Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial.
Author
Turner, Rebecca MTurkova, Anna
Moore, Cecilia L
Bamford, Alasdair
Archary, Moherndran
Barlow-Mosha, Linda N
Cotton, Mark F
Cressey, Tim R
Kaudha, Elizabeth
Lugemwa, Abbas
Lyall, Hermione
Mujuru, Hilda A
Mulenga, Veronica
Musiime, Victor
Rojo, Pablo
Tudor-Williams, Gareth
Welch, Steven B
Gibb, Diana M
Ford, Deborah
White, Ian R
Publication date
2022-02-20Subject
Paediatrics
Metadata
Show full item recordAbstract
Background: Clinical trial investigators may need to evaluate treatment effects in a specific subgroup (or subgroups) of participants in addition to reporting results of the entire study population. Such subgroups lack power to detect a treatment effect, but there may be strong justification for borrowing information from a larger patient group within the same trial, while allowing for differences between populations. Our aim was to develop methods for eliciting expert opinions about differences in treatment effect between patient populations, and to incorporate these opinions into a Bayesian analysis. Methods: We used an interaction parameter to model the relationship between underlying treatment effects in two subgroups. Elicitation was used to obtain clinical opinions on the likely values of the interaction parameter, since this parameter is poorly informed by the data. Feedback was provided to experts to communicate how uncertainty about the interaction parameter corresponds with relative weights allocated to subgroups in the Bayesian analysis. The impact on the planned analysis was then determined. Results: The methods were applied to an ongoing non-inferiority trial designed to compare antiretroviral therapy regimens in 707 children living with HIV and weighing ≥ 14 kg, with an additional group of 85 younger children weighing < 14 kg in whom the treatment effect will be estimated separately. Expert clinical opinion was elicited and demonstrated that substantial borrowing is supported. Clinical experts chose on average to allocate a relative weight of 78% (reduced from 90% based on sample size) to data from children weighing ≥ 14 kg in a Bayesian analysis of the children weighing < 14 kg. The total effective sample size in the Bayesian analysis was 386 children, providing 84% predictive power to exclude a difference of more than 10% between arms, whereas the 85 younger children weighing < 14 kg provided only 20% power in a standalone frequentist analysis. Conclusions: Borrowing information from a larger subgroup or subgroups can facilitate estimation of treatment effects in small subgroups within a clinical trial, leading to improved power and precision. Informative prior distributions for interaction parameters are required to inform the degree of borrowing and can be informed by expert opinion. We demonstrated accessible methods for obtaining opinions.Citation
Turner RM, Turkova A, Moore CL, Bamford A, Archary M, Barlow-Mosha LN, Cotton MF, Cressey TR, Kaudha E, Lugemwa A, Lyall H, Mujuru HA, Mulenga V, Musiime V, Rojo P, Tudor-Williams G, Welch SB, Gibb DM, Ford D, White IR; the ODYSSEY Trial Team. Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial. BMC Med Res Methodol. 2022 Feb 20;22(1):49. doi: 10.1186/s12874-022-01539-3.Type
ArticleAdditional Links
https://pubmed.ncbi.nlm.nih.gov/35184739/PMID
35184739Journal
BMC Medical Research MethodologyPublisher
BioMed Centralae974a485f413a2113503eed53cd6c53
10.1186/s12874-022-01539-3