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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. [ABSTRACT FROM AUTHOR]

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.

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.

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.