Parametric Frailty Model with Time-Dependent Covariates

The parametric frailty model has been used in this study where, the term frailty is used to represent an unobservable random effect shared by subjects with similar (unmeasured) risks in the analysis of mortality rate. In real-life environment, the application of frailty models have been widely used by biostatistician, economists and epidemiologist to donate proneness to disease, accidents and other events because there are persistent differences in susceptibility among individuals. When heterogeneity is ignored in a study of survival analysis the result will produce an incorrect estimation of parameters and standard errors. This study used gamma and Weibull distribution for the frailty model. The first objective of this study is to investigate parametric model with time dependent covariates on frailty model. The derivation is using either classical maximum likelihood or Monte Carlo integration. The second objective is to measure the effectiveness of Gamma and Weibull frailty model with and without time-dependent covariates. This is done by calculating the root mean square error (RMSE). The last objective is to assess the goodness of fit of Gamma and Weibull frailty model with and without time-dependent covariates using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Simulation is used in order to obtain the RMSE, AIC ad BIC value if time-dependent covariate does not exists. Between both models with time-dependent covariate, Weibull frailty distribution has lower AIC and BIC compared to Gamma frailty distribution. Therefore, Weibull frailty distribution with time-dependent covariate is preferable when a time-dependent covariate exists in a data.

Non-parametric frailty Cox models for hierarchical time-to-event data

Summary We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty. A tailored Expectation–Maximization algorithm is proposed for estimating the model parameters, methods of model selection are compared, and the code is assessed in simulation studies. This model is particularly useful for administrative data in which there are a limited number of covariates available to explain the heterogeneity associated with the risk of the event. We apply the model to a clinical administrative database recording times to hospital readmission, and related covariates, for patients previously admitted once to hospital for heart failure, and we explore latent clustering structures among healthcare providers.

EVALUATION OF RECURRENT HOSPITALIZATION PROBABILITY IN PATIENTS WITH DUODENAL AND GASTRIC ULCER DEPENDENT UPON REHABILITATION INPUT

Background: notoriously known worldwide cause of morbidity and disability duodenal (DU) and gastric ulcer (GU) experience their rise in Ukraine, demonstrating formidable increase by 38,4 % in last decade with the prevalence of 2299 per 100 000 population. Every second patient is treated in-patiently, every third experiences disability spell annually. Reduction in related risks confined not so much by absence of effective therapy but rather shortcomings in patient management and patient devotion. By WHO data 50 % of patients fail to follow physician prescriptions, 60 % can’t recollect physician recommendations in first 20 minutes. Ubiquitous belated timing of rehabilitation initiation in post hospital stage appeared to be cardinal obstacle of its efficiency with low (up to 20 %) coverage, and ensuring clinical effect in 8 % cases only. Aim: to evaluate efficacy of rehabilitation program detailed at first episode of in-patient treatment at gastroenterological department. Data: organized by cohort design. Control cohort comprised 180 patients with first episode of hospitalization due to DU or GU in gastroenterological Vinnitsa city department in 2009–2010 years. Experimental cohort consisted of 220 alike patients who enter rehabilitation program (RP). RP was administered randomly. Randomness was statistically verified on principal confounders. Cases were traced 4 years. Methods: we applied three modifications of semi-parametric frailty model to study effect of program on the risk of recurrent hospitalization. Results: all three modifications coincided in that program secured typically at least 39 days to recurrent hospitalization per patient with drop in risk at least at RR=0,774.