Preventing and Treating Missing Data in Longitudinal Clinical Trials: A Practical Guide
Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset.
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Missing Data Mechanisms
Trial Design Considerations
Trial Conduct Considerations
Models and Modeling Considerations
ANALYSES AND THE ANALYTIC ROAD
Choosing Primary Estimands and Analyses
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Analyses of Complete appropriate assess assumed assumptions baseline value beneﬁt best ﬁt categorical Chapter complete data correlation structure covariates data set deﬁne Diepenbeek difference between treatments distribution dosing drug efﬁcacy endpoint contrast example factors ﬁrst follow-up follow—up data hypothetical data illustrated implemented imputation model imputed values inclusive models incomplete inference inﬂuence inﬂuential inverse probability least squares likelihood function likelihood-based LOCF and BOCF logistic regression longitudinal clinical trials LSMEANS Mallinckrodt maximum likelihood MCAR mechanism missing data missing values missingness MNAR analyses MNAR methods Molenberghs and Kenward multiple imputation normal distribution NRC guidance observed data observed outcomes outcome variable parameters Placebo planned endpoint primary analysis primary estimand Primary Outcome probability of dropout random effects random-effects model reﬂect regression repeated measures rescue medications scenarios selection model sensitivity analyses signiﬁcant speciﬁc standard errors statistical summarized in Table treatment contrast treatment effect Universiteit Hasselt weight wGEE