#Sequential testing epidemiology skin#
#Sequential testing epidemiology series#

Includes a clear description of the many types of indirect effects and how they are calculated.īaron, R. M., and D. A. Mathematical description of how total effects are decomposed into direct and indirect effects based on prior research. The decomposition of effects in path analysis. MacKinnon and Dwyer 1993 formalizes these developments for regression models and applies them to prevention and treatment research.Īlwin, D. F., and R. M. Robins and Greenland 1992 describes a counterfactual-based approach to mediated effects that form the basis of subsequent causal mediation analysis. 1991 applies tests of mediation in the analysis of an intervention study. Sobel 1982 and Sobel 1986 describe matrix formulas for the calculation of indirect effects and their standard errors that can be used to form confidence intervals and significance testing for the indirect effect.

#Sequential testing epidemiology series#

Hyman 1955 and Lazarsfeld 1955 provide the original descriptions of how a third variable affects the relation between two variables through a series of statistical tests that were later translated into decomposition of effects by Alwin and Hauser 1975, mediation in psychology by James and Brett 1984 and Baron and Kenny 1986, and in evaluation by Judd and Kenny 1981. Wright 1920 was the first work to describe effects on variables that are indirect through other variables in the study of heritability of guinea pigs. Innovations in mediation analyses have been rapid in the last forty years, and recently progress in understanding the causal basis of mediation analysis has been a major breakthrough. Methodological developments and applications of mediation analysis have dramatically increased since ideas about mediation in the social sciences were first formalized by Herbert Hyman and Paul Lazarsfeld in 1955.

#Sequential testing epidemiology skin#

Sewall Wright’s work in the 1920s first applied the notion of mediated or indirect effects in path models for the inheritance of skin color in guinea pigs. Information about mediating mechanisms improves programs by providing information about the critical ingredients of successful programs. Although mediation analysis is useful for observational studies, it is perhaps most compelling for answering questions of cause and effect in randomized treatment and prevention programs. Mediation analysis is a statistical method used to quantify the causal sequence by which an antecedent variable causes a mediating variable that causes a dependent variable.