Joint meeting with Manchester University's Biostats Group

Theme: "Problems in Survival Analysis"

MILENA FALCARO and ANDREW PICKLES Univeristy of Manchester

Analysis of multivariate survival data with complex structure and interval censoring

The multivariate normal model provides great flexibility for complex model specification through the approach of covariance structure analysis. We describe how the 3-category multivariate ordinal probit framework can be used in this context and made more appropriate to survival data by the joint estimation of a Box-Cox transformation of the time-scale. We illustrate the method by the joint analysis of 4 survival times, censored ages of onset for two measures where both measures are recorded for each twin in a twin-pair, in order to assess the correlation in genetic and environmental effects that influence each measure.

Milena's talk

ROBIN HENDERSON University of Lancaster

Joint Modelling of Longitudinal and Event Time Data

Many scientific investigations generate both longitudinal measurement data, with repeated measurements of a response variable at a number of time points, and event history data, in which times to recurrent or terminating events are recorded. This talk provides an overview of methods and models for the joint behaviour of such data, concentrating on the most common situation of Gaussian longitudinal data and proportional hazard/intensity event time data. Modelling, estimation and diagnostics are discussed and illustrated.

Robin's talk

JONATHAN STERNE (Bristol)

Use of marginal structural models to estimate causal effects in longitudinal studies

Use of standard regression models for the analysis of cohort studies with time-updated measurements may result in biased estimates of treatment effects if time-dependent confounders affected by prior treatment are present. A covariate is a time-dependent confounder if it predicts future treatment, and future outcome, conditional on past treatment. If, additionally, past treatment predicts current covariate value (e.g. if the covariate is on the causal pathway between treatment and the outcome) then standard survival analyses with time-updated treatment effects will give biased treatment effect estimates. Marginal structural models address this problem, and can thus be used to make causal inferences about the effect of treatments in longitudinal studies. I will illustrate the use of marginal structural models to estimate the effect of highly active antiretroviral therapy (HAART) in the Swiss HIV Cohort Study.

Jonathan's talk