Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation

Cluster randomized trials (CRTs) are widely used in epidemiological and public health studies assessing population‐level effect of group‐based interventions. One important application of CRTs is the control of vector‐borne disease, such as malaria. However, a particular challenge for designing these...

Full description

Saved in:
Bibliographic Details
Published inBiometrical journal Vol. 63; no. 5; pp. 1052 - 1071
Main Authors Li, Fan, Tong, Guangyu
Format Journal Article
LanguageEnglish
Published Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.06.2021
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Cluster randomized trials (CRTs) are widely used in epidemiological and public health studies assessing population‐level effect of group‐based interventions. One important application of CRTs is the control of vector‐borne disease, such as malaria. However, a particular challenge for designing these trials is that the primary outcome involves counts of episodes that are subject to right truncation. While sample size formulas have been developed for CRTs with clustered counts, they are not directly applicable when the counts are right truncated. To address this limitation, we discuss two marginal modeling approaches for the analysis of CRTs with truncated counts and develop two corresponding closed‐form sample size formulas to facilitate the design of such trials. The proposed sample size formulas allow investigators to explore the power under a large number of scenarios without computationally intensive simulations. The proposed formulas are validated in extensive simulations. We further explore the implication of right truncation on power and apply the proposed formulas to illustrate the power calculation for a malaria control CRT where the primary outcome is subject to right truncation.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
content type line 14
ObjectType-Feature-3
ObjectType-Evidence Based Healthcare-1
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0323-3847
1521-4036
1521-4036
DOI:10.1002/bimj.202000230