Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs.
Ontology highlight
ABSTRACT:
SUBMITTER: Ballarini NM
PROVIDER: S-EPMC8251960 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup.
| S-EPMC6613177 | biostudies-literature
Project description:Bioequivalence (BE) studies are most often conducted as crossover trials, and therefore establishing their required sample size necessitates specification of the within-person variance. Given that this specification is often difficult in practice, there has been great interest in recent years in the use of adaptive designs for BE trials. However, while numerous methods for this have now been presented, their focus has been solely on two-treatment BE studies. In some instances, it will be desired to incorporate more than a single test and reference formulation into a BE trial. It would therefore be useful to establish methodology for the design of adaptive multi-treatment BE trials, to acquire the benefits in the two-treatment setting in this more complex situation. Here, we achieve this for three-treatment studies by extending previously proposed designs for two-treatment trials. First, we discuss the additional design considerations that arise when multiple comparisons are made. Next, an extensive simulation study is employed to compare the performance of the proposed procedures. With this, we demonstrate that two-stage designs with desirable statistical operating characteristics can be readily identified for three-treatment BE trials. Supplementary materials for this article are available online.
| S-EPMC7612189 | biostudies-literature
Project description:Simon's optimal two-stage design has been widely used in early phase clinical trials for Oncology and AIDS studies with binary endpoints. With this approach, the second-stage sample size is fixed when the trial passes the first stage with sufficient activity. Adaptive designs, such as those due to Banerjee and Tsiatis (2006) and Englert and Kieser (2013), are flexible in the sense that the second-stage sample size depends on the response from the first stage, and these designs are often seen to reduce the expected sample size under the null hypothesis as compared with Simon's approach. An unappealing trait of the existing designs is that they are not associated with a second-stage sample size, which is a non-increasing function of the first-stage response rate. In this paper, an efficient intelligent process, the branch-and-bound algorithm, is used in extensively searching for the optimal adaptive design with the smallest expected sample size under the null, while the type I and II error rates are maintained and the aforementioned monotonicity characteristic is respected. The proposed optimal design is observed to have smaller expected sample sizes compared to Simon's optimal design, and the maximum total sample size of the proposed adaptive design is very close to that from Simon's method. The proposed optimal adaptive two-stage design is recommended for use in practice to improve the flexibility and efficiency of early phase therapeutic development.
| S-EPMC4777673 | biostudies-literature
Project description: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.
| S-EPMC7785132 | biostudies-literature
Project description:If the response to treatment depends on genetic biomarkers, it is important to identify predictive biomarkers that define (sub-)populations where the treatment has a positive benefit risk balance. One approach to determine relevant subpopulations are subgroup analyses where the treatment effect is estimated in biomarker positive and biomarker negative groups. Subgroup analyses are challenging because several types of risks are associated with inference on subgroups. On the one hand, by disregarding a relevant subpopulation a treatment option may be missed due to a dilution of the treatment effect in the full population. Furthermore, even if the diluted treatment effect can be demonstrated in an overall population, it is not ethical to treat patients that do not benefit from the treatment when they can be identified in advance. On the other hand, selecting a spurious subpopulation increases the risk to restrict an efficacious treatment to a too narrow fraction of a potential benefiting population. We propose to quantify these risks with utility functions and investigate nonadaptive study designs that allow for inference on subgroups using multiple testing procedures as well as adaptive designs, where subgroups may be selected in an interim analysis. The characteristics of such adaptive and nonadaptive designs are compared for a range of scenarios.
| S-EPMC4314682 | biostudies-literature
Project description:Recently, several study designs incorporating treatment effect assessment in biomarker-based subpopulations have been proposed. Most statistical methodologies for such designs focus on the control of type I error rate and power. In this paper, we have developed point estimators for clinical trials that use the two-stage adaptive enrichment threshold design. The design consists of two stages, where in stage 1, patients are recruited in the full population. Stage 1 outcome data are then used to perform interim analysis to decide whether the trial continues to stage 2 with the full population or a subpopulation. The subpopulation is defined based on one of the candidate threshold values of a numerical predictive biomarker. To estimate treatment effect in the selected subpopulation, we have derived unbiased estimators, shrinkage estimators, and estimators that estimate bias and subtract it from the naive estimate. We have recommended one of the unbiased estimators. However, since none of the estimators dominated in all simulation scenarios based on both bias and mean squared error, an alternative strategy would be to use a hybrid estimator where the estimator used depends on the subpopulation selected. This would require a simulation study of plausible scenarios before the trial.
| S-EPMC6175016 | biostudies-literature
Project description:We propose an adaptive enrichment approach to test an active factor, which is a factor whose effect is non-zero in at least one subpopulation. We implement a two-stage play-the-winner design where all subjects in the second stage are enrolled from the subpopulation that has the highest observed effect in the first stage. We recommend a weighted Fisher's combination of the most powerful test for each stage, respectively: the first stage Hotelling's test and the second stage noncentral chi-square test. The test is further extended to cover binary outcomes and time-to-event outcomes.
| S-EPMC6881517 | biostudies-literature
Project description:Our increased understanding of the mechanistic heterogeneity of diseases has pushed the development of targeted therapeutics. We do not expect all patients with a given disease to benefit from a targeted drug; only those in the target population. That is, those with sufficient dysregulation in the biomolecular pathway targeted by treatment. However, due to complexity of the pathway, and/or technical issues with our characterizing assay, it is often hard to characterize the target population until well into large-scale clinical trials. This has stimulated the development of adaptive enrichment trials; clinical trials in which the target population is adaptively learned; and enrollment criteria are adaptively updated to reflect this growing understanding. This paper proposes a framework for group-sequential adaptive enrichment trials. Building on the work of Simon & Simon (2013). Adaptive enrichment designs for clinical trials. Biostatistics 14(4), 613-625), it includes a frequentist hypothesis test at the end of the trial. However, it uses Bayesian methods to optimize the decisions required during the trial (regarding how to restrict enrollment) and Bayesian methods to estimate effect size, and characterize the target population at the end of the trial. This joint frequentist/Bayesian design combines the power of Bayesian methods for decision making with the use of a formal hypothesis test at the end of the trial to preserve the studywise probability of a type I error.
| S-EPMC6075009 | biostudies-literature
Project description:We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization to find extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial. Our proposed designs include the celebrated Simon's two-stage design and its extension that allows two target response rates to be specified for the drug. We show that discrete particle swarm optimization not only frequently outperforms greedy algorithms, which are currently used to find such designs when there are only a few parameters; it is also capable of solving design problems posed here with more parameters that greedy algorithms cannot solve. In stage 1 of our proposed designs, futility is quickly assessed and if there are sufficient responders to move to stage 2, one tests one of the three target response rates of the drug, subject to various user-specified testing error rates. Our designs are therefore more flexible and interestingly, do not necessarily require larger expected sample size requirements than two-stage adaptive designs. Using a real adaptive trial for melanoma patients, we show our proposed design requires one half fewer subjects than the implemented design in the study.
| S-EPMC5515697 | biostudies-literature
Project description:Due to the small sample sizes in early-phase clinical trials, the toxicity and efficacy profiles of the dose-schedule regimens determined for subsequent trials may not be well established. The recent development of novel anti-tumor treatments and combination therapies further complicates the problem. Therefore, there is an increasing recognition of the essential place of optimizing dose-schedule regimens, and new strategies are now urgently needed. Bayesian adaptive designs provide a potentially effective way to evaluate several doses and schedules simultaneously in a single clinical trial with higher efficiency, but real-world implementation examples of such adaptive designs are still few. In this paper, we cover the critical factors associated with dose-schedule optimization and review the related innovative Bayesian adaptive designs. The assumptions, characteristics, limitations, and application scenarios of those designs are introduced. The review also summarizes some unresolved issues and future research opportunities for dose-schedule optimization.
| S-EPMC10701540 | biostudies-literature