Bayesian clinical trial design using historical data that inform the treatment effect.
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ABSTRACT:
SUBMITTER: Psioda MA
PROVIDER: S-EPMC6587921 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this paper, we develop a general Bayesian clinical trial design methodology, tailored for time-to-event trials with a cured fraction in scenarios where a previously completed clinical trial is available to inform the design and analysis of the new trial. Our methodology provides a conceptually appealing and computationally feasible framework that allows one to construct a fixed, maximally informative prior a priori while simultaneously identifying the minimum sample size required for the new trial so that the design has high power and reasonable type I error control from a Bayesian perspective. This strategy is particularly well suited for scenarios where adaptive borrowing approaches are not practical due to the nature of the trial, complexity of the model, or the source of the prior information. Control of a Bayesian type I error rate offers a sensible balance between wanting to use high-quality information in the design and analysis of future trials while still controlling type I errors in an equitable way. Moreover, sample size determination based on our Bayesian view of power can lead to a more adequately sized trial by virtue of taking into account all the uncertainty in the treatment effect. We demonstrate our methodology by designing a cancer clinical trial in high-risk melanoma.
| S-EPMC6288795 | biostudies-literature
Project description:BackgroundCentronuclear myopathies are severe rare congenital diseases. The clinical variability and genetic heterogeneity of these myopathies result in major challenges in clinical trial design. Alternative strategies to large placebo-controlled trials that have been used in other rare diseases (e.g., the use of surrogate markers or of historical controls) have limitations that Bayesian statistics may address. Here we present a Bayesian model that uses each patient's own natural history study data to predict progression in the absence of treatment. This prospective multicentre natural history evaluated 4-year follow-up data from 59 patients carrying mutations in the MTM1 or DNM2 genes.MethodsOur approach focused on evaluation of forced expiratory volume in 1 s (FEV1) in 6- to 18-year-old children. A patient was defined as a responder if an improvement was observed after treatment and the predictive probability of such improvement in absence of intervention was less than 0.01. An FEV1 response was considered clinically relevant if it corresponded to an increase of more than 8%.ResultsThe key endpoint of a clinical trial using this model is the rate of response. The power of the study is based on the posterior probability that the rate of response observed is greater than the rate of response that would be observed in the absence of treatment predicted based on the individual patient's previous natural history. In order to appropriately control for Type 1 error, the threshold probability by which the difference in response rates exceeds zero was adapted to 91%, ensuring a 5% overall Type 1 error rate for the trial.ConclusionsBayesian statistical analysis of natural history data allowed us to reliably simulate the evolution of symptoms for individual patients over time and to probabilistically compare these simulated trajectories to actual observed post-treatment outcomes. The proposed model adequately predicted the natural evolution of patients over the duration of the study and will facilitate a sufficiently powerful trial design that can cope with the disease's rarity. Further research and ongoing dialog with regulatory authorities are needed to allow for more applications of Bayesian statistics in orphan disease research.
| S-EPMC7789189 | biostudies-literature
Project description:In this paper, we develop the fixed-borrowing adaptive design, a Bayesian adaptive design which facilitates information borrowing from a historical trial using subject-level control data while assuring a reasonable upper bound on the maximum type I error rate and lower bound on the minimum power. First, one constructs an informative power prior from the historical data to be used for design and analysis of the new trial. At an interim analysis opportunity, one evaluates the degree of prior-data conflict. If there is too much conflict between the new trial data and the historical control data, the prior information is discarded and the study proceeds to the final analysis opportunity at which time a noninformative prior is used for analysis. Otherwise, the trial is stopped early and the informative power prior is used for analysis. Simulation studies are used to calibrate the early stopping rule. The proposed design methodology seamlessly accommodates covariates in the statistical model, which the authors argue is necessary to justify borrowing information from historical controls. Implementation of the proposed methodology is straightforward for many common data models, including linear regression models, generalized linear regression models, and proportional hazards models. We demonstrate the methodology to design a cardiovascular outcomes trial for a hypothetical new therapy for treatment of type 2 diabetes mellitus and borrow information from the SAVOR trial, one of the earliest cardiovascular outcomes trials designed to assess cardiovascular risk in antidiabetic therapies.
| S-EPMC6327964 | biostudies-literature
Project description:High quality historical control data, if incorporated, may reduce sample size, trial cost, and duration. A too optimistic use of the data, however, may result in bias under prior-data conflict. Motivated by well-publicized two-arm comparative trials in stroke, we propose a Bayesian design that both adaptively incorporates historical control data and selectively adapt the treatment allocation ratios within an ongoing trial responsively to the relative treatment effects. The proposed design differs from existing designs that borrow from historical controls. As opposed to reducing the number of subjects assigned to the control arm blindly, this design does so adaptively to the relative treatment effects only if evaluation of cumulated current trial data combined with the historical control suggests the superiority of the intervention arm. We used the effective historical sample size approach to quantify borrowed information on the control arm and modified the treatment allocation rules of the doubly adaptive biased coin design to incorporate the quantity. The modified allocation rules were then implemented under the Bayesian framework with commensurate priors addressing prior-data conflict. Trials were also more frequently concluded earlier in line with the underlying truth, reducing trial cost, and duration and yielded parameter estimates with smaller standard errors.
| S-EPMC6221103 | biostudies-literature
Project description:BackgroundClinical trial enrollment and completion is challenging, with nearly half of all trials not being completed or not completed on time. In 2014, the National Institutes of Health StrokeNet in collaboration with stroke epidemiologists from GCNKSS (Greater Cincinnati/Northern Kentucky Stroke Study) began providing proposed clinical trials with formal trial feasibility assessments. Herein, we describe the process of prospective feasibility analyses using epidemiological data that can be used to improve enrollment and increase the likelihood a trial is completed.MethodsIn 2014, DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trialists, National Institutes of Health StrokeNet, and stroke epidemiologists from GCNKSS collaborated to evaluate the initial inclusion/exclusion criteria for the DEFUSE 3 study. Trial criteria were discussed and an assessment was completed to evaluate the percent of the stroke population that might be eligible for the study. The DEFUSE 3 trial was stopped early with the publication of DAWN (Thrombectomy 6 to 24 Hours After Stroke With a Mismatch Between Deficit and Infarct), and the Wilcoxon rank-sum statistic was used to analyze whether the trial would have been stopped had the proposed changes not been made, following the DEFUSE 3 statistical analysis plan.ResultsAfter initial epidemiological analysis, 2.4% of patients with acute stroke in the GCNKSS population would have been predicted to be eligible for the study. After discussion with primary investigators and modifying 4 key exclusion criteria (upper limit of age increased to 90 years, baseline modified Rankin Scale broadened to 0-2, time since last well expanded to 16 hours, and decreased lower limit of National Institutes of Health Stroke Scale score to <6), the number predicted to be eligible for the trial increased to 4%. At the time of trial conclusion, 57% of the enrolled patients qualified only by the modified criteria, and the trial was stopped at an interim analysis that demonstrated efficacy. We estimated that the Wilcoxon rank-sum value for the unadjusted predicted enrollment would not have crossed the threshold for efficacy and the trial not stopped.ConclusionsObjectively assessing trial inclusion/exclusion criteria using a population-based resource in a collaborative and iterative process including epidemiologists can lead to improved recruitment and can increase the likelihood of successful trial completion.
| S-EPMC10050115 | biostudies-literature
Project description:BACKGROUND:Performing well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible. However, with the increasing availability of historical data, incorporating existing information into trials with small sample sizes is appealing in order to increase the power. Bayesian approaches enable one to incorporate historical data into a trial's analysis through a prior distribution. METHODS:Motivated by a RCT intended to evaluate the impact on event-free survival of mifamurtide in patients with osteosarcoma, we performed a simulation study to evaluate the impact on trial operating characteristics of incorporating historical individual control data and aggregate treatment effect estimates. We used power priors derived from historical individual control data for baseline parameters of Weibull and piecewise exponential models, while we used a mixture prior to summarise aggregate information obtained on the relative treatment effect. The impact of prior-data conflicts, both with respect to the parameters and survival models, was evaluated for a set of pre-specified weights assigned to the historical information in the prior distributions. RESULTS:The operating characteristics varied according to the weights assigned to each source of historical information, the variance of the informative and vague component of the mixture prior and the level of commensurability between the historical and new data. When historical and new controls follow different survival distributions, we did not observe any advantage of choosing a piecewise exponential model compared to a Weibull model for the new trial analysis. However, we think that it remains appealing given the uncertainty that will often surround the shape of the survival distribution of the new data. CONCLUSION:In the setting of Sarcome-13 trial, and other similar studies in rare diseases, the gains in power and accuracy made possible by incorporating different types of historical information commensurate with the new trial data have to be balanced against the risk of biased estimates and a possible loss in power if data are not commensurate. The weights allocated to the historical data have to be carefully chosen based on this trade-off. Further simulation studies investigating methods for incorporating historical data are required to generalise the findings.
| S-EPMC6480797 | biostudies-literature
Project description:BackgroundClinical trials assessing new treatment effects require a control group to compare the pure treatment effects. However, in clinical trials on regenerative medicine, rare diseases, and intractable diseases, it may be ethically difficult to assign participants to the control group. In recent years, the use of historical control data has attracted attention as a method for supplementing the number of participants in the control group. When combining historical control data with new randomized controlled trial (RCT) data, the assessment of heterogeneity using outcome data is not sufficient. Therefore, several statistical methods that consider participant outcomes and baseline characteristics, including the propensity score (PS) method have been proposed.MethodsWe propose a new method considering "information on whether the data are RCT data or not" in the PS model when combining the RCT and historical control data. The performance of the proposed method in estimating the treatment effect is evaluated using simulation data.ResultsWhen the distribution of covariates is similar between the RCT and historical control data, not much difference in performance is found between the proposed and conventional methods to estimate the treatment effect. On the other hand, when the distribution of covariates is not similar between the two kinds of data, the proposed method shows higher performance.ConclusionsEven when it is not known whether RCT and historical control data are similar, the proposed PS model is useful to estimate the treatment effect appropriately in RCTs using historical control data.
| S-EPMC10882803 | biostudies-literature
Project description:BACKGROUND:The basket trial evaluates the treatment effect of a targeted therapy in patients with the same genetic or molecular aberration, regardless of their cancer types. Bayesian hierarchical modeling has been proposed to adaptively borrow information across cancer types to improve the statistical power of basket trials. Although conceptually attractive, research has shown that Bayesian hierarchical models cannot appropriately determine the degree of information borrowing and may lead to substantially inflated type I error rates. METHODS:We propose a novel calibrated Bayesian hierarchical model approach to evaluate the treatment effect in basket trials. In our approach, the shrinkage parameter that controls information borrowing is not regarded as an unknown parameter. Instead, it is defined as a function of a similarity measure of the treatment effect across tumor subgroups. The key is that the function is calibrated using simulation such that information is strongly borrowed across subgroups if their treatment effects are similar and barely borrowed if the treatment effects are heterogeneous. RESULTS:The simulation study shows that our method has substantially better controlled type I error rates than the Bayesian hierarchical model. In some scenarios, for example, when the true response rate is between the null and alternative, the type I error rate of the proposed method can be inflated from 10% up to 20%, but is still better than that of the Bayesian hierarchical model. LIMITATION:The proposed design assumes a binary endpoint. Extension of the proposed design to ordinal and time-to-event endpoints is worthy of further investigation. CONCLUSION:The calibrated Bayesian hierarchical model provides a practical approach to design basket trials with more flexibility and better controlled type I error rates than the Bayesian hierarchical model. The software for implementing the proposed design is available at http://odin.mdacc.tmc.edu/~yyuan/index_code.html.
| S-EPMC5891374 | biostudies-literature
Project description:Delivering radiation to eradicate a solid tumor while minimizing damage to nearby critical organs remains a challenge. For esophageal cancer, radiation therapy may damage the heart or lungs, and several qualitatively different, possibly recurrent toxicities associated with chemoradiation or surgery may occur, each at two or more possible grades. In this article, we describe a Bayesian group sequential clinical trial design, based on total toxicity burden (TTB) and progression-free survival duration, for comparing two radiation therapy modalities for esophageal cancer. Each patient's toxicities are modeled as a multivariate doubly stochastic Poisson point process, with marks identifying toxicity grades. Each grade of each type of toxicity is assigned a severity weight, elicited from clinical oncologists familiar with the disease and treatments. TTB is defined as a severity-weighted sum over the different toxicities that may occur up to 12 months from the start of treatment. Latent frailties are used to formulate a multivariate model for all outcomes. Group sequential decision rules are based on posterior mean TTB and progression-free survival time. The proposed design is shown to provide both larger power and smaller mean sample size when compared to a conventional bivariate group sequential design.
| S-EPMC4809549 | biostudies-literature
Project description:In this article, we develop a Bayesian adaptive design methodology for oncology basket trials with binary endpoints using a Bayesian model averaging framework. Most existing methods seek to borrow information based on the degree of homogeneity of estimated response rates across all baskets. In reality, an investigational product may only demonstrate activity for a subset of baskets, and the degree of activity may vary across the subset. A key benefit of our Bayesian model averaging approach is that it explicitly accounts for the possibility that any subset of baskets may have similar activity and that some may not. Our proposed approach performs inference on the basket-specific response rates by averaging over the complete model space for the response rates, which can include thousands of models. We present results that demonstrate that this computationally feasible Bayesian approach performs favorably compared to existing state-of-the-art approaches, even when held to stringent requirements regarding false positive rates.
| S-EPMC7846150 | biostudies-literature