No abstract

No abstract

The AFFIRM Study enrolled 4060 predominantly elderly patients with atrial fibrillation to compare ventricular rate control with rhythm control. The patients in the AFFIRM Study were representative of patients at high risk for complications from atrial fibrillation, which indicates that the results of this large clinical trial will be relevant to patient care.

Objective Few studies have compared the risk of recurrent falls across different types of analgesic use, and were limited to adjust for potential confounders (e.g., pain/depression severity). We aimed to assess analgesic use and the subsequent risk of recurrent falls, among participants with or at risk of knee osteoarthritis (OA). Methods A longitudinal analysis included 4,231 participants aged 45–79 years at baseline with 4-year follow-up from the Osteoarthritis Initiative (OAI) cohort study. We grouped participants into six mutually exclusive subgroups based on annually assessed analgesic use in the following hierarchical order of analgesic/central nervous system potency: use of (1)opioids, (2)antidepressants, (3)other prescription pain medications, (4)over-the-counter pain medications, (5)nutraceuticals, and (6)no analgesics. We used multivariable modified Poisson regression models with a robust error variance to estimate the effect of analgesic use on the risk of recurrent falls(≥2) in the following year, adjusted for demographics and health status/behavior factors. Results Opioid use increased from 2.7% at baseline to 3.6% at the 36-month visit (>80% using other analgesics/nutraceuticals), while other prescription pain medication use decreased from 16.7% to 11.9% over this time period. Approximately 15% of participants reported recurrent falls. Compared to those not using analgesics, participants used opioids and/or antidepressants had a 22–25% increased risk of recurrent falls (opioids: RRadjusted=1.22, 95%CI=1.04–1.45; antidepressants: RRadjusted=1.25, 95%CI=1.10–1.41). Conclusion Participants with or at risk of knee OA who were on opioids and antidepressants with/without other analgesics/nutraceuticals may have an increased risk of recurrent falls after adjusting for potential confounders. Use of opioids and antidepressants warrants caution.

Background In many clinical trials continuous outcomes are dichotomized to compare proportions of patients who respond. A common and recommended approach to handling missing data in responder analysis is to impute as non-responders, despite known biases. Multiple imputation is another natural choice but when a continuous outcome is ultimately dichotomized, the specifications of the imputation model come into question. Practitioners can either impute the missing outcome before dichotomizing or dichotomize then impute. In this study we compared multiple imputation of the continuous and dichotomous forms of the outcome, and imputing responder status as non-response in responder analysis. Methods We simulated four response profiles representing a two-arm randomized controlled trial with a continuous outcome at four time points. We omitted data using six missing at random mechanisms, and imputed missing observations three ways: 1) replacing as non-responder; 2) multiply imputing before dichotomizing; and 3) multiply imputing the dichotomized response. Imputation models included the continuous response at all timepoints, and additional auxiliary variables for some scenarios. We assessed bias, power, coverage of the 95% confidence interval, and type 1 error. Finally, we applied these methods to a longitudinal trial for patients with major depressive disorder. Results Both forms of multiple imputation performed better than non-response imputation in terms of bias and type 1 error. When approximately 30% of responses were missing, bias was less than 7.3% for multiple imputation scenarios but when 50% of responses were missing, imputing before dichotomizing generally had lower bias compared to dichotomizing before imputing. Non-response imputation resulted in biased estimates, both underestimates and overestimates. In the example trial data, non-response imputation estimated a smaller difference in proportions than multiply imputed approaches. Conclusions With moderate amounts of missing data, multiply imputing the continuous outcome variable prior to dichotomizing performed similar to multiply imputing the binary responder status. With higher rates of missingness, multiply imputing the continuous variable was less biased and had well-controlled coverage probabilities of the 95% confidence interval compared to imputing the dichotomous response. In general, multiple imputation using the longitudinally measured continuous outcome in the imputation model performed better than imputing missing observations as non-responders.