6533b836fe1ef96bd12a0953

RESEARCH PRODUCT

Estimating completeness in cancer registries--comparing capture-recapture methods in a simulation study.

Schmidtmann Irene

subject

Statistics and ProbabilityModels StatisticalComputer scienceIncidenceLinear modelEstimatorBreast NeoplasmsGeneral MedicineCancer registryMark and recaptureStatistical simulationSimulated dataStatisticsEconometricsProbability distributionHumansComputer SimulationFemaleRegistriesStatistics Probability and UncertaintyCompleteness (statistics)Epidemiologic Methods

description

Completeness of registration is one of the quality indicators usually reported by cancer registries. This allows researchers to assess how useful and representative the data is. Several methods have been suggested to estimate completeness. In this paper a multi-state model for the process of cancer diagnosis and treatment is presented. In principle, every contact with a doctor during diagnosis, treatment, and aftercare can give rise to a cancer registry notification with a certain probability. Therefore the states included in the model are "incident tumour" and "death" but also contacts with doctors such as consultation of a general practitioner or specialised doctor, diagnostic procedures, therapeutic interventions, and aftercare. In this model transitions between states and possible notifications to a cancer registry after entering a state are simulated. Transition intensities are derived and used in simulation. Several capture-recapture methods have been applied to the simulated data. Simulated "true" numbers of new cases and simulated numbers of registrations are both available. This allows to assess the validity of the completeness estimates and to compare the relative merits of the methods. In the scenarios investigated here, all capture-recapture estimators tended to underestimate completeness. While a modified DCN method and one type of log-linear model yielded quite reasonable estimates other methods exhibited large variability or grossly underestimated completeness.

10.1002/bimj.200810483https://pubmed.ncbi.nlm.nih.gov/19067337