Collaborative Modeling to Compare Different Breast Cancer Screening Strategies: A Decision Analysis for the US Preventive Services Task Force.

Importance: The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known. Objective: To estimate outcomes of various mammography screening strategies. Design, Setting, and Population: Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses. Exposures: Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and "real-world" treatment. Main Outcomes and Measures: Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, overdiagnosis), and number of mammograms per 1000 women. Results: Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening) was associated with a 30.0% breast cancer mortality reduction, 1376 false-positive recalls, and 14 overdiagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and overdiagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women. Conclusions: This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening for women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities.

A cost/benefit analysis of clinical trial designs for COVID-19 vaccine candidates.

We compare and contrast the expected duration and number of infections and deaths averted among several designs for clinical trials of COVID-19 vaccine candidates, including traditional and adaptive randomized clinical trials and human challenge trials. Using epidemiological models calibrated to the current pandemic, we simulate the time course of each clinical trial design for 756 unique combinations of parameters, allowing us to determine which trial design is most effective for a given scenario. A human challenge trial provides maximal net benefits-averting an additional 1.1M infections and 8,000 deaths in the U.S. compared to the next best clinical trial design-if its set-up time is short or the pandemic spreads slowly. In most of the other cases, an adaptive trial provides greater net benefits.

A Quality Assessment of a Collaborative Model of a Pediatric Antimicrobial Stewardship Program.

BACKGROUND: Infectious Diseases Society of America guidelines recommend that key antimicrobial stewardship program (ASP) personnel include an infectious disease (ID) physician leader and dedicated ID-trained clinical pharmacist. Limited resources prompted development of an alternative model by using ID physicians and service-based clinical pharmacists at a pediatric hospital. The aim of this study was to analyze the effectiveness and impact of this alternative ASP model. METHODS: The collaborative ASP model incorporated key strategies of education, antimicrobial restriction, day 3 audits, and practice guidelines. High-use and/or high-cost antimicrobial agents were chosen with audits targeting vancomycin, caspofungin, and meropenem. The electronic medical record was used to identify patients requiring day 3 audits and to communicate ASP recommendations. Segmented regression analyses were used to analyze quarterly antimicrobial agent prescription data for the institution and selected services over time. RESULTS: Initiation of ASP and day 3 auditing was associated with blunting of a preexisting increasing trend for caspofungin drug starts and use and a significant downward trend for vancomycin drug starts (relative change -12%) and use (-25%), with the largest reduction in critical care areas. Although meropenem use was already low due to preexisting requirements for preauthorization, a decline in drug use (-31%, P = .021) and a nonsignificant decline in drug starts (-21%, P = .067) were noted. A 3-month review of acceptance of ASP recommendations found rates of 90%, 93%, and 100% for vancomycin, caspofungin, and meropenem, respectively. CONCLUSIONS: This nontraditional ASP model significantly reduced targeted drug usage demonstrating acceptance of integration of service-based clinical pharmacists and ID consultants.

Benefits, harms, and costs for breast cancer screening after US implementation of digital mammography.

BACKGROUND: Compared with film, digital mammography has superior sensitivity but lower specificity for women aged 40 to 49 years and women with dense breasts. Digital has replaced film in virtually all US facilities, but overall population health and cost from use of this technology are unclear. METHODS: Using five independent models, we compared digital screening strategies starting at age 40 or 50 years applied annually, biennially, or based on density with biennial film screening from ages 50 to 74 years and with no screening. Common data elements included cancer incidence and test performance, both modified by breast density. Lifetime outcomes included mortality, quality-adjusted life-years, and screening and treatment costs. RESULTS: For every 1000 women screened biennially from age 50 to 74 years, switching to digital from film yielded a median within-model improvement of 2 life-years, 0.27 additional deaths averted, 220 additional false-positive results, and $0.35 million more in costs. For an individual woman, this translates to a health gain of 0.73 days. Extending biennial digital screening to women ages 40 to 49 years was cost-effective, although results were sensitive to quality-of-life decrements related to screening and false positives. Targeting annual screening by density yielded similar outcomes to targeting by age. Annual screening approaches could increase costs to $5.26 million per 1000 women, in part because of higher numbers of screens and false positives, and were not efficient or cost-effective. CONCLUSIONS: The transition to digital breast cancer screening in the United States increased total costs for small added health benefits. The value of digital mammography screening among women aged 40 to 49 years depends on women's preferences regarding false positives.

Addressing the incremental benefit of histamine dihydrochloride when added to interleukin-2 in treating acute myeloid leukemia: a Bayesian meta-analysis.

IL-2 has been investigated as maintenance therapy for patients with AML in five randomized trials. None has shown a statistically significant benefit. A randomized trial of HDC + IL-2 showed a statistically significant benefit for leukemia-free survival (LFS) in comparison with standard of care. Because HDC + IL-2 has not been randomized against IL-2 alone, the question remains as to whether and to what extent HDC + IL-2 is an improvement compared to IL-2 alone. This is a literature-based meta-analysis. We employ two versions of a Bayesian hierarchical model to compare HDC + IL-2 versus IL-2 alone on the basis of LFS in patients in remission from AML.

Bayesian meta-analyses for comparative effectiveness and informing coverage decisions.

BACKGROUND: Evidence-based medicine is increasingly expected in health care decision-making. The Centers for Medicare and Medicaid have initiated efforts to understand the applicability of Bayesian techniques for synthesizing evidence. As a case study, a Bayesian analysis of clinical trials of implantable cardioverter defibrillators was undertaken using patient-level data not typically available for analysis. PURPOSE: Conduct Bayesian meta-analyses of the defibrillator trials using published results to demonstrate a Bayesian approach useful to policy makers. DATA SOURCES, STUDY SELECTION, DATA EXTRACTION: We reconsidered trials in a 2007 systematic review by Ezekowitz et al (Ann Intern Med. 2007;147:251-262) and extracted information from the original published articles. Employing a Bayesian hierarchical approach, we developed a base model and 2 variants, and modeled hazard ratios separately within each year of follow-up. We considered sequential meta-analyses over time and found the predictive distribution of the results of the next trial, given its sample size. DATA SYNTHESIS: For the most robust of 3 models, the probability that the mean defibrillator effect (in the population of trials) is beneficial is greater than 0.999. In that model, about 5% of trials in the population of trials would have a detrimental effect. Despite the moderate amount of heterogeneity across the trials, there was stability of conclusions after the first 3 of the 12 total trials had been conducted. This stability enabled reasonable predictions for the results of future trials. LIMITATIONS: Inability to assess treatment effects within subsets of patients. CONCLUSIONS: Bayesian meta-analyses based on literature surveys can effectively inform coverage decisions. Bayesian modeling for endpoints such as mortality can elucidate treatment effects over time. The Bayesian approach used in a sequential manner over time can predict results and help assess the utility of future clinical trials.

Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms.

BACKGROUND: Despite trials of mammography and widespread use, optimal screening policy is controversial. OBJECTIVE: To evaluate U.S. breast cancer screening strategies. DESIGN: 6 models using common data elements. DATA SOURCES: National data on age-specific incidence, competing mortality, mammography characteristics, and treatment effects. TARGET POPULATION: A contemporary population cohort. TIME HORIZON: Lifetime. PERSPECTIVE: Societal. INTERVENTIONS: 20 screening strategies with varying initiation and cessation ages applied annually or biennially. OUTCOME MEASURES: Number of mammograms, reduction in deaths from breast cancer or life-years gained (vs. no screening), false-positive results, unnecessary biopsies, and overdiagnosis. RESULTS OF BASE-CASE ANALYSIS: The 6 models produced consistent rankings of screening strategies. Screening biennially maintained an average of 81% (range across strategies and models, 67% to 99%) of the benefit of annual screening with almost half the number of false-positive results. Screening biennially from ages 50 to 69 years achieved a median 16.5% (range, 15% to 23%) reduction in breast cancer deaths versus no screening. Initiating biennial screening at age 40 years (vs. 50 years) reduced mortality by an additional 3% (range, 1% to 6%), consumed more resources, and yielded more false-positive results. Biennial screening after age 69 years yielded some additional mortality reduction in all models, but overdiagnosis increased most substantially at older ages. RESULTS OF SENSITIVITY ANALYSIS: Varying test sensitivity or treatment patterns did not change conclusions. LIMITATION: Results do not include morbidity from false-positive results, patient knowledge of earlier diagnosis, or unnecessary treatment. CONCLUSION: Biennial screening achieves most of the benefit of annual screening with less harm. Decisions about the best strategy depend on program and individual objectives and the weight placed on benefits, harms, and resource considerations. PRIMARY FUNDING SOURCE: National Cancer Institute.

Bayesian model averaging in meta-analysis: vitamin E supplementation and mortality.

CONTEXT: The strength and relevance of a meta-analysis depends on the validity of the statistical methods used. Of special importance is appropriately assessing different sources of variability. Many studies including meta-analyses have evaluated the efficacy and safety of vitamin E and have yielded varying results. Illuminating and resolving these disparities requires addressing study variability and model uncertainty. OBJECTIVE: To describe Bayesian meta-analysis methods for combining data from clinical trials, using recent studies that analyzed the relationship between vitamin E dose and all-cause mortality. DATA SOURCES: Studies used in a previously published meta-analysis appended by studies identified by a search of MEDLINE from August 2004 to December 2005 using the MeSH terms vitamin e and alpha tocopherol. INCLUSION CRITERIA: men and nonpregnant women; use of vitamin E alone or in combination with other vitamins or minerals; random allocation of participants to either vitamin E or a placebo or other control group; intervention and follow-up duration greater than 1 year; 10 or more deaths. DATA EXTRACTION: Independent data extraction by one author was reviewed and confirmed by a second author. Corresponding authors of the original publications were contacted when questions arose. DATA SYNTHESIS: Data collection included the number of patients and deaths, percent men, use of other vitamins or minerals, mean age, and length of follow-up. We combined study results using Bayesian hierarchical model averaging. Analyses used Markov chain Monte Carlo computational techniques. CONCLUSIONS: Vitamin E intake is unlikely to affect mortality regardless of dose. The Bayesian meta-analyses presented here are ideal for incorporating disparate sources of variability, including trial effect and model uncertainty.

Bayesian decision-theoretic group sequential clinical trial design based on a quadratic loss function: a frequentist evaluation.

The decision to terminate a controlled clinical trial at the time of an interim analysis is perhaps best made by weighing the value of the likely additional information to be gained if further subjects are enrolled against the various costs of that further enrollment. The most commonly used statistical plans for interim analysis (eg, O'Brien-Fleming), however, are based on a frequentist approach that makes no such comparison. A two-armed Bayesian decision-theoretic clinical trial design is developed for a disease with two possible outcomes, incorporating a quadratic decision loss function and using backward induction to quantify the cost of future enrollment. Monte Carlo simulation is used to compare frequentist error rates and mean required sample sizes for these Bayesian designs with the two-tailed frequentist group-sequential designs of, O'Brien-Fleming and Pocock. When the terminal decision loss function is chosen to yield typical frequentist error rates, the mean sample sizes required by the Bayesian designs are smaller than those of the corresponding O'Brien-Fleming frequentist designs, largely due to the more frequent interim analyses typically used with the Bayesian designs and the ability of the Bayesian designs to terminate early and conclude equivalence. Adding stochastic curtailment to the frequentist designs and using the same number of interim analyses results in largely equivalent trials. An example of a Bayesian design for the data safety monitoring of a clinical trial is given. Our design assumes independence of the probabilities of success in the two trial arms. Additionally, we have chosen non-informative priors and selected loss functions to produce trials with appealing frequentist error rates, rather than choosing priors that reflect realistic prior information and loss functions that reflect true costs. Our Bayesian designs allow interpretation of the final results along either Bayesian or frequentist lines. For the Bayesian, they minimize the total cost and allow the direct calculation of the probability density function for the difference in efficacy. For the frequentist, they have well-characterized type I and II error rates and in some cases lead to a reduction in the mean sample size.

Determining joint carrier probabilities of cancer-causing genes using Markov chain Monte Carlo methods.

In genetic counseling for cancer risk, the probability of carrying a mutation of a cancer-causing gene plays an important role. Family history of various cancers is important in calculating this probability. BRCAPRO is a widely used software for calculating the probability of carrying mutations in BRCA1 and BRCA2 genes given the family history of breast and ovarian cancer in first- and second-degree relatives. BRCAPRO uses an analytical (exact) calculational procedure. Using Markov chain Monte Carlo (MCMC) methods, we extend BRCAPRO to handle, in principle, any type of cancer, family history, any number of genes and alleles that each gene may have. When the information used in this MCMC approach is the same as for BRCAPRO (two genes: BRCA1 and BRCA2; two cancers: breast and ovarian; first- and second-degree relatives only), the two approaches give essentially the same answer. Extending the model to include (1) prostate cancer, (2) two mutated alleles of BRCA2, namely, mutations in Ovarian Cancer Cluster Region (OCCR) and non-OCCR region, and (3) relatives of degree greater than second-degree, leads to different carrier probabilities. The MCMC approach is a useful tool in building a comprehensive model to give accurate estimates of carrier probabilities. Such an approach will be even more important as additional information about the genetics of various cancers becomes available.

Seamlessly expanding a randomized phase II trial to phase III.

A sequential Bayesian phase II/III design is proposed for comparative clinical trials. The design is based on both survival time and discrete early events that may be related to survival and assumes a parametric mixture model. Phase II involves a small number of centers. Patients are randomized between treatments throughout, and sequential decisions are based on predictive probabilities of concluding superiority of the experimental treatment. Whether to stop early, continue, or shift into phase III is assessed repeatedly in phase II. Phase III begins when additional institutions are incorporated into the ongoing phase II trial. Simulation studies in the context of a non-small-cell lung cancer trial indicate that the proposed method maintains overall size and power while usually requiring substantially smaller sample size and shorter trial duration when compared with conventional group-sequential phase III designs.