AS TREATED ANALYSES OF CLUSTER RANDOMIZED TRIALS

• 成果类型: Article
• 署名作者: Fogelson, Ari I. F.; Landsiedel, Kirsten E.; Dufault, Suzanne M.; Jewell, Nicholas P.
• 署名单位: University of London; London School of Hygiene & Tropical Medicine; University of California System; University of California Berkeley; University of California System; University of California San Francisco; University of London; London School of Hygiene & Tropical Medicine
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1846
• 发表日期: 2024
• 页码: 1506-1518
• 关键词: wolbachia; Intention; DESIGN

摘要

Test -negative designs have rapidly become an appealing approach to assess disease interventions when randomization is not feasible and specifically used to measure the effectiveness of vaccines in the field (Vaccine 31 (2013) 2165-2168). An innovative extension of the test -negative design was recently used to assess the impact of a mosquito intervention where the intervention was applied at a cluster level with cluster assignment chosen at random, the AWED (applying Wolbachia to eliminate dengue) trial. The primary analysis reported was intention -to -treat (ITT) (Trials 19 (2018) 302; N. Engl. J. Med. 384 (2021) 2177-2186). However, the level of uptake of the intervention on mosquitoes was routinely captured in all clusters over time, and, furthermore, participants' mobility across clusters was measured in the time immediately preceding the onset of symptoms (whether test -positive or testnegative). Combinations of these measurements provide proxies for the true exposure to the intervention, thereby permitting an as treated assessment. We consider the use of marginal generalized estimating equations (GEE) and conditional generalized inear mixed models (GLMM) to estimate as treated efficacy, contrasting both with the ITT. We illustrate the strengths and challenges of these methods in the context of the AWED trial, highlighting several ways that common approaches to analysis of clustered data can yield incorrect results that can in turn be obscured and compounded by limitations in routine software. In addition, we estimate a greater level of intervention efficacy than shown in the ITT analysis.