{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T05:46:29Z","timestamp":1758087989816,"version":"3.44.0"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Appl. Math. Stat."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Dropout is a major source of missing data in repeated measures studies and can bias statistical inference if not handled properly. This study compares the performance of two common methods for addressing dropout under the missing at random (MAR) assumption: multiple imputation (MI) and inverse probability weighting (IPW).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A simulation study was conducted using repeated measures data generated from a marginal regression model with continuous outcomes. Two sample sizes (100 and 250) and three dropout rates (5%, 15%, and 30%) were considered. The methods were evaluated based on bias, coverage, and mean square error (MSE). In addition, the approaches were applied to serum cholesterol data from the National Cooperative Gallstone Study to illustrate their practical performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Simulation results showed that MI consistently produced lower bias and MSE and higher coverage compared to IPW, particularly at moderate to high dropout rates. The empirical analysis of serum cholesterol data indicated that while both methods yielded similar inferential conclusions regarding treatment effects, MI estimates were more stable and precise than those obtained from IPW.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This analysis demonstrates that MI outperforms IPW in handling MAR dropout for continuous repeated measures data. The findings support the use of MI as a more reliable approach, especially in studies with moderate to high dropout rates, although IPW may remain acceptable under low dropout rate.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fams.2025.1636153","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T04:14:09Z","timestamp":1757996049000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating two statistical methods for handling dropout impact in repeated measures data: a comparative study on missing at random assumption"],"prefix":"10.3389","volume":"11","author":[{"given":"Mohyaldein","family":"Salih","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Satty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henry","family":"Mwambi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1093\/biomet\/63.3.581","article-title":"Inference and missing data","volume":"63","author":"Rubin","year":"1976","journal-title":"Biometrika."},{"key":"B2","first-page":"20","article-title":"Multiple imputations in sample surveys \u2013 a phenomenological Bayesian approach to nonresponse","volume":"1","author":"Rubin","year":"1978","journal-title":"Proc Survey Res Methods Sect Am Stat Assoc."},{"journal-title":"Statistical Analysis with Missing Data","year":"2019","author":"Little","key":"B3"},{"key":"B4","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1037\/1082-989X.7.2.147","article-title":"Missing data: our view of the state of the art","volume":"7","author":"Schafer","year":"2002","journal-title":"Psychol Methods."},{"key":"B5","doi-asserted-by":"crossref","DOI":"10.1002\/9780470510445","author":"Molenberghs","year":"2007","journal-title":"Missing Data in Clinical Studies"},{"journal-title":"Models for Discrete Longitudinal Data","year":"2005","author":"Molenberghs","key":"B6"},{"key":"B7","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1037\/1082-989X.6.4.330-351","article-title":"A comparison of inclusive and restrictive strategies in modern missing data procedures","volume":"6","author":"Collins","year":"2001","journal-title":"Psychol Methods."},{"key":"B8","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1080\/01621459.1995.10476493","article-title":"Analysis of semiparametric regression models for repeated outcomes in the presence of missing data","volume":"90","author":"Robins","year":"1995","journal-title":"J Am Stat Assoc."},{"key":"B9","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1177\/0962280210395740","article-title":"Review of inverse probability weighting for dealing with missing data","volume":"22","author":"Seaman","year":"2011","journal-title":"Stat Methods Med Res."},{"key":"B10","doi-asserted-by":"crossref","DOI":"10.1002\/9781119513469","volume-title":"Applied Longitudinal Analysis","author":"Fitzmaurice","year":"2011"},{"key":"B11","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1111\/j.2517-6161.1995.tb02056.x","article-title":"Regression models for longitudinal binary responses with informative drop-outs","volume":"57","author":"Fitzmaurice","year":"1995","journal-title":"J R Stat Soc B."},{"key":"B12","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1111\/j.1467-985X.2006.00407.x","article-title":"A comparison of multiple imputation and doubly robust estimation for analyses with missing data","volume":"169","author":"Carpenter","year":"2006","journal-title":"J R Stat Soc A."},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.2202\/1557-4679.1106","article-title":"Missing confounding data in marginal structural models: a comparison of inverse probability weighting and multiple imputation","author":"Moodie","year":"2008","journal-title":"Int J Biostat"},{"key":"B14","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1097\/MLR.0000000000001063","article-title":"Missing data in marginal structural models: a plasmode simulation study comparing multiple imputation and inverse probability weighting","volume":"57","author":"Liu","year":"2019","journal-title":"Med Care."},{"key":"B15","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1111\/j.1541-0420.2011.01666.x","article-title":"Combining multiple imputation and inverse-probability weighting","volume":"68","author":"Seaman","year":"2012","journal-title":"Biometrics."},{"key":"B16","doi-asserted-by":"publisher","first-page":"46","DOI":"10.58498\/eajahme.v6i6.46","article-title":"Using multiple imputation and inverse probability weighting to adjust for missing data in HIV prevalence estimates: a cross-sectional study in Mwanza, North Western Tanzania","volume":"6","author":"Mhike","year":"2023","journal-title":"East Afr. J. Appl. Health Monit. Eval"},{"key":"B17","doi-asserted-by":"publisher","first-page":"4282","DOI":"10.1002\/sim.9860","article-title":"Accounting for nonmonotone missing data using inverse probability weighting","volume":"42","author":"Ross","year":"2023","journal-title":"Stat Med."},{"key":"B18","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1177\/09622802231226328","article-title":"Comparison between inverse-probability weighting and multiple imputation in Cox model with missing failure subtype","volume":"33","author":"Guo","year":"2024","journal-title":"Stat Methods Med Res."},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316696","author":"Rubin","year":"1987","journal-title":"Multiple Imputation for Nonresponse in Surveys"},{"key":"B20","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1093\/biomet\/73.1.13","article-title":"Longitudinal data analysis using generalized linear models","volume":"73","author":"Liang","year":"1986","journal-title":"Biometrika."},{"key":"B21","first-page":"31","article-title":"Advances in missing data methods and implications for educational research","volume-title":"Real Data Analysis","author":"Peng","year":"2006"},{"key":"B22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1191\/096228099671525676","article-title":"Multiple imputation: a primer","volume":"8","author":"Schafer","year":"1999","journal-title":"Stat Methods Med Res."},{"key":"B23","doi-asserted-by":"publisher","first-page":"4279","DOI":"10.1002\/sim.2673","article-title":"The design of simulation studies in medical statistics","volume":"25","author":"Burton","year":"2006","journal-title":"Stat Med."},{"key":"B24","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1080\/01621459.1984.10478093","article-title":"Two-sample asymptotically distribution-free tests for incomplete multivariate observations","volume":"79","author":"Wei","year":"1984","journal-title":"J Am Stat Assoc."},{"key":"B25","first-page":"131","article-title":"Selection and pattern mixture models for modelling longitudinal data with dropout: an application study","volume":"37","author":"Satty","year":"2013","journal-title":"SORT"},{"key":"B26","doi-asserted-by":"publisher","first-page":"5025","DOI":"10.1002\/sim.9899","article-title":"Addressing missing data in the estimation of time-varying treatments in comparative effectiveness research","volume":"42","author":"Segura-Buisan","year":"2023","journal-title":"Statist Med."},{"key":"B27","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1177\/00491241221113873","article-title":"A comparison of three popular methods for handling missing data: complete-case analysis, inverse probability weighting, and multiple imputation","volume":"53","author":"Little","year":"2022","journal-title":"Sociol Methods Res."},{"key":"B28","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1177\/0962280216628902","article-title":"Responsiveness-informed multiple imputation and inverse probability-weighting in cohort studies with missing data that are non-monotone or not missing at random","volume":"27","author":"Doidge","year":"2018","journal-title":"Stat Methods Med Res."},{"key":"B29","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1016\/j.csda.2007.04.020","article-title":"A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data","volume":"52","author":"Beunckens","year":"2008","journal-title":"Comput Stat Data Anal."},{"key":"B30","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1002\/sim.1821","article-title":"Analysis of longitudinal studies with death and drop-out: a case study","volume":"23","author":"Dufouil","year":"2004","journal-title":"Stat Med."}],"container-title":["Frontiers in Applied Mathematics and Statistics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fams.2025.1636153\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T04:14:11Z","timestamp":1757996051000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fams.2025.1636153\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"references-count":30,"alternative-id":["10.3389\/fams.2025.1636153"],"URL":"https:\/\/doi.org\/10.3389\/fams.2025.1636153","relation":{},"ISSN":["2297-4687"],"issn-type":[{"type":"electronic","value":"2297-4687"}],"subject":[],"published":{"date-parts":[[2025,9,16]]},"article-number":"1636153"}}