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 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.

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.

The use of therapeutic interchange for biologic therapies.

Therapeutic interchange is the practice of switching or dispensing drugs that are chemically distinct but therapeutically similar in terms of their efficacy, safety, and tolerability profiles. The stated goal of therapeutic interchange is to achieve an improved or neutral outcome with the new agent while reducing overall treatment costs. Until recently, most interchange programs have been limited to switches within drug classes, such as angiotensin-converting enzyme (ACE) inhibitors, proton pump inhibitors (PPIs), HMG-CoA reductase inhibitors (statins), and selective serotonin reuptake inhibitors (SSRIs), and generally to drugs that use the same routes of administration. Therapeutic interchange now is being applied to some biologic agents, such as those used to treat psoriasis and rheumatoid arthritis (RA). In some cases, these agents differ in structure and mode of administration. Patients who require a biologic agent are often difficult to manage, and the comorbidities that are prevalent in these patients further complicate management and agent selection. Population-based outcomes among various agents may not appear notably different, but because there is no a priori means to determine the effects of a given biologic agent on any individual patient, therapeutic interchange is inadvisable once a patient receiving RA or psoriasis therapy has been stabilized. However, if a biologic agent has been designated as preferred on a formulary, it is reasonable to initiate treatment with that agent in a patient who is naive to biologic therapy if that agent is not contraindicated. Respectful, two-way communication between health care professionals and managed care organizations (MCOs) will help ensure that a patient receives the appropriate therapy at the right time.

Lessons learned combining N-of-1 trials to assess fibromyalgia therapies.

OBJECTIVE: Applying population research to individual treatment requires understanding the connections between patient-specific characteristics, population-based studies, and treatment responses. Conducting practice-based research using individual-focused (N-of-1) trials may aid this process. We combined N-of-1 trials to compare fibromyalgia therapies and to assess the feasibility and outcomes of this approach for practice-based effectiveness research. METHODS: Community- and center-based rheumatologists enrolled patients with fibromyalgia syndrome in randomized, double-blind, multi-crossover, N-of-1 trials comparing amitriptyline and the combination amitriptyline and fluoxetine. Fibromyalgia Impact Questionnaire outcomes were used for the individuals' treatment and combined across patients for sample-based analyses. Outcomes were compared with results from more standard trial designs. RESULTS: Eight rheumatologists enrolled 58 patients in N-of-1 trials. Most physicians and patients had not previously participated in clinical trials. Using several analytic methods, the pooled results showed a better outcome score (mean difference: -6.1 +/- 2.0 to -8.0 +/- 3.7 points) in patients taking combination therapy. These population results are similar to published outcomes from a more traditional crossover trial. Neither practice type nor patient characteristics were significantly associated with the observed treatment-effect variation. Most participants, irrespective of selected treatment, felt their individual N-of-1 trials were helpful. CONCLUSION: Implementation of the combined N-of-1 methodology is feasible in rheumatology practices and results confirm greater fibromyalgia improvement with combination therapy. This research approach broadens participation, although our trials' specifics likely influenced enrollment eligibility. In addition to individual benefits, combining N-of-1 trial data provides population research benefits. This patient-focused approach should be further explored to bridge research and practice.

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-alpha (ESR1), estrogen receptor-beta (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.

Association of estrogen receptor beta gene polymorphisms with left ventricular mass and wall thickness in women.

BACKGROUND: Left ventricular (LV) hypertrophy is a significant risk factor for cardiovascular disease. Given sex-based differences in cardiac structure and remodeling, we hypothesized that variation in estrogen pathway genes might be associated with alteration of LV structure. METHODS: We studied 1249 unrelated individuals, 547 men and 702 women (mean age 59 years) from the Framingham Heart Study. Eight single nucleotide polymorphisms in the genes for estrogen receptor alpha and estrogen receptor beta (ESR2) were tested for association with 5 LV measures: LV mass (LVM), LV wall thickness (LVWT), LV internal diameter at end-diastole and end-systole, and fractional shortening. Sex-specific multiple regression analyses were performed adjusting for age, weight, height, systolic and diastolic blood pressure, hypertension treatment, diabetes, and in women, menopausal status. RESULTS: In men, there was no evidence of association between the estrogen pathway polymorphisms tested and LV structure or function. In women, however, two polymorphisms, ESR2 rs1256031 and ESR2 rs1256059, in linkage disequilibrium with one another, were associated with LVM and LVWT (P = .0007 to .03); the association was most pronounced in those women with hypertension (P = .0006 to .01). The association did not appear to be explained by variation in blood pressure, plasma lipoprotein levels, or hyperglycemia. CONCLUSIONS: The ESR2 polymorphisms are associated with LV structural differences in women with hypertension in a community-based population. These data are consistent with the hypothesis that genetic factors may mediate part of the observed sex-based differences in LV structure and remodeling.

Evaluating the nondrug costs of formulary coverage restrictions.

PURPOSE: Clinicians often are required to switch prescribed therapy for their patients in response to health plan initiatives for controlling drug expenditures. To explore the effect of these initiatives, we sought clinicians' feedback regarding their practices and processes for switching patients' medications to accommodate insurance coverage. DESIGN: Self-administered Intranet-based survey of clinicians at an urban, tertiary-care hospital. METHODOLOGY: Using survey responses, we calculate nondrug costs induced by formulary cost-saving measures. PRINCIPAL FINDINGS: A total of 91 responses were received from 569 providers who were sent a request to complete the questionnaire via electronic mail (18 percent response rate). It took an average of 11.1, 18.9, and 16.4 minutes for physicians, nurses, and nurse practitioners/physician assistants, respectively, to make the medication switch. The mean number of switches per month ranged from 10.6 to 36.9. More than half the time spent on these switches is not directly reimbursed. Specific switch-induced intervention costs differed for different drug types. The effect on clinician workload tended to be an inconvenience. While the majority of physicians and nurse practitioners/physician assistants did not feel this process damaged patient-provider relations, most nurses did. CONCLUSIONS: In response to formulary restrictions, other costs are induced and incurred by providers and patients. The extent of patient costs, including those from adverse drug reactions, needs further study. More research is needed to elucidate costs and burden shifts as all parties involved evaluate and modify plans to moderate prescription drug expenditures.