Nonparametric analysis of dependently interval-censored failure time data
Yayuan Zhu1, Jerald F Lawless1, Cecilia A Cotton1
1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
Abstract:
Failure time studies based on observational cohorts often have to deal with irregular intermittent observation of individuals, which produces interval-censored failure times. When the observation times depend on factors related to a person's failure time, the failure times may be dependently interval censored. Inverse-intensity-of-visit weighting methods have been developed for irregularly observed longitudinal or repeated measures data and recently extended to parametric failure time analysis. This article develops nonparametric estimation of failure time distributions using weighted generalized estimating equations and monotone smoothing techniques. Simulations are conducted for examination of the finite sample performance of proposed estimators. This research is motivated in part by the Toronto Psoriatic Arthritis Cohort Study, and the proposed methodology is applied to this study.
More Related Videos
Related Concept Videos
Censoring Survival Data
The Integrated Rate Law: The Dependence of Concentration on Time
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Chronopharmacokinetics: Time-Dependent Pharmacokinetics
Time-dependent pharmacokinetics refers to non-cyclical changes in drug rate processes over a period of time. It can lead to nonlinear pharmacokinetics, where the relationship between drug concentration and time is not proportional. Non-cyclical...
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Introduction to Nonparametric Statistics
One of...


