A Comparison of the Statistical Power of Different Methods for the Analysis of Repeated Cross-Sectional Cluster Randomization Trials with Binary Outcomes

Austin, Peter C.

doi:10.2202/1557-4679.1179

Cited by 9 publications

(11 citation statements)

References 23 publications

(41 reference statements)

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“…While population inference is the goal of clinical research, assuming that individuals recruited to a center are independent of others at the same centre may reduce the statistical power needed to show a treatment effect when the clustering of outcomes occurs. Our results show how changing statistical assumptions can confirm the magnitude of the treatment effect and builds on previous works addressing statistical analysis in multicenter trials [ 8 , 26 , 27 ].…”

Section: Discussionsupporting

confidence: 82%

Accounting for center in the Early External Cephalic Version trials: an empirical comparison of statistical methods to adjust for center in a multicenter trial with binary outcomes

Reitsma

Chu²,

Thorpe

et al. 2014

Trials

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BackgroundClustering of outcomes at centers involved in multicenter trials is a type of center effect. The Consolidated Standards of Reporting Trials Statement recommends that multicenter randomized controlled trials (RCTs) should account for center effects in their analysis, however most do not. The Early External Cephalic Version (EECV) trials published in 2003 and 2011 stratified by center at randomization, but did not account for center in the analyses, and due to the nature of the intervention and number of centers, may have been prone to center effects. Using data from the EECV trials, we undertook an empirical study to compare various statistical approaches to account for center effect while estimating the impact of external cephalic version timing (early or delayed) on the outcomes of cesarean section, preterm birth, and non-cephalic presentation at the time of birth.MethodsThe data from the EECV pilot trial and the EECV2 trial were merged into one dataset. Fisher’s exact method was used to test the overall effect of external cephalic version timing unadjusted for center effects. Seven statistical models that accounted for center effects were applied to the data. The models included: i) the Mantel-Haenszel test, ii) logistic regression with fixed center effect and fixed treatment effect, iii) center-size weighted and iv) un-weighted logistic regression with fixed center effect and fixed treatment-by-center interaction, iv) logistic regression with random center effect and fixed treatment effect, v) logistic regression with random center effect and random treatment-by-center interaction, and vi) generalized estimating equations.ResultsFor each of the three outcomes of interest approaches to account for center effect did not alter the overall findings of the trial. The results were similar for the majority of the methods used to adjust for center, illustrating the robustness of the findings.ConclusionsDespite literature that suggests center effect can change the estimate of effect in multicenter trials, this empirical study does not show a difference in the outcomes of the EECV trials when accounting for center effect.Trial registrationThe EECV2 trial was registered on 30 July 30 2005 with Current Controlled Trials: ISRCTN%2056498577.

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Section: Discussionsupporting

confidence: 82%

Accounting for center in the Early External Cephalic Version trials: an empirical comparison of statistical methods to adjust for center in a multicenter trial with binary outcomes

Reitsma

Chu²,

Thorpe

et al. 2014

Trials

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“…20 A concern with either approach is that the MLM may fail to converge if the CRT has few individuals per cluster. 37,38 …”

Section: Methodsmentioning

confidence: 99%

Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials

et al. 2011

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Aim. Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Methods. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering—seemingly unrelated regression (SUR) without a robust standard error (SE)—and 4 methods that recognized clustering—SUR and generalized estimating equations (GEEs), both with robust SE, a “2-stage” nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Results. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92–0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. Conclusions. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters.

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“…One situation where between-cluster heterogeneity is likely to be important is in designs with differential clustering between arms. 14,15 However, to date, these models have concentrated on situations in which it is natural to expect that the correlation between the observations within the treatment clusters is greater than that in the control clusters. Situations like this arise, for example, when the treatment induces clustering, for example, group therapy.…”

Section: Parallel Crtsmentioning

confidence: 99%

Modeling clustering and treatment effect heterogeneity in parallel and stepped‐wedge cluster randomized trials

Hemming

Taljaard²,

Forbes

2018

Statistics in Medicine

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Cluster randomized trials are frequently used in health service evaluation. It is common practice to use an analysis model with a random effect to allow for clustering at the analysis stage. In designs where clusters are exposed to both control and treatment conditions, it may be of interest to examine treatment effect heterogeneity across clusters. In designs where clusters are not exposed to both control and treatment conditions, it can also be of interest to allow heterogeneity in the degree of clustering between arms. These two types of heterogeneity are related. It has been proposed in both parallel cluster trials, stepped‐wedge, and other cross‐over designs that this heterogeneity can be allowed for by incorporating additional random effect(s) into the model. Here, we show that the choice of model parameterization needs careful consideration as some parameterizations for additional heterogeneity induce unnecessary or implausible assumptions. We suggest more appropriate parameterizations, discuss their relative advantages, and demonstrate the implications of these model choices using a real example of a parallel cluster trial and a simulated stepped‐wedge trial.

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A Comparison of the Statistical Power of Different Methods for the Analysis of Repeated Cross-Sectional Cluster Randomization Trials with Binary Outcomes

Cited by 9 publications

References 23 publications

Accounting for center in the Early External Cephalic Version trials: an empirical comparison of statistical methods to adjust for center in a multicenter trial with binary outcomes

Accounting for center in the Early External Cephalic Version trials: an empirical comparison of statistical methods to adjust for center in a multicenter trial with binary outcomes

Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials

Modeling clustering and treatment effect heterogeneity in parallel and stepped‐wedge cluster randomized trials

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