IDDI presenting at MSKCC Biostatistics Seminar Series

The presentation will be delivered at 16:30 CEST / 10:30 ET on 26th October 2022 titled:

GENERALIZED PAIRWISE COMPARISONS AS A STATISTICAL METHOD FOR PATIENT-CENTRIC MEDICINE

Join Marc in this insightful presentation to learn more about IDDI’s latest innovative statistical method – Generalized Pairwise Comparisons (GPC).

GPC is the core of our BENEFIT project which aims at developing new tools for the design and analysis of clinical trials, with the objective of involving patients and clinicians in the analysis and place them at the center of the therapeutic decision.

This seminar will be hybrid with an in-person external speaker.

Abstract

The era of “precision medicine” is in full swing. Precision medicine, which aims at giving the right treatment to the right patient at the right time, takes advantage of predictive biomarkers to deliver targeted drugs. This is particularly important in oncology, given the high stakes of treatment benefit vs. treatment harm. Precision medicine may be taken one step further if individual patient preferences are factored into decision-making to reach what might be truly called “patient-centric medicine”. Allowing patients to make individualized treatment decisions is currently done informally, since no statistical methods integrate several indicators of efficacy and toxicity into a single, quantitative measure. A new statistical method, named “generalized pairwise comparisons” (GPC), allows formal decisions based on the totality of the available information in a rigorous way. Using GPC, all efficacy, toxicity and quality of life data from patients enrolled in clinical trials comparing competing interventions can be used to analyse any number of prioritized outcomes of any type (binary, continuous, time to event, etc.), possibly with thresholds of clinical relevance for continuous or ordered outcomes. The method compares all possible pairs of patients formed by taking one patient from the experimental group and one patient from the control group of a randomized trial. We have proposed a new measure of the overall treatment effect, called the “Net Treatment Benefit” (NTB), as the difference between the probability that a patient taken at random in the experimental group has a better outcome than a patient taken at random in the control group. The NTB is an absolute measure that directly addresses patient-centric questions about the probabilities of benefits and harms from treatment. As such, the GPC method can be used to individualize treatment choices. Other measures of treatment benefit include the win ratio, which has received a lor of attention in cardiovascular trials, and the win odds. The general properties of GPC and the associated measures of treatment effect will discussed, and illustrated in actual applications.