Fix predict() calls in cf_auc and cf_calibration for ml_fitted objects

Author: boyercb

R/cf_auc.R

@@ -207,7 +207,7 @@ cf_auc <- function(predictions,
 
   # Propensity scores
   if (!is.null(propensity_model)) {
-    ps <- predict(propensity_model, type = "response")
+    ps <- .predict_nuisance(propensity_model, as.data.frame(covariates), type = "response")
     if (treatment_level == 0) {
       ps <- 1 - ps
     }
@@ -219,7 +219,7 @@ cf_auc <- function(predictions,
     if (is.null(full_data)) {
       full_data <- cbind(Y = outcomes, as.data.frame(covariates))
     }
-    q_hat <- predict(outcome_model, newdata = full_data, type = "response")
+    q_hat <- .predict_nuisance(outcome_model, full_data, type = "response")
   }
 
   # Indicator for treatment level

R/cf_calibration.R

@@ -86,7 +86,7 @@ cf_calibration <- function(predictions,
 
   # Get propensity scores
   if (estimator == "ipw") {
-    ps <- predict(propensity_model, type = "response")
+    ps <- .predict_nuisance(propensity_model, as.data.frame(covariates), type = "response")
     if (treatment_level == 0) {
       ps <- 1 - ps
     }

README.md

@@ -12,7 +12,7 @@
 
 - A prediction model will be deployed in settings where treatment policies differ from the training setting
 - Predictions are meant to support decisions about treatment initiation
-- You need valid performance estimates even when the prediction model is misspecified
+- You want to assess model performance after transporting from a source (e.g., RCT) to a target population
 
 Based on Boyer, Dahabreh & Steingrimsson (2025). "Estimating and evaluating counterfactual prediction models." *Statistics in Medicine*, 44(23-24), e70287. [doi:10.1002/sim.70287](https://doi.org/10.1002/sim.70287)
 
@@ -102,7 +102,7 @@ cf_compare(
 
 ## Transportability Analysis
 
-The package also implements transportability estimators from Voter et al. (2025) for evaluating prediction model performance when transporting from a source population (typically an RCT) to a target population:
+The package also implements transportability estimators from Steingrimsson et al. (2022) and Voter et al. (2025) for evaluating prediction model performance when transporting from a source population (typically an RCT) to a target population:
 
 ```r
 # Load transportability example data
@@ -189,6 +189,8 @@ Boyer CB, Dahabreh IJ, Steingrimsson JA. Estimating and evaluating counterfactua
 
 For transportability methods, also cite:
 
+Steingrimsson JA, Gatsonis C, Li B, Dahabreh IJ. Transporting a Prediction Model for Use in a New Target Population. *American Journal of Epidemiology*. 2022; 192(2):296-304. doi:[10.1093/aje/kwac128](https://doi.org/10.1093/aje/kwac128)
+
 Voter SR, et al. Transportability of machine learning-based counterfactual prediction models with application to CASS. *Diagnostic and Prognostic Research*. 2025; 9(4). doi:[10.1186/s41512-025-00201-y](https://doi.org/10.1186/s41512-025-00201-y)
 
 ```bibtex
@@ -203,9 +205,20 @@ Voter SR, et al. Transportability of machine learning-based counterfactual predi
   doi={10.1002/sim.70287}
 }
 
+@article{10.1093/aje/kwac128,
+    title = {Transporting a Prediction Model for Use in a New Target Population},
+    author = {Steingrimsson, Jon A. and Gatsonis, Constantine and Li, Bing and Dahabreh, Issa J.},
+    journal = {American Journal of Epidemiology},
+    volume = {192},
+    number = {2},
+    pages = {296-304},
+    year = {2022},
+    doi = {10.1093/aje/kwac128}
+}
+
 @article{voter2025transportability,
   title={Transportability of machine learning-based counterfactual prediction models with application to CASS},
-  author={Voter, Sarah R. and others},
+  author = {Voter, Sarah C. and Dahabreh, Issa J. and Boyer, Christopher B. and Rahbar, Habib and Kontos, Despina and Steingrimsson, Jon A.},
   journal={Diagnostic and Prognostic Research},
   volume={9},
   number={4},

vignettes/introduction.Rmd

@@ -55,6 +55,7 @@ head(cvd_sim)
 ```
 
 The `cvd_sim` dataset contains simulated cardiovascular data with:
+
 - `age`, `bp`, `chol`: Patient covariates  
 - `treatment`: Binary treatment indicator (confounded by covariates)
 - `event`: Binary outcome (cardiovascular event)