Calibration weights require specification of tuning parameter \(delta\) or \(lambda\). This function uses K-fold cross-validation to select tuning parameter used for calibration weights, with standardized net benfeit (sNB) as objective function. Either one of \(delta\) or \(lambda\) must be specificed. The sequence of tuning parameters can be obtained from the RAWgrid function.
cvWtTuning(p,y,r,rl,ru,kFold=5,cvParm,tuneSeq,cv.seed=1111)Vector of binary outcomes, with 1 indicating event (cases) and 0 indicating no event (controls)
Vector of risk score values
Clinically relevant risk threshold
Lower bound of clinically relevant region
Upper bound of clinically relevant region
Number of folds for cross-validation
Parameter to be selected via cross-validation. Can be either \(delta\) the weight assigned to observations outside the clinically relevant region [R_l,R_u], or the \(lambda\) tuning parameter controlling exponential decay within the clinically relevant region [R_l,R_u]
Sequence of values of tuning parameters to perform cross-validation over
Intial seed set for random splitting of data into K folds
Matrix containing sequence of tuning parameters and corresponding cross-validation sNB
Value of tuning parameter selected via cross validation
Matrix of cross-validation results for all folds
Mishra, A. (2019). Methods for Risk Markers that Incorporate Clinical Utility (Doctoral dissertation). (Available Upon Request)