title: A comparison of several methods to test for the existence of a monotonic dose-response relationship in clinical and epidemiological studies.
abstract: The Mantel-extension chi-square test for overall trend and an asymptotically equivalent test based on logistic regression are commonly used to test for a monotonic dose-response relationship between exposure and disease in epidemiological and clinical studies. However, these tests present two important disadvantages, as they (i) make the restrictive assumption of a parametric model of linear form on the logit scale and (ii) impose the a priori choice of scores to code for the exposure categories. Indeed, the linear assumption, if made incorrectly, can lead to an invalid conclusion, and the choice of scores lends arbitrariness to the test results. Some alternative tests have been proposed in the literature. We have considered several of these tests, namely one based on isotonic regression, the T-test based on contrasts and a recently published test based on adjacent contrasts (Dosemeci-Benichou test). The aim of our study was to compare the statistical properties (type I error and power) of these tests and of the commonly used Mantel-extension test for overall trend. We generated cohort and case-control data and considered one- and two-sided versions of the tests. Moreover, we studied the tests under the null hypothesis of no relationship between exposure and disease and under various alternative patterns of monotonic or non-monotonic dose-response relationships. This study confirms that the commonly used trend tests can lead to erroneous conclusion of a monotonic dose-response relationship. The test based on isotonic regression does not represent a favourable alternative, as it tends to be too powerful in case of non-monotonic dose-response relationship patterns. The tests based on contrasts seem to possess more favourable properties by combining close to nominal type I error, high power for monotonic alternatives and low power for non-monotonic alternatives.
authors.affiliation: University of Rouen Medical School and Rouen University Hospital, CHU de Rouen, Department of Biostatistics, 1, rue de Germont, 76 031 Rouen Cedex, France. Biostatistique@chu-rouen.fr
authors.forename: K
authors.initials: K
authors.lastname: Leuraud
authors.forename: J
authors.initials: J
authors.lastname: Benichou
mesh_terms: D016022:Case-Control Studies; D016009:Chi-Square Distribution; D002986:Clinical Trials as Topic / Q000379:methods*; D015331:Cohort Studies; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D016021:Epidemiologic Studies; D005260:Female; D006801:Humans; D016273:Occupational Exposure / Q000009:adverse effects; D012044:Regression Analysis; D001749:Urinary Bladder Neoplasms / Q000453:epidemiology