Randomized clinical trials are commonly conducted in pharmaceutical companies and medical research institutes to evaluate a certain intervention effect. Standard intention-to-treat (ITT) analysis is routinely performed to test if there is a difference in outcome between the randomized groups. Besides ITT analysis, explanatory analyses are often performed. The purpose of explanatory analyses could be: (1) to examine effects of treatment received on the outcome, (2) to further address the reason for a difference between randomized groups, (3) to address other related research questions rather than the original study objective. This research was motivated by a dose-controlled randomized PK/PD clinical trial and a randomized clinical trial of Modification of Diet in Renal Disease (MDRD).

In this research, we focus on challenges which we encounter in practice, while we apply the Instrumental Variables (IV) method. More specifically we focus on investigating (1) the exclusion restriction assumption, (2) censored outcome measurements in G-estimation, (3) surrogacy concepts in explanatory analyses. The dissertation is organized by the following four parts. First, we review the compliance issue and introduce causal concepts and related notations. Second, we propose to apply the IV method to characterizing the relationship between pharmacokinetics (PK) and pharmacodynamics (PD) to deal with unmeasured confounding problem, and discuss the exclusion restriction assumption. Third, we focus on how to deal with censored data issue while we apply G-estimation method. We propose to use artificial censoring method, and extend it to more general settings. Fourth, we discuss the surrogacy concepts in explanatory analyses. We propose to use the direct and indirect effects to define the estimands. Many explanatory analyses fall into surrogacy concepts framework although they are not traditional surrogate biomarker problems. We bridge the surrogate endpoint concept to the definition of instrumental variables and the concept of direct and indirect effects. We propose to utilize baseline covariate information to characterize both direct and indirect effects simultaneously, and relate our approach to meta-analytical approach and principal stratification approach. We apply the direct and indirect effect concepts to our explanatory analyses of MDRD trial and dose-controlled PK/PD trial.