Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial

BMC Medical Research Methodology

Hermione Lyall¹  Steven B. Welch²  Mark F. Cotton³  Pablo Rojo⁴  Gareth Tudor-Williams⁵  Elizabeth Kaudha⁶  Victor Musiime⁷  Abbas Lugemwa⁸  Moherndran Archary⁹  Linda N. Barlow-Mosha^1,10  Diana M. Gibb^1,11  Cecilia L. Moore^1,11  Ian R. White^1,11  Anna Turkova^1,11  Deborah Ford^1,11  Rebecca M. Turner^1,11  Alasdair Bamford^1,12  Tim R. Cressey^1,13  Veronica Mulenga^1,14  Hilda A. Mujuru^1,15 

[1]Department of Paediatric Infectious Diseases, Imperial College Healthcare NHS Trust, London, UK

[2]Department of Paediatrics, Birmingham Chest Clinic and Heartlands Hospital, University Hospitals Birmingham, Birmingham, UK

[3]Family Center for Research With Ubuntu, Department of Paediatrics and Child Health, Tygerberg Hospital and Stellenbosch University, Cape Town, South Africa

[4]Hospital, 12 de Octubre, Madrid, Spain

[5]Imperial College, London, UK

[6]Joint Clinical Research Centre, Kampala, Uganda

[7]Joint Clinical Research Centre, Kampala, Uganda

[8]Department of Paediatrics and Child Health, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda

[9]Joint Clinical Research Centre, Mbarara, Uganda

[10]King Edward VIII Hospital, Durban, South Africa

[11]Department of Paediatrics and Child Health, University of KwaZulu Natal, Durban, South Africa

[12]Makerere University- Johns Hopkins University Research Collaboration, Kampala, Uganda

[13]Medical Research Council Clinical Trials Unit at University College London, 90 High Holborn, WC1V 6LJ, London, UK

[14]Medical Research Council Clinical Trials Unit at University College London, 90 High Holborn, WC1V 6LJ, London, UK

[15]Department of Paediatric Infectious Diseases, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK

[16]UCL Great Ormond Street Institute of Child Health, London, UK

[17]PHPT/IRD UMI174, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand

[18]Department of Molecular & Clinical Pharmacology, University of Liverpool, Liverpool, UK

[19]University Teaching Hospital, Lusaka, Zambia

[20]University of Zimbabwe Clinical Research Centre, Harare, Zimbabwe

关键词: Paediatric trials;    Subgroups;    Small samples;    Bayesian analysis;    Borrowing information;   

DOI  :  10.1186/s12874-022-01539-3

来源: Springer

【 摘 要 】

BackgroundClinical 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.MethodsWe 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.ResultsThe 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.ConclusionsBorrowing 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.

【 授权许可】

CC BY