Utility-Based Bayesian Personalized Treatment Selection for Advanced Breast Cancer

Abstract

A Bayesian method is proposed for personalized treatment selection in settings where data are available from a randomized clinical trial with two or more outcomes. The motivating application is a randomized trial that compared letrozole plus bevacizumab to letrozole alone as first‐line therapy for hormone receptor‐positive advanced breast cancer. The combination treatment arm had larger median progression‐free survival time, but also a higher rate of severe toxicities. This suggests that the risk‐benefit trade‐off between these two outcomes should play a central role in selecting each patient's treatment, particularly since older patients are less likely to tolerate severe toxicities. To quantify the desirability of each possible outcome combination for an individual patient, we elicited from breast cancer oncologists a utility function that varied with age. The utility was used as an explicit criterion for quantifying risk‐benefit trade‐offs when making personalized treatment selections. A Bayesian nonparametric multivariate regression model with a dependent Dirichlet process prior was fit to the trial data. Under the fitted model, a new patient's treatment can be selected based on the posterior predictive utility distribution. For the breast cancer trial dataset, the optimal treatment depends on the patient's age, with the combination preferable for patients 70 years or younger and the single agent preferable for patients older than 70.

Adaptive phase I–II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies

“…Besides, many other phase I-II clinical trial designs have been proposed to handle more complicated clinical settings for targeted agents and immunotherapies such as the late-onset outcomes, 24,47,48 drug-drug combination, [49][50][51][52] dose schedule, [53][54][55][56][57][58] and personalized medicine. 40,45,46,[59][60][61][62][63][64][65][66] The phase I-II clinical trial designs belong to the class of seamless designs and are dedicated to the early stages of drug development. Many other types of seamless designs, such as the phase II-III design, have also been developed, focusing on the later stage of the drug development, such as the treatment effect confirmation and validation.…”

Interpreting Randomized Controlled Trials

“…Perfect patient relevance would require a statistical model to account for every aspect of a patient's biology, environment, and other covariates that can influence the outcome of interest. However, this is an unrealistic goal, and the relevance of a statistical model must be balanced with robustness and practicality [3,4,68,138,139].…”

“…The goal of a randomized controlled trial (RCT) is to generate data that can be used to compare treatments fairly, which in turn may guide patient-centered medical decisions [1][2][3][4]. Worldwide, results of approximately 140 RCTs are published each day, comprising an immense compendium of data that can be daunting for clinical practitioners and other stakeholders to digest efficiently [5].…”

Risk–benefit trade-offs and precision utilities in phase I-II clinical trials

“…Examples include U(response, toxicity, subgroup) in a dose-finding trial 4 or U(PFS, QOL, subgroup) in a randomized comparative trial, 10 where PFS denotes progression-free survival and QOL denotes quality of life. For example, Lee et al 11 provided a decision analysis based on the preference of older patients with advanced breast cancer for treatments associated with lower toxicity and thus better QOL at the cost of lower biological efficacy for extending PFS time. While long PFS time with no toxicity typically has high utility for patients of all ages, the utility assigned to low toxicity but shorter PFS time is greater for older patients compared to younger patients.…”

“…Such covariate-specific utility functions were integrated with data from a phase 3 randomized controlled trial (RCT) using a flexible multivariate Bayesian nonparametric regression model to inform the selection of letrozole alone versus the combination of letrozole plus bevacizumab as first-line therapy for hormone receptor–positive advanced breast cancer. 11 The utility functions were defined to vary with age because older patients with this type of breast cancer typically are less willing to accept more intensive therapies that can prolong survival outcomes at the cost of severe toxicities. The practical requirements of these approaches include eliciting covariate-specific utilities to quantify risk–benefit trade-offs.…”