Bayesian adaptive design scope of utilizing it for research in palliative care

Abstract Background Bevacizumab has promising activity against recurrent glioblastoma (GBM). However, acquired resistance to this agent results in tumor recurrence. We hypothesized that vorinostat, a histone deacetylase (HDAC) inhibitor with anti-angiogenic effects, would prevent acquired resistance to bevacizumab. Methods This multicenter phase II trial used a Bayesian adaptive design to randomize patients with recurrent GBM to bevacizumab alone or bevacizumab plus vorinostat with the primary endpoint of progression-free survival (PFS) and secondary endpoints of overall survival (OS) and clinical outcomes assessment (MD Anderson Symptom Inventory Brain Tumor module [MDASI-BT]). Eligible patients were adults (≥18 y) with histologically confirmed GBM recurrent after prior radiation therapy, with adequate organ function, KPS ≥60, and no prior bevacizumab or HDAC inhibitors. Results Ninety patients (bevacizumab + vorinostat: 49, bevacizumab: 41) were enrolled, of whom 74 were evaluable for PFS (bevacizumab + vorinostat: 44, bevacizumab: 30). Median PFS (3.7 vs 3.9 mo, P = 0.94, hazard ratio [HR] 0.63 [95% CI: 0.38, 1.06, P = 0.08]), median OS (7.8 vs 9.3 mo, P = 0.64, HR 0.93 [95% CI: 0.5, 1.6, P = 0.79]) and clinical benefit were similar between the 2 arms. Toxicity (grade ≥3) in 85 evaluable patients included hypertension (n = 37), neurological changes (n = 2), anorexia (n = 2), infections (n = 9), wound dehiscence (n = 2), deep vein thrombosis/pulmonary embolism (n = 2), and colonic perforation (n = 1). Conclusions Bevacizumab combined with vorinostat did not yield improvement in PFS or OS or clinical benefit compared with bevacizumab alone or a clinical benefit in adults with recurrent GBM. This trial is the first to test a Bayesian adaptive design with adaptive randomization and Bayesian continuous monitoring in patients with primary brain tumor and demonstrates the feasibility of using complex Bayesian adaptive design in a multicenter setting.

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Bayesian adaptive design: the future for regional anesthesia trials?

Regional Anesthesia and Pain Medicine ◽

10.1136/rapm-2018-100248 ◽

2019 ◽

Vol 44 (6) ◽

pp. 617-622

Author(s):

Robert Gotmaker ◽

Michael J Barrington ◽

John Reynolds ◽

Lorenzo Trippa ◽

Stephane Heritier

Keyword(s):

Regional Anesthesia ◽

Adaptive Design ◽

Bayesian Adaptive Design ◽

The Future

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A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate

Journal of Probability and Statistics ◽

10.1155/2018/8654173 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Márcio Augusto Diniz ◽

Sungjin Kim ◽

Mourad Tighiouart

Keyword(s):

Phase I ◽

Adaptive Design ◽

Cytotoxic Agents ◽

Dose Finding ◽

Model Parameters ◽

Baseline Characteristic ◽

Phase I Clinical Trials ◽

Bayesian Adaptive Design ◽

Dose Combination ◽

Toxicity Relationship

A Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility is described. The estimation of the maximum tolerated dose (MTD) curve is a function of a baseline covariate using two cytotoxic agents. A logistic model is used to describe the relationship between the doses, baseline covariate, and the probability of dose limiting toxicity (DLT). Trial design proceeds by treating cohorts of two patients simultaneously using escalation with overdose control (EWOC), where at each stage of the trial, the next dose combination corresponds to the α quantile of the current posterior distribution of the MTD of one of two agents at the current dose of the other agent and the next patient’s baseline covariate value. The MTD curves are estimated as function of Bayes estimates of the model parameters at the end of trial. Average DLT, pointwise average bias, and percent of dose recommendation at dose combination neighborhoods around the true MTD are compared between the design that uses the covariate and the one that ignores the baseline characteristic. We also examine the performance of the approach under model misspecifications for the true dose-toxicity relationship. The methodology is further illustrated in the case of a prespecified discrete set of dose combinations.

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What Works for Whom? A Bayesian Approach to Channeling Big Data Streams for Public Program Evaluation

American Journal of Evaluation ◽

10.1177/1098214017737173 ◽

2017 ◽

Vol 39 (1) ◽

pp. 109-122 ◽

Cited By ~ 5

Author(s):

Mariel McKenzie Finucane ◽

Ignacio Martinez ◽

Scott Cody

Keyword(s):

Bayesian Approach ◽

Data Streams ◽

Adaptive Design ◽

Public Programs ◽

Bayesian Adaptive Design ◽

Program Evaluations ◽

What Works ◽

Standard Design ◽

Specific Subgroup ◽

Big Data Streams

In the coming years, public programs will capture even more and richer data than they do now, including data from web-based tools used by participants in employment services, from tablet-based educational curricula, and from electronic health records for Medicaid beneficiaries. Program evaluators seeking to take full advantage of these data streams will require novel statistical methods, such as Bayesian approach. A Bayesian approach to randomized program evaluations efficiently identifies what works for whom. The Bayesian approach design adapts to accumulating evidence: Over the course of an evaluation, more study subjects are allocated to treatment arms that are more promising, given the specific subgroup from which each subject comes. We identify conditions under which there is more than a 90% chance that inference from the Bayesian adaptive design is superior to inference from a standard design, using less than one third the sample size.

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