# Model Evaluation and Comparison

    library(clinpubr)
    library(dplyr)
    library(survival)
    library(ggplot2)

## Introduction

Evaluating clinical prediction models requires assessing discrimination, calibration, and clinical utility. This vignette demonstrates a comprehensive evaluation workflow:

1. Model Comparison — Compare multiple models using discrimination and calibration metrics
2. Time-Dependent ROC — Evaluate survival prediction models at specific time points
3. C-Index — Global discrimination measure for survival models
4. Variable Importance — Identify key predictive features

## Preparing Data

We’ll use the NCCTG Lung Cancer dataset and fit three logistic regression models of increasing complexity:

    data(cancer, package = "survival")
    cancer$dead <- cancer$status == 2
    cancer$event <- ifelse(cancer$status == 2, 1, 0)

    cancer_clean <- cancer %>%
      mutate(sex = factor(sex, labels = c("Male", "Female"))) %>%
      na.omit()

    # Fit three models with increasing complexity
    cancer_clean$pred_model1 <- predict(
      glm(dead ~ age + sex + ph.karno, data = cancer_clean, family = binomial),
      type = "response"
    )
    cancer_clean$pred_model2 <- predict(
      glm(dead ~ age + sex + ph.karno + pat.karno, data = cancer_clean, family = binomial),
      type = "response"
    )
    cancer_clean$pred_model3 <- predict(
      glm(dead ~ age + sex + ph.karno + wt.loss, data = cancer_clean, family = binomial),
      type = "response"
    )

    knitr::kable(head(cancer_clean[, c("dead", "pred_model1", "pred_model2", "pred_model3")]),
      caption = "Model Predictions vs Actual Outcomes (First 6 Rows)"
    )

Model Predictions vs Actual Outcomes (First 6 Rows)
| dead | pred_model1 | pred_model2 | pred_model3
--- | --- | --- | --- | ---
2 | TRUE | 0.7868731 | 0.7661243 | 0.7858287
4 | TRUE | 0.7436147 | 0.8215218 | 0.7434448
6 | FALSE | 0.9260244 | 0.8954047 | 0.9273628
7 | TRUE | 0.7052554 | 0.7566565 | 0.7049340
8 | TRUE | 0.7704063 | 0.7279677 | 0.7724185
9 | TRUE | 0.8207969 | 0.8021612 | 0.8203195

## Model Comparison

`classif_model_compare()` provides a comprehensive comparison using multiple metrics (AUC, Accuracy, Sensitivity, Specificity, PPV, NPV, PRAUC, Brier Score) and generates ROC, PR, calibration, and DCA plots:

    model_comparison <- classif_model_compare(
      data = cancer_clean,
      target_var = "dead",
      model_names = c("pred_model1", "pred_model2", "pred_model3"),
      save_output = FALSE
    )

    knitr::kable(model_comparison$metric_table, caption = "Model Performance Comparison")

Model Performance Comparison
| Model | AUC | PRAUC | Accuracy | Sensitivity | Specificity | Pos Pred Value | Neg Pred Value | F1 | Kappa | Brier | cutoff | Youden | HosLem
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
3 | pred_model3 | 0.684 (0.594, 0.774) | 0.820 | 0.581 | 0.50 | 0.787 | 0.857 | 0.381 | 0.632 | 0.217 | 0.185 | 0.777 | 0.287 | 0.985
1 | pred_model1 | 0.684 (0.594, 0.774) | 0.817 | 0.557 | 0.45 | 0.830 | 0.871 | 0.371 | 0.593 | 0.203 | 0.185 | 0.786 | 0.280 | 0.967
2 | pred_model2 | 0.684 (0.590, 0.777) | 0.821 | 0.605 | 0.55 | 0.745 | 0.846 | 0.393 | 0.667 | 0.232 | 0.182 | 0.765 | 0.295 | 0.353

### Visualization

    model_comparison$roc_plot

    model_comparison$pr_plot

    model_comparison$calibration_plot

    model_comparison$dca_plot

## Time-Dependent ROC Analysis

For survival outcomes, time-dependent ROC accounts for the timing of events. First, we fit a Cox proportional hazards model and obtain risk scores:

    # Fit Cox model for survival prediction
    cox_model <- coxph(Surv(time, event) ~ age + sex + ph.karno, data = cancer_clean)
    cancer_clean$risk_score <- predict(cox_model, type = "risk")

Now we can evaluate the model using time-dependent ROC at specific time points:

    time_roc_result <- time_roc_plot(
      data = cancer_clean,
      event_var = "event",
      time_var = "time",
      marker_var = "risk_score",
      times = c(200, 365),
      time_unit = "days",
      save_plot = FALSE
    )

    time_roc_result
    #> $time_roc
    #> Time-dependent-Roc curve estimated using IPCW  (n=167, without competing risks).
    #>       Cases Survivors Censored AUC (%)
    #> t=200    48       111        8   67.32
    #> t=365    87        49       31   60.85
    #>
    #> Method used for estimating IPCW:marginal
    #>
    #> Total computation time : 0  secs.
    #>
    #> $plot

## C-Index

The C-index (concordance index) measures discrimination for survival models. Interpretation: 0.5 = no discrimination, 0.7-0.8 = acceptable, >0.8 = excellent.

    c_index <- calc_cindex(cancer_clean, "time", "event", "risk_score")
    c_index
    #>   C Index
    #> 0.6243374

## Variable Importance

The variable importance list could be obtained from the different models like random forest, gradient boosting machine, etc. Here, we assume we have the variable importance scores from some random model.

    importance_scores <- c(
      Age = 0.25, Sex = 0.15, Karnofsky = 0.30,
      WeightLoss = 0.12, Calories = 0.08, PatKarnofsky = 0.10
    )

    importance_plot(x = importance_scores, x_lab = "Variable Importance")

Show only top variables with custom styling:

    importance_plot(
      x = importance_scores,
      x_lab = "Relative Importance",
      top_n = 4,
      color = "steelblue"
    )

## Summary

### Key Functions

- `classif_model_compare()`: Comprehensive model comparison with ROC, PR, calibration, and DCA plots
- `time_roc_plot()`: Time-dependent ROC analysis for survival outcomes
- `calc_cindex()`: Calculate concordance index for survival models
- `importance_plot()`: Visualize variable importance

### Evaluation Checklist

1. Discrimination: AUC (binary) or C-index (survival), sensitivity/specificity at clinical thresholds
2. Calibration: Calibration plots, Brier score
3. Clinical utility: Decision curve analysis, net benefit at relevant thresholds
4. External validation: Performance in independent populations