916 · Cited by 161
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 141
Comparison of Meta-Analysis Versus Analysis of Variance of Individual Patient Data
Olkin, I., & Sampson, A. R. (1998) · Biometrics · doi:10.2307/2534018 · Cited by 138
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 103
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 94
Gueyffier, F., Boutitie, F., Boissel, J., Coope, J., Cutler, J., Ekbom, T., Fagard, R., Friedman, L., Perry, H. M., & Pocock, S. (1995) · PubMed · Cited by 69
Self-citationOn the Equivalence of Meta‐Analysis Using Literature and Using Individual Patient Data
Mathew, T., & Nordström, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.01221.x · Cited by 64
The Value of the Aggregate Data Approach in Meta-Analysis with Time-to-Event Outcomes
Smith, C. T., Williamson, P. R., Khan, S. A., & Best, L. (2001) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/1467-985x.00207 · Cited by 49
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
Between-subject and within-subject Statistical Information in Dental Research
Mancl, L., Leroux, B. G., & DeRouen, T. A. (2000) · Journal of Dental Research · doi:10.1177/00220345000790100801 · Cited by 37
Dwyer, T., & Blizzard, L. (2005) · Paediatric and Perinatal Epidemiology · doi:10.1111/j.1365-3016.2005.00615.x · Cited by 20
Cited by
Systematic reviews: CRD's guidance for undertaking reviews in health care
Tacconelli, E. (2010) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(10)70065-7 · Cited by 3,245
Stewart, L. A., Clarke, M., Rovers, M., Riley, R. D., Simmonds, M., Stewart, G., & Tierney, J. F. (2015) · JAMA · doi:10.1001/jama.2015.3656 · Cited by 2,134
Self-citationMeta-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
Self-citationBurke, D. L., Ensor, J., & Riley, R. D. (2017) · Statistics in Medicine · doi:10.1002/sim.7141 · Cited by 538
Self-citationDias, S., Sutton, A. J., Welton, N. J., & Ades, A. E. (2013) · Medical Decision Making · doi:10.1177/0272989x13485157 · Cited by 466
Metadata
- Authors
- Riley, R. D., Lambert, P. C., Staessen, J. A., Wang, J., Gueyffier, F., Thijs, L., & Boutitie, F.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2007-12-11
- Volume
- 27
- Issue
- 11
- Pages
- 1870-1893
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2043349008
- Cited by
- 279
- References
- 43
- FWCI
- 7.06
- Type
- journal article
- Language
- en
- Open access
- bronze
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.