Interval-censored Survival Data Research Articles

Regression models for interval censored survival data: application to HIV infection in Danish homosexual men.

This paper shows how to fit excess and relative risk regression models to interval censored survival data, and how to implement the models in standard statistical software. The methods developed are used for the analysis of HIV infection rates in a cohort of Danish homosexual men.

INTERVAL CENSORED SURVIVAL DATA: A GENERALIZED LINEAR MODELLING APPROACH

A method is described for weak parametric modelling of arbitrarily interval censored survival data using generalized linear models. The method makes use of an associated Bernoulli model, with standard errors based on the observed information matrix. Three types of models are discussed: additive and multiplicative hazard models with piecewise constant baseline hazard, and a proportional hazards model with discrete baseline survivor function. These models may be fitted in the statistical package GLIM.

Interval censored survival data and multistate compartmental models in the analysis of first appearance of Plasmodium falciparum parasites in infants.

A statistical model for interval censored data is described. Assuming a piecewise constant incidence enables us to analyse very unbalanced data in a generalized linear model. The distribution of age at first appearance of P. falciparum parasites in infants in Liberia has been estimated. A new graphical method for presentation of test results on all children was developed. In an illness--death model it is described how the proportion of undetected and detected malaria parasitemias depends on parasite rates and testing frequency. The incidence of detectable malaria parasitemia was 0.14 per month in infants under 4 months of age, and 0.60 per month in children over 4 months (p < 0.001). The congenital resistance to malaria in African infants living in a highly endemic area had largely disappeared by the age of 4 months; before this age children were partly protected.

Modelling for Survival Data in Medical Research

Some non-parametric procedures. Modelling survival data. The Cox Regression Model. Design of clinical trials. Some other models for survival data. Model checking. Time dependent co-variates. Interval censored survival data. Multi-state survival models. Some additional topics. Use of computer software in survival analysis. Appendices: Example data sets. Maximum liklihood estimation score statistics and information. GLIM macros for survival analysis.