Direct comparison of logistic regression and recursive partitioning to predict chemotherapy response of breast cancer based on clinical pathological variables

Roman Rouzier*, Charles Coutant, Bénédicte Lesieur, Chafika Mazouni, Roberto Incitti, René Natowicz, Lajos Pusztai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The purpose was to compare logistic regression model (LRM) and recursive partitioning (RP) to predict pathologic complete response to preoperative chemotherapy in patients with breast cancer. The two models were built in a same training set of 496 patients and validated in a same validation set of 337 patients. Model performance was quantified with respect to discrimination (evaluated by the areas under the receiver operating characteristics curves (AUC)) and calibration. In the training set, AUC were similar for LRM and RP models (0.77 (95% confidence interval, 0.74-0.80) and 0.75 (95% CI, 0.74-0.79), respectively) while LRM outperformed RP in the validation set (0.78 (95% CI, 0.74-0.82) versus 0.64 (95% CI, 0.60-0.67). LRM model also outperformed RP model in term of calibration. In these real datasets, LRM model outperformed RP model. It is therefore more suitable for clinical use.

Original languageEnglish (US)
Pages (from-to)325-331
Number of pages7
JournalBreast Cancer Research and Treatment
Issue number2
StatePublished - Sep 2009


  • Breast cancer
  • Logistic regression model
  • Pathological complete response
  • Prediction
  • Recursive partitioning model

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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