| dc.description.abstracteng | Cardiovascular diseases are diseases of the heart and blood vessels constituting a major cause of death and disability worldwide. Cardiovascular drug development aims to deliver efficacious drugs to address the public health burden of cardiovascular diseases. However, the high costs associated with cardiovascular drug development, for example due to long-running clinical trials, sometimes including thousands of patients, place a high burden on the development of new efficacious treatments for cardiovascular diseases. Proposals for improving the efficiency of cardiovascular drug development include better disease characterization, more defined target populations, and the use of adaptive clinical trial designs. This dissertation focuses on adaptive clinical trial designs for cardiovascular research.
Adaptive clinical trial designs, commonly referred to as adaptive designs, are clinical trial designs with a preplanned modification of design aspects, under some constraints such as preserving integrity and validity of the trials, based on interim data of the ongoing trial. Design aspects which are commonly modified include the sample size, number of doses or treatments, or endpoints. Adaptive designs offer flexibility compared to traditional clinical trials with a fixed design to accommodate newly gained information. However, with the flexibility comes an increased statistical complexity, as adaptive designs require an increased effort to control the probability that the clinical trial declares efficacy of an inefficacious treatment, that is the type I error rate, and to plan the number of patients required such that an efficacious treatment is detected with a high statistical power.
The focus of this dissertation is on two types of adaptive designs: group sequential designs and designs with a nuisance parameter based sample size re-estimation. In group sequential designs, the efficacy of a treatment is tested repeatedly during the conduct of the trial and the trial is stopped early if efficacy of the treatment can be shown with statistical significance. Thus, an efficacious treatment can be detected early in clinical trials with a group sequential design. In designs with a nuisance parameter based sample size re-estimation, the final sample size is adjusted using estimates of the potentially several nuisance parameters based on interim data. Nuisance parameters are for example the outcome variance in trials with continuous outcomes and the overall event rate in trials with count outcomes. The nuisance parameter based sample size re-estimation aims to assure that a clinical trial achieves the target power independently of the initially planned sample size.
The first objective of this dissertation is to study group sequential designs with recurrent events, motivated by clinical trials with patients suffering from chronic heart failure. In clinical trials with patients suffering from chronic heart failure, a common clinical relevant recurrent event outcome is the number of heart failure hospitalizations, which can also be part of a composite endpoint in combination with cardiovascular death. To model heart failure hospitalizations and the respective composite, a negative binomial model and a more robust semiparametric model have been proposed in the literature. However, group sequential designs have not been studied for these models. Therefore, I propose statistical methods for planning and analyzing group sequential designs for negative binomial models and more robust semiparametric models and study their asymptotic properties. Moreover, I show that the proposed planning and analysis methods result in an appropriate power and type I error rate, respectively, for parameter combinations common in clinical trials with patients suffering from chronic heart failure. I put a particular focus on the longitudinal nature of the recurrent events, i.e., a single subject can experience new events throughout the trial, and its consequential on the group sequential designs. The longitudinal natures of the outcomes distinguishes group sequential designs with recurrent events from group sequential designs for other common models, such a continuous, binary, or survival data.
A second objective of this dissertation is to study nuisance parameter based sample size re-estimation in three-arm trials with normal outcomes; an investigation motivated by clinical trials with patients suffering from hypertension. A common endpoint in these trials modeled as normally distributed is the change of blood pressure between the baseline measurement and the end of the trial. I show that the ideas for nuisance parameter based sample size re-estimation in two-arm trials can be adapted to three-arm trials and highlight that the corresponding approaches do not result in the desired target power. Furthermore, I modify one of the sample size re-estimation procedures such that it results in appropriately powered three-arm clinical trials.
The third objective of this dissertation is to study incorporating prior information on the variance into the nuisance parameter based sample size re-estimation in two-arm trials with normal outcomes. This objective, too, is motivated by clinical trials with patients suffering from hypertension. I propose several ad hoc rules for incorporating prior information into the sample size re-estimation and by means of Monte Carlo simulation studies I show that the incorporation of prior information can reduce the variability of the final sample size when no prior-data conflict is present. However, I illustrate that in the presence of a prior-data conflict, the designs with a sample size re-estimation incorporating prior information do not convey the target power. I also highlight that common approaches of robustifying the prior information cannot completely mitigate the negative effects of a prior-data conflict without also nullifying the benefits of incorporating prior information on the nuisance parameter into the sample size re-estimation. | de |