PubMed HealthSearch

Biomedical subjects

G M Tallis

Publications and source records attributed to G M Tallis.

12 recordsLinked to original sources

A review of the technical features of breast cancer screening illustrated by a specific model using South Australian cancer registry data.

This paper illustrates many of the concepts and issues relating to breast cancer screening by reference to a case study of screening in South Australia and by a survey of the mathematical screening models. This work is motivated by the observation that, for some women, the prospects for a breast cancer cure appear to be enhanced if the disease is detected early. We use data from the South Australian Cancer Registry to estimate some of the parameters which describe the pattern of this disease in the South Australian female population, and the underlying model is then used to estimate the impact of various screening designs on the efficacy of a variety of breast cancer screening programmes.

Breast Neoplasms

Compartmental models and competing risk.

General compartmental models are derived using competing risk arguments. When the risk variables are exponential, the results specialize to the standard stationary Markov compartmental model. Iterative methods of solving the fundamental integral equation are given, and the uniqueness of the solution is incidentally established. The analysis is extended to include fixed inputs, orderly and nonorderly stream infusions, and time dependency. The study is motivated by a biological system that evolves through various stages over time.

Biological Evolution

Conditioned life tables from registries with unidentified random losses.

We identify two types of random loss that may afflict registries which use 'passive follow-up'. The effects of these errors on estimates related to survival are examined mathematically and numerically. A procedure for correcting the attendant biases is suggested, and its strengths and limitations are explored.

Bias

The analysis of survival data from a central cancer registry with passive follow-up.

We present analyses for survival data obtained from a central cancer registry with passive follow-up. This method of data collection has the potential to produce unknown random losses which would affect estimates of survival. We show that non-parametrically estimated conditional distributions remove any effect of these unknown losses and that a compound mixture model estimates their magnitude. Lung cancer data are used to illustrate the procedures.

Actuarial Analysis

The effect of accessing medical records by date of death on estimates of survival.

An inefficient, but quickly and easily calculated, estimate of a survival-time distribution is described. Conditions for the estimate to be unbiased specify bounds on the length of time a study has been in progress and the way in which people must enter a study. These appear to be frequently met when a large medical registry is examined with a view to establishing base line survival experience for a selected class of patients.

Epidemiologic Methods

A migration model.

Explore the source record for details and available documents.

Genetics, Population