4.3 Article

Dealing with missing observations in the outcome and covariates in randomized controlled trials

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Pharmacology & Pharmacy

Imputation of Missing Covariates in Randomized Controlled Trials with Continuous Outcomes: Simple, Unbiased and Efficient Methods

Mutamba T. Kayembe et al.

Summary: The literature suggests using sophisticated methods (such as multiple imputation and maximum likelihood) for dealing with missing covariates in nonrandomized studies, but these methods may not be optimal in randomized studies. This study extends the analysis to multiple baseline covariates with missingness and compares the performance of multiple imputation and maximum likelihood with simple alternative methods in various scenarios. The results show that all simple methods provide unbiased treatment effect estimation, but with increased mean squared residual. Mean imputation and the missing-indicator method perform best in all covariate missingness scenarios.

JOURNAL OF BIOPHARMACEUTICAL STATISTICS (2022)

Article Cardiac & Cardiovascular Systems

Effectiveness of an exercise training programme COPD in primary care: A randomized controlled trial

Annemieke Fastenau et al.

RESPIRATORY MEDICINE (2020)

Article Health Care Sciences & Services

Should multiple imputation be the method of choice for handling missing data in randomized trials?

Thomas R. Sullivan et al.

STATISTICAL METHODS IN MEDICAL RESEARCH (2018)

Article Medicine, Research & Experimental

Comparison of intent-to-treat analysis strategies for pre-post studies with loss to follow-up

Wenna Xi et al.

CONTEMPORARY CLINICAL TRIALS COMMUNICATIONS (2018)

Article Health Care Sciences & Services

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

Jonathan W. Bartlett et al.

STATISTICAL METHODS IN MEDICAL RESEARCH (2015)

Article Mathematics, Interdisciplinary Applications

ANCOVA Versus CHANGE From Baseline in Nonrandomized Studies: The Difference

Gerard J. P. van Breukelen

MULTIVARIATE BEHAVIORAL RESEARCH (2013)

Article Medicine, General & Internal

Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis

Rolf H. H. Groenwold et al.

CANADIAN MEDICAL ASSOCIATION JOURNAL (2012)

Article Mathematical & Computational Biology

Multiple imputation using chained equations: Issues and guidance for practice

Ian R. White et al.

STATISTICS IN MEDICINE (2011)

Article Social Sciences, Mathematical Methods

How much can we learn about missing data?: an exploration of a clinical trial in psychiatry

Dan Jackson et al.

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY (2010)

Article Mathematical & Computational Biology

Specification of covariance structure in longitudinal data analysis for randomized clinical trials

Kaifeng Lu et al.

STATISTICS IN MEDICINE (2010)

Article Mathematical & Computational Biology

Should baseline be a covariate or dependent variable in analyses of change from baseline in clinical trials?

Guanghan F. Liu et al.

STATISTICS IN MEDICINE (2009)

Article Medicine, Research & Experimental

Randomized clinical trials with a pre- and a post-treatment measurement: Repeated measures versus ANCOVA models

Bjorn Winkens et al.

CONTEMPORARY CLINICAL TRIALS (2007)

Article Mathematical & Computational Biology

Adjusting for partially missing baseline measurements in randomized trials

IR White et al.

STATISTICS IN MEDICINE (2005)