schisto

Data on types of water exposure and schistosomiasis infection.

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

schisto function

A data frame containing the outcome variable (stoolpos -- 1 = positive stool sample (infected), 0 = negative stool sample), 7 water contact exposure variables (total time subject spent doing each activity: laundry, tool washing, bathing, swimming, ditch digging, rice planting, fishing), and two categorical covariates (village id number and age category).<p></p><p>Note: Village label does not correspond to the id numbers from the paper.</p>

format

Spear RC, Seto E, Liang S, Birkner M, Hubbard A, Qiu D, Yang C, Zhong B, Xu F, Gu X, Davis GM (2004). "Factors Influencing the Transmission of Schistosoma Japonicum in the Mountains of Sichuan Province of China." The American Journal of Tropical Medicine and Hygiene, 70(1), 48-56.

schisto: Schistosomiasis Data Set

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