NNTintervalsProspective

Npositives

Nnegatives

Observed or anticipated number of "BestToTreat" among the positives.

NtruePositives

Observed or anticipated number of "BestToWait"  among the negatives.

NtrueNegatives

= 0.15 Prevalence of "BestToTreat" characteristic.

alpha

Beta parameters for prior. Default is the Jeffreys prior = c(1/2,1/2). Jaynes prior = c(0,0) won't work when #fp=1.

prior


Produce Bayesian and classical intervals for NNT from
observations in a prospective study.
Useful for "anticipated results" when designing a study,
The setting: patients will be tested immediately, and followed to determine the BestToTreat/BestToWait classification.
as well as analyzing study results.
There were (or will be) Npositives patients with a positive test, Nnegatives with a negative test.
The observed NNTs in each group were (or will be) NNTpos and NNTneg.


Helps a clinical trial team  discuss
the clinical goals  of a well-defined biomarker with a diagnostic,
staging, prognostic, or predictive purpose.  From this discussion will
come a statistical plan for a (non-randomized) validation trial.
Both prospective and retrospective trials are supported. In a specific
focused discussion, investigators should determine the range of
"discomfort" for the NNT, number needed to treat.  The meaning of
the discomfort range, [NNTlower, NNTupper], is that within this range
most physicians would feel discomfort either in treating or withholding
treatment. A pair of NNT values bracketing that range, NNTpos and NNTneg,
become the targets of the study's design.  If the trial can demonstrate
that a positive biomarker test yields an NNT less than NNTlower,
and that a negative biomarker test yields an NNT less than NNTlower,
then the biomarker may be useful for patients. A highlight of the package
is visualization of a "contra-Bayes" theorem, which produces criteria for
retrospective case-controls studies.

NNTintervalsProspective: NNTintervalsProspective

Description

Usage

Arguments

Value