A stratified survival analysis was compared with a non-stratified model to identify significant factors affecting the productive life of commercial sows and to estimate the heritability for sow productive life. Data for 3,074 sows were used for the survival analysis under a Cox model. The stratified model identified factors associated with sow survival that were not statistically significant in...

Causal inference in survival analysis has been centered on treatment effect assessment with adjustment of covariates. The direct adjustment method is usually employed to find the survival function of a treatment. A Cox model that stratifies the cumulative hazard by treatment is an ideal choice for performing direct adjustment because the treatment effects are allowed to vary over time. A SAS ma...

Stratified Cox regression models with large number of strata and small stratum size are useful in many settings, including matched case-control family studies. In the presence of measurement error in covariates and a large number of strata, we show that extensions of existing methods fail either to reduce the bias or to correct the bias under nonsymmetric distributions of the true covariate or ...

The life cycle cost of a system is influenced by its maintainability. Maintainability is a design parameter, whose operational conditions can affect it significantly. Hence, the effects of these operational conditions should be quantified early in the design phase. The proportional repair model (PRM), which is developed based on the proportional hazard model (PHM), can be used to analyze mainta...

Background. Findings on the association between posttransplant anemia (PTA) and mortality in posttransplant patients are scarce. This study explored whether PTA shortly after kidney transplantation (KT) predicts mortality at up to 10 years' follow-up, stratified for chronic kidney disease (CKD) stages. Methods. PTA was divided into 3 categories according to the hemoglobin (Hb) value: severe (Hb...

Background: In many studies, Cox regression was used to assess the important factors that affect the survival of cancer patients based on demographic and clinical variables. The aim of this study was to determine the factors affecting the survival of patients with Hodgkin's lymphoma using the random survival forest (RSF) method and compare it with the Cox model. Methods: In this retrospective ...

BACKGROUND Regression models for survival data have traditionally been based on the Cox regression model. However, its validity relies heavily on assumption of proportional hazards. Another restriction of the Cox model is insufficiency in dealing with time-varying covariate effects, since the regression coefficients are assumed constant. These weaknesses have generated interest in alternative a...