Date of Completion

4-3-2020

Embargo Period

3-31-2020

Keywords

clinical trials, multiple imputation, non-inferiority

Major Advisor

Ofer Harel

Associate Advisor

Ming-Hui Chen

Associate Advisor

Haim Bar

Field of Study

Statistics

Degree

Doctor of Philosophy

Open Access

Open Access

Abstract

Clinical trials are an essential part of the drug development life cycle. There are different types of clinical trials, and in this dissertation, we focus on non-inferiority (NI) trials. In NI trials the goal is to show that the effectiveness of a new treatment is not considerably worse than of a standard one by an acceptable margin. Although, the new treatment could be slightly less efficacious, it can offer other benefits such as less severe adverse reactions.

Several methodological challenges have been reported regarding the design, analysis and interpretation of NI trials. These include incomplete data analysis, specification of an acceptable margin, and overall benefit of the new non-inferior treatment. Therefore, the aim of this dissertation was to address each of these challenges and provide practical solutions for researchers involved with NI trials.

First, we focus on incomplete data. Specifically, we evaluate how different statistical strategies perform under several NI scenarios and various types of missingness. We provide a set of recommendations for practitioners to use when confronted with incomplete data to avoid false non-inferiority conclusions. Second, while performing a thorough investigation of proper statistical strategies for incomplete data analysis, we discovered that combination rules of multiply imputed data when inference is done using a Newcombe’s method did not exist. As a result, we developed these combination rules. Third, we proposed a new framework that allows for a transparent and objective justification of an acceptable margin. The framework is based on combining results of NI study and clinical experts survey data using multiple imputation (MI). Fourth, we developed a new approach for a comprehensive benefit-risk assessment of a non-inferior treatment. We focus on preference elicitation regarding benefits and risks from a small sample of NI trial participants, and use MI to restore preferences of all study participants.

This dissertation provides an important contribution to the field of Statistics, and drug development. The novel methods and techniques outlined in this dissertation facilitate practitioners involved with NI trails to make more efficient and transparent evaluations of treatment effectiveness.

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