Controlling for unobserved confounders in observational studies using large health care databases by means of instrumental variables in time-to-event analysis

Since 2011, the United States Food and Drug Administration is authorized to require post-authorization safety surveillance (PASS) studies to monitor recently approved drugs. Such PASS studies are a useful instrument to assess the effectiveness and safety of a drug after approval, since randomized controlled trials, the gold standard during the drug approval process, cannot provide all necessary information about drug reactions as they are limited by several factors. First, they often have a small sample size, second, they are additionally restricted to specific patient populations, that means they are not representative for the population of subsequent users, and, third, they are conducted in a controlled environment that does not reflect routine clinical practice. Observational studies in free-living populations are therefore necessary to evaluate the safety and effectiveness of drugs after drug marketing. Large health care databases are frequently used for this purpose, which, however, also suffer from certain limitations that may lead to biased results. Statistical methods for controlling bias due to confounding include stratification, regression adjustment and propensity score approaches. None of these methods is able to remove this type of bias, unless all confounders are recorded in the data. A common problem of analyses based on administrative databases is that confounding variables such as smoking status or body mass index are often not recorded in health care claims databases, so that exposure effects are inconsistently estimated. Under certain conditions, instrumental variables can reduce or eliminate confounding bias in observational studies, so that IV estimators can consistently estimate treatment effects even in the absence of information on important confounders. Instrumental variable methods are well established for continuous outcomes using linear regression models, where two-stage least squares are typically used in observational studies. However, in time-to-event analysis no such common instrumental variable method exists which may be due to a number of complications that result from censoring and survivorship bias. Even if the proportional hazards model, the most popular model in time-to-event analysis, is used, two-stage estimators to account for instrumental variables are only justified for rare events. The aim of this thesis is therefore to explore two-stage instrumental variable estimation for time-toevent outcomes in large health care databases if the assumption of rare events does not hold true. The thesis consists of five chapters. Chapter 2 describes the German Pharmacoepidemiological Research Database (GePaRD) as an example for a large health care database. Considering the advantages and drawbacks of this database, studies of adverse drug effects pose a number of methodological challenges to be addressed by the research design. Three studies illustrating these challenges are presented in this chapter and the concept of bias and confounding is briefly introduced (Arfè et al., 2016; Kollhorst et al., 2015; Schröder et al., 2017). The chapter concludes with a description of methods to deal with measured confounding which are applied in a study on the comparative risk of death of antidepressants in older people with depression based on GePaRD presented in a forthcoming paper. Subsequently, Chapter 3 provides details on instrumental variable methods to reduce unmeasured confounding. The core conditions for a valid instrumental variable are outlined and the validity of the physician’s preference as an instrumental variable in GePaRD is investigated (Kollhorst et al., 2016). Moreover, details on the two-stage predictor substitution method and the control function approach for instrumental variable estimation using linear models and the two-stage residual inclusion method for nonlinear models are given. The first two approaches are considered again in Chapter 4, where the focus is on the two-stage predictor substitution method and the control function approach in time-to-event analysis. Due to the noncollapsibility of the hazard ratio, instrumental variable methods such as the two-stage predictor substitution method and the control function approach cannot be readily applied to the Cox model. Therefore, two situations are presented where the use of these methods is justified in the context of proportional hazards models: first, there is no causal effect of the exposure on the outcome; second, the outcome is rare in the sense that the cumulative incidence remains low over the follow-up period. Finally, a simulation study that investigates the performance of these estimators for non-rare events or a non-null causal effect is conducted and results of the simulation study are transferred to an observational study based on GePaRD. Since the results of the simulation study and the study comparing mortality risks between elderly new users of conventional and atypical antipsychotics have not been published yet, more details are provided. Finally, the thesis concludes with an overall discussion of results and a suggestion for future research and perspectives regarding the problem of unmeasured confounding in large health care databases.