Biomedical subjects

T H Scheike

Publications and source records attributed to T H Scheike.

3 recordsLinked to original sources

PubMed1997

A discrete survival model with random effects: an application to time to pregnancy.

Time to pregnancy, the number of menstrual cycles it takes a couple to conceive, and various covariates have been collected among couples ultimately achieving conception. To assess the influence of the covariates, we constructed a discrete survival model that allows time-dependent covariates. A random effect was included to account for unobserved heterogeneity. The collected waiting times are obtained through retrospective ascertainment and are analyzed as truncated data. Maximum likelihood estimation was implemented by Fisher scoring through iteratively reweighted least squares.

Adult↗

PubMed1996

Estimation from current-status data in continuous time.

The nonparametric maximum likelihood estimator for current-status data has been known for at least 40 years, but only recently have the mathematical-statistical properties been clarified. This note provides a case study in the important and often studied context of estimating age-specific immunization intensities from a seroprevalence survey. Fully parametric and spline-based alternatives (also based on continuous-time models) are given. The basic reproduction number R0 exemplifies estimation of a functional. The limitations implied by the necessarily rather restrictive epidemiological assumptions are briefly discussed.

Adolescent↗

PubMed1993

Marker-dependent hazard estimation: an application to AIDS.

The acquired immunodeficiency syndrome (AIDS) results from infection with the human immunodeficiency virus (HIV). The time of infection is generally unknown since transmission usually occurs during the course of repeated sexual contacts or needle sharing. Brookmeyer and Gail describe the biases that may arise in survival analyses using the recruitment time rather than the unknown infection time as the origin in prevalent cohorts of HIV-infected individuals. We apply a non-parametric hazard estimator, introduced by Nielsen, that assumes the hazard of an AIDS diagnosis depends upon the unknown time of infection solely through the value of possibly multidimensional markers of HIV-disease progression such as CD4+ T lymphocyte cell counts. Essentially, we estimate the hazard for a specific marker value y by dividing the number of occurrences among subjects with marker measurements in a neighbourhood of y by the total risk time in that neighbourhood. We present this estimator, which relies upon kernel estimator techniques to produce a smooth estimate, within a counting process framework. We apply this method to marker data from the San Francisco Men's Health Study.

Acquired Immunodeficiency Syndrome↗