simple_screenr: An Overly Simple Approach to Test Screening

In sgutreuter/screenr: Construction of Binary Test-Screening Rules

An Overly Simple Approach to Test Screening

Description

simple_screenr implements the method described in Bandason et al. (2016).

Usage

simple_screenr(
  formula,
  data,
  partial_auc = c(0.8, 1),
  partial_auc_focus = "sensitivity",
  partial_auc_correct = TRUE,
  conf_level = 0.95
)

Arguments

formula	an object of class formula defining the testing outcome and predictor covariates.
data	the "training" sample; a data frame containing the testing outcome and predictive covariates to be used for testing screening. The testing outcome must be binary (0,1) indicating negative and positive test results, respectively, or logical (TRUE/FALSE), and the screening scores are the row-wise sums of the values of those covariates. The covariates are typically binary (0 = no, 1 = yes) responses to questions, but the responses may also be ordinal numeric values.
partial_auc	either a logical FALSE or a numeric vector of the form c(left, right) where left and right are numbers in the interval [0, 1] specifying the endpoints for computation of the partial area under the ROC curve (pAUC). The total AUC is computed if partial\_auc = FALSE. Default: c(0.8, 1.0)
partial_auc_focus	one of "sensitivity" or specificity, specifying for which the pAUC should be computed. partial.auc.focus is ignored if partial\_auc = FALSE. Default: "sensitivity".
partial_auc_correct	logical value indicating whether the pAUC should be transformed the interval from 0.5 to 1.0. partial\_auc\_correct is ignored if partial\_auc = FALSE. Default: TRUE).
conf_level	a number between 0 and 1 specifying the confidence level for confidence intervals for the (partial)AUC. Default: 0.95.

Details

simple_screenr computes the in-sample (overly optimistic) performances for development of a very simple test screening tool based on the sums of affirmative questionnaire responses. simpleScreener is not optimized and is intended only for comparision with lasso_screenr, logreg_screenr or gee_screenr, any of which will almost certainly out-perform simple_screenr.

Value

simple_screenr returns (invisibly) an object of class simple_screenr containing the elements:

Call

The function call.

Prevalence

Prevalence of the test condition in the training sample.

ISroc

An object of class roc containing the "in-sample" (overly-optimistic) receiver operating characteristics, and additional functions for use with this object are available in the pROC package.

Scores

The training sample, including the scores.

References

Bandason T, McHugh G, Dauya E, Mungofa S, Munyati SM, Weiss HA, Mujuru H, Kranzer K, Ferrand RA. Validation of a screening tool to identify older children living with HIV in primary care facilities in high HIV prevalence settings. AIDS. 2016;30(5):779-785 http://dx.doi.org/10.1097/QAD.0000000000000959

Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Muller M. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12(77):1-8. http://doi.org/10.1186/1471-2105-12-77

See Also

easy_tool for a better approach to simplification using the results from lasso_screenr, logreg_screenr or gee_screenr.

lasso_screenr, logreg_screenr

Examples

data(unicorns)
toosimple <- simple_screenr(testresult ~ Q1 + Q2 + Q3 + Q4 + Q5 + Q6 + Q7,
                           data = unicorns)
methods(class = class(toosimple))
summary(toosimple)

sgutreuter/screenr documentation built on Nov. 20, 2022, 2:41 a.m.