Dataset Information

Use of Bayesian Decision Analysis to Minimize Harm in Patient-Centered Randomized Clinical Trials in Oncology.

ABSTRACT: Randomized clinical trials (RCTs) currently apply the same statistical threshold of alpha?=?2.5% for controlling for false-positive results or type 1 error, regardless of the burden of disease or patient preferences. Is there an objective and systematic framework for designing RCTs that incorporates these considerations on a case-by-case basis?To apply Bayesian decision analysis (BDA) to cancer therapeutics to choose an alpha and sample size that minimize the potential harm to current and future patients under both null and alternative hypotheses.We used the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) database and data from the 10 clinical trials of the Alliance for Clinical Trials in Oncology.The NCI SEER database was used because it is the most comprehensive cancer database in the United States. The Alliance trial data was used owing to the quality and breadth of data, and because of the expertise in these trials of one of us (D.J.S.).The NCI SEER and Alliance data have already been thoroughly vetted. Computations were replicated independently by 2 coauthors and reviewed by all coauthors.Our prior hypothesis was that an alpha of 2.5% would not minimize the overall expected harm to current and future patients for the most deadly cancers, and that a less conservative alpha may be necessary. Our primary study outcomes involve measuring the potential harm to patients under both null and alternative hypotheses using NCI and Alliance data, and then computing BDA-optimal type 1 error rates and sample sizes for oncology RCTs.We computed BDA-optimal parameters for the 23 most common cancer sites using NCI data, and for the 10 Alliance clinical trials. For RCTs involving therapies for cancers with short survival times, no existing treatments, and low prevalence, the BDA-optimal type 1 error rates were much higher than the traditional 2.5%. For cancers with longer survival times, existing treatments, and high prevalence, the corresponding BDA-optimal error rates were much lower, in some cases even lower than 2.5%.Bayesian decision analysis is a systematic, objective, transparent, and repeatable process for deciding the outcomes of RCTs that explicitly incorporates burden of disease and patient preferences.

SUBMITTER: Montazerhodjat V

PROVIDER: S-EPMC5824294 | biostudies-other | 2017 Sep

REPOSITORIES: biostudies-other

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Use of Bayesian Decision Analysis to Minimize Harm in Patient-Centered Randomized Clinical Trials in Oncology.

Montazerhodjat Vahid V Chaudhuri Shomesh E SE Sargent Daniel J DJ Lo Andrew W AW

JAMA oncology 20170914 9

<h4>Importance</h4>Randomized clinical trials (RCTs) currently apply the same statistical threshold of alpha = 2.5% for controlling for false-positive results or type 1 error, regardless of the burden of disease or patient preferences. Is there an objective and systematic framework for designing RCTs that incorporates these considerations on a case-by-case basis?<h4>Objective</h4>To apply Bayesian decision analysis (BDA) to cancer therapeutics to choose an alpha and sample size that minimize the ...[more]

PMID: 28418507

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Project description:BackgroundPoor patient-provider communication, among other reasons, is a notable barrier to contraceptive decision-making among Latinas. Patient-centered approaches to contraceptive counseling that optimize communication align with shared decision-making (SDM) -which is associated with satisfaction and continued contraceptive use among various populations.ObjectiveTo examine associations of patient-provider communication and importance of SDM tenets with consistent contraceptive use among a population of Latinas.Patient involvementFormative work for this study included prior qualitative and quantitative research with Latinas who expressed the importance of patient-provider communication during contraceptive counseling and therefore were instrumental in problem definition.MethodsCross-sectional surveys were administered to Latinas ages 15-29 years. Patient-provider communication, patient-reported importance of specific SDM tenets, and consistent contraception use were measured and analyzed for associations.Results103 Latinas (mean age = 21.4) participated. 33% of participants<21 years were using contraception consistently vs. 67% for those ≥ 21 (p = 0.003). Among participants ≥ 21, consistent users reported higher communication scores compared to inconsistent users and non-users (p = 0.042). For participants< 21, consistent users were more likely than inconsistent users and non-users to report that 2 SDM tenets (discussion of contraceptive preferences and avoidance of race/ethnic-based judgement) are important (p = 0.052, 0.028, respectively).DiscussionPatient-provider communication was especially important for Latinas ≥ 21 while using an SDM approach during counseling was highly valued by those<21. Patient-centered approaches to contraceptive counseling provide opportunities to optimize healthcare delivery for this vulnerable population.Practical valueResults from this research demonstrate that patient-centered communication is highly valued by Latina study participants and is an important consideration in their contraceptive counseling. Clinicians should consider employing techniques such as SDM as they seek to provide patient-centered care during contraceptive counseling for this patient population.

Project description:PURPOSE:Precision oncology is hindered by the lack of decision support for determining the functional and therapeutic significance of genomic alterations in tumors and relevant clinically available options. To bridge this knowledge gap, we established a Precision Oncology Decision Support (PODS) team that provides annotations at the alteration-level and subsequently determined if clinical decision-making was influenced. METHODS:Genomic alterations were annotated to determine actionability based on a variant's known or potential functional and/or therapeutic significance. The medical records of a subset of patients annotated in 2015 were manually reviewed to assess trial enrollment. A web-based survey was implemented to capture the reasons why genotype-matched therapies were not pursued. RESULTS:PODS processed 1,669 requests for annotation of 4,084 alterations (2,254 unique) across 49 tumor types for 1,197 patients. 2,444 annotations for 669 patients included an actionable variant call: 32.5% actionable, 9.4% potentially, 29.7% unknown, 28.4% non-actionable. 66% of patients had at least one actionable/potentially actionable alteration. 20.6% (110/535) patients annotated enrolled on a genotype-matched trial. Trial enrolment was significantly higher for patients with actionable/potentially actionable alterations (92/333, 27.6%) than those with unknown (16/136, 11.8%) and non-actionable (2/66, 3%) alterations (p=0.00004). Actionable alterations in PTEN, PIK3CA, and ERBB2 most frequently led to enrollment on genotype-matched trials. Clinicians cited a variety of reasons why patients with actionable alterations did not enroll on trials. CONCLUSION:Over half of alterations annotated were of unknown significance or non-actionable. Physicians were more likely to enroll a patient on a genotype-matched trial when an annotation supported actionability. Future studies are needed to demonstrate the impact of decision support on trial enrollment and oncologic outcomes.

Dataset Information

Use of Bayesian Decision Analysis to Minimize Harm in Patient-Centered Randomized Clinical Trials in Oncology.

Publications

Use of Bayesian Decision Analysis to Minimize Harm in Patient-Centered Randomized Clinical Trials in Oncology.

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