ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals.

BACKGROUND AND OBJECTIVES: Marginal models with generalized estimating equations (GEE) are usually recommended for analyzing correlated ordinal outcomes which are commonly seen in a longitudinal study or clustered randomized trial (CRT). Within-cluster association is often of interest in longitudinal studies or CRTs, and can be estimated with paired estimating equations. However, the estimators for within-cluster association parameters and variances may be subject to finite-sample biases when the number of clusters is small. The objective of this article is to introduce a newly developed R package ORTH.Ord for analyzing correlated ordinal outcomes using GEE models with finite-sample bias corrections. METHODS: The R package ORTH.Ord implements a modified version of alternating logistic regressions with estimation based on orthogonalized residuals (ORTH), which use paired estimating equations to jointly estimate parameters in marginal mean and association models. The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). The R package also provides a finite-sample bias correction to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and bias-corrected sandwich estimators with different options for covariance estimation. RESULTS: A simulation study shows that MMORTH provides less biased global POR estimates and coverage of their 95% confidence intervals closer to the nominal level than uncorrected ORTH. An analysis of patient-reported outcomes from an orthognathic surgery clinical trial illustrates features of ORTH.Ord. CONCLUSIONS: This article provides an overview of the ORTH method with bias-correction on both estimating equations and sandwich estimators for analyzing correlated ordinal data, describes the features of the ORTH.Ord R package, evaluates the performance of the package using a simulation study, and finally illustrates its application in an analysis of a clinical trial.

Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics.

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.

Fungal endophytes associated with three South American Myrtae (Myrtaceae) exhibit preferences in the colonization at leaf level.

Fungal endophytes associated with Myrtaceae from Brazil and Argentina were isolated at three levels of nesting: leaf, individual host trees, and site collection. The alternating logistic regression (ALR) was used to model the data because it offers a computationally convenient method for fitting regression structures involving large clusters. The objectives of this study were to determine: (i) whether the colonization pattern is influenced by environmental variables, (ii) if there is some leaf part they prefer to colonize; (iii) if there is some fungal endophyte aggregation between hierarchical levels; (iv) what the distance effect is on the fungal association. The environmental variables were statistically significant only for Xylaria, i.e., when the elevation and water precipitation increase and the temperature decreases, the odds ratio of finding another fungal endophyte of that genus previously found increases. Sordariomycetes, Xylariales, and Xylaria exhibited leaf fragment preference to petiole and tip. Fungal endophytes showed association within leaf. The horizontal transmission mode and the dispersal limitation may explain this association at the leaf level. Moreover, our results suggest that when a fungal endophyte infects a leaf or host tree individual, the odds ratio of dispersal inside them is greater.