ORTH: R and SAS software for regression models of correlated binary data based on orthogonalized residuals and alternating logistic regressions

By, Kunthel; Qaqish, Bahjat F.; Preisser, John S.; Perin, Jamie; Zink, Richard C.

doi:10.1016/j.cmpb.2013.09.017

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…The alternating logistic regression approach 45 for binary and ordinal outcomes can be used to account for correlation due to repeated measures on individuals within groups and can be implemented within a GEE framework in both R (the package) and SAS (PROC GEE). 46 The second-order GEE approach which, in contrast to regular GEE, models the working correlation structure as a function of covariates, can be implemented in R ( in R 47 ). 48 For more general working correlation matrices, the user typically needs to perform additional programming in order to provide the appropriate covariance matrix and convergence may not be achieved.…”

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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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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“…Furthermore, ALR allows one to distinguish between odds-ratios within clusters and within subclusters (in the current case subjects); however, the within-subject correlation must be modelled as exchangeable. An adapted ALR macro for 3-level of clustering (Kunthel et al, 2014) is available when estimation of the association struc-ture is of primary interest, though it was not included in our simulation study. For two-level binary repeated measures data, both GEE with an exchangeable correlation structure and ALR yield asymptotically unbiased estimates, which can be nearly efficient relative to GEE with a correctly specified working correlation structure (Masaoud and Stryhn, 2010) and to maximum-likelihood estimates in a fully and correctly specified model (Diggle et al, 2002, Chapter 8).…”

Section: Marginal Estimation Proceduresmentioning

confidence: 99%

“…This is an interesting result that we think needs more research to validate it in different settings and designs of binary data. Furthermore, we suggest that further research may include the relatively recently adapted ALR macro for 3-level of clustering (Kunthel et al, 2014).…”

Section: Marginal Estimation Proceduresmentioning

confidence: 99%

A comparison of statistical methods for the analysis of binary repeated measures data with additional hierarchical structure

Masaoud

Stryhn

2020

JSR

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The objective of the study was to compare statistical methods for the analysis of binary repeated measures data with an additional hierarchical level. Random effects true models with autocorrelated ($\rho=1$, 0.9 or 0.5) subject random effects were used in this simulation study. The settings of the simulation were chosen to reflect a real veterinary somatic cell count dataset, except that the within--subject time series were balanced, complete and of fixed length (4 or 8 time points). Four fixed effects parameters were studied: binary predictors at the subject and cluster levels, respectively, a linear time effect, and the intercept. The following marginal and random effects statistical procedures were considered: ordinary logistic regression (OLR), alternating logistic regression (ALR), generalized estimating equations (GEE), marginal quasi-likelihood (MQL), penalized quasi-likelihood (PQL), pseudo likelihood (REPL), maximum likelihood (ML) estimation and Bayesian Markov chain Monte Carlo (MCMC). The performance of these estimation procedures was compared specifically for the four fixed parameters as well as variance and correlation parameters. The findings of this study indicate that in data generated by random intercept models ($\rho=1$), the ML and MCMC procedures performed well and had fairly similar estimation errors. The PQL regression estimates were attenuated while the variance estimates were less accurate than ML and MCMC, but the direction of the bias depended on whether binomial or extra-binomial dispersion was assumed. In datasets with autocorrelation ($\rho<1$), random effects estimates procedures gave downwards biased estimates, while marginal estimates were little affected by the presence of autocorrelation. The results also indicate that in addition to ALR, a GEE procedure that accounts for clustering at the highest hierarchical level is sufficient.

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“…SAS PROC GENMOD was used in our applications, and more details about the required data formats are illustrated in Supplementary Materials- S2. Another computation tool is the “orth” package in R (By et al, 2014), and data of similar formats need to be prepared for using this package.…”

Section: Estimation and Computationmentioning

confidence: 99%

Analysis of Longitudinal Multivariate Outcome Data From Couples Cohort Studies: Application to HPV Transmission Dynamics

Kong¹,

Wang²,

Gray³

2015

Journal of the American Statistical Association

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We consider a specific situation of correlated data where multiple outcomes are repeatedly measured on each member of a couple. Such multivariate longitudinal data from couples may exhibit multi-faceted correlations which can be further complicated if there are polygamous partnerships. An example is data from cohort studies on human papillomavirus (HPV) transmission dynamics in heterosexual couples. HPV is a common sexually transmitted disease with 14 known oncogenic types causing anogenital cancers. The binary outcomes on the multiple types measured in couples over time may introduce inter-type, intra-couple, and temporal correlations. Simple analysis using generalized estimating equations or random effects models lacks interpretability and cannot fully utilize the available information. We developed a hybrid modeling strategy using Markov transition models together with pairwise composite likelihood for analyzing such data. The method can be used to identify risk factors associated with HPV transmission and persistence, estimate difference in risks between male-to-female and female-to-male HPV transmission, compare type-specific transmission risks within couples, and characterize the inter-type and intra-couple associations. Applying the method to HPV couple data collected in a Ugandan male circumcision (MC) trial, we assessed the effect of MC and the role of gender on risks of HPV transmission and persistence.

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ORTH: R and SAS software for regression models of correlated binary data based on orthogonalized residuals and alternating logistic regressions

Cited by 8 publications

References 29 publications

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

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

A comparison of statistical methods for the analysis of binary repeated measures data with additional hierarchical structure

Analysis of Longitudinal Multivariate Outcome Data From Couples Cohort Studies: Application to HPV Transmission Dynamics

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