Actuarial predictions from population-based disease registries with unidentified random losses.
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Biomedical subjects
Publications and source records attributed to G M Tallis.
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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.
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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.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.