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Incorporating individual historical controls and aggregate treatment effect estimates into a Bayesian survival trial: a simulation study
- Source :
- BMC Medical Research Methodology, BMC Medical Research Methodology, 2019, 19 (1), pp.85. ⟨10.1186/s12874-019-0714-z⟩, BMC Medical Research Methodology, Vol 19, Iss 1, Pp 1-17 (2019), BMC Medical Research Methodology, BioMed Central, 2019, 19 (1), pp.85. ⟨10.1186/s12874-019-0714-z⟩
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- 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. Electronic supplementary material The online version of this article (10.1186/s12874-019-0714-z) contains supplementary material, which is available to authorized users.
- Subjects :
- Simulation study
Epidemiology
Computer science
Bayesian probability
Health Informatics
[SDV.CAN]Life Sciences [q-bio]/Cancer
Power prior
law.invention
03 medical and health sciences
0302 clinical medicine
Adjuvants, Immunologic
[SDV.CAN] Life Sciences [q-bio]/Cancer
Randomized controlled trial
law
Statistics
Prior probability
Humans
Computer Simulation
030212 general & internal medicine
Set (psychology)
Baseline (configuration management)
Bayesian randomised survival trial
Randomized Controlled Trials as Topic
Weibull distribution
Osteosarcoma
lcsh:R5-920
Phosphatidylethanolamines
030503 health policy & services
Aggregate (data warehouse)
Bayes Theorem
Variance (accounting)
Models, Theoretical
[SDV.SP]Life Sciences [q-bio]/Pharmaceutical sciences
Control Groups
Mixture prior
3. Good health
[SDV.SP] Life Sciences [q-bio]/Pharmaceutical sciences
Research Design
Sample Size
Individual control data
0305 other medical science
lcsh:Medicine (General)
Aggregate treatment effect
Acetylmuramyl-Alanyl-Isoglutamine
Rare disease
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 14712288
- Database :
- OpenAIRE
- Journal :
- BMC Medical Research Methodology, BMC Medical Research Methodology, 2019, 19 (1), pp.85. ⟨10.1186/s12874-019-0714-z⟩, BMC Medical Research Methodology, Vol 19, Iss 1, Pp 1-17 (2019), BMC Medical Research Methodology, BioMed Central, 2019, 19 (1), pp.85. ⟨10.1186/s12874-019-0714-z⟩
- Accession number :
- edsair.doi.dedup.....88efbaa13ba605dfc4ed34d72e77e42d