A STATISTICAL-METHOD FOR ASSESSING A THRESHOLD IN EPIDEMIOLOGIC STUDIES

被引:138
作者
ULM, K
机构
[1] Institute for Medical Statistics and Epidemiology, Technical University Munich, Munich, 8000
关键词
D O I
10.1002/sim.4780100306
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
I describe a method for estimating and testing a threshold value in epidemiological studies. A threshold effect indicates an association between a risk factor and a defined outcome above the threshold value but none below it. An important field of application is occupational medicine where, for a lot of chemical compounds and other agents which are non-carcinogenic health hazards, so-called threshold limit values of TLVs are specified. The method is presented within the framework of the logistic regression model, which is widely used in the analysis of the relationship between some explanatory variables and a dependent dichotomous outcome. In most available programs for this and also for other models the concept of a threshold is disregarded. The method for assessing a threshold consists of an estimation procedure using the maximum-likelihood technique and a test procedure based on the likelihood-ratio statistic R, following under the null hypothesis (no threshold) a quasi one-sided chi-2 distribution with one degree of freedom. This use of this distribution is supported by a simulation study. The method is applied to data from an epidemiological study of the relationship between occupational dust exposure and chronic bronchitic reactions. The results are confirmed by bootstrap resampling.
引用
收藏
页码:341 / 349
页数:9
相关论文
共 22 条
  • [1] Woitowitz H.-J., Maximum concentrations at the workplace in the Federal Republic of Germany, American Journal of Industrial Medicine, 14, pp. 223-229, (1988)
  • [2] Armitage P., The assessment of low‐dose carcinogenicity, Biometrics, Supplement: Current Topics in Biostatistics and Epidemiology, 37, pp. 119-129, (1982)
  • [3] Brown C.C., Mathematical aspects of dose‐response studies in carcinogenesis ‐ the concept of thresholds, Oncology, 33, pp. 62-65, (1976)
  • [4] Gallant A.R., Fuller W.A., Fitting segmented polynomial regression models whose join point have to be estimated, Journal of the American Statistical Association, 68, pp. 144-147, (1973)
  • [5] Feder P.I., The log likelihood ratio in segmented regression, The Annals of Statistics, 3, pp. 84-97, (1975)
  • [6] Schulze U., A method of estimation of change points in multiphasic growth models, Biometrical Journal, 26, pp. 495-504, (1984)
  • [7] Jones R.H., Molitoris B.A., A statistical method for determining the breakpoint of two lines, Analytical Biochemistry, 141, pp. 287-290, (1984)
  • [8] Edler L., Berger J., Computational methods to determine a break point in linear regression, EDV in Medizin und Biologie, 16, pp. 128-134, (1985)
  • [9] Freeman J.M., An unknown change point and goodness of fit, The Statistician, 35, pp. 335-344, (1986)
  • [10] Cox Ch., Threshold dose‐response models in toxicology, Biometrics, 43, pp. 511-523, (1987)