Diagnosis of Random‐Effect Model Misspecification in Generalized Linear Mixed Models for Binary Response

Huang, Xing

doi:10.1111/j.1541-0420.2008.01103.x

Cited by 37 publications

(38 citation statements)

References 15 publications

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“…In this case, the CPI model yields nearly the same fitted values as the MEM model, so we consider only the MEM model from this point. A variety of methods have been proposed to assess the assumption of independent normally distributed random effects . We use several of these methods to assess the assumption in this case; details are in Appendix S3.…”

Section: Resultsmentioning

confidence: 99%

Novel methods for the analysis of stepped wedge cluster randomized trials

Kennedy‐Shaffer

Gruttola

Lipsitch

2019

Statistics in Medicine

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Stepped wedge cluster randomized trials (SW‐CRTs) have become increasingly popular and are used for a variety of interventions and outcomes, often chosen for their feasibility advantages. SW‐CRTs must account for time trends in the outcome because of the staggered rollout of the intervention. Robust inference procedures and nonparametric analysis methods have recently been proposed to handle such trends without requiring strong parametric modeling assumptions, but these are less powerful than model‐based approaches. We propose several novel analysis methods that reduce reliance on modeling assumptions while preserving some of the increased power provided by the use of mixed effects models. In one method, we use the synthetic control approach to find the best matching clusters for a given intervention cluster. Another method makes use of within‐cluster crossover information to construct an overall estimator. We also consider methods that combine these approaches to further improve power. We test these methods on simulated SW‐CRTs, describing scenarios in which these methods have increased power compared with existing nonparametric methods while preserving nominal validity when mixed effects models are misspecified. We also demonstrate theoretical properties of these estimators with less restrictive assumptions than mixed effects models. Finally, we propose avenues for future research on the use of these methods; motivation for such research arises from their flexibility, which allows the identification of specific causal contrasts of interest, their robustness, and the potential for incorporating covariates to further increase power. Investigators conducting SW‐CRTs might well consider such methods when common modeling assumptions may not hold.

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Section: Resultsmentioning

confidence: 99%

Novel methods for the analysis of stepped wedge cluster randomized trials

Kennedy‐Shaffer

Gruttola

Lipsitch

2019

Statistics in Medicine

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“…If the mixed effects model used to estimate conditional effects is misspecified, the estimates are difficult to interpret and, even if regression diagnostics can help, 29 standard errors (SEs) are not robust. Fortunately, Murray et al 30 and Fu 31 have shown that mixed models are robust to substantial violation of the normality assumptions for member- and group-level errors, so long as balance is maintained at the group level.…”

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

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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“…However, this class of models makes a strong assumption about the distribution of random effects. For computational convenience, random effects are assumed to be normal, but this assumption may be unrealistic for some applications [1]. Since the random effects are not observable, checking the assumption is difficult, and if the true distribution of the random effects is far from normality, the estimation and inferences could be considerably affected.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

Hernández

Giampaoli

2018

Stats

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Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

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Diagnosis of Random‐Effect Model Misspecification in Generalized Linear Mixed Models for Binary Response

Cited by 37 publications

References 15 publications

Novel methods for the analysis of stepped wedge cluster randomized trials

Novel methods for the analysis of stepped wedge cluster randomized trials

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

The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

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