title: Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation.
abstract: Protocol deviations, for example, due to early withdrawal and noncompliance, are unavoidable in clinical trials. Such deviations often result in missing data. Additional assumptions are then needed for the analysis, and these cannot be definitively verified from the data at hand. Thus, as recognized by recent regulatory guidelines and reports, clarity about these assumptions and their implications is vital for both the primary analysis and framing relevant sensitivity analysis. This article focuses on clinical trials with longitudinal quantitative outcome data. For the target population, we define two estimands, the de jure estimand, "does the treatment work under the best case scenario," and the de facto estimand, "what would be the effect seen in practice." We then carefully define the concept of a deviation from the protocol relevant to the estimand, or for short a deviation. Each patient's postrandomization data can then be divided into predeviation data and postdeviation data. We set out an accessible framework for contextually appropriate assumptions relevant to de facto and de jure estimands, that is, assumptions about the joint distribution of pre- and postdeviation data relevant to the clinical question at hand. We then show how, under these assumptions, multiple imputation provides a practical approach to estimation and inference. We illustrate with data from a longitudinal clinical trial in patients with chronic asthma.
authors.affiliation: a Medical Statistics Department , London School of Hygiene & Tropical Medicine , London , UK.
authors.forename: James R
authors.initials: JR
authors.lastname: Carpenter
authors.forename: James H
authors.initials: JH
authors.lastname: Roger
authors.forename: Michael G
authors.initials: MG
authors.lastname: Kenward
mesh_terms: D000465:Algorithms; D018927:Anti-Asthmatic Agents / Q000008:administration & dosage / Q000009:adverse effects; D001249:Asthma / Q000188:drug therapy / Q000503:physiopathology; D001499:Bayes Theorem; D002908:Chronic Disease; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D008168:Lung / Q000187:drug effects / Q000503:physiopathology; D008390:Markov Chains; D055118:Medication Adherence; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D010352:Patient Dropouts; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012107:Research Design / Q000706:statistics & numerical data; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D018927:Anti-Asthmatic Agents