Design and analysis of group-randomized trials in cancer: A review of current practices

Murray, David M.; Pals, Sherri; George, Stephanie M.; Kuzmichev, Andrey; Lai, Gabriel Y.; Lee, Jocelyn A.; Myles, Ranell L.; Nelson, Shakira M.

doi:10.1016/j.ypmed.2018.03.010

Cited by 30 publications

(29 citation statements)

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“…In fact, a recent review by Fiero et al (2016) found that, of the 86 studies included, about 50% randomized 24 or fewer clusters. In CRTs related to cancer published between 2002 and 2006, Murray et al (2008) found similar results, with about 50% randomizing 24 or fewer clusters. Additionally, in their review of 300 CRTs published between 2000 and 2008, Ivers et al (2011) found that, of the 285 studies reporting the number of clusters randomized, at least 50% randomized 21 or fewer clusters.…”

Section: Introductionmentioning

confidence: 60%

xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials

Gallis

Turner

2020

The Stata Journal: Promoting communications on statistics and S

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Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.

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Section: Introductionmentioning

confidence: 60%

xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials

Gallis

Turner

2020

The Stata Journal: Promoting communications on statistics and S

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“…However, the use of these statistical adjustments is inconsistent. Murray et al [5] reviewed the common analytic methods used in CRTs and determined several appropriate analytical methods, including the 1-stage generalized linear mixed model and a 2-stage cluster mean approach. Varnell et al [32] reviewed 60 group randomized trials from 1998 to 2002.…”

Section: Discussionmentioning

confidence: 99%

“…outcomes) or odds ratio (for binary outcomes) suffices as a measure of treatment effect. In practice, results for many CRTs have also been reported using similar (unadjusted) estimators [4,5]. However, the internal validity of CRTs is challenged when treatment arms are not comparable.…”

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confidence: 99%

Impact of baseline covariate imbalance on bias in treatment effect estimation in cluster randomized trials: Race as an example

Yang

Starks

Hernandez

et al. 2020

Contemporary Clinical Trials

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A R T I C L E I N F O Keywords: (MeSH terms): Bias Cluster analysis Multivariable analysis Randomized controlled trials as topic Research design A B S T R A C TIndividual-level baseline covariate imbalance could happen more frequently in cluster randomized trials, and may influence the observed treatment effect. Using computer and real-data simulations, this paper quantifies the extent and impact of covariate imbalance on the estimated treatment effect for both continuous and binary outcomes, and relates it to the degree of imbalance for different numbers of clusters, cluster sizes, and covariate intraclass correlation coefficients. We focused on the impact of race as a covariate, given the emphasis of regulatory and funding bodies on understanding the influence of demographic characteristics on treatment effectiveness. We found that bias in the treatment effect is proportional to both the degree of baseline covariate imbalance and the covariate effect size. Larger numbers of clusters result in lower covariate imbalance, and increasing cluster size is less effective in reducing imbalance compared to increasing the number of clusters. Models adjusted for important baseline confounders are superior to unadjusted models for minimizing bias in both model-based simulations and an innovative simulation based on real clinical trial data. Higher outcome intraclass correlation coefficients did not affect bias but resulted in greater variance in treatment estimates. outcomes) or odds ratio (for binary outcomes) suffices as a measure of treatment effect. In practice, results for many CRTs have also been reported using similar (unadjusted) estimators [4,5]. However, the internal validity of CRTs is challenged when treatment arms are not comparable. Brierley et al. [6] found that about 30% of CRTs may suffer from selection bias, resulting in substantial covariate imbalance. Previous literature recommended that researchers could control selection bias in CRTs via cautious design, such as identifying patients prior to randomization and blinding [7][8][9]. Aside from selection bias, another important source of covariate imbalance is random chance, and that is the focus of the current paper. We show that baseline covariate imbalance may happen more frequently in CRTs compared to RCTs. If the covariates are predictive of the clinical outcomes, they act as confounders and may cause a spurious association between intervention and clinical outcome when not accounted for properly. As a result, it is important to understand how to accurately account for important individual-level covariates in the design and analysis of CRTs where confounders typically can impact the outcome.

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“…42 A discussion on the importance of these advantages, with details, is provided in an online supplementary appendix and is based on the references cited here. 20,32,34,36,38,40–45,47–58…”

Section: Discussionmentioning

confidence: 99%

A readily available improvement over method of moments for intra-cluster correlation estimation in the context of cluster randomized trials and fitting a GEE–type marginal model for binary outcomes

Westgate

2018

Clinical Trials

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Background/aims: Cluster randomized trials are popular in health-related research due to the need or desire to randomize clusters of subjects to different trial arms as opposed to randomizing each subject individually. As outcomes from subjects within the same cluster tend to be more alike than outcomes from subjects within other clusters, an exchangeable correlation arises that is measured via the intra-cluster correlation coefficient. Intra-cluster correlation coefficient estimation is especially important due to the increasing awareness of the need to publish such values from studies in order to help guide the design of future cluster randomized trials. Therefore, numerous methods have been proposed to accurately estimate the intra-cluster correlation coefficient, with much attention given to binary outcomes. As marginal models are often of interest, we focus on intra-cluster correlation coefficient estimation in the context of fitting such a model with binary outcomes using generalized estimating equations. Traditionally, intra-cluster correlation coefficient estimation with generalized estimating equations has been based on the method of moments, although such estimators can be negatively biased. Furthermore, alternative estimators that work well, such as the analysis of variance estimator, are not as readily applicable in the context of practical data analyses with generalized estimating equations. Therefore, in this article we assess, in terms of bias, the readily available residual pseudo-likelihood approach to intracluster correlation coefficient estimation with the GLIMMIX procedure of SAS (SAS Institute, Cary, NC). Furthermore, we study a possible corresponding approach to confidence interval construction for the intra-cluster correlation coefficient. Methods: We utilize a simulation study and application example to assess bias in intra-cluster correlation coefficient estimates obtained from GLIMMIX using residual pseudo-likelihood. This estimator is contrasted with method of moments and analysis of variance estimators which are standards of comparison. The approach to confidence interval construction is assessed by examining coverage probabilities. Results: Overall, the residual pseudo-likelihood estimator performs very well. It has considerably less bias than moment estimators, which are its competitor for general generalized estimating equation-based analyses, and therefore, it is a major improvement in practice. Furthermore, it works almost as well as analysis of variance estimators when they are applicable. Confidence intervals have near-nominal coverage when the intra-cluster correlation coefficient estimate has negligible bias. Conclusion: Our results show that the residual pseudo-likelihood estimator is a good option for intra-cluster correlation coefficient estimation when conducting a generalized estimating equation-based analysis of binary outcome data arising from cluster randomized trials. The estimator is practical in that it is simply a result from fitting a marginal model with GLIMMIX, ...

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Design and analysis of group-randomized trials in cancer: A review of current practices

Cited by 30 publications

References 50 publications

xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials

xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials

Impact of baseline covariate imbalance on bias in treatment effect estimation in cluster randomized trials: Race as an example

A readily available improvement over method of moments for intra-cluster correlation estimation in the context of cluster randomized trials and fitting a GEE–type marginal model for binary outcomes

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