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Estimate Transportable False Positive Rate in the Target Population

Source: R/tr_sens_spec.R
tr_fpr.Rd

Estimates the false positive rate (1 - specificity) of a binary classifier. This is a convenience wrapper around tr_specificity(). Supports both counterfactual and factual prediction model transportability.

Usage

tr_fpr(
  predictions,
  outcomes,
  treatment = NULL,
  source,
  covariates,
  threshold = 0.5,
  treatment_level = NULL,
  analysis = c("transport", "joint"),
  estimator = c("dr", "om", "ipw", "naive"),
  selection_model = NULL,
  propensity_model = NULL,
  outcome_model = NULL,
  se_method = c("none", "bootstrap", "influence"),
  n_boot = 200,
  conf_level = 0.95,
  stratified_boot = TRUE,
  cross_fit = FALSE,
  n_folds = 5,
  ps_trim = NULL,
  parallel = FALSE,
  ncores = NULL,
  ...
)

Arguments

predictions

Numeric vector of model predictions.

outcomes

Numeric vector of observed outcomes.

treatment

Numeric vector of treatment indicators (0/1), or NULL for factual prediction model transportability (no treatment/intervention). When NULL, only the selection model is used for weighting.

source

Numeric vector of population indicators (1=source/RCT, 0=target).

covariates

A matrix or data frame of baseline covariates.

threshold

Numeric vector of classification thresholds. Predictions above this value are classified as positive. Can be a single value or a vector for computing sensitivity at multiple thresholds simultaneously. Default is 0.5.

treatment_level

The treatment level of interest (default: NULL). Required when treatment is provided; should be NULL when treatment is NULL (factual mode).

analysis

Character string specifying the type of analysis:

  • "transport": Use source outcomes for target estimation (default)

  • "joint": Pool source and target data

estimator

Character string specifying the estimator:

  • "naive": Naive estimator (biased)

  • "om": Outcome model estimator

  • "ipw": Inverse probability weighting estimator

  • "dr": Doubly robust estimator (default)

selection_model

Optional fitted selection model for P(S=0|X). If NULL, a logistic regression model is fit using the covariates.

propensity_model

Optional fitted propensity score model for P(A=1|X,S=1). If NULL, a logistic regression model is fit using source data.

outcome_model

Optional fitted outcome model for E[L(Y,g)|X,A,S]. If NULL, a regression model is fit using the relevant data. For binary outcomes, this should be a model for E[Y|X,A] (binomial family). For continuous outcomes, this should be a model for E[L|X,A] (gaussian family).

se_method

Method for standard error estimation:

  • "bootstrap": Bootstrap standard errors (default)

  • "influence": Influence function-based standard errors

  • "none": No standard error estimation

n_boot

Number of bootstrap replications (default: 500).

conf_level

Confidence level for intervals (default: 0.95).

stratified_boot

Logical indicating whether to use stratified bootstrap that preserves the source/target ratio (default: TRUE). Recommended for transportability analysis.

cross_fit

Logical indicating whether to use cross-fitting for nuisance model estimation (default: FALSE).

n_folds

Number of folds for cross-fitting (default: 5).

ps_trim

Propensity score trimming specification. Controls how extreme propensity scores are handled. Can be:

  • NULL (default): Uses absolute bounds c(0.01, 0.99)

  • "none": No trimming applied

  • "quantile": Quantile-based trimming with default c(0.01, 0.99)

  • "absolute": Explicit absolute bounds with default c(0.01, 0.99)

  • A numeric vector of length 2: c(lower, upper) absolute bounds

  • A single numeric: Symmetric bounds c(x, 1-x)

  • A list with method ("absolute"/"quantile"/"none") and bounds

parallel

Logical indicating whether to use parallel processing for bootstrap (default: FALSE).

ncores

Number of cores for parallel processing (default: NULL, which uses all available cores minus one).

...

Additional arguments passed to internal functions.

Value

An object of class c("tr_fpr", "tr_performance") with the same structure as tr_specificity(), but with estimate containing 1 - specificity.

See also

tr_specificity(), tr_sensitivity()

Examples

set.seed(123)
n <- 500
x <- rnorm(n)
s <- rbinom(n, 1, 0.6)
a <- rbinom(n, 1, 0.5)
y <- rbinom(n, 1, plogis(-1 + x))
pred <- plogis(-1 + 0.8 * x)

result <- tr_fpr(
  predictions = pred, outcomes = y, treatment = a,
  source = s, covariates = data.frame(x = x),
  se_method = "none"
)
print(result)
#> 
#> Transportable False Positive Rate Estimate
#> ==========================================
#> 
#> Estimator: DR 
#> Analysis: transport 
#> Treatment level: 1 
#> N (source): 312 
#> N (target): 188 
#> 
#> Threshold: 0.5 
#> Estimate: 0.0388 
#> Naive estimate: 0.0351