pred_model_binout: Predictive model estimation in diagnosis scenarios

In sMSROC: Assessment of Diagnostic and Prognostic Markers

Predictive model estimation in diagnosis scenarios

Description

Estimation of the predictive models in diagnosis scenarios.

Usage

pred_model_binout(marker, status, meth)

Arguments

marker	vector with the biomarker values.
status	numeric response vector. The highest value is assumed to stand for the subjects having the event under study. The lowest value, for those who do not. Any other value will not be considered.
meth	method for approximating the predictive model P(D|X=x). The options are: “L”, for Linear logistic regression models. “S”, for Smooth models.

Details

• If meth = “L”, the logit transformation of the predicitive model is approximated by a linear logistic regression model:

  P (D|X=x) = 1/(1 + \exp{- \{ \beta_0 + \beta_1 x \}),}

  with \beta_0, \beta_1 \in {\cal R}.

• If meth = “S”, the logit transformation of the predicitive model is estimated by the smooth logistic regression,

  P(D | X=x) = 1 / ( 1 + \exp \{ - s(x) \}),

  being s(\cdot) the smooth function (splines, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.4780080504")}).

Value

The returned value is a list with the two components:

marker	vector containing the ordered marker values.
probs	vector with the probabilities corresponding to each marker value estimated through the predictive model.

See Also

sMS_binout and sMSROC

sMSROC documentation built on May 29, 2024, 1:43 a.m.