Biomedical subjects

Jerald F Lawless

Publications and source records attributed to Jerald F Lawless.

3 recordsLinked to original sources

PubMed2005

Statistical methods for multivariate interval-censored recurrent events.

Multi-type recurrent event data arise when two or more different kinds of events may occur repeatedly over a period of observation. The scientific objectives in such settings are often to describe features of the marginal processes and to study the association between the different types of events. Interval-censored multi-type recurrent event data arise when the precise event times are unobserved, but intervals are available during which the events are known to have occurred. This type of data is common in studies of patients with advanced cancer, for example, where the events may represent the development of different types of metastatic lesions which are only detectable by conducting bone scans of the entire skeleton. In this setting it is of interest to characterize the incidence of the various types of bone lesions, to estimate the impact of treatment and other covariate effects on the development of new lesions, and to understand the relationship between the processes generating the bone lesions. We develop joint models for multi-type interval-censored recurrent events which accommodate dependencies between different types of events and enable one to examine the covariate effects via regression. However, since the marginal likelihood resulting from the multivariate random effect model is intractable, we describe a Gibbs sampling algorithm to facilitate model fitting and inference. We use generalized estimating equations for estimation and inference based on marginal models. The finite sample properties of the marginal approach are studied via simulation. The estimates of both the regression coefficients and the variance-covariance parameters are shown to have negligible bias and 95 per cent confidence intervals based on the asymptotic variance formula are shown to have excellent empirical coverage probabilities in all of the settings considered. The application of these methods to data from a trial of women with advanced breast cancer provides insight into the clinical course of bone metastases in this population.

Bayes Theorem↗

PubMed2005

Reporting of mortality in a psoriatic arthritis clinic is primarily a function of the number of clinic contacts and not disease severity.

OBJECTIVE: To identify processes that influence data collection, particularly in the reporting of deaths in mortality studies, using patient registry data. METHODS: The University of Toronto Psoriatic Arthritis Clinic has mechanisms for patient followup and identification of deaths. Logistic regression was used to identify patient characteristics that discriminate between 2 populations of deaths, those reported under regular followup and those reported in the context of special studies. Factors examined were based on information available at the patients' last clinic visit and the pattern of patients' clinic visits. RESULTS: A clear relationship was found between the number of contacts with the clinic and rapid death reporting. However, no particular link between severity of disease and the reporting of death was apparent in this study. CONCLUSION: It is recommended that research databases routinely record the time between death and reporting of death and the method of ascertaining and reporting death. More detailed information on the scheduling of clinic visits may also be helpful.

Adult↗

PubMed2003

Flexible maximum likelihood methods for bivariate proportional hazards models.

This article presents methodology for multivariate proportional hazards (PH) regression models. The methods employ flexible piecewise constant or spline specifications for baseline hazard functions in either marginal or conditional PH models, along with assumptions about the association among lifetimes. Because the models are parametric, ordinary maximum likelihood can be applied; it is able to deal easily with such data features as interval censoring or sequentially observed lifetimes, unlike existing semiparametric methods. A bivariate Clayton model (1978, Biometrika 65, 141-151) is used to illustrate the approach taken. Because a parametric assumption about association is made, efficiency and robustness comparisons are made between estimation based on the bivariate Clayton model and "working independence" methods that specify only marginal distributions for each lifetime variable.

Analysis of Variance↗