Causal inference in randomized trials with partial clustering.
Background: Participant dependence, if present, must be accounted for in the analysis of randomized trials. This dependence, also referred to as "clustering," can occur in one or more trial arms. This dependence may predate randomization or arise after randomization. We examine three trial designs:...
| Publicado en: | Clinical Trials Vol. 22; no. 5; pp. 547 - 559 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications, Ltd.
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188284828&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188284828 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17407745 AB6 jtl: Clinical Trials issn: 17407745 maglogo: N pubinfo: dt: Oct2025 vid: 22 iid: 5 pid: 33180 pub: Sage Publications, Ltd. place: <Blank> artinfo: ui: 188284828 184859973 188284828 188284828 10.1177/17407745251333779 188284828 ppf: 547 ppct: 12 formats: tig: atl: Causal inference in randomized trials with partial clustering. aug: au: Nugent, Joshua R Kakande, Elijah Chamie, Gabriel Kabami, Jane Owaraganise, Asiphas Havlir, Diane V Kamya, Moses Balzer, Laura B affil: Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA sug: subj: Randomized Controlled Trials Methods Cluster Sample Research Subjects Control Group Models, Statistical Machine Learning Human Uganda Health Personnel Comparative Studies Tuberculosis Prevention and Control Multiple Regression Descriptive Statistics Data Analysis Software Funding Source Nonparametric Statistics ab: Background: Participant dependence, if present, must be accounted for in the analysis of randomized trials. This dependence, also referred to as "clustering," can occur in one or more trial arms. This dependence may predate randomization or arise after randomization. We examine three trial designs: one "fully clustered" (where all participants are dependent within clusters or groups) and two "partially clustered" (where some participants are dependent within clusters and some participants are completely independent of all others). Methods: For these three designs, we (1) use causal models to non-parametrically describe the data generating process and formalize the dependence in the observed data distribution; (2) develop a novel implementation of targeted minimum loss-based estimation for analysis; (3) evaluate the finite-sample performance of targeted minimum loss-based estimation and common alternatives via a simulation study; and (4) apply the methods to real-data from the SEARCH-IPT trial. Results: We show that the two randomization schemes resulting in partially clustered trials have the same dependence structure, enabling use of the same statistical methods for estimation and inference of causal effects. Our novel targeted minimum loss-based estimation approach leverages covariate adjustment and machine learning to improve precision and facilitates estimation of a large set of causal effects. In simulations, we demonstrate that targeted minimum loss-based estimation achieves comparable or markedly higher statistical power than common alternatives for these partially clustered designs. Finally, application of targeted minimum loss-based estimation to real data from the SEARCH-IPT trial resulted in 20%–57% efficiency gains, demonstrating the real-world consequences of our proposed approach. Conclusions: Partially clustered trial analysis can be made more efficient by implementing targeted minimum loss-based estimation, assuming care is taken to account for the dependent nature of the observed data. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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