Platform Trials - Beware the Noncomparable Control Group.
Ontology highlight
ABSTRACT:
SUBMITTER: Dodd LE
PROVIDER: S-EPMC8117966 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:When testing multiple hypotheses, a suitable error rate should be controlled even in exploratory trials. Conventional methods to control the False Discovery Rate 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 different times during its conduct. Therefore, the actual 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 False Discovery Rate was introduced. We propose several heuristic variations of the LOND procedure (significance Levels based On Number of Discoveries) that incorporate interim analyses for platform trials, and study their online False Discovery Rate via simulations. To adjust for the interim looks spending functions are applied with O'Brien-Fleming or Pocock type group-sequential boundaries. The power depends on the prior distribution of effect sizes, for example, whether true alternatives are uniformly distributed over time or not. We consider the choice of design parameters for the LOND procedure to maximize the overall power and investigate the impact on the False Discovery Rate by including both concurrent and non-concurrent control data.
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Project description: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 necessarily pre-specified. Online error rate 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 multiple hypothesis testing framework, hypotheses are tested one-by-one over time, where at each time-step an analyst decides whether to reject the current null hypothesis without knowledge of future tests but based solely on past decisions. Methodology has recently been developed for online control of the false discovery rate as well as the familywise error rate (FWER). In this article, we describe how to apply online error rate control to the platform trial setting, present extensive simulation results, and give some recommendations for the use of this new methodology in practice. We show that the algorithms for online error rate control can have a substantially lower FWER than uncorrected testing, while still achieving noticeable gains in power when compared with the use of a Bonferroni correction. We also illustrate how online error rate control would have impacted a currently ongoing platform trial.
| S-EPMC7614610 | biostudies-literature
Project description:For the past few years, platform trials have experienced a significant increase, recently amplified by the COVID-19 pandemic. The implementation of a platform trial is particularly useful in certain pathologies, particularly when there is a significant number of drug candidates to be assessed, a rapid evolution of the standard of care or in situations of urgent need for evaluation, during which the pooling of protocols and infrastructure optimizes the number of patients to be enrolled, the costs, and the deadlines for carrying out the investigation. However, the specificity of platform trials raises methodological, ethical, and regulatory issues, which have been the subject of the round table and which are presented in this article. The round table was also an opportunity to discuss the complexity of sponsorship and data management related to the multiplicity of partners, funding, and governance of these trials, and the level of acceptability of their findings by the competent authorities.
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Project description:The progress made over the past 50 years in disease-directed clinical trials has significantly increased cure rates for children and adolescents with cancer. The Children's Oncology Group (COG) is now conducting more studies that emphasize improving quality of life for young people with cancer. These types of clinical trials are classified as cancer control (CCL) studies by the National Cancer Institute and require different resources and approaches to facilitate adequate accrual and implementation at COG institutions. Several COG institutions that had previously experienced problems with low accruals to CCL trials have successfully implemented local nursing leadership for these types of studies. Successful models of nurses as institutional leaders and "champions" of CCL trials are described.
| S-EPMC5013656 | biostudies-literature
Project description:BackgroundPlatform trials allow adding new experimental treatments to an on-going trial. This feature is attractive to practitioners due to improved efficiency. Nevertheless, the operating characteristics of a trial that adds arms have not been well-studied. One controversy is whether just the concurrent control data (i.e. of patients who are recruited after a new arm is added) should be used in the analysis of the newly added treatment(s), or all control data (i.e. non-concurrent and concurrent).MethodsWe investigate the benefits and drawbacks of using non-concurrent control data within a two-stage setting. We perform simulation studies to explore the impact of a linear and a step trend on the inference of the trial. We compare several analysis approaches when one includes all the control data or only concurrent control data in the analysis of the newly added treatment.ResultsWhen there is a positive trend and all the control data are used, the marginal power of rejecting the corresponding hypothesis and the type one error rate can be higher than the nominal value. A model-based approach adjusting for a stage effect is equivalent to using concurrent control data; an adjustment with a linear term may not guarantee valid inference when there is a non-linear trend.ConclusionsIf strict error rate control is required then non-concurrent control data should not be used; otherwise it may be beneficial if the trend is sufficiently small. On the other hand, the root mean squared error of the estimated treatment effect can be improved through using non-concurrent control data.
| S-EPMC7315495 | biostudies-literature
Project description:Important considerations for exercise trials in cancer patients are contamination and differential drop-out among the control group members that might jeopardize the internal validity. This systematic review provides an overview of different control groups design characteristics of exercise-oncology trials and explores the association with contamination and drop-out rates.Randomized controlled exercise-oncology trials from two Cochrane reviews were included. Additionally, a computer-aided search using Medline (Pubmed), Embase and CINAHL was conducted after completion date of the Cochrane reviews. Eligible studies were classified according to three control group design characteristics: the exercise instruction given to controls before start of the study (exercise allowed or not); and the intervention the control group was offered during (any (e.g., education sessions or telephone contacts) or none) or after (any (e.g., cross-over or exercise instruction) or none) the intervention period. Contamination (yes or no) and excess drop-out rates (i.e., drop-out rate of the control group minus the drop-out rate exercise group) were described according to the three design characteristics of the control group and according to the combinations of these three characteristics; so we additionally made subgroups based on combinations of type and timing of instructions received.40 exercise-oncology trials were included based on pre-specified eligibility criteria. The lowest contamination (7.1% of studies) and low drop-out rates (excess drop-out rate -4.7±9.2) were found in control groups offered an intervention after the intervention period. When control groups were offered an intervention both during and after the intervention period, contamination (0%) and excess drop-out rates (-10.0±12.8%) were even lower.Control groups receiving an intervention during and after the study intervention period have lower contamination and drop-out rates. The present findings can be considered when designing future exercise-oncology trials.
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Project description:The EORTC GastroIntestinal Tract Cancer Group and the EORTC HeadQuarters wish to set up a European screening platform for advanced colo-rectal cancer (CRC) patients. The goal of this screening platform is to provide quick access to new drugs to patients by offering a new structure for clinical trials.
Currently some of the most challenging clinical questions arise from the molecular sub-division of CRC that would theoretically allow to inhibit the specific, altered pathways in the patients.
A major problem for trials in this "personalized medicine" is that the low frequency of the different mutations requires a high effort for screening and identifying the patients.
The EORTC CRC screening platform will hopefully offer a feasible and efficient way to characterize the patients on the molecular basis of their tumors and allow to offer them rapid and preferential participation in clinical studies with new drugs targeted to their specific pathway alterations.
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