wcgs

A subset of the data from the Western Collaborative Group Study (described in Rosenman et al. 1975)

datasets

Performs variable importance analysis using a causal inference approach. This is done by fitting Population Intervention Models. The default is to use a Targeted Maximum Likelihood Estimator (TMLE). The other available estimators are Inverse Probability of Censoring Weighted (IPCW), Double-Robust IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators. Inference can be obtained from the influence curve (plug-in) or by bootstrapping.

Stephan Ritter

multiPIM

Variable Importance Analysis with Population Intervention Models

wcgs function

A data frame containing the following variables (all are type integer except for bmi, which is double):
<ul><li>chd69: Outcome variable -- did subject get CHD by end of follow up (1969)?</li><li>sbp0: Systolic blood pressure at baseline.</li><li>dbp0: Diastolic blood pressure at baseline.</li><li>chol0: Fasting serum cholesterol at baseline (contains 12 missing values -- NA).</li><li>ncigs0: Cigarettes per day.</li><li>dibpat0: Behavior type -- 1 = type A, 2 = type B.</li><li>age0: Age in years at baseline.</li><li>height0: Height in inches.</li><li>weight0: Weight in lbs.</li><li>bmi0: BMI based on height0 and weight0.</li></ul>

format

Rosenman RH, Brand RJ, Jenkins CD, Friedman M, Straus R, Wurm M (1975). "Coronary Heart Disease in the Western Collaborative Group Study. Final Follow-up Experience of 8 1/2 Years." JAMA, 233(8), 872-7.

wcgs: Subset of Data from Western Collaborative Group Study

Description

Usage

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