... The semiparametric Cox proportional hazards model, together with the partial likelihood principle, has been widely used to study the effects of potentially time-dependent covariates on a possibly censored event time. We propose a computationally efficient method for fitting the Cox model to big data involving...

... function within a transformation. The proposed models provide a highly flexible and versatile class of semiparametric models that include the transformation models and the Cox–Aalen model as special cases. Specifically, it extends the transformation models by allowing potentially time-dependent covariates...

... transplantations is employed to illustrate that the dynamic RMST model can predict patients’ expected survival times from any prediction time, considering the time-dependent covariates and time-varying effects of covariates. dynamic prediction dynamic restricted mean survival time longitudinal data non...

... effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing...

...-validated predictive performance, in this case, outperforming joint modelling. cystic fibrosis dynamic prediction landmark model machine learning ensemble survival analysis time-dependent covariates Predictive models for time-to-event outcomes are used widely in medicine to identify individuals...

.... To construct a feasible estimating equation for , one may replace in the above display by its Kaplan-Meier estimator, which is denoted as , and obtain (6) (1) The AFT model with time-dependent covariates ( Lin and Ying, 1995 ) relates the failure time with a baseline failure time τ...

... on the hazard of death from lung cancer. Age at death is the outcome variable, and the effects of cumulative exposure, calendar year, and number of years since initial employment are modelled as time dependent covariates. This work quantifies and tests formally the dose-response effect observed...

...Trond Petersen Address for correspondence: Trond Petersen, Assistant Professor, Department of Sociology, WJH 512, Harvard University, Cambridge, MA 02138, USA. Appl. Statist. (1986) 35. No. 3.pp. 281-288 Fitting Parametric Survival Models With Time- Dependent Covariates By TROND PETERSEN...

... ) Summary Cox’s (1972) proportional hazards model is applied to a prospective study of infection in aplastic anaemia and acute leukaemia patients following bone marrow transplantation. The usefulness of time-dependent covariates and cause-specific hazard functions is highlighted. cause-specific hazard...

...-response association Penalized additive models Reproducible research Survival analysis Time-dependent covariates The remainder of this article is structured as follows. Our model, which is an extension of the piece-wise exponential model (PEM), is described in detail in Section 2 . The proposed...

... characteristics (smoking, marital status, etc.). To understand the pathways by which such characteristics may affect the occurrence of health events (death, diabetes onset, etc.), it is common to incorporate them as time-dependent covariates in models for a time-to-event outcome. Hazard models are often adopted...

... present a novel model and estimation procedure for right-censored survival data with time-dependent covariates. Summary Since survival data occur over time, often important covariates that we wish to consider also change over time. Such covariates are referred as time-dependent covariates. Quantile...

... sampling Time-dependent covariates NHLBI 10.13039/100000050 R01 HL94786 R01 HL079266 Vanderbilt University 10.13039/100006537 N01-HR-16044 16045 16046 16047 16048 16049 16050 16051 16052 NHLBI 10.13039/100000050 GCRC 10.13039/100008658 M01RR00051 M01RR0099718-24...

...Patrick J. Heagerty; Bryan A. Comstock Dependence Longitudinal data analysis Time-dependent covariates NIH 10.13039/100000002 R01 HL072966 UL1 RR024379 A key advantage of conducting a longitudinal study is the opportunity to link prospectively measured exposure variables to subsequent...

... stand point but cannot be fully explored in this data set. Therefore, in our first analysis we assume that partial resection also completely prevents subsequent risk of cancer. The model proposed is then formulated as follows: we fit the onset of diabetes as a time-dependent covariate in a Cox...

...-specific genotype data. We also assume that these data would be ascertained by analyzing stored blood samples, the cost of which limits the sample size. Binary data Epidemiological study design Longitudinal data analysis Marginal models Marginalized models Outcome-dependent sampling Time-dependent...

... using their longitudinal CD4 cell count measurements. Area under the curve Discrimination ROC methodology Shared parameter model Survival analysis Time-dependent covariates In longitudinal studies often interest lies in the relation between a longitudinally measured marker and a time-to-event...

... studies. Accelerated failure time model Heteroscedasticity Information criteria Interval censoring Proportional hazards model Proportional odds model Seminonparametric (SNP) density Time-dependent covariates NIH 10.13039/100000002 R01-CA085848 R37-AI031789 Regression analysis of censored time...

... of outcome-dependent longitudinal sampling designs and to both outline and evaluate the basic conditional likelihood analysis allowing for valid statistical inference. Binary data Longitudinal data analysis Marginal models Marginalized models Outcome-dependent sampling Time-dependent covariates We...

... Publication Model ( https://dbpia.nl.go.kr/journals/pages/open_access/funder_policies/chorus/standard_publication_model ) Summary We develop a new approach to using estimating equations to estimate marginal regression models for longitudinal data with time-dependent covariates. Our approach classifies time...