Background: N-of-1 trials offer an innovative approach to delivering personalized clinical care together with population-level research. While increasingly used, these methods have raised some statistical concerns in the healthcare community. Methods: We discuss concerns of selection bias, carryover effects from treatment, and trial data analysis conceptually, then rigorously evaluate concerns of effect sizes, power and sample size through simulation study. Four variance structures for patient heterogeneity and model error are considered in a series of 5000 simulated trials with 3 cycles, which compare aggregated N-of-1 trials to parallel randomized controlled trials (RCTs) and crossover trials. Results: Aggregated N-of-1 trials outperformed both traditional parallel RCT and crossover designs when these trial designs were simulated in terms of power and required sample size to obtain a given power. N-of-1 designs resulted in a higher type-I error probability than parallel RCT and cross over designs when moderate-to-strong carryover effects were not considered or in the presence of modeled selection bias. However, N-of-1 designs allowed better estimation of patient-level random effects. These results reinforce the need to account for these factors when planning N-of-1 trials. Conclusion: N-of-1 trial designs offer a rigorous method for advancing personalized medicine and healthcare with the potential to minimize costs and resources. Interventions can be tested with adequate power with far fewer patients than traditional RCT and crossover designs. Operating characteristics compare favorably to both traditional RCT and crossover designs.

Background: Despite improvements in prognosis following myocardial infarction (MI), racial disparities persist. The objective of this study was to examine disparities between Black and White adults in cardiovascular disease (CVD), coronary heart disease, stroke, heart failure (HF), and mortality after MI and characteristics that may explain the disparities. Methods: This prospective cohort study included 1122 REGARDS (Reasons for Geographic and Racial Differences in Stroke) study participants with incident MI between 2003 and 2016. We followed participants for subsequent CVD events (MI, stroke, HF hospitalization, or death from CVD; n=431), coronary heart disease events (MI or death from coronary heart disease; (n=277), stroke (n=68), HF events (HF hospitalization or death from HF; n=191), and all-cause mortality (n=527; 3-year median follow-up after MI). Results: Among 1122 participants with incident MI, 37.5% were Black participants, 45.4% were women, and mean age was 73.2 (SD, 9.5) years. The unadjusted hazard ratio for CVD events comparing Black to White participants was 1.42 (95% CI, 1.17–1.71). Adjusting for sociodemographic characteristics did not attenuate the association (1.41 [95% CI, 1.14–1.73]), but further adjusting for pre-MI health status (1.25 [95% CI, 1.00–1.56]) and characteristics of the MI (1.01 [95% CI, 0.80–1.27]) resulted in substantial attenuation. Similar patterns were observed for the other outcomes, although the number of strokes was small. Conclusions: Black individuals had a higher risk of CVD events and mortality after MI than White individuals. The disparities were explained by health status before MI and characteristics of the MI. These findings suggest that both primordial prevention of risk factors and improved acute treatment strategies are needed to reduce disparities in post-MI outcomes.

To the Editor The recent article by Kravitz and colleagues reporting the results of a randomized clinical trial comparing n-of-1 trials with standard care for t

Purpose of review: The changing patterns of osteoporosis treatment and fragility fractures have led to what leaders are calling a ‘crisis in the osteoporosis.’ We address data on changing patterns in fractures, and highlight strengths and limitations of recently published data. Recent findings: Declines in hip fracture rates have been shown in studies from around the world. However, recently, using national Medicare data, Michael Lewiecki and colleagues show a plateau in the decline of hip fracture incidence in the United States from 2012 to 2015. Population-based data is integral for evaluating temporal trends; however, researchers must consider the biases associated with them including: age effects, period effects, and cohort effects. Rosengren and colleagues conducted the most comprehensive evaluation of age, period, and birth cohort effects in their study of hip fracture trends from 1987 to 2010 in Denmark and Sweden, in which they identified changes in hip fracture rates based on age, period, and cohort effects. Summary: Recent findings show clear temporal trends in changing fracture rates. Studies, which evaluated these biases largely attribute increased hip fracture rates to various age, period, and cohort effects, highlighting the importance of appropriate screening and treatment.