Indiana University-Purdue University Indianapolis (IUPUI) / The marker strategy design (MSGD) has been proposed to assess and validate predictive markers for targeted therapies and immunotherapies. Under this design, patients are randomized into two strategies: the marker-based strategy, which treats patients based on their marker status, and the non-marker-based strategy, which randomizes patients into treatments independent of their marker status in the same way as in a standard randomized clinical trial. The strategy effect is then tested by comparing the response rate between the two strategies and this strategy effect is commonly used to evaluate the predictive capability of the markers. We show that this commonly used between-strategy test is flawed, which may cause investigators to miss the opportunity to discover important predictive markers or falsely claim an irrelevant marker as predictive. Then we propose new procedures to improve the power of the MSGD to detect the predictive marker effect. One is based on a binary response endpoint; the second is based on survival endpoints. We conduct simulation studies to compare the performance of the MSGD with the widely used marker stratified design (MSFD). Numerical studies show that the MSGD and MSFD has comparable performance. Hence, contrary to popular belief that the MSGD is an inferior design compared with the MSFD, we conclude that using the MSGD with the proposed tests is an efficient and ethical way to find predictive markers for targeted therapies.

The most common type of confirmatory trial is a randomised trial comparing the experimental treatment of interest to a control treatment. Confirmatory trials are expensive and take a lot of time in the planning, set up and recruitment of patients. Efficient methodology in clinical trial design is critical to save both time and money and allow treatments to become available to patients quickly. Often there are data available on the control treatment from a previous trial. These historical data are often used to design new trials, forming the basis of sample size calculations, but are not used in the analysis of the new trial. Incorporating historical control data into the design and analysis could potentially lead to more efficient trials. When the historical and current control data agree, incorporating historical control data could reduce the number of control patients required in the current trial and therefore the duration of the trial, or increase the precision of parameter estimates. However, when the historical and current data are inconsistent, there is a potential for biased treatment effect estimates, inflated type I error and reduced power. We propose two novel weights to assess agreement between the current and historical control data: a probability weight based on tail area probabilities; and a weight based on the equivalence of the historical and current control data parameters. For binary outcome data, agreement is assessed using the posterior distributions of the response probability in the historical and current control data. For normally distributed outcome data, agreement is assessed using the marginal posterior distributions of the difference in means and the ratio of the variances of the current and historical control data. We consider an adaptive design with an interim analysis. At the interim, the agreement between the historical and current control data is assessed using the probability or equivalence probability weight approach. The allocation ratio is adapted to randomise fewer patients to control when there is agreement and revert back to a standard trial design when there is disagreement. The final analysis is Bayesian utilising the analysis approach of the power prior with a fixed weight. The operating characteristics of the proposed design are explored and we show how the equivalence bounds can be chosen at the design stage of the current study to control the maximum inflation in type I error. We then consider a design where a treatment arm is added to an ongoing clinical trial. For many disease areas, there are often treatments in different stages of the development process. We consider the design of a two-arm parallel group trial where it is planned to add a new treatment arm during the trial. This could potentially save money, patients, time and resources. The addition of a treatment arm creates a multiple comparison problem. Dunnett (1955) proposed a design that controls the family-wise error rate when comparing multiple experimental treatments to control and determined the optimal allocation ratio. We have calculated the correlation between test statistics for the method proposed by Dunnett when a treatment arm is added during the trial and only concurrent controls are used for each treatment comparison. We propose an adaptive design where the sample size of all treatment arms are increased to control the family-wise error rate. We explore adapting the allocation ratio once the new treatment arm is added to maximise the overall power of the trial.

To assess non-inferiority of an experimental product to an active control in a clinical trial, an ideal design is to include a placebo arm to ensure both the experimental product and the active control is superior to placebo. We aim to identify methodology to control Type I error rate and maintain adequate power in a superiority/non-inferiority seamless clinical trial defined as: 1. selecting the best experimental treatment dose vs. placebo out of multiple treatment doses in Stage I; and 2. assessing non-inferiority of the chosen experimental dose to an active control, after adding subjects to yield adequate power for non-inferiority, in Stage II. The trial design here is an antihypertensive trial with change in systolic blood pressure as the outcome. The trial has three experimental treatment doses arms of experimental, a placebo control arm, and an active control arm. A simulation study of 20,000 such trials was conducted. We apply multiple comparison methodologies in Stage I to detect the most beneficial experimental treatment dose versus placebo, and test non-inferiority of the selected experimental dose to the active control in Stage II. Simulated Type I error rate and power for claiming non-inferiority are calculated for various dose-response trends. The need to adjust alpha to control Type I error either stage is assessed, seeking the optimal approach for doing so. Next, type I error and power for various fixed and variable non-inferiority margins are evaluated, exploring a range of margins informed by the first stage results of the study. A variable non-inferiority margin informed completely by the first stage of the trial approach results in inflated error rate which cannot be controlled by suggested multiplicity adjustments. We assess a synthesis approach between the fixed and variable margins, to both control the family-wise error rates and reach adequate power, depending on a tuning parameter defined in our work. We conclude that well-designed and adequately controlled seamless superiority/non-inferiority trials are possible with appropriate multiple comparisons adjustments and could result in less development time and fewer subjects needed to assess efficacy than separate trials.

The clinical trial is the most import study for the development of successful novel drugs. The aim of this dissertation is to develop innovative statistical methods to overcome the three main obstacles in clinical trials: (1) lengthy trial duration and inaccurate maximum tolerated dose (MTD) in phase I trials; (2) heterogeneity in drug effect when patients are given the same prescription and same dose; and (3) high failure rates of expensive phase III confirmatory trials due to the discrepancy in the endpoints adopted in phase II and III trials. Towards overcoming the first obstacle, we originally develop a hybrid design for the time-to-event dose escalation method with overdose control using a normalized equivalent toxicity score (NETS) system. This hybrid design can substantially reduce sample size, shorten study length, and estimate accurate MTD by employing a parametric model and adaptive Bayesian approach. Toward overcoming the second obstacle, we propose a new approach to incorporate patients’ characteristic using our proposed design in phase I clinical trials which considers the personalized information for patients who participant in the trials. To conquer the third obstacle, we propose a novel two-stage screening design for phase II trials whereby the endpoint of percent change in of tumor size is used in an initial screening to select potentially effective agents within a short time interval followed by a second screening stage where progression free survival is estimated to confirm the efficacy of agents. These research projects will substantially benefit both cancer patients and researchers by improving clinical trial efficiency and reducing cost and trial duration. Moreover, they are of great practical meaning since cancer medicine development is of paramount importance to human health care.

Des données complexes sont fréquentes dans les essais cliniques récents et nécessitent des méthodes statistiques adaptées. La recherche vaccinale du VIH est un exemple d’un domaine avec des données complexes et une absence de critères de jugement validés dans les essais précoces. Cette thèse d’Université concerne des recherches méthodologiques sur la conception et les aspects statistiques des essais cliniques vaccinaux du VIH, en particulier sur les critères de jugement d’immunogénicité et les schémas d’essai de phase I-II. A l’aide des données cytokiniques multiplex, nous illustrons les aspects méthodologiques spécifiques à une technique de mesure. Nous proposons ensuite des définitions de critères de jugement et des méthodes statistiques adéquates pour l'analyse des données d'immunogénicité multidimensionnelles. En particulier, nous montrons l’intérêt des scores multivariés non-paramétriques, permettant de résumer l’information à travers différents marqueurs d’immunogénicité et de faire des comparaisons inter- et intra-groupe. Dans l’objectif de contribuer à la conception méthodologique des nouveaux essais vaccinaux, nous présentons la construction d’un schéma d’essai optimisé pour le développement clinique précoce. En imbriquant les phases I et II d’évaluation clinique, ce schéma permet d’accélerer le développement de plusieurs stratégies vaccinales en parallèle. L’intégration d’une règle d’arrêt est proposée dans des perspectives fréquentistes et Bayesiennes. Les méthodes mises en avant dans cette thèse sont transposables à d’autres domaines d’application avec des données complexes, telle que les données d’imagerie ou les essais d’autres immunothérapies. / Complex data are frequently recored in recent clinical trials and require the use of appropriate statistical methods. HIV vaccine research is an example of a domaine with complex data and a lack of validated endpoints for early-stage clinical trials. This thesis concerns methodological research with regards to the design and analysis aspects of HIV vaccine trials, in particular the definition of immunogenicity endpoints and phase I-II trial designs. Using cytokine multiplex data, we illustrate the methodological aspects specific to a given assay technique. We then propose endpoint definitions and statistical methods appropriate for the analysis of multidimensional immunogenicity data. We show in particular the value of non-parametric multivariate scores, which allow for summarizing information across different immunogenicity markers and for making statistical comparisons between and within groups. In the aim of contributing to the design of new vaccine trials, we present the construction of an optimized early-stage HIV vaccine design. Combining phase I and II assessments, the proposed design allows for accelerating the clinical development of several vaccine strategies in parallel. The integration of a stopping rule is proposed from both a frequentist and a Bayesian perspective. The methods advocated in this thesis are transposable to other research domains with complex data, such as imaging data or trials of other immune therapies.