Comments about Joint Modeling of Cluster Size and Binary and Continuous Subunit-Specific Outcomes

In longitudinal studies and in clustered situations often binary and continuous response variables are observed and need to be modeled together. In a recent publication Dunson, Chen, and Harry (2003, Biometrics 59, 521-530) (DCH) propose a Bayesian approach for joint modeling of cluster size and binary and continuous subunit-specific outcomes and illustrate this approach with a developmental toxicity data example. In this note we demonstrate how standard software (PROC NLMIXED in SAS) can be used to obtain maximum likelihood estimates in an alternative parameterization of the model with a single cluster-level factor considered by DCH for that example. We also suggest that a more general model with additional cluster-level random effects provides a better fit to the data set. An apparent discrepancy between the estimates obtained by DCH and the estimates obtained earlier by Catalano and Ryan (1992, Journal of the American Statistical Association 87, 651-658) is also resolved. The issue of bias in inferences concerning the dose effect when cluster size is ignored is discussed. The maximum-likelihood approach considered herein is applicable to general situations with multiple clustered or longitudinally measured outcomes of different type and does not require prior specification and extensive programming.     

Best linear unbiased prediction of host-range of the facultative parasite Colletotrichum gloeosporioides f. sp. salsolae, a potential biological control agent of Russian thistle

Russian thistle or tumbleweed (Salsola tragus L.) is an introduced invasive weed in N. America. It is widely distributed in the US and is a target of biological control efforts.The fungus Colletotrichum gloeosporioides (Penz.) Penz. & Sacc. in Penz. f. sp. salsolae (CGS) is a facultative parasite under evaluation for classical biological control of this weed. Host-range tests were conducted with CGS in quarantine to determine whether the fungus is safe to release in N. America. Ninetytwo accessions were analyzed from 19 families: Aizoaceae, Alliaceae, Amaranthaceae, Apiaceae, Asteraceae, Brassicaceae, Cactaceae, Campanulaceae, Chenopodiaceae, Cucurbitaceae, Cupressaceae, Fabaceae, Malvaceae, Nyctaginaceae, Phytolaccaceae, Poaceae, Polygonaceae, Sarcobataceae, and Solanaceae and 10 tribes within the Chenopodiaceae: Atripliceae, Beteae, Camphorosmeae, Chenopodieae, Corispermeae, Halopepideae, Polycnemeae, Salicornieae, Salsoleae, and Suaedeae. These included 62 genera and 120 species. To facilitate interpretation of results, disease reaction data were combined with a relationship matrix derived from internal transcribed spacer DNA sequences and analyzed with mixed model equations to produce Best Linear Unbiased Predictors (BLUPs) for each species. Twenty-nine species (30 accessions) from seven closely-related Chenopodiaceae tribes had significant levels of disease severity as indicated by BLUPs, compared to six species determined to be susceptible with least squares means estimates. The 29 susceptible species were: 1 from Atripliceae, 4 from Camphorosmeae, 1 from Halopepideae, 2 from Polycnemeae, 6 from Salicornieae, 8 from Salsolae, and 7 from Suaedeae. Most species in the genus Salsola, which are all introduced and weedy, were very susceptible and damaged by CGS. Statistical comparisons and contrasts of BLUPs indicated that these Salsola species were significantly more susceptible than non-target species, including 15 species from relatives in the closely-related genera Bassia (=Kochia), Nitrophila, Salicornia, Sarcocornia, and Suaeda. Of the 29 susceptible species, 10 native or commercially important species in N. America were identified as needing additional tests to determine the extent of any damage caused by infection.     

Statistical inference in mixed models and analysis of twin and family data

Analysis of data from twin and family studies provides the foundation for studies of disease inheritance. The development of advanced theory and computational software for general linear models has generated considerable interest for using mixed-effect models to analyze twin and family data, as a computationally more convenient and theoretically more sound alternative to the classical structure equation modeling. Despite the long history of twin and family data analysis, some fundamental questions remain unanswered. We addressed two important issues. One is to determine the necessary and sufficient conditions for the identifiability in the mixed-effects models for twin and family data. The other is to derive the asymptotic distribution of the likelihood ratio test, which is novel due to the fact that the standard regularity conditions are not satisfied. We considered a series of specific yet important examples in which we demonstrated how to formulate mixed-effect models to appropriately reflect the data, and our key idea is the use of the Cholesky decomposition. Finally, we applied our method and theory to provide a more precise estimate of the heritability of two data sets than the previously reported estimate.     

Pathogenic variants of PROC gene caused type I activity deficiency in a familial Chinese venous thrombosis

Pathogenic mutation of protein C (PROC) gene results into the deficiency of PROC activity. This study aimed to identify the pathogenic genetic variants and to explore the functional consequence in Chinese familial venous thrombosis (VTE). Whole exome sequencing was performed to identify the pathogenic variants of anticoagulant factors. Serum coagulation and anti‐coagulation factors activity were assayed to evaluate the genetic association. Functional study of PROC antigen secretion deficiency was conducted in VTE subjects and in vitro cell lines. One rare pathogenic variant (p.Ala178Pro) was identified in the four VTE subjects but not in the normal subjects from the family. An inframeshift variant (rs199469469) was also identified in a paediatric subject of the pedigree. Further evaluation of serum PROC activity levels in p.Ala178Pro variants VTE carriers showed significantly lower PROC activity compared to non‐carriers. Furthermore, in vitro study showed that the p.Ala178Pro mutant cells had a consistent reduction in concentration of PROC antigen. In conclusions, our study demonstrated the pathogenic variant (p.Ala178Pro) contributed to PROC type I activity deficiency, which may be due to decreased secretion of PROC.     

Analysis of twin data using SAS

Summary .  Twin studies are essential for assessing disease inheritance. Data generated from twin studies are traditionally analyzed using specialized computational programs. For many researchers, especially those who are new to twin studies, understanding and using those specialized computational programs can be a daunting task. Given that SAS (Statistical Analysis Software) is the most popular software for statistical analysis, we suggest that the use of SAS procedures for twin data may be a helpful alternative and demonstrate that we can obtain similar results from SAS to those produced by specialized computational programs. This numerical validation is practically useful, because a natural concern with general statistical software is whether it can deal with data that are generated from special study designs such as twin studies and if it can test a particular hypothesis. We concluded through our extensive simulation that SAS procedures can be used easily as a very convenient alternative to specialized programs for twin data analysis.     

Fitting semiparametric additive hazards models using standard statistical software

The Cox proportional hazards model has become the standard in biomedical studies, particularly for settings in which the estimation covariate effects (as opposed to prediction) is the primary objective. In spite of the obvious flexibility of this approach and its wide applicability, the model is not usually chosen for its fit to the data, but by convention and for reasons of convenience. It is quite possible that the covariates add to, rather than multiply the baseline hazard, making an additive hazards model a more suitable choice. Typically, proportionality is assumed, with the potential for additive covariate effects not evaluated or even seriously considered. Contributing to this phenomenon is the fact that many popular software packages (e.g., SAS, S-PLUS/R) have standard procedures to fit the Cox model (e.g., proc phreg, coxph), but as of yet no analogous procedures to fit its additive analog, the Lin and Ying (1994) semiparametric additive hazards model. In this article, we establish the connections between the Lin and Ying (1994) model and both Cox and least squares regression. We demonstrate how SAS's phreg and reg procedures may be used to fit the additive hazards model, after some straightforward data manipulations. We then apply the additive hazards model to examine the relationship between Model for End-stage Liver Disease (MELD) score and mortality among patients wait-listed for liver transplantation.