Lifetime Data Anal - Ornstein-Uhlenbeck threshold regression for time-to-event data with and without a cure fraction.

Tópicos

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Resumo

In this paper we propose a threshold regression (TR) model for time to event data related to subject health using a latent Ornstein-Uhlenbeck (OU) process that fails once it hits a boundary value for the first time. Baseline covariates are incorporated into the analysis using a log-link function for the initial state of the health process. The model provides clinically meaningful covariate effects and does not require the proportional hazards assumption of the commonly used Cox model. Unlike TR models based on the Wiener process, the OU model allows increments in the health process to depend on previous values and drifts toward a state of equilibrium or homeostasis, which are present in many biological applications. We also extend our model to incorporate a cure rate for applications with improper survival functions, such as time to tumor recurrence in a cancer clinical trial. Our models are applied to overall and relapse-free survival data of melanoma patients undergoing definitive surgery.

Resumo Limpo

paper propos threshold regress tr model time event data relat subject health use latent ornsteinuhlenbeck ou process fail hit boundari valu first time baselin covari incorpor analysi use loglink function initi state health process model provid clinic meaning covari effect requir proport hazard assumpt common use cox model unlik tr model base wiener process ou model allow increment health process depend previous valu drift toward state equilibrium homeostasi present mani biolog applic also extend model incorpor cure rate applic improp surviv function time tumor recurr cancer clinic trial model appli overal relapsefre surviv data melanoma patient undergo definit surgeri

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