title: [Unbiased estimation of factorial effect by using analysis of covariance or propensity score method for observational studies in laboratory medicine].
abstract: This paper deals with bias-reduction techniques for observational studies in evidence-based laboratory medicine (EBLM). In the field of laboratory medicine, many observational studies have been performed since it is difficult to design randomized experimental studies. The results of these observational studies have usually been affected by various types of biases in observational data that could not be controlled by the researchers. In randomized experiments, random assignment provides unbiased estimations of the treatment effect. In contrast, in observational studies, incorrect (biased) estimations arise from the imbalance between the covariates for the treatment/exposure group and the control group; therefore, information regarding confounding factors that affect both an outcome variable and assignment should be used to construct a multivariate model for minimizing bias. Covariate adjustment helps to reduce bias by correcting the imbalance in covariates. Analysis of covariance (ANCOVA) is an important method for covariate adjustment. The ANCOVA model is an extension of multiple regression models that can statistically control the effects of covariates. The propensity score method has recently been used as a covariate adjustment method in applied research. Because propensity scores concentrate the information on covariates, conditional expectations can be easily computed. In this paper, both methods were exemplified in a study on sex-based differences in HDL cholesterol levels. Similar unbiased estimates of sex-based differences were obtained using both methods, as opposed to an incorrect estimate obtained using univariate analysis. The results emphasize that covariate adjustment should be used to obtain credible evidence in observational studies.
authors.affiliation: Department of Clinical Laboratory, Toranomon Hospital, Minato-ku, Tokyo 105-8470, Japan. m-inada@pg8.so-net.ne.jp
authors.forename: Masanori
authors.initials: M
authors.lastname: Inada
mesh_terms: D000704:Analysis of Variance; D015982:Bias; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D015233:Models, Statistical; D057216:Propensity Score; D012107:Research Design / Q000706:statistics & numerical data