期刊
AMERICAN JOURNAL OF EPIDEMIOLOGY
卷 190, 期 8, 页码 1671-1680出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwab045
关键词
external validity; implementation; number needed to treat; subgroup analyses; target-specific subgroup analyses; transportability
资金
- National Institute of Mental Health [5F31 MH111346]
- National Institutes of Health [DP2 HD084070, K24 AI134413, AI 126597, AI064002]
- Bill & Melinda Gates Foundation
The process of transporting subgroup analyses from highly selected study populations to external target populations can provide target-specific subgroup effect estimates and numbers needed to treat, ultimately leading to more tailored and accurate guidance for resource allocation and cost-effectiveness analyses.
Subgroup analyses of randomized controlled trials guide resource allocation and implementation of new interventions by identifying groups of individuals who are likely to benefit most from the intervention. Unfortunately, trial populations are rarely representative of the target populations of public health or clinical interest. Unless the relevant differences between trial and target populations are accounted for, subgroup results from trials might not reflect which groups in the target population will benefit most from the intervention. Transportability provides a rigorous framework for applying results derived in potentially highly selected study populations to external target populations. The method requires that researchers measure and adjust for all variables that 1) modify the effect of interest and 2) differ between the target and trial populations. To date, applications of transportability have focused on the external validity of overall study results and understanding within-trial heterogeneity; however, this approach has not yet been used for subgroup analyses of trials. Through an example from the Iniciativa Prof ilaxis Pre-Exposicion (iPrEx) study (multiple countries, 2007-2010) of preexposure prophylaxis for human immunodeficiency virus, we illustrate how transporting subgroup analyses can produce target-specific subgroup effect estimates and numbers needed to treat. This approach could lead to more tailored and accurate guidance for resource allocation and cost-effectiveness analyses.
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