Biomedical subjects

J G Liao

Publications and source records attributed to J G Liao.

3 recordsLinked to original sources

PubMed1999

A hierarchical Bayesian model for combining multiple 2 x 2 tables using conditional likelihoods.

This paper introduces a hierarchical Bayesian model for combining multiple 2 x 2 tables that allows the flexibility of different odds ratio estimates for different tables and at the same time allows the tables to borrow information from each other. The proposed model, however, is different from a full Bayesian model in that the nuisance parameters are eliminated by conditioning instead of integration. The motivation is a more robust model and a faster and more stable Gibbs algorithm. We work out a Gibbs scheme using the adaptive rejection sampling for log concave density and an algorithm for the mean and variance of the noncentral hypergeometric distribution. The model is applied to a multicenter ulcer clinical trial.

Algorithms↗

PubMed1990

The analysis of delays in disease reporting: methods and results for the acquired immunodeficiency syndrome.

In order to monitor accurately trends in disease incidence, it is necessary to account for delays in the reporting of cases to central registries. The objective of the paper is to develop simple methods for the analysis of reporting delays in order to identify the main sources of heterogeneity and to adjust reported disease incidence data. The analysis is complicated because the data are right truncated. A simple and flexible method for the regression analysis of reporting delays is proposed, which can be easily implemented with standard computing tools for generalized linear models or logistic regression. The method was used to analyze delays in reporting the acquired immunodeficiency syndrome in the United States among cases who met the pre-1987 surveillance definition. This analysis showed significant geographic variation. Delays were shortest in the Northeast and longest in the South. The influences of risk groups and calendar year of diagnosis were not consistent across each of the geographic regions. Variation among risk groups was attributed primarily to slower reporting of transfusion-associated and pediatric acquired immunodeficiency syndrome cases. An overall trend toward longer delays with calendar time of diagnosis was attributed primarily to a trend toward longer delays in the Northeast. These methods and results are useful both for the evaluation of surveillance procedures in order to improve disease reporting and for adjustment of disease incidence data.

Acquired Immunodeficiency Syndrome↗

PubMed1990

Statistical modelling of the AIDS epidemic for forecasting health care needs.

The objective of this paper is to develop statistical methods for estimating current and future numbers of individuals in different stages of the natural history of the human immunodeficiency (AIDS) virus infection and to evaluate the impact of therapeutic advances on these numbers. The approach is to extend the method of back-calculation to allow for a multistage model of natural history and to permit the hazard functions of progression from one stage to the next to depend on calendar time. Quasi-likelihood estimates of key quantities for evaluating health care needs can be obtained through iteratively reweighted least squares under weakly parametric models for the infection rate. An approach is proposed for incorporating into the analysis independent estimates of human immunodeficiency virus (HIV) prevalence obtained from epidemiologic surveys. The methods are applied to the AIDS epidemic in the United States. Short-term projections are given of both AIDS incidence and the numbers of HIV-infected AIDS-free individuals with CD4 cell depletion. The impact of therapeutic advances on these numbers is evaluated using a change-point hazard model. A number of important sources of uncertainty must be considered when interpreting the results, including uncertainties in the specified hazard functions of disease progression, in the parametric model for the infection rate, in the AIDS incidence data, in the efficacy of treatment, and in the proportions of HIV-infected individuals receiving treatment.

Acquired Immunodeficiency Syndrome↗