Development and evaluating multimarker models for guiding treatment decisions

Parvin Tajik*, Mohammad Hadi Zafarmand, Aeilko H. Zwinderman, Ben W. Mol, Patrick M. Bossuyt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Background: Despite the growing interest in developing markers for predicting treatment response and optimizing treatment decisions, an appropriate methodology to identify, combine and evaluate such markers has been slow to develop. We propose a step-by-step strategy for analysing data from existing randomised trials with the aim of identifying a multi-marker model for guiding decisions about treatment. Methods: We start with formulating the treatment selection problem, continue with defining the treatment threshold, prepare a list of candidate markers, develop the model, apply the model to estimate individual treatment effects, and evaluate model performance in the study group of patients who meet the trial eligibility criteria. In this process, we rely on some well-known techniques for multivariable prediction modelling, but focus on predicting benefit from treatment, rather than outcome itself. We present our approach using data from a randomised trial in which 808 women with multiple pregnancy were assigned to cervical pessary or control, to prevent adverse perinatal outcomes. Overall, cervical pessary did not reduce the risk of adverse perinatal outcomes. Results: The treatment threshold was zero. We had a preselected list of 5 potential markers and developed a logistic model including the markers, treatment and all marker-by-treatment interaction terms. The model was well calibrated and identified 35% (95% confidence interval (CI) 32 to 39%) of the trial participants as benefitting from pessary insertion. We estimated that the risk of adverse outcome could be reduced from 13.5 to 8.1% (5.4% risk reduction; 95% CI 2.1 to 8.6%) through model-based selective pessary insertion. The next step is external validation upon existence of independent trial data. Conclusions: We suggest revisiting existing trials data to explore whether differences in treatment benefit can be explained by differences in baseline characteristics of patients. This could lead to treatment selection tools which, after validation in comparable existing trials, can be introduced into clinical practice for guiding treatment decisions in future patients.

Original languageEnglish
Article number52
JournalBMC Medical Informatics and Decision Making
Volume18
Issue number1
Early online date30 May 2018
DOIs
Publication statusPublished - 28 Jun 2018

Keywords

  • Biomarker
  • Individualised medicine
  • Model development
  • Prediction models
  • Prognostic
  • Randomised controlled trials
  • Stratified medicine
  • Subgroup analysis
  • Treatment selection
  • Validation

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