An analysis for a cross-over cohort study with an application to the study of triggers of Menière's disease
ABSTRACT When studying the effect of a transient exposure on the risk of a rare illness, for time and cost effectiveness it is desirable to follow a cohort of individuals who are 'prone' to the illness over an observation period. In this paper, we present a method of analysis for data arising from such a study. The proposed method can be used to estimate the relative risk of an exposure triggering the illness and the distribution of the time delay from exposure to the onset of illness. The model is extended to include covariate effects and to the situation where there are two types of exposure. For the two types of exposures situation, a model to handle a possible synergism of the exposures is proposed. Finally, the method is applied to study the potential triggers of attacks of Menière's disease.
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ABSTRACT: Simple tests are given for consistency of the data with additive and with multiplicative effects of two risk factors on a binary outcome. A combination of the procedures will show whether data are consistent with neither, one or both of the models of no additive or no multiplicative interaction. Implications for the size of the study needed to detect differences between the models are also addressed. Because of the simple form of the test statistics, combination of evidence from different studies or strata is straightforward. Illustration of how the method could be extended to data from a 2xRxC table is also given.Biostatistics 02/2005; 6(1):1-9. DOI:10.1093/biostatistics/kxh024 · 2.24 Impact Factor
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ABSTRACT: A case-control design involving only cases may be used when brief exposure causes a transient change in risk of a rare acute-onset disease. The design resembles a retrospective nonrandomized crossover study but differs in having only a sample of the base population-time. The average incidence rate ratio for a hypothesized effect period following the exposure is estimable using the Mantel-Haenszel estimator. The duration of the effect period is assumed to be that which maximizes the rate ratio estimate. Self-matching of cases eliminates the threat of control-selection bias and increases efficiency. Pilot data from a study of myocardial infarction onset illustrate the control of within-individual confounding due to temporal association of exposures.American Journal of Epidemiology 02/1991; 133(2):144-53. · 4.98 Impact Factor
Article: Analysis of case-crossover designs[Show abstract] [Hide abstract]
ABSTRACT: The case-crossover design provides a means to study the effects of transient exposures on the risk of acute illness, for example, the effects of drinking alcohol on the immediate risk of a heart attack. Only cases are required by the design, since each case is effectively its own control; what a case was doing at the time of an acute event is compared with what the case would have been doing usually. Maclure has described an approach based on the Mantel-Haenszel method of analysis. It is shown here how the analysis of case-crossover designs can be achieved by a method of maximum likelihood. The method is quite general and, in principle, can be used to analyse the joint effects of many transient exposures. For binary exposures the Mantel-Haenszel approach is an approximate solution to the likelihood equations. In practice, case-crossover designs are limited by the information available on each case's 'usual' behaviour. Extracting such information requires in-depth questioning, but, in principle, it can be obtained. To do so requires careful questionnaire design. The approach is illustrated by analysis of 24 hour alcohol consumption and the risk of myocardial infarction. The problem with this analysis is how to estimate the probability of what a case would 'usually' have been doing from information on drinking frequency.Statistics in Medicine 12/1993; 12(24):2333-41. DOI:10.1002/sim.4780122409 · 2.04 Impact Factor