Predictive modeling analysis: nested-CV classifiers and survival models with calibration reporting

Key facts

What is predictive modeling?

Predictive modeling fits supervised algorithms, including logistic regression, gradient-boosted trees, or survival forests, to estimate probabilities or risk scores for a pre-specified endpoint from high-dimensional biological or clinical predictors [3]. Biomarker discovery focuses on compact feature lists and selection stability. Predictive modeling asks whether those features generalize to unseen samples, with reported discrimination and calibration [1]. Unsupervised clustering does not train against labeled outcomes; these models do. A 2023 systematic review screened 682 PubMed articles (2017-2023) and critically reviewed 30 cancer ML biomarker studies, noting data-leakage risks and inconsistent evaluation among common limitations [4]. See the predictive modeling glossary.

When should you use predictive modeling?

Use predictive modeling when you have labeled outcomes and need out-of-sample performance estimates. A ranked feature list without outcome-model reporting, or a sample size too small for even regularized models, is a poor fit [5].

• Breast cancer therapy response: Sammut et al. [2] integrated multi-omic and digital pathology features from 168 pre-treatment biopsies and predicted pathological complete response with AUROC 0.87 on an external 75-patient cohort.
• Cancer of unknown primary: Moon et al. [7] trained OncoNPC on 36,445 tumors across 22 cancer types. High-confidence predictions (≥0.9) achieved weighted F1 0.942 on held-out samples and identified CUP subgroups with distinct survival.
• Targeted therapy response: Sinha et al. [8] developed PERCEPTION, using single-cell tumor transcriptomics to predict targeted-therapy response and resistance in multiple myeloma and breast cancer clinical cohorts.

How the analysis works — step by step

1. 1. Scope outcome, cohort, and validation plan

   Pepkio confirms the prediction target, endpoint type, primary metrics, and whether a holdout or external cohort exists [1, 7]. Sample-size rationale uses pmsampsize when binary or time-to-event endpoints apply [6].

   Tools and outputs

   Tools used: pmsampsize (R); project scoping templates

   Output: Signed scope document with primary and secondary metrics

2. 2. Validate inputs and harmonize metadata

   Sample IDs are matched between feature matrix and outcome table. Missingness, duplicates, class balance, and batch structure are audited.

   Tools and outputs

   Tools used: Custom Python/R validation scripts; pandas 2.2 / data.table

   Output: sample_manifest.csv; feature_qc_summary.csv

3. 3. Preprocess and normalize features

   Features are filtered by missing-value fraction and variance. Continuous predictors are scaled within cross-validation pipelines. Batch correction with limma or ComBat is applied within training folds only when batch is not confounded with the outcome [5].

   Tools and outputs

   Tools used: scikit-learn 1.6.1 ColumnTransformer; limma 3.60; sva/ComBat when scoped

   Output: Analysis-ready feature matrix; preprocessing parameter log

4. 4. Audit for data leakage

   Pepkio applies the seven guiding questions from Bernett et al. [9] before partitioning, checking whether preprocessing, feature selection, or repeated measures could leak test-fold information.

   Tools and outputs

   Tools used: Documented leakage checklist per Bernett et al. [9]

   Output: leakage_audit.md

5. 5. Partition data with patient-level nested cross-validation

   Outer cross-validation estimates generalization. An inner loop tunes hyperparameters and embedded feature selection [1]. Group-aware splitters keep repeated measures from the same patient in one fold.

   Tools and outputs

   Tools used: scikit-learn 1.6.1 StratifiedKFold, GridSearchCV; scikit-survival 0.24.1 for censored outcomes

   Output: fold_assignments.csv (sample_id, outer_fold, inner_fold, split_role)

6. 6. Train and tune candidate models

   Pepkio compares elastic-net logistic regression, XGBoost, and survival forests against simpler baselines [3], with feature selection embedded inside each outer fold. Deep learning is scoped separately when justified by cohort size.

   Tools and outputs

   Tools used: scikit-learn 1.6.1; XGBoost 2.1.3; glmnet 4.1-8; scikit-survival 0.24.1

   Output: Candidate model list with hyperparameter grids; serialized best models per fold

7. 7. Evaluate discrimination and calibration

   Discrimination (AUROC, AUPRC, or C-index) and calibration (reliability diagrams, Brier score, calibration slope) are reported from outer-fold predictions [7]. External performance is reported separately when a holdout or external cohort is available [5].

   Tools and outputs

   Tools used: scikit-learn 1.6.1 calibration_curve, brier_score_loss; scikit-survival 0.24.1 concordance_index_censored

   Output: model_performance_summary.csv; calibration_metrics.csv

8. 8. Select operating threshold and decision metrics

   Pepkio documents threshold selection (Youden index on outer-fold predictions or a clinical cutoff) and reports sensitivity, specificity, and PPV at that threshold. Decision-curve analysis is included when scoped.

   Tools and outputs

   Tools used: scikit-learn 1.6.1 roc_curve; custom threshold summary scripts

   Output: threshold_summary.csv (threshold, sensitivity, specificity, PPV, NPV)

9. 9. Interpret features and biological context

   SHAP values and coefficient tables identify top predictors. GO/KEGG annotation is included when gene or protein lists are in scope. Interpretability does not support causal claims [10].

   Tools and outputs

   Tools used: SHAP 0.46.0; clusterProfiler 4.20.0 when scoped

   Output: SHAP summary plots; selected_features.csv; pathway enrichment tables

10. 10. Package deliverables and draft Methods

    Pepkio assembles figures, serialized models, DOME summary table, TRIPOD+AI checklist when scoped, environment locks, README, and Methods draft.

    Tools and outputs

    Tools used: conda or renv lock files; DOME and TRIPOD+AI checklists

    Output: Final deliverable bundle

What Pepkio delivers

Technical decisions we make — and why

Validation design: patient-level nested CV vs single holdout split

Nested cross-validation avoids unstable estimates when hyperparameters are tuned on the same partition used for scoring [1]. A holdout or external cohort supplements nested CV when available.

Model family: elastic-net baseline before tree ensembles

Regularized logistic or Cox models are compared against XGBoost or survival forests. Simpler models are retained unless complexity improves outer-fold performance [3, 5].

Calibration: isotonic or Platt scaling on outer-fold predictions

Scores are recalibrated so predicted probabilities match observed event rates [7]. Uncalibrated scores are reported alongside calibrated outputs.

Class imbalance: stratified folds and balanced class weights vs SMOTE

Pepkio uses stratified cross-validation and class_weight='balanced' rather than resampling the full dataset before partitioning, because pre-split oversampling can share information between training and test folds.

Batch correction: limma or ComBat within training folds when batch is not confounded with outcome

Technical batch is corrected within training folds when batch and condition are separable. Fully confounded designs are flagged at kickoff because correction would remove biological signal [5].

Common questions

Related services

• Biomarker discovery — Compact signature construction and feature stability analysis when the primary deliverable is a ranked panel rather than a full outcome model.
• Multi-omics integration — Fused feature matrices across transcriptomics, proteomics, and metabolomics as predictors for outcome models.
• Bulk RNA-seq — Expression matrix generation and normalization when raw RNA-seq counts are the modeling input.
• Statistical analysis — Experimental design, covariate planning, and sample-size rationale before committing to ML.
• Bioinformatics consulting — Feasibility assessment and validation planning before model development.