Handling Partially Observed Trial Data After Treatment Withdrawal: Introducing Retrieved Dropout Reference-Base Centred Multiple Imputation.

The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires the monitoring of patients and collection of primary outcome data following termination of randomised treatment. However, when patients withdraw from a study early before completion this creates true missing data complicating the analysis.

Missing data sensitivity analysis for recurrent event data using controlled imputation.

Statistical analyses of recurrent event data have typically been based on the missing at random assumption. One implication of this is that, if data are collected only when patients are on their randomized treatment, the resulting de jure estimator of treatment effect corresponds to the situation in which the patients adhere to this regime throughout the study. For confirmatory analysis of clinical trials, sensitivity analyses are required to investigate alternative de facto estimands that depart from this assumption.

Platelet aggregation unchanged by lipoprotein-associated phospholipase A₂ inhibition: results from an in vitro study and two randomized phase I trials.

Background: We explored the theorized upregulation of platelet-activating factor (PAF)- mediated biologic responses following lipoprotein-associated phospholipase A2 (Lp-PLA2) inhibition using human platelet aggregation studies in an in vitro experiment and in 2 clinical trials.

Methods And Results: Full platelet aggregation concentration response curves were generated in vitro to several platelet agonists in human plasma samples pretreated with rilapladib (selective Lp-PLA2 inhibitor) or vehicle. This was followed by a randomized, double-blind crossover study in healthy adult men (n = 26) employing a single-agonist dose assay of platelet aggregation, after treatment of subjects with 250 mg oral rilapladib or placebo once daily for 14 days.

Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation.

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.

Multiplicity: discussion points from the Statisticians in the Pharmaceutical Industry multiplicity expert group.

In May 2012, the Committee of Health and Medicinal Products issued a concept paper on the need to review the points to consider document on multiplicity issues in clinical trials. In preparation for the release of the updated guidance document, Statisticians in the Pharmaceutical Industry held a one-day expert group meeting in January 2013. Topics debated included multiplicity and the drug development process, the usefulness and limitations of newly developed strategies to deal with multiplicity, multiplicity issues arising from interim decisions and multiregional development, and the need for simultaneous confidence intervals (CIs) corresponding to multiple test procedures.

The use of baseline covariates in crossover studies.

It is our experience that in many settings, crossover trials that have within-period baseline measurements are analyzed wrongly. A "conventional" analysis of covariance in this setting uses each baseline as a covariate for the following outcome variable in the same period but not for any other outcome. If used with random subject effects such an analysis leads to biased treatment comparisons; this is an example of cross-level bias.

Practical usage of O'Brien's OLS and GLS statistics in clinical trials.

Multivariate techniques of O'Brien's OLS and GLS statistics are discussed in the context of their application in clinical trials. We introduce the concept of an operational effect size and illustrate its use to evaluate power. An extension describing how to handle covariates and missing data is developed in the context of Mixed models.