adherence

<p>This is a simulated pooled repeated measures data set of patient medication adherence summaries with a binary outcome.  The independent units are the patients, and each patient has one or more measurements made at different time points in treatment, each associated with a binary outcome that could represent a diagnostic test result.  This data set is meant to be used with the <code>ci.pooled.cvAUC</code> function.</p>

datasets

Tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.

Erin LeDell

cvAUC

Cross-Validated Area Under the ROC Curve Confidence Intervals

adherence function

<p>A data frame.  The first column, <code>id</code>, is the patient id.  Columns 2-5 represent medication adherence (as a percentage) averaged over the past 2, 7, 14, 21 and 28 days, respectively.  The last column is a binary outcome that could represent a test result.</p>

adherence: Data set: Simulated Pooled Repeated Measures Data

Description

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See Also