Missing data: A statistical framework for practice
Open accessAbstract
Missing data are ubiquitous in medical research, yet there is still uncertainty over when restricting to the complete records is likely to be acceptable, when more complex methods (e.g. maximum likelihood, multiple imputation and Bayesian methods) should be used, how they relate to each other and the role of sensitivity analysis. This article seeks to address both applied practitioners and researchers interested in a more formal explanation of some of the results. For practitioners, the framework, illustrative examples and code should equip them with a practical approach to address the issues raised by missing data (particularly using multiple imputation), alongside an overview of how the various approaches in the literature relate. In particular, we describe how multiple imputation can be readily used for sensitivity analyses, which are still infrequently performed. For those interested in more formal derivations, we give outline arguments for key results, use simple examples to show how methods relate, and references for full details. The ideas are illustrated with a cohort study, a multi‐centre case control study and a randomised clinical trial.
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
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?
MI assumes data are missing at random (MAR), but missingness in loneliness and anxiety/depression was likely due to participants experiencing these conditions, making MI unreliable.
“MI assumes data are missing at random (MAR), but missingness in loneliness and anxiety/depression was likely due to participants experiencing these conditions, making MI unreliable [111].”
From The prospective association of adolescent loneliness and low resilience with anxiety and depression in young adulthood: The HUNT study (2025) · cites this paper as [111]
The Multiple imputation section (page unavailable) says standard MI draws missing values from a distribution estimated under MAR. The Beyond Mar: Sensitivity Analysis section (page unavailable) also describes MI approaches for departures from MAR. The paper does not assess loneliness or anxiety/depression missingness, or establish that MI was unreliable for those variables. (LLM, conf=0.88)
Experimental — accuracy not yet measuredWeighting does not fully remove bias when missingness relates to the outcome.
“Weighting does not fully remove bias when missingness relates to the outcome [111].”
From The prospective association of adolescent loneliness and low resilience with anxiety and depression in young adulthood: The HUNT study (2025) · cites this paper as [111]
The Inverse probability weighting section says weighting will not always eliminate bias, which supports the concern in the claim. But it also gives an example where weighting eliminates bias even though missingness depends on the data value; the claim overstates a possibility as an invariable result. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works
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
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,384 · see citing works
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
the CONSORT Group, Schulz, K. F., Altman, D. G., & Moher, D. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-18 · Cited by 13,371 · see citing works
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925 · see citing works
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
Schulz, K. F., Altman, D. G., Moher, D., & for the CONSORT Group (2010) · BMJ · doi:10.1136/bmj.c332 · Cited by 9,329 · see citing works
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473 · see citing works
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009) · BMJ · doi:10.1136/bmj.b2393 · Cited by 7,299 · see citing works
Self-citationAnalysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012 · see citing works
Analysis of Incomplete Multivariate Data
Booth, D. E., & Schafer, J. L. (2000) · Technometrics · doi:10.2307/1271466 · Cited by 5,627 · see citing works
A generalization of sampling without replacement from a finite universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483446 · Cited by 4,293 · see citing works
Multiple imputation of discrete and continuous data by fully conditional specification
van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827 · see citing works
A comparison of inclusive and restrictive strategies in modern missing data procedures.
Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.4.330 · Cited by 2,389 · see citing works
Flexible Imputation of Missing Data, Second Edition
van Buuren, S. (2018) · doi:10.1201/9780429492259 · Cited by 2,273 · see citing works
Finding the Observed Information Matrix When Using the EM Algorithm
Louis, T. A. (1982) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1982.tb01203.x · Cited by 2,234 · see citing works
Statistical Analysis with Missing Data, Third Edition
Little, R. J. A., & Rubin, D. B. (2019) · Wiley series in probability and statistics · doi:10.1002/9781119482260 · Cited by 2,089 · see citing works
A multivariate technique for multiply imputing missing values using a sequence of regression models
Raghunathan, T. E., Lepkowski, J. M., Hoewyk, J. V., & Solenberger, P. W. (2001) · Survey methodology · Cited by 1,981 · see citing works
The prevention and treatment of missing data in clinical trials
Little, R. J., D'Agostino, R., Cohen, M. L., Dickersin, K., Emerson, S. S., Farrar, J. T., Frangakis, C., Hogan, J. W., Molenberghs, G., Murphy, S. A., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmsr1203730 · Cited by 1,884 · see citing works
Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
Robins, J. M., Rotnitzky, A. G., & Zhao, L. P. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476493 · Cited by 1,574 · see citing works
Principal Stratification in Causal Inference
Frangakis, C., & Rubin, D. B. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00021.x · Cited by 1,496 · see citing works
Applications of Mathematics to Medical Problems
M'kendrick, A. G. (1925) · Proceedings of the Edinburgh Mathematical Society · doi:10.1017/s0013091500034428 · Cited by 1,189 · see citing works
Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
Scharfstein, D. O., Rotnitzky, A. G., & Robins, J. M. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473862 · Cited by 1,021 · see citing works
Multiple Imputation and its Application
Carpenter, J. R., & Kenward, M. G. (2013) · doi:10.1002/9781119942283 · Cited by 913 · see citing works
Self-citationGeneralized Multilevel Structural Equation Modeling
Rabe‐Hesketh, S., Skrondal, A., & Pickles, A. R. (2004) · Psychometrika · doi:10.1007/bf02295939 · Cited by 833 · see citing works
Shah, A. D., Bartlett, J. W., Carpenter, J. R., Nicholas, O., & Hemingway, H. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwt312 · Cited by 810 · see citing works
Self-citationMultiple-Imputation Inferences with Uncongenial Sources of Input
Meng, X. (1994) · Statistical Science · doi:10.1214/ss/1177010269 · Cited by 761 · see citing works
Wood, A., White, I. R., & Thompson, S. G. (2004) · Clinical Trials · doi:10.1191/1740774504cn032oa · Cited by 546 · see citing works
Bartlett, J. W., Seaman, S. R., White, I. R., Carpenter, J. R., & Initiative, A. D. N. (2014) · Statistical Methods in Medical Research · doi:10.1177/0962280214521348 · Cited by 483 · see citing works
Self-citationEpidemiology and reporting of randomised trials published in PubMed journals
Chan, A.-W., & Altman, D. G. (2005) · The Lancet · doi:10.1016/s0140-6736(05)71879-1 · Cited by 474 · see citing works
A MISSING INFORMATION PRINCIPLE: THEORY AND APPLICATIONS
Orchard, T., & Woodbury, M. A. (1972) · University of California Press eBooks · doi:10.1525/9780520325883-036 · Cited by 451 · see citing works
The Prevention and Treatment of Missing Data in Clinical Trials
National Research Council (2010) · National Academies Press eBooks · doi:10.17226/12955 · Cited by 439 · see citing works
REALCOM-IMPUTESoftware for Multilevel Multiple Imputation with Mixed Response Types
Carpenter, J. R., Goldstein, H., & Kenward, M. G. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i05 · Cited by 406 · see citing works
Self-citationHandling missing data in RCTs; a review of the top medical journals
Bell, M. L., Fiero, M., Horton, N. J., & Hsu, C.-H. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-118 · Cited by 395 · see citing works
Strategies for multiple imputation in longitudinal studies.
Spratt, M., Carpenter, J., Sterne, J., Carlin, J., Heron, J., Henderson, J., & Tilling, K. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwq137 · Cited by 395 · see citing works
Self-citationHOW TO IMPUTE INTERACTIONS, SQUARES, AND OTHER TRANSFORMED VARIABLES
von Hippel, P. V. (2009) · Sociological Methodology · doi:10.1111/j.1467-9531.2009.01215.x · Cited by 383 · see citing works
Bayesian inference for causal effects in randomized experiments with noncompliance
Imbens, G. W., & Rubin, D. B. (1997) · The Annals of Statistics · doi:10.1214/aos/1034276631 · Cited by 382 · see citing works
Intent-to-Treat Analysis for Longitudinal Studies with Drop-Outs
Little, R. J. A., & Yau, L. (1996) · Biometrics · doi:10.2307/2532847 · Cited by 380 · see citing works
Lee, K. J., Tilling, K. M., Cornish, R., Little, R. J. A., Bell, M. L., Goetghebeur, E. J. T., Hogan, J. W., & Carpenter, J. R. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.01.008 · Cited by 380 · see citing works
Self-citationPerforming likelihood ratio tests with multiply-imputed data sets
Meng, X., & Rubin, D. B. (1992) · Biometrika · doi:10.1093/biomet/79.1.103 · Cited by 357 · see citing works
Handbook of Missing Data Methodology
Molenberghs, G., Fitzmaurice, G. M., Kenward, M. G., Tsiatis, A. A., & Verbeke, G. N. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b17622 · Cited by 354 · see citing works
Combining Multiple Imputation and Inverse‐Probability Weighting
Seaman, S. R., White, I. R., Copas, A. J., & Li, L. (2011) · Biometrics · doi:10.1111/j.1541-0420.2011.01666.x · Cited by 308 · see citing works
Carpenter, J. R., Roger, J. H., & Kenward, M. G. (2013) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2013.834911 · Cited by 286 · see citing works
Self-citationCarpenter, R. G., McGarvey, C., Mitchell, E. A., Tappin, D. M., Vennemann, M. M., Smuk, M., & Carpenter, J. R. (2013) · BMJ Open · doi:10.1136/bmjopen-2012-002299 · Cited by 268 · see citing works
Self-citationLeyrat, C., Seaman, S. R., White, I. R., Douglas, I., Smeeth, L. L., Kim, J., Resche‐Rigon, M., Carpenter, J. R., & Williamson, E. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217713032 · Cited by 254 · see citing works
Self-citationLi, K. H., Raghunathan, T. E., & Rubin, D. B. (1991) · Journal of the American Statistical Association · doi:10.1080/01621459.1991.10475152 · Cited by 246 · see citing works
The Multiple Adaptations of Multiple Imputation
Reiter, J. P., & Raghunathan, T. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214507000000932 · Cited by 245 · see citing works
Significance levels from repeated p-values with multiply imputed data
Li, K. H., Meng, X., Raghunathan, T. E., & Rubin, D. B. (1991) · Statistica Sinica · Cited by 241 · see citing works
A Comparison of Multiple Imputation and Doubly Robust Estimation for Analyses with Missing Data
Carpenter, J. R., Kenward, M. G., & Vansteelandt, S. (2006) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2006.00407.x · Cited by 240 · see citing works
Self-citationUse of Multiple Imputation in the Epidemiologic Literature
Klebanoff, M. A., & Cole, S. R. (2008) · American Journal of Epidemiology · doi:10.1093/aje/kwn071 · Cited by 238 · see citing works
Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables
White, I., Daniel, R., & Royston, P. (2010) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2010.04.005 · Cited by 223 · see citing works
Cro, S., Morris, T. P., Kenward, M. G., & Carpenter, J. R. (2020) · Statistics in Medicine · doi:10.1002/sim.8569 · Cited by 203 · see citing works
Self-citationMultilevel models with multivariate mixed response types
Goldstein, H., Carpenter, J. R., Kenward, M. G., & Levin, K. A. (2009) · Statistical Modelling · doi:10.1177/1471082x0800900301 · Cited by 201 · see citing works
Self-citationLi, K. H., Raghunathan, T. E., & Rubin, D. B. (1991) · Journal of the American Statistical Association · doi:10.2307/2290525 · Cited by 186 · see citing works
Busse, W. W., Chervinsky, P., Condemi, J. J., Lumry, W. R., Petty, T. L., Rennard, S. I., & Townley, R. G. (1998) · Journal of Allergy and Clinical Immunology · doi:10.1016/s0091-6749(98)70353-7 · Cited by 184 · see citing works
Seaman, S. R., Bartlett, J. W., & White, I. R. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-46 · Cited by 177 · see citing works
Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression
Bartlett, J. W., Harel, O., & Carpenter, J. R. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv114 · Cited by 161 · see citing works
Self-citationjomo: A Flexible Package for Two-level Joint Modelling Multiple Imputation
Quartagno, M., Grund, S., & Carpenter, J. R. (2019) · The R Journal · doi:10.32614/rj-2019-028 · Cited by 142 · see citing works
Self-citationOn the Bias of the Multiple-Imputation Variance Estimator in Survey Sampling
Kim, J. K., Brick, J. M., Fuller, W. A., & Kalton, G. (2006) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2006.00546.x · Cited by 130 · see citing works
Goldstein, H., Carpenter, J. R., & Browne, W. J. (2013) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12022 · Cited by 121 · see citing works
Self-citationQuartagno, M., & Carpenter, J. R. (2015) · Statistics in Medicine · doi:10.1002/sim.6837 · Cited by 119 · see citing works
Self-citationSchafer, J. L. (2001) · American Psychological Association eBooks · doi:10.1037/10409-012 · Cited by 112 · see citing works
Multiple Imputation of Covariates by Substantive-model Compatible Fully Conditional Specification
Bartlett, J., & Morris, T. (2015) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x1501500206 · Cited by 104 · see citing works
Appropriate inclusion of interactions was needed to avoid bias in multiple imputation
Tilling, K. M., Williamson, E., Spratt, M., Sterne, J. A. C., & Carpenter, J. R. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.07.004 · Cited by 99 · see citing works
Self-citationNevalainen, J., Kenward, M. G., & Virtanen, S. (2009) · Statistics in Medicine · doi:10.1002/sim.3731 · Cited by 93 · see citing works
Analysis of Incomplete Data Using Inverse Probability Weighting and Doubly Robust Estimators
Vansteelandt, S., Carpenter, J. R., & Kenward, M. G. (2010) · Methodology · doi:10.1027/1614-2241/a000005 · Cited by 85 · see citing works
Self-citationJoint modelling rationale for chained equations
Hughes, R. A., White, I. R., Seaman, S. R., Carpenter, J. R., Tilling, K. M., & Sterne, J. A. C. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-28 · Cited by 84 · see citing works
Self-citationReiter, J. P. (2007) · Biometrika · doi:10.1093/biomet/asm028 · Cited by 79 · see citing works
Analysis of Longitudinal Binary Data from Multiphase Sampling
Clayton, D. G., Spiegelhalter, D. J., Dunn, G., & Pickles, A. R. (1998) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00109 · 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
Subsample Ignorable Likelihood for Regression Analysis with Missing Data
Little, R. J. A., & Zhang, N. (2011) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2011.00763.x · Cited by 71 · see citing works
On the use of the not‐at‐random fully conditional specification (NARFCS) procedure in practice
Tompsett, D., Leacy, F. P., Moreno‐Betancur, M., Heron, J., & White, I. R. (2018) · Statistics in Medicine · doi:10.1002/sim.7643 · Cited by 64 · see citing works
Multiple Imputation with Survey Weights: A Multilevel Approach
Quartagno, M., Carpenter, J. R., & Goldstein, H. (2019) · Journal of Survey Statistics and Methodology · doi:10.1093/jssam/smz036 · Cited by 64 · see citing works
Self-citationWelch, C., Petersen, I., Bartlett, J., White, I., Marston, L., Morris, R., Nazareth, I., Walters, K., & Carpenter, J. (2014) · Statistics in Medicine · doi:10.1002/sim.6184 · Cited by 60 · see citing works
Self-citationMultiple imputation for discrete data: Evaluation of the joint latent normal model
Quartagno, M., & Carpenter, J. R. (2019) · Biometrical Journal · doi:10.1002/bimj.201800222 · Cited by 51 · see citing works
Self-citationInformation-Anchored Sensitivity Analysis: Theory and Application
Cro, S., Carpenter, J. R., & Kenward, M. G. (2018) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12423 · Cited by 47 · see citing works
Self-citationMason, A. J., Gomes, M., Grieve, R., Ulug, P., Powell, J. T., & Carpenter, J. R. (2017) · Clinical Trials · doi:10.1177/1740774517711442 · Cited by 44 · see citing works
Self-citationMultiple imputation in Cox regression when there are time‐varying effects of covariates
Keogh, R. H., & Morris, T. P. (2018) · Statistics in Medicine · doi:10.1002/sim.7842 · Cited by 42 · see citing works
Kalaycıoğlu, O., Copas, A. J., King, M., & Omar, R. (2015) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12140 · Cited by 41 · see citing works
Common Methods for Handling Missing Data in Marginal Structural Models: What Works and Why
Leyrat, C., Carpenter, J. R., Bailly, S., & Williamson, E. (2020) · American Journal of Epidemiology · doi:10.1093/aje/kwaa225 · Cited by 39 · see citing works
Self-citationComparison of imputation variance estimators
Hughes, R. A., Sterne, J. A. C., & Tilling, K. M. (2014) · Statistical Methods in Medical Research · doi:10.1177/0962280214526216 · Cited by 38 · see citing works
Reference‐based sensitivity analysis for time‐to‐event data
Atkinson, A., Kenward, M. G., Clayton, T. C., & Carpenter, J. R. (2019) · Pharmaceutical Statistics · doi:10.1002/pst.1954 · Cited by 32 · see citing works
Self-citationMissing covariates in competing risks analysis
Bartlett, J. W., & Taylor, J. M. G. (2016) · Biostatistics · doi:10.1093/biostatistics/kxw019 · Cited by 31 · see citing works
Multiple imputation for an incomplete covariate that is a ratio
Morris, T. P., White, I. R., Royston, P. J., Seaman, S. R., & Wood, A. (2013) · Statistics in Medicine · doi:10.1002/sim.5935 · Cited by 30 · see citing works
Smuk, M., Carpenter, J. R., & Morris, T. P. (2017) · BMC Medical Research Methodology · doi:10.1186/s12874-017-0301-0 · Cited by 22 · see citing works
Self-citationA method for increasing the robustness of multiple imputation
Daniel, R., & Kenward, M. G. (2011) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2011.10.006 · Cited by 21 · see citing works
Analysing Longitudinal Studies with Non-response: Issues and Statistical Methods
Carpenter, J. R., & Plewis, I. (2014) · SAGE Publications Ltd eBooks · doi:10.4135/9781446268261.n28 · Cited by 20 · see citing works
Self-citationA framework for extending trial design to facilitate missing data sensitivity analyses
Mason, A. J., Grieve, R., Richards‐Belle, A., Mouncey, P. R., Harrison, D. A., & Carpenter, J. R. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-00930-2 · Cited by 9 · see citing works
Self-citationLee, K. J., Tilling, K. M., Cornish, R., & Carpenter, J. R. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyab168.371 · Cited by 7 · see citing works
Self-citationHeitjan, D. F. (2017) · Clinical Trials · doi:10.1177/1740774517711443 · Cited by 6 · see citing works
Sensitivity Analysis with Multiple Imputation
Carpenter, M. P., & Kenward, M. (2014) · CRC Press eBooks · doi:10.1201/9781439854624-25 · Cited by 5 · see citing works
Missing Data Methodology: Sensitivity analysis after multiple imputation
Smuk, M. (2015) · LSHTM Research Online (London School of Hygiene and Tropical Medicine) · doi:10.17037/pubs.02212896 · Cited by 4 · see citing works
Self-citationMultiple Imputation‐Based Sensitivity Analysis
Carpenter, J. R. (2019) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat07852 · Cited by 2 · see citing works
Self-citationMultiple imputation of missing blood pressure covariates in survival analysis
van Buuren, S., Boshuizen, H. C., & Knook, D. L. (1999) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19990330)18:6
Flagged first, then by citations
1 of the 94 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Carpenter JR Kenward MG Development of methods and critique of ad-hoc methods Molenberghs G Fitzmaurice G Kenward MG Tsiatis A Verbeke G Handbook of missing data methodology 2015b 23 46 CRC press
- Carpenter JR Kenward MG Vansteelandt S A comparison of multiple imputation and inverse probability weighting for analyses with missing data Journal of the Royal Statistical Society, Series A (Statistics in Society) 2006 169 571 584
- Cro S Carpenter JR Kenward MG Information anchored sensitivity analysis Journal of the Royal Statistical Society: Series A (Statistics in Society) 2019 182 2 623 645 30828138 PMC6378615
- Kennickel AB Imputation of the 1989 survey of consumer finances Proceedings of the Section on Survey Research Methods, 1990 1991
- Mason AJ Gomes M Grieve R Ulug P Powell JT CarpenterC JR Rejoinder to commentary on ‘Development of a practical approach to expert elicitation for randomised controlled trials with missing health outcomes: Application to the IMPROVE Trial’ Clinical Trials 2017b 14 372 373 28675302 PMC5648050
Cited by
Sorted by citations
Guo, B., Guo, Y., Nima, Q., Feng, Y., Wang, Z., Lu, R., Baimayangji, Ma, Y., Zhou, J., Xu, H. Y., et al. (2021) · Journal of Hepatology · doi:10.1016/j.jhep.2021.10.016 · Cited by 238 · see citing works
Little, R. J. A., Carpenter, J. R., & Lee, K. J. (2022) · Sociological Methods & Research · doi:10.1177/00491241221113873 · Cited by 116 · see citing works
Self-citationTreating gaps and biases in biodiversity data as a missing data problem
Bowler, D. E., Boyd, R. J., Callaghan, C. T., Robinson, R. A., Isaac, N. J. B., & Pocock, M. J. O. (2024) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.13127 · Cited by 90 · see citing works
Hou, W., Lin, C., Yu, L., Qin, J., Yu, R., & Wang, L. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3253760 · Cited by 74 · see citing works
Explainable Machine Learning Model for Chronic Kidney Disease Prediction
Arif, M. S., Rehman, A. U., & Asif, D. (2024) · Algorithms · doi:10.3390/a17100443 · Cited by 56 · see citing works
Junaid, M. A., Slack-Smith, L. M., Wong, K., Bourke, J., Baynam, G. S., Calache, H., & Leonard, H. (2022) · Pediatric Research · doi:10.1038/s41390-022-02024-9 · Cited by 34 · see citing works
Meijs, D. A. M., van Bussel, B. C. T., Stessel, B., Mehagnoul-Schipper, J., Hana, A., Scheeren, C. I. E., Peters, S. A. E., van Mook, W., van der Horst, I. C. C., Marx, G., et al. (2022) · Scientific Reports · doi:10.1038/s41598-021-04531-x · Cited by 30 · see citing works
Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation
Karthick, K., Shanmugasundaram, R., & Priyanka, R. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15020060 · Cited by 31 · see citing works
Fleurence, R., Kent, S., Adamson, B. J. S., Tcheng, J. E., Balicer, R., Ross, J. S., Haynes, K., Muller, P., Campbell, J., Bouée-Benhamiche, E., et al. (2024) · Value in Health · doi:10.1016/j.jval.2024.01.019 · Cited by 30 · see citing works
Baddoo, T. D., Li, Z., Odai, S. N., Boni, K. R. C., Nooni, I. K., & Andam‐Akorful, S. A. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18168375 · Cited by 29 · see citing works
Addressing missing outcome data in randomised controlled trials: A methodological scoping review
Medcalf, E., Turner, R. M., Espinoza, D., He, V., & Bell, K. (2024) · Contemporary Clinical Trials · doi:10.1016/j.cct.2024.107602 · Cited by 29 · see citing works
Conitz, F., Zingraff‐Hamed, A., Lupp, G., & Pauleit, S. (2021) · Hydrology · doi:10.3390/hydrology8040167 · Cited by 27 · see citing works
Missing data: Issues, concepts, methods
Pham, T. M., Pandis, N., & White, I. R. (2024) · Seminars in Orthodontics · doi:10.1053/j.sodo.2024.01.007 · Cited by 25 · see citing works
Fatigue in early rheumatoid arthritis: data from the Early Rheumatoid Arthritis Network
Ifesemen, O. S., McWilliams, D. F., Norton, S., Kiely, P. D. W., Young, A., & Walsh, D. A. (2021) · Lara D. Veeken · doi:10.1093/rheumatology/keab947 · Cited by 24 · see citing works
Curnow, E., Carpenter, J. R., Heron, J., Cornish, R., Rach, S., Didelez, V., Langeheine, M., & Tilling, K. M. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2023.06.011 · Cited by 24 · see citing works
Self-citationBravo, L., Simoes, J. F. F., Cardoso, V. R., Adisa, A., Aguilera, M. L., Arnaud, A. P., Biccard, B. M., Calvache, J. A., Chernbumroong, S., Elhadi, M., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00065-7 · Cited by 22 · see citing works
Data cleaning for clinician researchers: Application and explanation of a data-quality framework
Pilowsky, J. K., Elliott, R. M., & Roche, M. A. (2024) · Australian Critical Care · doi:10.1016/j.aucc.2024.03.004 · Cited by 28 · see citing works
Comprehensive analysis of stress factors affecting students: a machine learning approach
de Filippis, R., & Foysal, A. A. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00169-6 · Cited by 33 · see citing works
Mathur, M. B. (2022) · American Journal of Epidemiology · doi:10.1093/aje/kwac207 · Cited by 21 · see citing works
Regional Climate Change and Drought Dynamics in Tunceli, Turkey: Insights from Drought Indices
Korkmaz, M., & Kuriqi, A. (2024) · Water Conservation Science and Engineering · doi:10.1007/s41101-024-00281-9 · Cited by 20 · see citing works
Holfeld, J., Nägele, F., Pölzl, L., Engler, C., Graber, M., Hirsch, J., Schmidt, S., Mayr, A., Troger, F., Pamminger, M., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae341 · Cited by 19 · see citing works
Deforth, M. E., Heinze, G., & Held, U. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111539 · Cited by 19 · see citing works
Sarri, G., Bennett, D., Debray, T. P. A., Déruaz‐Luyet, A., Gabarró, M. S., Largent, J. A., Li, X., Liu, W., Lund, J. L., Moga, D. C., et al. (2022) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2560 · Cited by 18 · see citing works
Random Assignment with Nonrandom Peers: A Structural Approach to Counterfactual Treatment Assessment
Griffith, A. (2022) · The Review of Economics and Statistics · doi:10.1162/rest_a_01197 · Cited by 18 · see citing works
Xuan, J., Mt‐Isa, S., Latimer, N. R., Bell‐Gorrod, H., Malbecq, W., Vandormael, K., Yorke-Edwards, V., & White, I. R. (2024) · Statistical Methods in Medical Research · doi:10.1177/09622802241289559 · Cited by 15 · see citing works
Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach
Ahmed, H. M., Abdulrazak, B., Blanchet, F. G., Aloulou, H., & Mokhtari, M. (2022) · IEEE Access · doi:10.1109/access.2022.3218785 · Cited by 17 · see citing works
Gomes, M., Turner, A. J., Sammon, C., Dawoud, D., Ramagopalan, S. V., Simpson, A., & Siebert, U. (2024) · Value in Health · doi:10.1016/j.jval.2024.01.020 · Cited by 16 · see citing works
Sondhi, A., Weberpals, J. G., Yerram, P., Jiang, C., Taylor, M., Samant, M. K., & Cherng, S. T. (2023) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12998 · Cited by 15 · see citing works
Infant Sleep Hazards and the Risk of Sudden Unexpected Death in Infancy
MacFarlane, M., Thompson, J., Wilson, J., LAWTON, B. A., Taylor, B. J., Elder, D. E., Baker, N., McDonald, G., ZUCCOLLO, J. M., Schlaud, M., et al. (2022) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2022.01.044 · Cited by 14 · see citing works
Handling missing covariate data in clinical studies in haematology
Bonneville, E. F., Schetelig, J., Putter, H., & de Wreede, L. C. (2023) · Best Practice & Research Clinical Haematology · doi:10.1016/j.beha.2023.101477 · Cited by 13 · see citing works
Missing values and inconclusive results in diagnostic studies – A scoping review of methods
Stahlmann, K., Reitsma, J. B., & Zapf, A. (2023) · Statistical Methods in Medical Research · doi:10.1177/09622802231192954 · Cited by 13 · see citing works
A comprehensive survey of imputation methods in medical missing data analysis
Benhamza, K., Benselim, R., Naidja, H., & Séridi, H. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06602-2 · Cited by 13 · see citing works
Bann, D., Wright, L., Goisis, A., Hardy, R., Johnson, W., Maddock, J., McElroy, E., Moulton, V., Patalay, P., Scholes, S., et al. (2022) · Discover Social Science and Health · doi:10.1007/s44155-022-00021-1 · Cited by 13 · see citing works
Curnow, E., Cornish, R., Heron, J., Carpenter, J. R., & Tilling, K. M. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02353-9 · Cited by 12 · see citing works
Self-citationCurnow, E., Tilling, K. M., Heron, J., Cornish, R., & Carpenter, J. R. (2023) · Frontiers in Epidemiology · doi:10.3389/fepid.2023.1237447 · Cited by 11 · see citing works
Self-citationSegura‐Buisan, J., Leyrat, C., & Gomes, M. (2023) · Statistics in Medicine · doi:10.1002/sim.9899 · Cited by 11 · see citing works
Cole, E. M., Crouch, R., Baxter, M. A., Wang, C., Sivapathasuntharam, D., Peck, G., Jennings, C., & Jarman, H. (2024) · Scandinavian Journal of Trauma Resuscitation and Emergency Medicine · doi:10.1186/s13049-023-01169-8 · Cited by 11 · see citing works
Resurrecting complete-case analysis: a defense
Mathur, M. B., Shpitser, I., & VanderWeele, T. J. (2025) · American Journal of Epidemiology · doi:10.1093/aje/kwaf284 · Cited by 10 · see citing works
Assessing alternative imputation strategies for infrequently missing items on multi-item scales
Rezvan, P. H., Comulada, W. S., Fernandez, M. I., & Belin, T. R. (2022) · Communications in Statistics Case Studies Data Analysis and Applications · doi:10.1080/23737484.2022.2115430 · Cited by 10 · see citing works
Weberpals, J. G., Raman, S. R., Shaw, P. A., Lee, H., Russo, M., Hammill, B. G., Toh, S., Connolly, J. G., Dandreo, K. J., Tian, F., et al. (2024) · Clinical Epidemiology · doi:10.2147/clep.s436131 · Cited by 10 · see citing works
Oberle, E., Fan, S., Molyneux, T. M., Ji, X. R., & BRUSSONI, M. J. (2025) · BMC Public Health · doi:10.1186/s12889-025-21857-7 · Cited by 11 · see citing works
Risk of bias assessment tool for systematic review and meta-analysis of the gut microbiome
Lampeter, T., Love, C., Tang, T. T., Marella, A. S., Lee, H. Y., Oganyan, A. K., Moffat, D., Kareem, A., Rusling, M., Massmann, A., et al. (2023) · Gut Microbiome · doi:10.1017/gmb.2023.12 · Cited by 9 · see citing works
Paneru, B., Mainali, D. P., Paneru, B., & Sapkota, S. C. (2024) · Systems and Soft Computing · doi:10.1016/j.sasc.2024.200157 · Cited by 9 · see citing works
Kawabata, E., Major‐Smith, D., Clayton, G., Shapland, C. Y., Morris, T. P., Carter, A. R., Fernández‐Sanlés, A., Borges, M. C., Tilling, K. M., Griffith, G. J., et al. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02382-4 · Cited by 9 · see citing works
Zhou, C. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1475863 · Cited by 9 · see citing works
Missing data in ecology: Syntheses, clarifications, and considerations
Dumelle, M., Trangucci, R., Nahlik, A. M., Olsen, A. R., Irvine, K. M., Blocksom, K. A., Hoef, J. M. V., & Fuentes, C. (2025) · Ecological Monographs · doi:10.1002/ecm.70037 · Cited by 9 · see citing works
Vinke, P. C., Combalia, M., de Bock, G. H., Leyrat, C., Spanjaart, A. M., Dalle, S., da Silva, M. G., Essongue, A., Rabier, A., Pannard, M., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-069090 · Cited by 9 · see citing works
Cano, M. N., Jones, A. A., Silverstein, S. M., Daniulaityte, R., & LoVecchio, F. (2024) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2024.104678 · Cited by 8 · see citing works
Li, W., Wang, B., Li, T., Ma, Y., Jin, H., Zhao, J., Xue, Z., Su, N., He, Y., Shi, J., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02370-6 · Cited by 8 · see citing works
Buick, J. E., Austin, P. C., Cheskes, S., Ko, D. T., & Atzema, C. L. (2023) · Academic Emergency Medicine · doi:10.1111/acem.14756 · Cited by 8 · see citing works
Skarstein, E. S., Martino, S., & Muff, S. (2023) · Biometrical Journal · doi:10.1002/bimj.202300078 · Cited by 8 · see citing works
Yiend, J., Taher, R., Fialho, C., Hampshire, C., Hsu, C.-W., Kabir, T., Keppens, J., McGuire, P. K., Mouchlianitis, E. D., PETERS, E. R., et al. (2024) · Trials · doi:10.1186/s13063-024-08570-3 · Cited by 7 · see citing works
Bed-sharing is a risk for sudden unexpected death in infancy
Tappin, D. M., Mitchell, E. A., Carpenter, J. R., Hauck, F. R., & Allan, L. (2021) · Archives of Disease in Childhood · doi:10.1136/archdischild-2021-322480 · Cited by 8 · see citing works
Self-citationWei, X., Yan, X., Guo, Y., Zhang, J., Wang, G., Fayyaz, A., & Yu, J. (2022) · World Journal of Gastroenterology · doi:10.3748/wjg.v28.i36.5338 · Cited by 8 · see citing works
Parlikar, N., Strand, L. B., Kvaløy, K., Espnes, G. A., & Moksnes, U. K. (2025) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-025-02888-2 · Cited by 7 · see citing works
“Likewise, loneliness and social isolation during adolescence precipitate a state of vulnerability Weighting does not fully remove bias when missingness relates to the outcome [111].”
Yan, M.-Q., Zhou, L., Zhao, C., Shi, C., & Ou, C. (2025) · Health and Quality of Life Outcomes · doi:10.1186/s12955-025-02364-0 · Cited by 7 · see citing works
Development and Validation of a Hybrid Machine Learning Model to Predict Lung Transplant Outcomes
Sharma, G., Kamal, V. K., Bollineni, S., Timofte, I., Plasencia, J. D., Lesaja, S., Kaza, V., Keshavamurthy, S., Murala, J. S. K., Peltz, M., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.45369 · Cited by 7 · see citing works
Fu, H., Wang, C., Afzal, I., Kader, D. F., Asopa, V., & Sochart, D. H. (2023) · The Knee · doi:10.1016/j.knee.2023.02.016 · Cited by 7 · see citing works
Imputation methods for informative censoring in survival analysis with time dependent covariates
Jin, M. (2023) · Contemporary Clinical Trials · doi:10.1016/j.cct.2023.107401 · Cited by 7 · see citing works
Statistical Methods for Pre-Post Intervention Design
Dziadkowiec, O. (2023) · Journal of Obstetric, Gynecologic & Neonatal Nursing · doi:10.1016/j.jogn.2023.11.010 · Cited by 7 · see citing works
Anderegg, J., Brefin, S. L., Nigg, C. R., Koschnick, D., Paul, C., & Ketelhut, S. (2025) · European Journal of Sport Science · doi:10.1002/ejsc.70031 · Cited by 7 · see citing works
Hazewinkel, A., Bowden, J., Wade, K. H., Palmer, T., Wiles, N., & Tilling, K. M. (2022) · Statistics in Medicine · doi:10.1002/sim.9299 · Cited by 7 · see citing works
Ly, A., Heron, J., Rai, D., & Wright, C. (2022) · Psychological Medicine · doi:10.1017/s0033291722000940 · Cited by 7 · see citing works
Harrer, M., Ebert, D. D., Kuper, P. R., Paganini, S., Schlicker, S., Terhorst, Y., Reuter, B., Sander, L., & Baumeister, H. (2023) · Internet Interventions · doi:10.1016/j.invent.2023.100634 · Cited by 7 · see citing works
Mayer, B., Elbing, U., & Ostermann, T. (2023) · Journal of Intellectual Disability Research · doi:10.1111/jir.13076 · Cited by 6 · see citing works
Feasibility and acceptability of an online epilepsy stigma self-management program
Margolis, S. A., Prieto, S., Goldstein, A., Kaden, S. J., Castillo, E., Sadhu, S., Solanki, D., Larracey, E. T., Tremont, G. N., Mankodiya, K., et al. (2025) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2025.110331 · Cited by 6 · see citing works
Gambazza, S., Bargeri, S., Campanini, I., Meroni, R., Turolla, A., Castellini, G., Gianola, S., Gambazza, S., Bargeri, S., Bertozzi, L., et al. (2021) · Archives of Physiotherapy · doi:10.1186/s40945-021-00125-y · Cited by 6 · see citing works
Rzigui, H., Bogacki, W., Hörmann, G., & Hachicha, M. (2022) · Irrigation and Drainage · doi:10.1002/ird.2723 · Cited by 6 · see citing works
Schröder, T., Grube, H. C. B., Kühn, G., Sina, C., Thaçi, D., Nitschke, M. F., & König, I. R. (2022) · Trials · doi:10.1186/s13063-022-06933-2 · Cited by 6 · see citing works
Do We Really Need Imputation in AutoML Predictive Modeling?
Paterakis, G., Fafalios, S., Charonyktakis, P., Christophides, V., & Tsamardinos, I. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3643643 · Cited by 6 · see citing works
Evaluating a targeted support program for mental health carers: a randomised controlled trial
Fitzgeraldson, E., Fitzpatrick, S., Dizon, J., & KAY‐LAMBKIN, F. J. (2024) · Advances in Mental Health · doi:10.1080/18387357.2024.2324103 · Cited by 6 · see citing works
Mathur, M. B., VanderWeele, T. J., & Shpitser, I. (2023) · doi:10.31219/osf.io/ths4e · Cited by 6 · see citing works
Rop, M., Maposa, I., Young, T., & Machekano, R. N. (2025) · BMC Medical Research Methodology · doi:10.1186/s12874-025-02633-y · Cited by 5 · see citing works
Tolani, E., Carpenter, M., Petersen, I., & Carpenter, J. R. (2025) · Clinical Epidemiology · doi:10.2147/clep.s528401 · Cited by 5 · see citing works
Self-citationKaewwinud, J., Pienchitlertkajorn, S., Koomtanapat, K., Lumkul, L., Wongyikul, P., & Phinyo, P. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e23440 · Cited by 5 · see citing works
Graul, E. L., Nordon, C., Rhodes, K. M., Menon, S., Al-Ammouri, M., Kallis, C., Ioannides, A. E., Whittaker, H. R., Peters, N. S., & Quint, J. K. (2024) · BMC Pulmonary Medicine · doi:10.1186/s12890-024-03035-4 · Cited by 5 · see citing works
A Multiple Imputation Workflow for Handling Missing Covariate Data in Pharmacometrics Modeling
Vuong, M. L., Verbeke, G. N., & Dreesen, E. (2025) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.70039 · Cited by 5 · see citing works
Multiple Imputation of Missing Covariates When Using the Fine–Gray Model
Bonneville, E. F., Beyersmann, J., Keogh, R. H., Bartlett, J. W., Morris, T. P., Polverelli, N., de Wreede, L. C., & Putter, H. (2025) · Statistics in Medicine · doi:10.1002/sim.70166 · Cited by 5 · see citing works
DIVIS: a semantic DIstance to improve the VISualisation of heterogeneous phenotypic datasets
Eid, R., Landès, C., Pernet, A., Benoît, E., Santagostini, P., Ghaziri, A. E., & Bourbeillon, J. (2022) · BioData Mining · doi:10.1186/s13040-022-00293-y · Cited by 5 · see citing works
Luijken, K., van de Wall, B. V. D., Hooft, L., Leenen, L., Houwert, R., & Groenwold, R. (2022) · European Journal of Trauma and Emergency Surgery · doi:10.1007/s00068-022-02031-9 · Cited by 2 · see citing works
Methods for Informative Censoring in Time-to-Event Data Analysis
Jin, M., & Fang, Y. (2023) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2023.2182355 · Cited by 5 · see citing works
Multiple imputation strategies for missing event times in a multi‐state model analysis
Curnow, E., Hughes, R. A., Birnie, K., Tilling, K. M., & Crowther, M. J. (2024) · Statistics in Medicine · doi:10.1002/sim.10011 · Cited by 5 · see citing works
YENİ BİR ÇOK KRİTERLİ KARAR VERME YÖNTEMİ: BULUT ENDEKS-BETA (BE-β)
Top, M., & Bulut, T. (2022) · Verimlilik dergisi · doi:10.51551/verimlilik.1031366 · Cited by 5 · see citing works
Enhancing data integrity in Electronic Health Records: Review of methods for handling missing dataPreprint
Vahdati, A., Cotterill, S., Marsden, A., & Kontopantelis, E. (2024) · medRxiv · doi:10.1101/2024.05.13.24307268 · Cited by 5 · see citing works
Saengsin, K., Sittiwangkul, R., Borisuthipandit, T., Wongyikul, P., Tanasombatkul, K., Phanacharoensawad, T., Moonsawat, G., Trongtrakul, K., & Phinyo, P. (2024) · Frontiers in Pediatrics · doi:10.3389/fped.2024.1346198 · Cited by 4 · see citing works
Dissecting Through the Literature: A Review of the Critical Appraisal Process
Almutairi, R., Alsarraf, A., Alkandari, D., Ashkanani, H. K. A. H. A., & AlBazali, A. A. (2024) · Cureus · doi:10.7759/cureus.59658 · Cited by 4 · see citing works
Bayesian semiparametric inference in longitudinal metabolomics data
Sarkar, A., Cominetti, O., Montoliu, I., Hosking, J., Pinkney, J. H., Martin, F., & Dunson, D. B. (2024) · Scientific Reports · doi:10.1038/s41598-024-82718-8 · Cited by 4 · see citing works
Qiu, Y., Yang, Y., Wang, J., Wang, Q., Zhao, S., & Ding, X. (2025) · Nurse Education Today · doi:10.1016/j.nedt.2025.106758 · Cited by 4 · see citing works
Necessary conditions for valid causal inference from observational data
Cox, L. A. (2025) · Critical Reviews in Toxicology · doi:10.1080/10408444.2025.2611827 · Cited by 4 · see citing works
Goudman, L., Molenberghs, G., Duarte, R. V., & Moens, M. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10214897 · Cited by 4 · see citing works
Gutman, C. K., Lion, K. C., Aronson, P. L., Fisher, C. L., Bylund, C. L., McFarlane, A., Lou, X., Patterson, M., Lababidi, A., & Fernandez, R. (2022) · BMJ Open · doi:10.1136/bmjopen-2022-063611 · Cited by 4 · see citing works
Spatial Language and Cognition in Autistic Preschoolers
Prescott, K. E., Crespo, K., & Weismer, S. E. (2023) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-022-05883-7 · Cited by 4 · see citing works
Hazewinkel, A., Tilling, K. M., Wade, K. H., & Palmer, T. (2023) · Biometrical Journal · doi:10.1002/bimj.202200116 · Cited by 4 · see citing works
Missing Step Count Data? Step Away From the Expectation–Maximization Algorithm
Tackney, M. S., Stahl, D. R., Williamson, E., & Carpenter, J. R. (2022) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2022-0002 · Cited by 4 · see citing works
Self-citationChen, L., Sun, Q., Chou, R., Anderson, D., Shi, B., Chen, Y., Liu, X., Feng, S., Zhou, H., & Ferreira, M. L. (2023) · International Journal of Surgery · doi:10.1097/js9.0000000000000951 · Cited by 4 · see citing works
Richter, C., Wildisen, L., Rohrmann, S., & Haile, S. R. (2024) · European Journal of Cancer Prevention · doi:10.1097/cej.0000000000000881 · Cited by 4 · see citing works
Missing data, part 4. Missing data in the outcomes vs in the covariates
Pham, T. M., Pandis, N., & White, I. R. (2022) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2022.06.001 · Cited by 4 · see citing works
Evaluating the statistical realism of LLM-generated social science data
Xie, Y., Liang, L., Li, S., Lu, Y., Xiao, Z., Shi, M., Huang, J., Wang, M., & Xie, Y. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2538145123 · Cited by 3 · see citing works
Nested Random Forest: A Personalized Imputation Method for Missing Data
Wang, K., Luo, M., Deng, M., & Chen, H. (2022) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia56350.2022.9874127 · Cited by 3 · see citing works
Zhang, P., & Xie, S. X. (2025) · Statistics in Biosciences · doi:10.1007/s12561-025-09493-6 · Cited by 3 · see citing works
- Authors
- Carpenter, J. R., & Smuk, M.
- Affiliations
- London School of Hygiene & Tropical Medicine (GB)
- Corresponding author
- Carpenter, James R.
- Venue
- Biometrical Journal
- Publisher
- Wiley
- Published
- 2021-02-24
- Volume
- 63
- Issue
- 5
- Pages
- 915-947
- ISSN
- 0323-3847 (ISSN-L)
- 1521-4036
- PMC
- PMC7615108
- OpenAlex
- W3130968222
- Cited by
- 198
- References
- 94
- Citations by year
- 2026: 38 · 2025: 54 · 2024: 36 · 2023: 32 · 2022: 31 · 2021: 7
- FWCI
- 4.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Biometrical Journal (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by-nc)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Funders
- Medical Research Council (MC_UU_12023/21, MC_UU_12023/29, MC_UU_00004/07)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- missing data, multiple imputation, sensitivity analysis, maximum likelihood estimation, Bayesian analysis, missing data mechanisms, cohort studies, case-control study, randomized controlled trials
- MeSH terms
- Bayes Theorem, Data Interpretation, Statistical, Humans, Cohort Studies, Case-Control Studies, Uncertainty
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
