Deborah Zucker

Individual (N-of-1) trials can be combined to give population comparative treatment effect estimates: methodologic considerations

OBJECTIVE To compare different statistical models for combining N-of-1 trials to estimate a population treatment effect. STUDY DESIGN AND SETTING Data from a published series of N-of-1 trials comparing amitriptyline (AMT) therapy and combination treatment (AMT+fluoxetine [FL]) were analyzed to compare summary and individual participant data meta-analysis; repeated-measure models; Bayesian hierarchical models; and single-period, single-pair, and averaged outcome crossover models. RESULTS The best-fitting model included a random intercept (response on AMT) and fixed treatment effect (added FL). Results supported a common, uncorrelated within-patient covariance structure that is equal between treatments and across patients. Assuming unequal within-patient variances, a random-effect model was favored. Bayesian hierarchical models improved precision and were highly sensitive to within-patient variance priors. CONCLUSION Optimal models for combining N-of-1 trials need to consider goals, data sources, and relative within- and between-patient variances. Without sufficient patients, between-patient variation will be hard to explain with covariates. N-of-1 data with few observations per patients may not support models with heterogeneous within-patient variation. With common variances, models appear robust. Bayesian models may improve parameter estimation but are sensitive to prior assumptions about variance components. With limited resources, improving within-patient precision must be balanced by increased participants to explain population variation.

Variation in estrogen-related genes and cross-sectional and longitudinal blood pressure in the Framingham Heart Study.

Objective To examine the association between variation in estrogen-related genes and cross-sectional and longitudinal blood pressure in men and women. Design In 1780 unrelated members of the community-based Framingham Heart Study offspring cohort, systolic blood pressure and diastolic blood pressure were measured over a total of six examination cycles encompassing 24 years of follow-up. Multivariate regression analyses were used to assess the relation between untreated cross-sectional and longitudinal blood pressure and polymorphisms at the estrogen receptor-α (ESR1), estrogen receptor-β (ESR2), aromatase (CYP19A1), and nuclear receptor coactivator 1 (NCOA1) genes after adjustment for common risk factors. Results In men, systolic blood pressure and pulse pressure (systolic blood pressure minus diastolic blood pressure) were associated with two polymorphisms in ESR1, while pulse pressure was also associated with variations in NCOA1 and CYP19A1. Polymorphisms in ESR1, CYP19A1, and NCOA1 were associated with diastolic blood pressure in women. Conclusions Although the underlying relations between genes involved in estrogen action and hypertension remain to be completely understood, our findings provide suggestive evidence of gender-specific contributions of estrogen-related genes to blood pressure variation. As no correction for multiple testing was performed in the analyses, we view these results as suggestive and not definitive. Further studies are warranted to confirm these results using a comprehensive set of polymorphisms in order to shed more light on the involvement of estrogen in blood pressure regulation.

CONSORT extension for reporting N-of-1 trials (CENT) 2015 Statement

N-of-1 trials provide a mechanism for making evidence based treatment decisions for an individual patient. They use key methodological elements of group clinical trials to evaluate treatment effectiveness in a single patient, for situations that cannot always accommodate large scale trials: rare diseases, comorbid conditions, or in patients using concurrent therapies. Improvement in the reporting and clarity of methods and findings in N-of-1 trials is essential for reader to gauge the validity of trials and to replicate successful findings. A CONSORT extension for N-of-1 trials (CENT 2015) provides guidance on the reporting of individual and series of N-of-1 trials. CENT provides additional guidance for 14 of the 25 items of the CONSORT 2010 checklist and recommends a diagram for depicting an individual N-of-1 trial and modifies the CONSORT flow diagram to address the flow of a series of N-of-1 trials. The rationale, development process, and CENT 2015 checklist and diagrams are reported in this document.

Strategies for Diagnosing and Treating Suspected Acute Bacterial Sinusitis: A Cost-effectiveness Analysis

OBJECTIVE: Symptoms suggestive of acute bacterial sinusitis are common. Available diagnostic and treatment options generate substantial costs with uncertain benefits. We assessed the cost-effectiveness of alternative management strategies to identify the optimal approach.DESIGN: For such patients, we created a Markov model to examine four strategies: 1) no antibiotic treatment; 2) empirical antibiotic treatment; 3) clinical criteria-guided treatment; and 4) radiography-guided treatment. The model simulated a 14-day course of illness, included sinusitis prevalence, antibiotic side effects, sinusitis complications, direct and indirect costs, and symptom severity. Strategies costing less than

CONSORT extension for reporting N-of-1 trials (CENT) 2015: Explanation and elaboration

50,000 per quality-adjusted life year gained were considered “cost-effective.”MEASUREMENTS AND MAIN RESULTS: For mild or moderate disease, basing antibiotic treatment on clinical criteria was cost-effective in clinical settings where sinusitis prevalence is within the range of 15% to 93% or 3% to 63%, respectively. For severe disease, or to prevent sinusitis or antibiotic side effect symptoms, use of clinical criteria was cost-effective in settings with lower prevalence (below 51% or 44%, respectively); empirical antibiotics was cost-effective with higher prevalence. Sinus radiography-guided treatment was never cost-effective for initial treatment.CONCLUSIONS: Use of a simple set of clinical criteria to guide treatment is a cost-effective strategy in most clinical settings. Empirical antibiotics are cost-effective in certain settings; however, their use results in many unnecessary prescriptions. If this resulted in increased antibiotic resistance, costs would substantially rise and efficacy would fall. Newer, expensive antibiotics are of limited value. Additional testing is not cost-effective. Further studies are needed to find an accurate, low-cost diagnostic test for acute bacterial sinusitis.

CONSORT extension for reporting N-of-1 trials (CENT) 2015 Statement.

N-of-1 trials are a useful tool for clinicians who want to determine the effectiveness of a treatment in a particular individual. The reporting of N-of-1 trials has been variable and incomplete, hindering their usefulness in clinical decision making and by future researchers. This document presents the CONSORT (Consolidated Standards of Reporting Trials) extension for N-of-1 trials (CENT 2015). CENT 2015 extends the CONSORT 2010 guidance to facilitate the preparation and appraisal of reports of an individual N-of-1 trial or a series of prospectively planned, multiple, crossover N-of-1 trials. CENT 2015 elaborates on 14 items of the CONSORT 2010 checklist, totalling 25 checklist items (44 sub-items), and recommends diagrams to help authors document the progress of one participant through a trial or more than one participant through a trial or series of trials, as applicable. Examples of good reporting and evidence based rationale for CENT 2015 checklist items are provided.

Developing Your Career in an Age of Team Science

N-of-1 trials provide a mechanism for making evidence-based treatment decisions for an individual patient. They use key methodological elements of group clinical trials to evaluate treatment effectiveness in a single patient, for situations that cannot always accommodate large-scale trials: rare diseases, comorbid conditions, or in patients using concurrent therapies. Improvement in the reporting and clarity of methods and findings in N-of-1 trials is essential for reader to gauge the validity of trials and to replicate successful findings. A Consolidated Standards of Reporting Trials (CONSORT) extension for N-of-1 trials (CENT 2015) provides guidance on the reporting of individual and series of N-of-1 trials. CENT provides additional guidance for 14 of the 25 items of the CONSORT 2010 checklist, recommends a diagram for depicting an individual N-of-1 trial, and modifies the CONSORT flow diagram to address the flow of a series of N-of-1 trials. The rationale, development process, and CENT 2015 checklist and diagrams are reported in this document.

CONSORT extension for reporting N-of-1 trials (CENT) 2015: explanation and elaboration

Academic institutions and researchers are becoming increasingly involved in translational research to spur innovation in addressing many complex biomedical and societal problems and in response to the focus of the National Institutes of Health and other funders. One approach to translational research is to develop interdisciplinary research teams. By bringing together collaborators with diverse research backgrounds and perspectives, these teams seek to blend their science and the workings of the scientists to push beyond the limits of current research. While team science promises individual and team benefits in creating and implementing innovations, its increased complexity poses challenges. In particular, because academic career advancement commonly focuses on individual achievement, team science might differentially impact early stage researchers. The need to be recognized for individual accomplishments to move forward in an academic career may give rise to research team conflicts. Raising awareness to career-related aspects of team science will help individuals (particularly trainees and junior faculty) take steps to align their excitement and participation with the success of both the team and their personal career advancement.

Patient Protection and Risk Selection: Do Primary Care Physicians Encourage their Patients to Join or Avoid Capitated Health Plans According to the Patients' Health Status?

N-of-1 trials are a useful tool for clinicians who want to determine the effectiveness of a treatment in a particular individual. The reporting of N-of-1 trials has been variable and incomplete, hindering their usefulness in clinical decision making and by future researchers. This document presents the CONSORT (Consolidated Standards of Reporting Trials) extension for N-of-1 trials (CENT 2015). CENT 2015 extends the CONSORT 2010 guidance to facilitate the preparation and appraisal of reports of an individual N-of-1 trial or a series of prospectively planned, multiple, crossover N-of-1 trials. CENT 2015 elaborates on 14 items of the CONSORT 2010 checklist, totalling 25 checklist items (44 sub-items), and recommends diagrams to help authors document the progress of one participant through a trial or more than one participant through a trial or series of trials, as applicable. Examples of good reporting and evidence based rationale for CENT 2015 checklist items are provided.

Tools for Productively Managing Conflict

BACKGROUND: Individual physicians who are paid prospectively, as in capitated health plans, might tend to encourage patients to avoid or to join these plans according to the patient’s health status. Though insurance risk selection has been well documented among organizations paid on a prospective basis, such physician-level risk selection has not been studied. OBJECTIVE: To assess physician reports of risk selection in capitated health plans and explore potentially related factors. DESIGN AND PARTICIPANTS: National mailed survey of primary care physicians in 1997–1998, oversampling physicians in areas with more capitated health plans. RESULTS: The response rate was 63% (787 of 1,252 eligible recipients). Overall, 44% of physicians reported encouraging patients either to join or to avoid capitated health plans according to the patients’ health status: 40% encouraged more complex and ill patients to avoid capitated plans and 23% encouraged healthier patients to join capitated plans. In multivariable models, physicians with negative perceptions of capitated plan quality, with more negative experiences in capitated plans, and those who knew at each patient encounter how they were being compensated had higher odds of encouraging sicker patients to avoid capitated plans (odds ratios, 2.0, 2.2, and 2.0; all confidence intervals >1). CONCLUSIONS: Many primary care physicians report encouraging patients to join or avoid capitated plans according to the patient’s health status. Although these physicians’ recommendations might be associated primarily with concerns about quality, they can have the effect of insulating certain health plans from covering sicker and more expensive patients.