Platform trials offer a framework to study multiple interventions in a single trial with the opportunity of opening and closing arms. The use of a common control in platform trials can increase efficiency as compared to individual control arms or separate trials per treatment. However, the need for multiplicity adjustment as a consequence of common controls is currently a controversial debate among researchers, pharmaceutical companies, as well as regulators. We investigate the impact of a common control arm in platform trials on the type one error and power in comparison to what would have been obtained with a platform trial with individual control arms in a simulation study. Furthermore, we evaluate the impact on power in case multiplicity adjustment is required in a platform trial. In both study designs, the family-wise error rate (FWER) is inflated compared to a standard, two-armed randomized controlled trial when no multiplicity adjustment is applied. In case of a common control, the FWER inflation is smaller. In most circumstances, a platform trial with a common control is still beneficial in terms of sample size and power after multiplicity adjustment, whereas in some cases, the platform trial with a common control loses the efficiency gain. Therefore, we further discuss the need for adjustment in terms of a family definition or hypotheses dependencies.
When testing multiple hypotheses, a suitable error rate should be controlled even in exploratory trials. Conventional methods to control the False Discovery Rate (FDR) assume that all p-values are available at the time point of test decision. In platform trials, however, treatment arms enter and leave the trial at any time during its conduct. Therefore, the number of treatments and hypothesis tests is not fixed in advance and hypotheses are not tested at once, but sequentially. Recently, for such a setting the concept of online control of the FDR was introduced. We investigate the LOND procedu…
Platform trials can evaluate the efficacy of several treatments compared to a control. The number of treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel-group trials because of using shared control groups. For arms entering the trial later, not all patients in the control group are randomised concurrently. The control group is then divided into concurrent and non-concurrent controls. Using non-concurrent controls (NCC) can improve the trial's efficiency, but can introduce bias due to time trends. We focu…
Principles of Conditionality and Layering of Error Rates with Application to Platform Trials
University of California, Riverside（加州大学河滨分校）；Nektar Therapeutics University of California（Nektar治疗公司加州大学）；BeiGene（百济神州）；IO Biotech（IO生物技术）；The Ohio State University（俄亥俄州立大学）
Though platform trials have been touted for their flexibility and streamlined use of trial resources, their statistical efficiency is not well understood. We fill this gap by establishing their greater efficiency for comparing the relative efficacy of multiple interventions over using several separate, two-arm trials, where the relative efficacy of an arbitrary pair of interventions is evaluated by contrasting their relative risks as compared to control. In theoretical and numerical studies, we demonstrate that the inference of such a contrast using data from a platform trial enjoys identical …
Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not all necessarily pre-specified. Online error control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time. In the online te…
Trial level surrogates are useful tools for improving the speed and cost effectiveness of trials, but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial level surrogate evaluation, but none, to our knowledge, for the specific setting of Bayesian adaptive platform studies. As adaptive studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate e…
There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design that allows additional arms to be added in a platform trial in a pre-planned fashion, while still controlling the family wise error rate. A method is given to compute the sample size required to achieve a desired level of power and we …
Estimands and estimation in trials with time trends
Universitat Politècnica de Catalunya - BarcelonaTech（加泰罗尼亚理工大学）；Novartis Pharma AG（诺华制药）；Ghent University（根特大学）；Vrije Universiteit Brussel（布鲁塞尔自由大学）；Medical University of Vienna（维也纳医科大学）
In randomized trials, appropriately adjusting for baseline variables and short-term outcomes can lead to increased precision and reduced sample size. We examine the impact of such adjustment in group sequential designs, i.e., designs with preplanned interim analyses where enrollment may be stopped early for efficacy or futility. We address the following questions: how much precision gain can be obtained by appropriately adjusting for baseline variables and a short-term outcome? How is this precision gain impacted by factors such as the proportion of pipeline participants (those who enrolled bu…
Background: Experimental treatments pass through various stages of development. If a treatment passes through early phase experiments, the investigators may want to assess it in a late phase randomised controlled trial. An efficient way to do this is adding it as a new research arm to an ongoing trial. This allows to add the new treatment while the existing arms continue. The familywise type I error rate (FWER) is often a key quantity of interest in any multi-arm trial. We set out to clarify how it should be calculated when new arms are added to a trial some time after it has started. Methods:…
Stepped wedge designs (SWDs) are designs for cluster randomized trials that feature staggered, unidirectional cross-over, typically from a control to a treatment condition. Existing literature on statistical power for SWDs primarily focuses on designs with a single treatment. However, SWDs with multiple treatments are being proposed and conducted. We present a linear mixed model for a SWD with two treatments, with and without an interaction between them. We derive closed form solutions for the standard errors of the treatment effect coefficients for such models along with power calculation met…
GenTwoArmsTrialSize: An R Statistical Software Package to estimate Generalized Two Arms Randomized Clinical Trial Sample Size
Northeastern University（东北大学）；University of Calgary（卡尔加里大学）；Iowa State University（爱荷华州立大学）；University of South Florida（南佛罗里达大学）；Alberta Heath Services & University of Calgary（阿尔伯塔卫生局与卡尔加里大学）；ClinChoice Inc.（ClinChoice公司）
Many lifestyle intervention trials depend on collecting self-reported outcomes, like dietary intake, to assess the intervention's effectiveness. Self-reported outcome measures are subject to measurement error, which could impact treatment effect estimation. External validation studies measure both self-reported outcomes and an accompanying biomarker, and can therefore be used for measurement error correction. Most validation data, though, are only relevant for outcomes under control conditions. Statistical methods have been developed to use external validation data to correct for outcome measu…
While the gold standard for clinical trials is to blind all parties -- participants, researchers, and evaluators -- to treatment assignment, this is not always a possibility. When some or all of the above individuals know the treatment assignment, this leaves the study open to the introduction of post-randomization biases. In the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial, we were presented with the potential for the unblinded clinicians administering the treatment, as well as the individuals enrolled in the study, to introduce ascertainment bias into some bu…
