Abstract
The growing body of work in the epidemiology literature focused on G-computation includes theoretical explanations of the method but very few simulations or examples of application. The small number of G-computation analyses in the epidemiology literature relative to other causal inference approaches may be partially due to a lack of didactic explanations of the method targeted toward an epidemiology audience. The authors provide a step-by-step demonstration of G-computation that is intended to familiarize the reader with this procedure. The authors simulate a data set and then demonstrate both G-computation and traditional regression to draw connections and illustrate contrasts between their implementation and interpretation relative to the truth of the simulation protocol. A marginal structural model is used for effect estimation in the G-computation example. The authors conclude by answering a series of questions to emphasize the key characteristics of causal inference techniques and the G-computation procedure in particular.
Original language | English (US) |
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Pages (from-to) | 731-738 |
Number of pages | 8 |
Journal | American journal of epidemiology |
Volume | 173 |
Issue number | 7 |
DOIs | |
State | Published - Apr 1 2011 |
Externally published | Yes |
Keywords
- air pollution
- asthma
- causality
- methods
- regression analysis
ASJC Scopus subject areas
- Epidemiology