Design of adaptive multi-arm multi-stage clinical trials

Two-arm group sequential designs have been widely used for over forty years, especially for studies with mortality endpoints. The natural generalization of such designs to trials with multiple treatment arms and a common control (MAMS designs) has, however, been implemented rarely. While the statistical methodology for this extension is clear, the main limitation has been an efficient way to perform the computations. Past efforts were hampered by algorithms that were computationally explosive. With the increasing interest in adaptive designs, platform designs, and other innovative designs that involve multiple comparisons over multiple stages, the importance of MAMS designs is growing rapidly. This dissertation proposes a group sequential approach to design MAMS trial where the test statistic is the maximum of the cumulative score statistics for each pair-wise comparison, and is evaluated at each analysis time point with respect to efficacy and futility stopping boundaries while maintaining strong control of the family wise error rate (FWER). In this dissertation we start with a break-through algorithm that will enable us to compute MAMS boundaries rapidly. This algorithm will make MAMS design a practical reality. For designs with efficacy-only boundaries, the computational effort increases linearly with number of arms and number of stages. For designs with both efficacy and futility boundaries the computational effort doubles with successive increases in number of stages. Previous attempts to obtain MAMS boundaries were confined to smaller problems because their computational effort grew exponentially with number of arms and number of stages. We will next extend our proposed group sequential MAMS design to permit adaptive changes such as dropping treatment arms and increasing the sample size at each interim analysis time point. In order to control the FWER in the presence of these adaptations the early stopping boundaries must be re-computed by invoking the conditional error rate principle and the closed testing principle. This adaptive MAMS design is immensely useful in phase~2 and phase~3 settings. An alternative to the group sequential approach for MAMS design is the p-value combination approach. This approach has been in place for the last fifteen years.This alternative MAMS approach is based on combining independent p-values from the incremental data of each stage. Strong control of the FWER for this alternative approach is achieved by closed testing. We will compare the operating characteristics of the two approaches both analytically and empirically via simulation. In this dissertation we will demonstrate that the MAMS group sequential approach dominates the traditional p-value combination approach in terms of statistical power

Adaptive Survival Trials

Mid-study design modifications are becoming increasingly accepted in confirmatory clinical trials, so long as appropriate methods are applied such that error rates are controlled. It is therefore unfortunate that the important case of time-to-event endpoints is not easily handled by the standard theory. We analyze current methods that allow design modifications to be based on the full interim data, i.e., not only the observed event times but also secondary endpoint and safety data from patients who are yet to have an event. We show that the final test statistic may ignore a substantial subset of the observed event times. Since it is the data corresponding to the earliest recruited patients that is ignored, this neglect becomes egregious when there is specific interest in learning about long-term survival. An alternative test incorporating all event times is proposed, where a conservative assumption is made in order to guarantee type I error control. We examine the properties of our proposed approach using the example of a clinical trial comparing two cancer therapies.Comment: 22 pages, 7 figure

Levosimendan for the prevention of acute organ dysfunction in sepsis

BACKGROUND Levosimendan is a calcium-sensitizing drug with inotropic and other properties that may improve outcomes in patients with sepsis. METHODS We conducted a double-blind, randomized clinical trial to investigate whether levosimendan reduces the severity of organ dysfunction in adults with sepsis. Patients were randomly assigned to receive a blinded infusion of levosimendan (at a dose of 0.05 to 0.2 μg per kilogram of body weight per minute) for 24 hours or placebo in addition to standard care. The primary outcome was the mean daily Sequential Organ Failure Assessment (SOFA) score in the intensive care unit up to day 28 (scores for each of five systems range from 0 to 4, with higher scores indicating more severe dysfunction; maximum score, 20). Secondary outcomes included 28-day mortality, time to weaning from mechanical ventilation, and adverse events. RESULTS The trial recruited 516 patients; 259 were assigned to receive levosimendan and 257 to receive placebo. There was no significant difference in the mean (±SD) SOFA score between the levosimendan group and the placebo group (6.68±3.96 vs. 6.06±3.89; mean difference, 0.61; 95% confidence interval [CI], −0.07 to 1.29; P=0.053). Mortality at 28 days was 34.5% in the levosimendan group and 30.9% in the placebo group (absolute difference, 3.6 percentage points; 95% CI, −4.5 to 11.7; P=0.43). Among patients requiring ventilation at baseline, those in the levosimendan group were less likely than those in the placebo group to be successfully weaned from mechanical ventilation over the period of 28 days (hazard ratio, 0.77; 95% CI, 0.60 to 0.97; P=0.03). More patients in the levosimendan group than in the placebo group had supraventricular tachyarrhythmia (3.1% vs. 0.4%; absolute difference, 2.7 percentage points; 95% CI, 0.1 to 5.3; P=0.04). CONCLUSIONS The addition of levosimendan to standard treatment in adults with sepsis was not associated with less severe organ dysfunction or lower mortality. Levosimendan was associated with a lower likelihood of successful weaning from mechanical ventilation and a higher risk of supraventricular tachyarrhythmia. (Funded by the NIHR Efficacy and Mechanism Evaluation Programme and others; LeoPARDS Current Controlled Trials number, ISRCTN12776039.

Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous

Treatment Effect Quantification for Time-to-event Endpoints -- Estimands, Analysis Strategies, and beyond

A draft addendum to ICH E9 has been released for public consultation in August 2017. The addendum focuses on two topics particularly relevant for randomized confirmatory clinical trials: estimands and sensitivity analyses. The need to amend ICH E9 grew out of the realization of a lack of alignment between the objectives of a clinical trial stated in the protocol and the accompanying quantification of the "treatment effect" reported in a regulatory submission. We embed time-to-event endpoints in the estimand framework, and discuss how the four estimand attributes described in the addendum apply to time-to-event endpoints. We point out that if the proportional hazards assumption is not met, the estimand targeted by the most prevalent methods used to analyze time-to-event endpoints, logrank test and Cox regression, depends on the censoring distribution. We discuss for a large randomized clinical trial how the analyses for the primary and secondary endpoints as well as the sensitivity analyses actually performed in the trial can be seen in the context of the addendum. To the best of our knowledge, this is the first attempt to do so for a trial with a time-to-event endpoint. Questions that remain open with the addendum for time-to-event endpoints and beyond are formulated, and recommendations for planning of future trials are given. We hope that this will provide a contribution to developing a common framework based on the final version of the addendum that can be applied to design, protocols, statistical analysis plans, and clinical study reports in the future.Comment: 37 page

The randomized placebo-phase design: Evaluation, interim monitoring and analysis

The randomized placebo-phase design, also known as the randomized delayed-start design, has been proposed as an approach to circumvent the reluctance of patients and physicians to participate in trials with a placebo control. Although there is some practical appeal to the design and it has been used in an increasing number of active and ongoing trials, there are often overlooked issues relative to statistical power, estimating sample size and determining plans for interim analysis that may limit its usefulness. We developed a general model for describing the pattern of treatment response and based on the specified parameters of this model, derive and compare different strategies for interim monitoring. In addition to statistical power considerations, we also provide results from extensive simulations investigating the robustness of the proposed procedures since the efficiency of the randomized placebo-phase design is highly dependent on the assumptions made about the form of the alternative hypotheses. Public Health Relevance: The randomized clinical trial is the gold standard for evaluating new medical treatments/public health interventions. Indiscriminate use of the RPPD may result in failure to identify important new treatment/interventions because of low statistical power