Cox proportional hazards model is a widely used method to analyse time-to-event data for survival analysis. In the medical field, age is the most common confounder of any association between risk factor and disease since most risk factors and the risk of disease change with age. Usually, age recorded at the start of the study (age-at-baseline) will be adjusted for in the model to account for th...

This paper uses the Cox proportional hazards model to analyze recent Broadway show data to investigate the factors that relate to the longevity of shows The type of show whether a show is a revival and rst week attendance for the show are predictive for longevity Favorable critic re views in the Daily News are related to greater success but reviews in The New York Times are not Winning major To...

This paper studies Cox‘s proportional hazards model under covariate measurement error. Nakamura‘s (1990) methodology of corrected log-likelihood will be applied to the so called Breslow likelihood, which is, in the absence of measurement error, equivalent to partial likelihood. For a general error model with possibly heteroscedastic and non-normal additive measurement error, corrected estimator...

This paper proposes a discrete-time hazard regression approach based on the relation between hazard rate models and excess over threshold models, which are frequently encountered in extreme value modelling. The proposed duration model employs a exible link function and incorporates the grouped-duration analogue of the wellknown Cox proportional hazards model and the proportional odds model as s...

Methods We evaluated 153 patients (28males) diagnosed younger than 20 years old, managed during 1980 through 2013 (median 7.0 years of duration). Good or poor outcome (persistence or recurrence) was analyzed in 126 patients followed for at least 12 months. Predictors for recurrence were analyzed among 108 pediatric PTC patients. Adult PTC patients (n = 3093) were finally included in Cox proport...

Correlated survival times can be modelled by introducing a random effect, or frailty component, into the hazard function. For multivariate survival data we extend a non-PH model, the generalized time-dependent logistic survival model, to include random effects. The hierarchical-likelihood procedure, which obviates the need for marginalization over the random effect distribution, is derived for ...

Purpose – Most current condition-based maintenance (CBM) systems using proportional hazards model (PHM) assume that enough historical data are available. However, in many practical cases, it is usually costly to collect much historical data prior to real practice (model implementation). This paper aims to focus on the necessity and benefits of updating a PHM with new samples generated in the pr...

We introduce the R package CPHshape, which computes the effect parameters and the nonparametric maximum likelihood estimator of a shape constrained baseline hazard in the Cox proportional hazards model. The functionality of the package is illustrated using reproducible examples which are based on simulated data.