A bias‐corrected covariance estimate for improved inference with quadratic inference functions

Westgate, Philip M.

doi:10.1002/sim.5479

Cited by 24 publications

(44 citation statements)

References 29 publications

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“…Theoretically, the QIF approach is equally or more efficient than GEE. However, finite-sample covariance inflation of the regression parameter estimates must be taken into account, as is done in Westgate [19, 20]. Westgate [20] proposed a method that utilizes the TECM to select both a working correlation structure and one of these two methods, analogous to the approach we used in this manuscript.…”

Section: Discussionmentioning

confidence: 99%

A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: a study on its applicability with structured correlation matrices

Westgate

2015

Journal of Statistical Computation and Simulation

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When generalized estimating equations (GEE) incorporate an unstructured working correlation matrix, the variances of regression parameter estimates can inflate due to the estimation of the correlation parameters. In previous work, an approximation for this inflation that results in a corrected version of the sandwich formula for the covariance matrix of regression parameter estimates was derived. Use of this correction for correlation structure selection also reduces the over-selection of the unstructured working correlation matrix. In this manuscript, we conduct a simulation study to demonstrate that an increase in variances of regression parameter estimates can occur when GEE incorporates structured working correlation matrices as well. Correspondingly, we show the ability of the corrected version of the sandwich formula to improve the validity of inference and correlation structure selection. We also study the relative influences of two popular corrections to a different source of bias in the empirical sandwich covariance estimator.

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Section: Discussionmentioning

confidence: 99%

A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: a study on its applicability with structured correlation matrices

Westgate

2015

Journal of Statistical Computation and Simulation

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“…61 However, the SEs may be under-estimated for small and medium sample size or for variable group size. 62 More recent work by Westgate 63,64 provides improvements by using a bias-corrected sandwich covariance estimate and by simultaneously selecting the QIF or GEE while selecting the best working correlation structure. 65 Despite the many attractive properties of QIF, at this time there are few applications in public health.…”

Section: Developments In the Analysis Of Parallel Group-randomized Trmentioning

confidence: 99%

“…As of the writing, the authors have been unable to load the package and it only allows equal cluster size, but Westgate has modified the code for GRTs with variable cluster size in the appendix of his paper 63 …”

Section: Tablementioning

confidence: 99%

Review of Recent Methodological Developments in Group-Randomized Trials: Part 2—Analysis

et al. 2017

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In 2004, Murray et al. published a review of methodological developments in both the design and analysis of group-randomized trials (GRTs). Over the last 13 years, there have been many developments in both areas. The goal of the current paper is to review developments in analysis, with a companion paper to focus on developments in design. As a pair, these papers update the 2004 review. This analysis paper includes developments in topics included in the earlier review, such as methods for parallel-arm GRTs, inference for conditional and marginal effects, and new topics including methods to account for multiple levels of clustering and alternative estimation methods such as augmented GEE, targeted maximum likelihood and quadratic inference functions. We also examine developments in dealing with missing outcome data, including doubly robust approaches, software available for analysis, and analysis of alternative group designs (including stepped wedge GRTs, network-randomized trials, pseudo-cluster randomized trials and individually-randomized group treatment trials). These alternative designs, like the parallel-arm GRT, require clustering to be accounted for in both their design and analysis.

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“…As with the GMM approach of Lai and Small , finite‐sample covariance inflation occurs because of the use of

{bold-italicC}_{N} (\overset{bold-italicβ}{true})

in place of Σ N within the estimating equations . As a result, the small‐sample estimation performance of the modified QIF approach may not be as ideal as expected.…”

Section: Time‐dependent Covariates and Current Methodsmentioning

confidence: 99%

“…The reason for this is because these approaches utilize an empirical estimator for the optimal weighting matrix, and the use of this estimator can increase the variances of regression parameter estimates relative to their theoretical variances. The degree of variance inflation increases with the number of moment conditions and as the number of subjects decreases. The variance inflation can, at least partially, offset any efficiency gains because of the use of the modified QIF and GMM approaches.…”

Section: Introductionmentioning

confidence: 98%

Improved methods for the marginal analysis of longitudinal data in the presence of time‐dependent covariates

Chen

Westgate

2017

Statistics in Medicine

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Generalized estimating equations (GEEs) are commonly used for the marginal analysis of longitudinal data. In order to obtain consistent regression parameter estimates, these estimating equations must be unbiased. However, in the presence of certain types of time-dependent covariates, these equations can be biased unless they incorporate the independence working correlation structure. Moreover, in this case, regression parameter estimation can be very inefficient because not all valid moment conditions are incorporated within the corresponding estimating equations. Therefore, approaches based on the generalized method of moments or quadratic inference functions have been proposed in order to utilize all valid moment conditions. However, we have found in previous studies, as well as the current study, that such methods will not always provide valid inference and can also be improved upon in terms of finite-sample regression parameter estimation. Therefore, we propose both a modified GEE approach and a method selection strategy in order to ensure valid inference with the goal of improving regression parameter estimation. In a simulation study and application example, we compare existing and proposed methods and demonstrate that our modified GEE approach performs well, and the correlation information criterion has good accuracy with respect to selecting the best approach in terms of regression parameter estimation. Copyright © 2017 John Wiley & Sons, Ltd.

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A bias‐corrected covariance estimate for improved inference with quadratic inference functions

Cited by 24 publications

References 29 publications

A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: a study on its applicability with structured correlation matrices

A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: a study on its applicability with structured correlation matrices

Review of Recent Methodological Developments in Group-Randomized Trials: Part 2—Analysis

Improved methods for the marginal analysis of longitudinal data in the presence of time‐dependent covariates

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