Integrating value of research into NCI Clinical Trials Cooperative Group research review and prioritization: A pilot study

BackgroundThe Institute of Medicine has called for approaches to help maximize the return on investments (ROI) in cancer clinical trials. Value of Research (VOR) is a health economics technique that estimates ROI and can inform research prioritization. Our objective was to evaluate the impact of using VOR analyses on the clinical trial proposal review process within the SWOG cancer clinical trials consortium.MethodsWe used a previously developed minimal modeling approach to calculate VOR estimates for 9 phase II/III SWOG proposals between February 2015 and December 2016. Estimates were presented to executive committee (EC) members (N = 12) who determine which studies are sent to the National Cancer Institute for funding consideration. EC members scored proposals from 1 (best) to 5 based on scientific merit and potential impact before and after receiving VOR estimates. EC members were surveyed to assess research priorities, proposal evaluation process satisfaction, and the VOR process.ResultsValue of Research estimates ranged from −$2.1B to$16.46B per proposal. Following review of VOR results, the EC changed their score for eight of nine proposals. Proposal rankings were different in pre‐ vs postscores (P value: 0.03). Respondents had mixed views of the ultimate utility of VOR for their decisions with most supporting (42%) or neutral (41%) to the idea of adding VOR to the evaluation process.ConclusionsThe findings from this pilot study indicate use of VOR analyses may be a useful adjunct to inform proposal reviews within NCI Cooperative Clinical Trials groups.The Instiztute of Medicine has called for approaches to help maximize the return on investments in cancer clinical trials. The findings from this pilot study indicate use of value of research analyses may be a useful adjunct to inform proposal reviews within NCI Cooperative Clinical Trials groups.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146484/1/cam41657.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146484/2/cam41657_am.pd

Breast MRI in the Diagnostic and Preoperative Workup Among Medicare Beneficiaries With Breast Cancer

We compared the frequency and sequence of breast imaging and biopsy use for the diagnostic and preoperative workup of breast cancer according to breast MRI use among older women

Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents

<p>Abstract</p> <p>Background</p> <p>Quantiles are a staple of epidemiologic research: in contemporary epidemiologic practice, continuous variables are typically categorized into tertiles, quartiles and quintiles as a means to illustrate the relationship between a continuous exposure and a binary outcome.</p> <p>Discussion</p> <p>In this paper we argue that this approach is highly problematic and present several potential alternatives. We also discuss the perceived drawbacks of these newer statistical methods and the possible reasons for their slow adoption by epidemiologists.</p> <p>Summary</p> <p>The use of quantiles is often inadequate for epidemiologic research with continuous variables.</p

Feasibility study of a clinically-integrated randomized trial of modifications to radical prostatectomy

<p>Abstract</p> <p>Background</p> <p>Numerous technical modifications to radical prostatectomy have been proposed. Such modifications are likely to lead to only slight improvements in outcomes. Although small differences would be worthwhile, an appropriately powered randomized trial would need to be very large, and thus of doubtful feasibility given the expense, complexity and regulatory burden of contemporary clinical trials. We have proposed a novel methodology, the clinically-integrated randomized trial, which dramatically streamlines trial procedures in order to reduce the marginal cost of an additional patient towards zero. We aimed to determine the feasibility of implementing such a trial for radical prostatectomy.</p> <p>Methods</p> <p>Patients undergoing radical prostatectomy as initial treatment for prostate cancer were randomized in a factorial design to involvement of the fascia during placement of the anastomotic sutures, urethral irrigation, both or neither. Endpoint data were obtained from routine clinical documentation. Accrual and compliance rates were monitored to determine the feasibility of the trial.</p> <p>Results</p> <p>From a total of 260 eligible patients, 154 (59%) consented; 56 patients declined to participate, 20 were not approached on recommendation of the treating surgeon, and 30 were not approached for logistical reasons. Although recording by surgeons of the procedure used was incomplete (~80%), compliance with randomization was excellent when it was recorded, with only 6% of procedures inconsistent with allocation. Outcomes data was received from 71% of patients at one year. This improved to 83% as the trial progressed.</p> <p>Conclusions</p> <p>A clinically-integrated randomized trial was conducted at low cost, with excellent accrual, and acceptable compliance with treatment allocation and outcomes reporting. This demonstrates the feasibility of the methodology. Improved methods to ensure documentation of surgical procedures would be required before wider implementation.</p> <p>Trial registration</p> <p>ClinicalTrials.gov <a href="http://www.clinicaltrials.gov/ct2/show/NCT00928850">NCT00928850</a></p

Integrating value of research into NCI

BackgroundThe Institute of Medicine has called for approaches to help maximize the return on investments (ROI) in cancer clinical trials. Value of Research (VOR) is a health economics technique that estimates ROI and can inform research prioritization. Our objective was to evaluate the impact of using VOR analyses on the clinical trial proposal review process within the SWOG cancer clinical trials consortium.MethodsWe used a previously developed minimal modeling approach to calculate VOR estimates for 9 phase II/III SWOG proposals between February 2015 and December 2016. Estimates were presented to executive committee (EC) members (N = 12) who determine which studies are sent to the National Cancer Institute for funding consideration. EC members scored proposals from 1 (best) to 5 based on scientific merit and potential impact before and after receiving VOR estimates. EC members were surveyed to assess research priorities, proposal evaluation process satisfaction, and the VOR process.ResultsValue of Research estimates ranged from −$2.1B to$16.46B per proposal. Following review of VOR results, the EC changed their score for eight of nine proposals. Proposal rankings were different in pre‐ vs postscores (P value: 0.03). Respondents had mixed views of the ultimate utility of VOR for their decisions with most supporting (42%) or neutral (41%) to the idea of adding VOR to the evaluation process.ConclusionsThe findings from this pilot study indicate use of VOR analyses may be a useful adjunct to inform proposal reviews within NCI Cooperative Clinical Trials groups.The Instiztute of Medicine has called for approaches to help maximize the return on investments in cancer clinical trials. The findings from this pilot study indicate use of value of research analyses may be a useful adjunct to inform proposal reviews within NCI Cooperative Clinical Trials groups.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146484/1/cam41657.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146484/2/cam41657_am.pd

Covasim:An agent-based model of COVID-19 dynamics and interventions

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America