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Modern analysis of incomplete longitudinal outcomes involves formulating assumptions about the missingness mechanisms and then using a statistical method that produces valid inferences under this assumption. In this manuscript, we define missingness strategies for analyzing randomized clinical trials (RCTs) based on plausible clinical scenarios. Penalties for dropout are also introduced in an attempt to balance benefits against risks. Some missingness mechanisms are assumed to be non‐future dependent, which is a subclass of missing not at random. Non‐future dependent stipulates that missingness depends on the past and the present information but not on the future. Missingness strategies are implemented in the pattern‐mixture modeling framework using multiple imputation (MI), and it is shown how to estimate the marginal treatment effect. Next, we outline how MI can be used to investigate the impact of dropout strategies in subgroups of interest. Finally, we provide the reader with some points to consider when implementing pattern‐mixture modeling‐MI analyses in confirmatory RCTs. The data set that motivated our investigation comes from a placebo‐controlled RCT design to assess the effect on pain of a new compound. Copyright © 2016 John Wiley & Sons, Ltd.
Original Article: Implementation of pattern‐mixture models in randomized clinical trialsNEXT ARTICLE
Clinical trials are a set of procedures in medical research conducted to allow safety (or more specifically, information about adverse drug reactions and adverse effects of other treatments) and efficacy data to be collected for health interventions (e.g...
Pain is a feeling (sharp or dull) triggered in the nervous system which can be transient or constant. Pain can be specific to one area of the body eg back, abdomen or chest or more general all over the body eg muscles ache from the flu. Without pain ...