Comment: The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation

Zhang, Kai; Small, Dylan S.

doi:10.1214/09-sts274b

Cited by 15 publications

(12 citation statements)

References 3 publications

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“…have shown its benefits in clustered RCTs . Zhang and Small have shown that optimal nonbipartite matching using an MD may outperform other matching methods, and the current paper shows that the RMD may yield results superior to the MD when the perceived quality of the matching depends on the relative clinical importance of the variables. Moreover, the RMD may account for missing data in a sophisticated, yet highly automated, process.…”

Section: Discussionmentioning

confidence: 70%

“…Some reasons to exclude a unit may be obvious, such as logistical difficulties unique to that unit. When there is no clear choice, the matching method can optimally select which units to drop by removing those that would create the greatest imbalance between the groups . The user specifies a number of units to exclude, say k units.…”

Section: Methodsmentioning

confidence: 99%

“…When the assumption of equal cluster sizes is relaxed, as is appropriate for most practical scenarios, the MPCR that matches on cluster size and pre‐treatment covariates will improve the study's efficiency and power over unmatched cluster‐randomization, even with as few as six clusters. In a discussion of Imai et al ., Zhang and Small show the utility of optimal nonbipartite matching for achieving pre‐treatment covariate balance in MPCR and for optimally selecting a set of units for study when the number of units available is greater than the number needed . For observational studies utilizing matching, Rosenbaum presents a method of augmenting the distance matrix to optimally choose the number of units to study for a specified level of quality of match .…”

Section: Introductionmentioning

confidence: 99%

“…In a discussion of Imai et al, Zhang and Small show the utility of optimal nonbipartite matching for achieving pre-treatment covariate balance in MPCR and for optimally selecting a set of units for study when the number of units available is greater than the number needed. 4 For observational studies utilizing matching, Rosenbaum presents a method of augmenting the distance matrix to optimally choose the number of units to study for a specified level of quality of match. 5 When the quality of matches is of greater concern than the exact number of units included, this approach can be very useful in the MPCR setting.…”

Section: Introductionmentioning

confidence: 99%

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Reweighted Mahalanobis distance matching for cluster‐randomized trials with missing data

Greevy

Grijalva

Roumie

et al. 2012

Pharmacoepidemiology and Drug

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Purpose This paper introduces an improved tool for designing matched-pairs randomized trials. The tool allows the incorporation of clinical and other knowledge regarding the relative importance of variables used in matching and allows for multiple types of missing data. The method is illustrated in the context of a cluster-randomized trial. A web application and R package are introduced to implement the method and incorporate recent advances in the area. Methods Reweighted Mahalanobis Distance (RMD) matching incorporates user-specified weights and imputed values for missing data. Weight may be assigned to missingness indicators to match on missingness patterns. Three examples are presented, using real data from a cohort of 90 Veterans Health Administration sites that had at least 100 incident metformin users in 2007. Matching is utilized to balance seven factors aggregated at the site level. Covariate balance is assessed for 10,000 randomizations under each strategy: simple randomization, matched randomization using the Mahalanobis distance, and matched randomization using the RMD. Results The RMD matching achieved better balance than simple randomization or MD randomization. In the first example, simple and MD randomization resulted in a 10% chance of seeing an absolute mean difference of greater than 26% in the percent of nonwhite patients per site; the RMD dramatically reduced that to 6%. The RMD achieved significant improvement over simple randomization even with as much as 20% of the data missing. Conclusions RMD matching provides an easy-to-use tool that incorporates user knowledge and missing data.

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Section: Discussionmentioning

confidence: 70%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reweighted Mahalanobis distance matching for cluster‐randomized trials with missing data

Greevy

Grijalva

Roumie

et al. 2012

Pharmacoepidemiology and Drug

View full text Add to dashboard Cite

show abstract

“…We refer the readers to Imai et al [4] and Zhang and Small [11] for discussions on designing such experiments when resources permit. However, the success of such designs requires random allocation of the intervention among a large enough number of communities to ensure that all sources of confounding are distributed evenly across treatment arms.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Effect of a Community-Based Intervention with Two Communities

Laan

Petersen

Zheng

2013

Journal of Causal Inference

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Due to the need to evaluate the effectiveness of community-based programs in practice, there is substantial interest in methods to estimate the causal effects of community-level treatments or exposures on individual level outcomes. The challenge one is confronted with is that different communities have different environmental factors affecting the individual outcomes, and all individuals in a community share the same environment and intervention. In practice, data are often available from only a small number of communities, making it difficult if not impossible to adjust for these environmental confounders. In this paper we consider an extreme version of this dilemma, in which two communities each receives a different level of the intervention, and covariates and outcomes are measured on a random sample of independent individuals from each of the two populations; the results presented can be straightforwardly generalized to settings in which more than two communities are sampled. We address the question of what conditions are needed to estimate the causal effect of the intervention, defined in terms of an ideal experiment in which the exposed level of the intervention is assigned to both communities and individual outcomes are measured in the combined population, and then the clock is turned back and a control level of the intervention is assigned to both communities and individual outcomes are measured in the combined population. We refer to the difference in the expectation of these outcomes as the marginal (overall) treatment effect. We also discuss conditions needed for estimation of the treatment effect on the treated community. We apply a nonparametric structural equation model to define these causal effects and to establish conditions under which they are identified. These identifiability conditions provide guidance for the design of studies to investigate community level causal effects and for assessing the validity of causal interpretations when data are only available from a few communities. When the identifiability conditions fail to hold, the proposed statistical parameters still provide nonparametric treatment effect measures (albeit non-causal) whose statistical interpretations do not depend on model specifications. In addition, we study the use of a matched cohort sampling design in which the units of different communities are matched on individual factors. Finally, we provide semiparametric efficient and doubly robust targeted MLE estimators of the community level causal effect based on i.i.d. sampling and matched cohort sampling.

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Adaptive pair‐matching in randomized trials with unbiased and efficient effect estimation

Balzer

Petersen

Laan

2014

Statistics in Medicine

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In randomized trials, pair-matching is an intuitive design strategy to protect study validity and to potentially increase study power. In a common design, candidate units are identified, and their baseline characteristics used to create the best n/2 matched pairs. Within the resulting pairs, the intervention is randomized, and the outcomes measured at the end of follow-up. We consider this design to be adaptive, because the construction of the matched pairs depends on the baseline covariates of all candidate units. As a consequence, the observed data cannot be considered as n/2 independent, identically distributed (i.i.d.) pairs of units, as common practice assumes. Instead, the observed data consist of n dependent units. This paper explores the consequences of adaptive pair-matching in randomized trials for estimation of the average treatment effect, conditional the baseline covariates of the n study units. By avoiding estimation of the covariate distribution, estimators of this conditional effect will often be more precise than estimators of the marginal effect. We contrast the unadjusted estimator with targeted minimum loss-based estimation (TMLE) and show substantial efficiency gains from matching and further gains with adjustment. This work is motivated by the Sustainable East Africa Research in Community Health (SEARCH) study, an ongoing community randomized trial to evaluate the impact of immediate and streamlined antiretroviral therapy on HIV incidence in rural East Africa.

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Comment: The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation

Cited by 15 publications

References 3 publications

Reweighted Mahalanobis distance matching for cluster‐randomized trials with missing data

Reweighted Mahalanobis distance matching for cluster‐randomized trials with missing data

Estimating the Effect of a Community-Based Intervention with Two Communities

Adaptive pair‐matching in randomized trials with unbiased and efficient effect estimation

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