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Objectives: To understand the relationship between informed choice and long-acting reversible contraceptive (LARC) use among women aged 15-49 years in Uganda after adjusting for potential confounding., Methods: This cross-sectional study uses data from the 2016 Uganda Standard Demographic and Health Survey. Thomas-Rao corrections to a χ 2 test were used for the bivariable analysis. A design-adjusted multivariable logistic regression was used to estimate the association between informed choice and LARC use. Propensity score matching was conducted as a sensitivity analysis., Results: In all, 3646 women were included in the analysis and 975 reported using a LARC. In the design-adjusted multivariable analysis, the odds of reporting LARC usage were 1.98 (95% confidence interval 1.61-2.43) times higher among women who reported informed choice compared with those who did not. The subsequent propensity score analysis reported similar findings., Conclusion: Providing informed choice can help to increase the number of women who use LARC in Uganda. As such, the Ugandan Ministry of Health should further expand access to family planning counseling as it could contribute to the reduction of unplanned pregnancies across Uganda with the use of LARC., (© 2022 International Federation of Gynecology and Obstetrics.)

Pragmatic trials offer practical means of obtaining real-world evidence to help improve decision-making in comparative effectiveness settings. Unfortunately, incomplete adherence is a common problem in pragmatic trials. The commonly used methods in randomized control trials often cannot handle the added complexity imposed by incomplete adherence, resulting in biased estimates. Several naive methods and advanced causal inference methods (e.g., inverse probability weighting and instrumental variable-based approaches) have been used in the literature to deal with incomplete adherence. Practitioners and applied researchers are often confused about which method to consider under a given setting. This current work is aimed to review commonly used statistical methods to deal with non-adherence along with their key assumptions, advantages, and limitations, with a particular focus on pragmatic trials. We have listed the applicable settings for these methods and provided a summary of available software. All methods were applied to two hypothetical datasets to demonstrate how these methods perform in a given scenario, along with the R codes. The key considerations include the type of intervention strategy (point treatment settings, where treatment is administered only once versus sustained treatment settings, where treatment has to be continued over time) and availability of data (e.g., the extent of measured or unmeasured covariates that are associated with adherence, dependent confounding impacted by past treatment, and potential violation of assumptions). This study will guide practitioners and applied researchers to use the appropriate statistical method to address incomplete adherence in pragmatic trial settings for both the point and sustained treatment strategies., (© 2022 John Wiley & Sons Ltd.)

Participants in pragmatic clinical trials often partially adhere to treatment. However, to simplify the analysis, most studies dichotomize adherence (supposing that subjects received either full or no treatment), which can introduce biases in the results. For example, the popular approach of principal stratification is based on the concept that the population can be separated into strata based on how they will react to treatment assignment, but this framework does not include strata in which a partially adhering participant would belong. We expanded the principal stratification framework to allow partial adherers to have their own principal stratum and treatment level. The expanded approach is feasible in pragmatic settings. We have designed a Monte Carlo posterior sampling method to obtain the relevant parameter estimates. Simulations were completed under a range of settings where participants partially adhered to treatment, including a hypothetical setting from a published simulation trial on the topic of partial adherence. The inference method is additionally applied to data from a real randomized clinical trial that features partial adherence. Comparison of the simulation results indicated that our method is superior in most cases to the biased estimators obtained through standard principal stratification. Simulation results further suggest that our proposed method may lead to increased accuracy of inference in settings where study participants only partially adhere to assigned treatment., (© 2021 John Wiley & Sons Ltd.)

Objective: To elucidate the role of nonsteroidal antiinflammatory drugs (NSAIDs) in the increased risk of cardiovascular disease (CVD) among osteoarthritis (OA) patients., Methods: This longitudinal study was based on linked health administrative data from British Columbia, Canada. From a population-based cohort of 720,055 British Columbians, we selected 7,743 OA patients and 23,229 age- and sex-matched non-OA controls. We used multivariable Cox proportional hazards models to estimate the risk of developing incident CVD (primary outcome) as well as ischemic heart disease, congestive heart failure, and stroke (secondary outcomes). To estimate the mediating effect of NSAIDs, defined as current use of an NSAID according to linked PharmaNet data, in the OA-CVD relationship, we implemented a marginal structural model., Results: OA patients had a higher risk of developing CVD than controls without OA. After adjusting for socioeconomic status, body mass index, hypertension, diabetes, hyperlipidemia, chronic obstructive pulmonary disease, and Romano comorbidity score, the adjusted hazard ratio (HR) was 1.23 (95% confidence interval [95% CI] 1.17-1.28). The adjusted HRs for congestive heart failure, ischemic heart disease, and stroke were 1.42 (95% CI 1.33-1.51), 1.17 (95% CI 1.10-1.26), and 1.14 (95% CI 1.07-1.22), respectively. Approximately 41% of the total effect of OA on increased CVD risk was mediated through NSAIDs. For the secondary outcomes, the proportion mediated through NSAIDs was 23%, 56%, and 64% for congestive heart failure, ischemic heart disease, and stroke, respectively., Conclusion: The findings of this first study to evaluate the mediating role of NSAIDs in the relationship between OA and CVD suggest that NSAID use contributes substantially to the OA-CVD association., (© 2019, American College of Rheumatology.)

Correct specification of the inverse probability weighting (IPW) model is necessary for consistent inference from a marginal structural Cox model (MSCM). In practical applications, researchers are typically unaware of the true specification of the weight model. Nonetheless, IPWs are commonly estimated using parametric models, such as the main-effects logistic regression model. In practice, assumptions underlying such models may not hold and data-adaptive statistical learning methods may provide an alternative. Many candidate statistical learning approaches are available in the literature. However, the optimal approach for a given dataset is impossible to predict. Super learner (SL) has been proposed as a tool for selecting an optimal learner from a set of candidates using cross-validation. In this study, we evaluate the usefulness of a SL in estimating IPW in four different MSCM simulation scenarios, in which we varied the specification of the true weight model specification (linear and/or additive). Our simulations show that, in the presence of weight model misspecification, with a rich and diverse set of candidate algorithms, SL can generally offer a better alternative to the commonly used statistical learning approaches in terms of MSE as well as the coverage probabilities of the estimated effect in an MSCM. The findings from the simulation studies guided the application of the MSCM in a multiple sclerosis cohort from British Columbia, Canada (1995-2008), to estimate the impact of beta-interferon treatment in delaying disability progression. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

Background and Purpose: Beta-interferons (IFNβ) are the most widely prescribed drugs for patients with multiple sclerosis (MS). However, whether or not treatment with IFNβ can delay secondary progressive MS (SPMS) onset remains unknown. Our aim was to examine the association between IFNβ exposure and SPMS onset in patients with relapsing-remitting MS (RRMS)., Methods: A retrospective cohort study using British Columbia (Canada) population-based clinical and health administrative data (1985-2008) was conducted. RRMS patients treated with IFNβ (n = 794) were compared with untreated contemporary (n = 933) and historical (n = 837) controls. Cohort entry was the first clinic visit during which patients became eligible for IFNβ treatment (baseline). The outcome was time from baseline to SPMS onset. Cox regression models with IFNβ as a time-dependent exposure were adjusted for sex, and baseline age, disease duration, disability, *socioeconomic status and *comorbidities (*available for the contemporary cohorts only). Additional analyses included propensity score adjustment., Results: The median follow-up for the IFNβ-treated, untreated contemporary and historical controls were 5.7, 3.7 and 7.3 years, and the proportions of patients reaching SPMS were 9.2%, 11.8% and 32.9%, respectively. After adjustment for confounders, IFNβ exposure was not associated with the risk of reaching SPMS when either the contemporary or the historical untreated cohorts were considered (hazard ratio 1.07; 95% confidence interval 0.93-1.48, and hazard ratio 1.04; 95% confidence interval 0.74-1.46, respectively). Further adjustments and the propensity score yielded results consistent with the main analysis., Conclusions: Amongst patients with RRMS, use of IFNβ was not associated with a delayed onset of SPMS., (© 2015 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Background and Purpose: It was recently reported that there was no significant overall association between interferon beta exposure and disability progression in relapsing-remitting multiple sclerosis (RRMS) patients in an observational study from Canada. In the current study, the potential for heterogeneity in the association between exposure to interferon beta and disability progression across patients' baseline characteristics was investigated., Methods: RRMS patients treated with interferon beta (n = 868) and two cohorts of untreated patients (829 contemporary and 959 historical controls) were included. The main outcome was time from interferon beta treatment eligibility (baseline) to a confirmed and sustained Expanded Disability Status Scale (EDSS) score 6 using a multivariable Cox model, with treatment as a time-varying predictor, testing interaction effects for five pre-specified baseline characteristics: sex, age, disease duration, EDSS and annualized relapse rate (ARR) based on the previous 2 years., Results: Significant heterogeneity was found in the association of interferon beta exposure and disability progression only across ARR, and only when treated patients were compared with historical controls (P = 0.005 at a Bonferroni-adjusted alpha of 0.01). For patients with ARR>1, treatment-exposed time was associated with a hazard ratio of 0.38 (95%CI 0.20-0.75) for disability progression compared with the unexposed time., Conclusions: RRMS patients with more frequent relapses at baseline may be more likely to benefit from interferon beta treatment with respect to long-term disability progression., (© 2013 The Author(s) European Journal of Neurology © 2013 EAN.)