Removal of radiation dose response effects: an example of over-matching

doi:10.1136/bmj.325.7359.327

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J L Marsh, PhD studenta,
J L Hutton, senior lecturera,
Keith Binks, esearch group managerb

^aDepartment of Statistics, University of Newcastle upon Tyne NE1 7RU
^bOccupational Health and Medical Statistics, Westlakes Scientific Consulting, Moor Row, Cumbria CA24 3LN

Correspondence to: J L Marsh, Department of Statistics, University of Warwick, Coventry CV4 7AL

Accepted 24 January 2002

Over-matching can be a design fault in case-control studies and may lead to bias. J L Marsh and colleagues describe a case-control study of workers at a nuclear reprocessing plant in which over-matching obscured the relation between radiation exposure and mortality from leukaemia

This paper describes the rationale of a case-control study, explains the process of stratification through matching, and summarises the mechanism of confounding. We carried out a matched case-control study of workers at BNFL (British Nuclear Fuels) to clarify the results of past cohort studies, which had found a significant association between risk of leukaemia and cumulative external radiation dose. Fitted models from the current study contradicted these results. After examining the relation between the matching factors and the dose, we suggest that over-matching is a cause of the contradiction.

Summary points

Case-control studies are useful in examining the epidemiology of rare diseases

They can be biased through a design fault called over-matching

The current case-control study of Sellafield workers illustrates over-matching and shows the dependence of conclusions on the correct design

Background

Matching in case-control studies

In a case-control study, individuals with the disease of interest (cases) are taken from the population, together with a random sample of the remaining, healthy individuals (controls). Exposureof the two groups to the risk factors in question is examined. If a significant difference in exposure history is found, it can be inferred that these risk factors have some association with the probability of disease.

If other factors influence the probabilities of disease and exposure to risks, this can disguise the influence of the risk factors in question. This confounding can be removed by stratification. This must be done carefully: stratifying too finely will cause information to be lost, but stratifying too coarsely will not remove enough confounding. The ratio of cases to controls must remain the same in …