Incorporating individual historical controls and aggregate treatment effect estimates into a Bayesian survival trial: a simulation study

Brard, Caroline; Gaspar, Nathalie; Deley, Marie‐Cécile Le; Teuff, Gwénaël Le

doi:10.1186/s12874-019-0714-z

Cited by 10 publications

(7 citation statements)

References 42 publications

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“…8,9 For time-to-event endpoints, a method for estimating the power parameter of the pooled historical controls is proposed. 14,15 However, no method for estimating the power parameter based on the degree of conflict between the current control and each historical control has thus far been proposed for time-to-event endpoints. Therefore, in this study, only the power priors are considered when evaluating the binary endpoints.…”

Section: Power Priormentioning

confidence: 99%

Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials

Ohigashi

Maruo

Sozu

et al. 2022

Stat Methods Med Res

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Meta-analytic approaches and power priors are often used to incorporate historical controls into the analysis of a current randomized controlled trial. In this study, we propose a method for incorporating multiple historical controls based on a horseshoe prior, which is a type of global–local shrinkage prior. The method assumes that historical controls follow the same distribution as the current control. In the case in which only a few historical controls are heterogeneous, we consider them to follow a potentially biased distribution from the distribution of the current control. We analyze two clinical trial examples with binary and time-to-event endpoints and conduct simulation studies to compare the performance of the proposed and existing methods. In the analysis of the clinical trial example, the posterior standard deviation of the treatment effect is decreased by the proposed method by considering the bias between the current control and heterogeneous historical control. In the scenarios in which the current and historical controls follow the same distribution, the statistical power using the proposed method is higher than that using existing methods. The proposed method is advantageous when few or no heterogeneous historical controls are expected.

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Section: Power Priormentioning

confidence: 99%

Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials

Ohigashi

Maruo

Sozu

et al. 2022

Stat Methods Med Res

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“…However, these randomized trials require more patients and thus are longer than single-arm trials. Bayesian trial designs might be of value in rapidly evaluating multiple treatments for rare tumors as done in Ewing sarcoma (rEECur trial) 65 and to integrate previous historical data (sarcoma 13 trial) 45 , 66 , tying to minimize the number of patients required.…”

Section: Drug Development In Osteosarcoma: How To Improve Trial Desigmentioning

confidence: 99%

Recent advances in understanding osteosarcoma and emerging therapies

Gaspar¹,

Costa²,

Fromigué³

et al. 2020

Fac Rev

Self Cite

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Osteosarcoma is the most common bone cancer in adolescents and young adults, but it is a rare cancer with no improvement in patient survival in the last four decades. The main problem of this bone tumor is its evolution toward lung metastatic disease, despite the current treatment strategy (chemotherapy and surgery). To further improve survival, there is a strong need for new therapies that control osteosarcoma cells with metastatic potential and their favoring tumor microenvironment (ME) from the diagnosis. However, the complexity and heterogeneity of those tumor cell genomic/epigenetic and biology, the diversity of tumor ME where it develops, the sparsity of appropriate preclinical models, and the heterogeneity of therapeutic trials have rendered the task difficult. No tumor- or ME-targeted drugs are routinely available in front-line treatment. This article presents up-to-date information from preclinical and clinical studies that were recently published or presented in recent meetings which we hope might help change the osteosarcoma treatment landscape and patient survival in the near future.

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“…In medicine, it was proposed more than 45 years ago [21] and repeatedly advocated for [22][23][24][25]. Such external knowledge can improve the precision of estimates, lower error rates, grant better small sample properties, and improve the precision of survival estimates [24,[26][27][28][29][30][31]. While improper incorporation of external knowledge might bias estimates and increase error rate [32], safeguards against adverse effects of external knowledge exist.…”

Section: Introductionmentioning

confidence: 99%

Informed Bayesian survival analysis

Bartoš

Aust

Haaf

2022

BMC Med Res Methodol

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Background We provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of evidence, and incorporating uncertainty about the true data generating process. Methods We illustrate the application of the outlined Bayesian approaches on an example data set, retrospective re-analyzing a colon cancer trial. We assess the performance of Bayesian parametric survival analysis and maximum likelihood survival models with AIC/BIC model selection in fixed-n and sequential designs with a simulation study. Results In the retrospective re-analysis of the example data set, the Bayesian framework provided evidence for the absence of a positive treatment effect of adding Cetuximab to FOLFOX6 regimen on disease-free survival in patients with resected stage III colon cancer. Furthermore, the Bayesian sequential analysis would have terminated the trial 10.3 months earlier than the standard frequentist analysis. In a simulation study with sequential designs, the Bayesian framework on average reached a decision in almost half the time required by the frequentist counterparts, while maintaining the same power, and an appropriate false-positive rate. Under model misspecification, the Bayesian framework resulted in higher false-negative rate compared to the frequentist counterparts, which resulted in a higher proportion of undecided trials. In fixed-n designs, the Bayesian framework showed slightly higher power, slightly elevated error rates, and lower bias and RMSE when estimating treatment effects in small samples. We found no noticeable differences for survival predictions. We have made the analytic approach readily available to other researchers in the RoBSA R package. Conclusions The outlined Bayesian framework provides several benefits when applied to parametric survival analyses. It uses data more efficiently, is capable of considerably shortening the length of clinical trials, and provides a richer set of inferences.

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Incorporating individual historical controls and aggregate treatment effect estimates into a Bayesian survival trial: a simulation study

Cited by 10 publications

References 42 publications

Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials

Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials

Recent advances in understanding osteosarcoma and emerging therapies

Informed Bayesian survival analysis

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