Complementary Log Regression for Sufficient-Cause Modeling of Epidemiologic Data

Complementary Log Regression for Sufficient-Cause Modeling of Epidemiologic Data

The logistic regression model is the workhorse of epidemiological data analysis. The model helps to clarify the relationship between multiple exposures and a binary outcome. Logistic regression analysis is readily implemented using existing statistical software, and this has contributed to it becoming a routine procedure for epidemiologists. In this paper, the authors focus on a causal model wh...

Sufficient Cause I Sufficient Cause 1 Sufficient Cause

The conceptual framework for causes presented here is intended neither as a review nor an expansion of knowledge, but rather as a viewpoint which bridges the gap between metaphysical notions of cause and basic epidemiologic parameters. The focus, then, is neither metaphysics nor epidemiology, but the gulf between them. In the same spirit as recent discussion on these pages about definitions of ...

Relative risk regression analysis of epidemiologic data.

Relative risk regression methods are described. These methods provide a unified approach to a range of data analysis problems in environmental risk assessment and in the study of disease risk factors more generally. Relative risk regression methods are most readily viewed as an outgrowth of Cox's regression and life model. They can also be viewed as a regression generalization of more classical...

Log - Linear Modeling and Logistic Regression

Practice of Epidemiology Competing Risk Regression Models for Epidemiologic Data

Competing events can preclude the event of interest from occurring in epidemiologic data and can be analyzed by using extensions of survival analysis methods. In this paper, the authors outline 3 regression approaches for estimating 2 key quantities in competing risks analysis: the cause-specific relative hazard (csRH) and the subdistribution relative hazard (sdRH). They compare and contrast th...