Dataset Information

Performance of methods for analyzing continuous data from stratified cluster randomized trials - A simulation study.

ABSTRACT:

Background

The adoption of cluster randomized trials (CRTs) with the stratified design is currently gaining widespread interest. In the stratified design, clusters are first grouped into two or more strata and then randomized into treatment groups within each stratum. In this study, we evaluated the performance of several commonly used methods for analyzing continuous data from stratified CRTs.

Methods

This is a simulation study where we compared four methods: mixed-effects, generalized estimating equation (GEE), cluster-level (CL) linear regression and meta-regression methods to analyze the continuous data from stratified CRTs using a simulation study with varying numbers of clusters, cluster sizes, intra-cluster correlation coefficients (ICCs) and effect sizes. This study was based on a stratified CRT with one stratification variable with two strata. The performance of the methods was evaluated in terms of the type I error rate, empirical power, root mean square error (RMSE), and width and coverage of the 95% confidence interval (CI).

Results

GEE and meta-regression methods had high type I error rates, higher than 10%, for the small number of clusters. All methods had similar accuracy, measured through RMSE, except meta-regression. Similarly, all methods but meta-regression had similar widths of 95% CIs for the small number of clusters. For the same sample size, the empirical power for all methods decreased as the value of the ICC increased.

Conclusion

In this study, we evaluated the performance of several methods for analyzing continuous data from stratified CRTs. Meta-regression was the least efficient method compared to other methods.

SUBMITTER: Borhan S

PROVIDER: S-EPMC10313865 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Publications

Performance of methods for analyzing continuous data from stratified cluster randomized trials - A simulation study.

Borhan Sayem S Ma Jinhui J Papaioannou Alexandra A Adachi Jonathan J Thabane Lehana L

Contemporary clinical trials communications 20230314

<h4>Background</h4>The adoption of cluster randomized trials (CRTs) with the stratified design is currently gaining widespread interest. In the stratified design, clusters are first grouped into two or more strata and then randomized into treatment groups within each stratum. In this study, we evaluated the performance of several commonly used methods for analyzing continuous data from stratified CRTs.<h4>Methods</h4>This is a simulation study where we compared four methods: mixed-effects, gener ...[more]

PMID: 37397432

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Project description:BackgroundThe assessment of methods for analyzing over-dispersed zero inflated count outcome has received very little or no attention in stratified cluster randomized trials. In this study, we performed sensitivity analyses to empirically compare eight methods for analyzing zero inflated over-dispersed count outcome from the Vitamin D and Osteoporosis Study (ViDOS) - originally designed to assess the feasibility of a knowledge translation intervention in long-term care home setting.MethodForty long-term care (LTC) homes were stratified and then randomized into knowledge translation (KT) intervention (19 homes) and control (21 homes) groups. The homes/clusters were stratified by home size (<250/> = 250) and profit status (profit/non-profit). The outcome of this study was number of falls measured at 6-month post-intervention. The following methods were used to assess the effect of KT intervention on number of falls: i) standard Poisson and negative binomial regression; ii) mixed-effects method with Poisson and negative binomial distribution; iii) generalized estimating equation (GEE) with Poisson and negative binomial; iv) zero inflated Poisson and negative binomial - with the latter used as a primary approach. All these methods were compared with or without adjusting for stratification.ResultsA total of 5,478 older people from 40 LTC homes were included in this study. The mean (=1) of the number of falls was smaller than the variance (=6). Also 72% and 46% of the number of falls were zero in the control and intervention groups, respectively. The direction of the estimated incidence rate ratios (IRRs) was similar for all methods. The zero inflated negative binomial yielded the lowest IRRs and narrowest 95% confidence intervals when adjusted for stratification compared to GEE and mixed-effect methods. Further, the widths of the 95% confidence intervals were narrower when the methods adjusted for stratification compared to the same method not adjusted for stratification.ConclusionThe overall conclusion from the GEE, mixed-effect and zero inflated methods were similar. However, these methods differ in terms of effect estimate and widths of the confidence interval.Trial registrationClinicalTrials.gov: NCT01398527. Registered: 19 July 2011.

Project description:BackgroundIn order to correctly assess the effect of intervention from stratified cluster randomized trials (CRTs), it is necessary to adjust for both clustering and stratification, as failure to do so leads to misleading conclusions about the intervention effect. We have conducted a systematic survey to examine the current practices about analysis and reporting of stratified CRTs.MethodWe used the search terms to identify the stratified CRTs from MEDLINE since the inception to July 2019. In phase 1, we screened the title and abstract for English-only studies and selected, including the main results paper of the identified protocols, for the next phase. In phase 2, we screened the full text and selected studies for data abstraction. The data abstraction form was piloted and developed using the REDCap. We abstracted data on multiple design and methodological aspects of the study including whether the primary method adjusted for both clustering and stratification, reporting of sample size, randomization, and results.ResultsWe screened 2686 studies in the phase 1 and selected 286 studies for phase 2-among them 185 studies were selected for data abstraction. Most of the selected studies were two-arm 140/185 (76%) and parallel-group 165/185 (89%) trials. Among these 185 studies, 27 (15%) of them did not provide any sample size or power calculation, while 105 (57%) studies did not mention any method used for randomization within each stratum. Further, 43 (23%) and 150 (81%) of 185 studies did not provide the definition of all the strata, while more than 60% of the studies did not include all the stratification variable(s) in the flow chart or baseline characteristics table. More than half 114/185 (62%) of the studies did not adjust the primary method for both clustering and stratification.ConclusionStratification helps to achieve the balance among intervention groups. However, to correctly assess the intervention effect from stratified CRTs, it is important to adjust the primary analysis for both stratification and clustering. There are significant deficiencies in the reporting of methodological aspects of stratified CRTs, which require substantial improvements in several areas including definition of strata, inclusion of stratification variable(s) in the flow chart or baseline characteristics table, and reporting the stratum-specific number of clusters and individuals in the intervention groups.

Project description:In a cluster-randomized trial (CRT), the number of participants enrolled often varies across clusters. This variation should be considered during both trial design and data analysis to ensure statistical performance goals are achieved. Most methodological literature on the CRT design has assumed equal cluster sizes. This scoping review focuses on methodology for unequal cluster size CRTs. EMBASE, Medline, Google Scholar, MathSciNet and Web of Science databases were searched to identify English-language articles reporting on methodology for unequal cluster size CRTs published until March 2021. We extracted data on the focus of the paper (power calculation, Type I error etc.), the type of CRT, the type and the range of parameter values investigated (number of clusters, mean cluster size, cluster size coefficient of variation, intra-cluster correlation coefficient, etc.), and the main conclusions. Seventy-nine of 5032 identified papers met the inclusion criteria. Papers primarily focused on the parallel-arm CRT (p-CRT, n = 60, 76%) and the stepped-wedge CRT (n = 14, 18%). Roughly 75% of the papers addressed trial design issues (sample size/power calculation) while 25% focused on analysis considerations (Type I error, bias, etc.). The ranges of parameter values explored varied substantially across different studies. Methods for accounting for unequal cluster sizes in the p-CRT have been investigated extensively for Gaussian and binary outcomes. Synthesizing the findings of these works is difficult as the magnitude of impact of the unequal cluster sizes varies substantially across the combinations and ranges of input parameters. Limited investigations have been done for other combinations of a CRT design by outcome type, particularly methodology involving binary outcomes-the most commonly used type of primary outcome in trials. The paucity of methodological papers outside of the p-CRT with Gaussian or binary outcomes highlights the need for further methodological development to fill the gaps.

Dataset Information

Performance of methods for analyzing continuous data from stratified cluster randomized trials - A simulation study.

Background

Methods

Results

Conclusion

Publications

Performance of methods for analyzing continuous data from stratified cluster randomized trials - A simulation study.

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