Biomedical subjects

E T Ng

Publications and source records attributed to E T Ng.

3 recordsLinked to original sources

PubMed1999

Two-state mixed renewal processes for chronic disease.

We develop a model based on a two-state mixed renewal process and propose its use for modelling disease activity in studies of chronic conditions. We specify Weibull forms for the conditional transition intensities to allow for time trends, and bivariate frailties to accommodate subject-to-subject variability in the disease process. Extensions of this model are considered for stratified analyses in which strata are defined by the number of past exacerbations. Data from a motivating study of chronic bronchitis are analysed to illustrate the methodology.

Anti-Infective Agents↗

PubMed1997

Modeling two-state disease processes with random effects.

Many chronic medical conditions are manifested by alternating sojourns in symptom-free and symptomatic states. In many cases, in addition to their relapsing and remitting nature, these conditions lead to worsening disease patterns over time and may exhibit seasonal trends. We develop a mixed-effect two-state model for such disease processes in which covariate effects are modeled multiplicatively on transition intensities. The transition intensities, in turn, are functions of three time scales: the semi-Markov scale involving the backward recurrence time for the cyclical component, the Markov scale for the time trend component, and a seasonal time scale. Multiplicative bivariate log-normal random effects are introduced to accommodate heterogeneity in disease activity between subjects and to admit a possible negative correlation between the transition intensities. Maximum likelihood estimation is carried out using Gauss-Hermite integration and a standard Newton-Raphson procedure. Tests of homogeneity are presented based on score statistics. An application of the methodology to data from a multi-center clinical trial of chronic bronchitis is provided for illustrative purposes.

Anti-Infective Agents↗

PubMed1997

A logistic-bivariate normal model for overdispersed two-state Markov processes.

We describe a logistic-bivariate normal mixture model for a two-state Markov chain in which each individual makes transitions between states according to a subject-specific transition probability matrix. The use of the bivariate normal mixing distribution facilitates inferences regarding the correlation of the random effects and hence provides insight as to the nature of the subject-to-subject variability in the transition probabilities. Tests regarding the correlation can be based on likelihood ratio, score, or Wald statistics. Estimates of the transition intensities of a latent continuous time conditionally Markov process may also be computed. We illustrate this methodology by application to a parasitic infection field study and contrast our findings with those previously published on this data set.

Biometry↗