scholarly journals Quantifying the impact of fixed effects modeling of clusters in multiple imputation for cluster randomized trials

2011 ◽  
Vol 53 (1) ◽  
pp. 57-74 ◽  
Author(s):  
Rebecca R. Andridge
Keyword(s):  
Multiple Imputation ◽  
Fixed Effects ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Cluster Randomized ◽  
The Impact

Methodological issues in the design and analysis of cluster randomized trials

2021 ◽  
pp. 113-128
Author(s):  
Kathy J. Baisley ◽  
Richard J. Hayes ◽  
Lawrence H. Moulton
Keyword(s):  
Gold Standard ◽  
Randomized Trials ◽  
Controlled Trials ◽  
Cluster Randomized Trials ◽  
Methodological Issues ◽  
Eligibility Criteria ◽  
Experimental Conditions ◽  
Cluster Randomized ◽  
And Control ◽  
The Impact

Randomized controlled trials are the accepted gold standard for evaluating the effects of interventions to improve health. In the majority of such trials, individuals are randomly allocated to the experimental conditions under study, for example, to treatment and control arms. However, in some situations it is more appropriate to randomly allocate groups of individuals to the treatment arms. These groups are referred to as clusters, and trials of this kind are known as cluster randomized trials (CRTs). Examples of clusters include schools, villages, workplaces, or health facilities, but there are many other possible choices. In some CRTs, all individuals within the selected clusters are automatically included. In others, there may be additional eligibility criteria. Similarly, the impact of the intervention may be measured in all individuals in the cluster, or in a random subsample. This chapter aims to discuss methodological issues that arise in the design and analysis of CRTs

scholarly journals A comparison of imputation strategies in cluster randomized trials with missing binary outcomes

2016 ◽  
Vol 25 (6) ◽  
pp. 2650-2669 ◽  
Author(s):  
Agnès Caille ◽  
Clémence Leyrat ◽  
Bruno Giraudeau
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Multiple Imputation ◽  
Random Effects ◽  
Logistic Regression Model ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Complete Case ◽  
Intervention Effect ◽  
Cluster Randomized

In cluster randomized trials, clusters of subjects are randomized rather than subjects themselves, and missing outcomes are a concern as in individual randomized trials. We assessed strategies for handling missing data when analysing cluster randomized trials with a binary outcome; strategies included complete case, adjusted complete case, and simple and multiple imputation approaches. We performed a simulation study to assess bias and coverage rate of the population-averaged intervention-effect estimate. Both multiple imputation with a random-effects logistic regression model or classical logistic regression provided unbiased estimates of the intervention effect. Both strategies also showed good coverage properties, even slightly better for multiple imputation with a random-effects logistic regression approach. Finally, this latter approach led to a slightly negatively biased intracluster correlation coefficient estimate but less than that with a classical logistic regression model strategy. We applied these strategies to a real trial randomizing households and comparing ivermectin and malathion to treat head lice.

Inference for the treatment effect in longitudinal cluster randomized trials when treatment effect heterogeneity is ignored

2021 ◽  
pp. 096228022110417
Author(s):  
Rhys Bowden ◽  
Andrew B Forbes ◽  
Jessica Kasza
Keyword(s):  
Treatment Effect ◽  
Randomized Trials ◽  
Error Rates ◽  
Type I ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Treatment Effect Heterogeneity ◽  
Cluster Randomized ◽  
Effect Heterogeneity ◽  
The Impact

In cluster-randomized trials, sometimes the effect of the intervention being studied differs between clusters, commonly referred to as treatment effect heterogeneity. In the analysis of stepped wedge and cluster-randomized crossover trials, it is possible to include terms in outcome regression models to allow for such treatment effect heterogeneity yet this is not frequently considered. Outside of some simulation studies of specific cases where the outcome is binary, the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator is unknown. We analytically examine the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator, when outcomes are continuous. Using analysis of variance and feasible generalized least squares we provide expressions for this variance. For both the cluster-randomized crossover design and the stepped wedge design, our analytic derivations indicate that failing to include treatment effect heterogeneity results in the estimates for variance of the treatment effect that are too small, leading to inflation of type I error rates. We therefore recommend assessing the sensitivity of sample size calculations and conclusions drawn from the analysis of cluster randomized trials to the inclusion of treatment effect heterogeneity.

Power for Detecting Treatment by Moderator Effects in Two- and Three-Level Cluster Randomized Trials

2016 ◽  
Vol 41 (6) ◽  
pp. 605-627 ◽  
Author(s):  
Jessaca Spybrook ◽  
Benjamin Kelcey ◽  
Nianbo Dong
Keyword(s):  
Statistical Power ◽  
Randomized Trials ◽  
A Priori ◽  
Individual Characteristics ◽  
Cluster Randomized Trials ◽  
Moderator Effects ◽  
Effect Of Treatment ◽  
Cluster Randomized ◽  
Level Cluster ◽  
The Impact

Recently, there has been an increase in the number of cluster randomized trials (CRTs) to evaluate the impact of educational programs and interventions. These studies are often powered for the main effect of treatment to address the “what works” question. However, program effects may vary by individual characteristics or by context, making it important to also consider power to detect moderator effects. This article presents a framework for calculating statistical power for moderator effects at all levels for two- and three-level CRTs. Annotated R code is included to make the calculations accessible to researchers and increase the regularity in which a priori power analyses for moderator effects in CRTs are conducted.

scholarly journals Statistical approaches to computing sample size in cluster randomized trials: a simulation study

2020 ◽  
Author(s):  
Ashutosh Ranjan ◽  
Guangzi Song ◽  
Christopher S Coffey ◽  
Leslie A McClure
Keyword(s):  
Sample Size ◽  
Cluster Size ◽  
Randomized Trials ◽  
Sample Size Calculation ◽  
Minimum Variance ◽  
Type I ◽  
Cluster Randomized Trials ◽  
Planning Stage ◽  
Cluster Randomized ◽  
The Impact

Abstract Background: Cluster randomized trials, which randomize groups of individuals to an intervention, are common in health services research when one wants to evaluate improvement in a subject's outcome by intervening at an organizational level. For many such trials, sample size calculation is performed under the assumption of equal cluster size. For a variety of reasons, many trials that set out to recruit clusters of the same size end up with unequal clusters. This leads to a misalignment between the method used for sample size calculation and the data analysis, which may affect trial power. Various weighted analysis methods for analyzing cluster means have been suggested to overcome the problem introduced by unbalanced clusters; however, the performance of such methods has not been evaluated extensively. Methods: We examine the use of the general linear model for analysis of clustered randomized trials that assume equal cluster sizes during the planning stage, but for which the realized cluster sizes are unequal. We demonstrate the performance of three approaches using different weights for analyzing the cluster means: (1) the standard analysis of cluster means, (2) weighting by cluster size, and (3) minimum variance weights. Several distributions are used to generate cluster sizes to assess a range of patterns of imbalance. The variability in cluster size is measured by the coefficient of variation (CV). We assess the impact of using each of the three methods of analysis with respect to type I error and power of the study and how each are impacted by the variability in cluster size via simulations. Results: Analyses that assumes equal clusters provide a reasonable approximation when cluster sizes vary minimally (CV < 0.30). For analyses weighted by cluster size type I errors were inflated, and that worsened as the variation in cluster size increases, despite reasonable power. However, minimum variance weighted analyses best maintain target power and level of significance under scenarios considered. Conclusion: Unweighted analyses work well as an approximate method when variation in cluster size is minimal. However, using minimum variance weights performs much better across the full range of variation in cluster size and is recommended.

scholarly journals Estimating intervention effectiveness in trials of malaria interventions with contamination

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Lea Multerer ◽  
Fiona Vanobberghen ◽  
Tracy R. Glass ◽  
Alexandra Hiscox ◽  
Steven W. Lindsay ◽  
...  
Keyword(s):  
Randomized Trials ◽  
Credible Interval ◽  
Mass Trapping ◽  
Cluster Randomized Trials ◽  
Buffer Zones ◽  
Cluster Boundary ◽  
Cost Efficient ◽  
Cluster Randomized ◽  
The Impact ◽  
Malaria Interventions

Abstract Background In cluster randomized trials (CRTs) or stepped wedge cluster randomized trials (SWCRTs) of malaria interventions, mosquito movement leads to contamination between trial arms unless buffer zones separate the clusters. Contamination can be accounted for in the analysis, yielding an estimate of the contamination range, the distance over which contamination measurably biases the effectiveness. Methods A previously described analysis for CRTs is extended to SWCRTs and estimates of effectiveness are provided as a function of intervention coverage. The methods are applied to two SWCRTs of malaria interventions, the SolarMal trial on the impact of mass trapping of mosquitoes with odor-baited traps and the AvecNet trial on the effect of adding pyriproxyfen to long-lasting insecticidal nets. Results For the SolarMal trial, the contamination range was estimated to be 146 m ($$95\%$$ 95 % credible interval $$[0.052,\,0.923]$$ [ 0.052 , 0.923 ]  km), together with a $$31.9\%$$ 31.9 % ($$95\%$$ 95 % credible interval $$[15.3,\,45.8]\%$$ [ 15.3 , 45.8 ] % ) reduction of Plasmodium infection, compared to the $$30.0\%$$ 30.0 % reduction estimated without accounting for contamination. The estimated effectiveness had an approximately linear relationship with coverage. For the AvecNet trial, estimated contamination effects were minimal, with insufficient data from the cluster boundary regions to estimate the effectiveness as a function of coverage. Conclusions The contamination range in these trials of malaria interventions is much less than the distances Anopheles mosquitoes can fly. An appropriate analysis makes buffer zones unnecessary, enabling the design of more cost-efficient trials. Estimation of the contamination range requires information from the cluster boundary regions and trials should be designed to collect this.

Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness

2019 ◽  
Vol 29 (5) ◽  
pp. 1338-1353
Author(s):  
Elizabeth L Turner ◽  
Lanqiu Yao ◽  
Fan Li ◽  
Melanie Prague
Keyword(s):  
Multiple Imputation ◽  
Randomized Trials ◽  
Simulated Data ◽  
Real Data ◽  
Cluster Randomized Trial ◽  
Outcome Data ◽  
Binary Outcomes ◽  
Cluster Randomized Trials ◽  
Cluster Randomized ◽  
Multilevel Multiple Imputation

The generalized estimating equation (GEE) approach can be used to analyze cluster randomized trial data to obtain population-averaged intervention effects. However, most cluster randomized trials have some missing outcome data and a GEE analysis of available data may be biased when outcome data are not missing completely at random. Although multilevel multiple imputation for GEE (MMI-GEE) has been widely used, alternative approaches such as weighted GEE are less common in practice. Using both simulations and a real data example, we evaluate the performance of inverse probability weighted GEE vs. MMI-GEE for binary outcomes. Simulated data are generated assuming a covariate-dependent missing data pattern across a range of missingness clustering (from none to high), where all covariates are measured at baseline and are fully observed (i.e. a type of missing-at-random mechanism). Two types of weights are estimated and used in the weighted GEE: (1) assuming no clustering of missingness (W-GEE) and (2) accounting for such clustering (CW-GEE). Results show that, even in settings with high missingness clustering, CW-GEE can lead to more bias and lower coverage than W-GEE, whereas W-GEE and MMI-GEE provide comparable results. W-GEE should be considered a viable strategy to account for missing outcomes in cluster randomized trials.

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