Evaluating the comparability of osteoporosis treatments using propensity score and negative control outcome methods in UK and Denmark electronic health record databases.

Evidence on the comparative effectiveness of osteoporosis treatments is heterogeneous. This may be attributed to different populations and clinical practice, but also to differing methodologies ensuring comparability of treatment groups before treatment effect estimation and the amount of residual confounding by indication. This study assessed the comparability of denosumab vs oral bisphosphonate (OBP) groups using propensity score (PS) methods and negative control outcome (NCO) analysis. A total of 280 288 women aged ≥50 yr initiating denosumab or OBP in 2011-2018 were included from the UK Clinical Practice Research Datalink (CPRD) and the Danish National Registries (DNR). Balance of observed covariates was assessed using absolute standardized mean difference (ASMD) before and after PS weighting, matching, and stratification, with ASMD >0.1 indicating imbalance. Residual confounding was assessed using NCOs with ≥100 events. Hazard ratio (HR) and 95%CI between treatment and NCO were estimated using Cox models. Presence of residual confounding was evaluated with 2 approaches (1) >5% of NCOs with 95% CI excluding 1, (2) >5% of NCOs with an upper CI <0.75 or lower CI >1.3. The number of imbalanced covariates before adjustment (CPRD 22/87; DNR 18/83) decreased, with 2%-11% imbalance remaining after weighting, matching, or stratification. Using approach 1, residual confounding was present for all PS methods in both databases (≥8% of NCOs), except for stratification in DNR (3.8%). Using approach 2, residual confounding was present in CPRD with PS matching (5.3%) and stratification (6.4%), but not with weighting (4.3%). Within DNR, no NCOs had HR estimates with upper or lower CI limits beyond the specified bounds indicating residual confounding for any PS method. Achievement of covariate balance and determination of residual bias were dependent upon several factors including the population under study, PS method, prevalence of NCO, and the threshold indicating residual confounding.

Treatment groups in clinical practice may not be comparable as patient characteristics differ according to the need for the prescribed medication, known as confounding. We assessed comparability of 2 common osteoporosis treatments, denosumab and oral bisphosphonate, in 280 288 postmenopausal women using electronic health records from UK Clinical Practice Research Datalink (CPRD) and Danish National Registries (DNR). We evaluated comparability of recorded patient characteristics with 3 propensity score (PS) methods, matching, stratification, and weighting. We assessed residual confounding from unrecorded patient characteristics via negative control outcomes (NCOs), events known not to be associated with treatment such as delirium. We found that achieving comparability of osteoporosis treatment groups depended on the study population, PS method, and definition of residual confounding. Weighting and stratification performed the best in DNR and CPRD, respectively. Using a stricter threshold based on statistical significance for the NCO suggested that the treatment groups were not comparable, except for PS stratification in DNR. Applying clinically significant thresholds of treatment effect size showed comparability using weighting in CPRD and all PS methods in DNR. Studies should consider more than one PS method to test robustness and identify the largest number of NCO to give the greatest flexibility in detecting residual confounding.

Integration of population genetics with oceanographic models reveals strong connectivity among coral reefs across Seychelles.

Many countries with tropical reef systems face hard choices preserving coral reefs in the face of climate change on limited budgets. One approach to maximising regional reef resilience is targeting management efforts and resources at reefs that export large numbers of larvae to other reefs. However, this requires reef connectivity to be quantified. To map coral connectivity in the Seychelles reef system we carried out a population genomic study of the Porites lutea species complex using 241 sequenced colonies from multiple islands. To identify oceanographic drivers of this connectivity and quantify variability, we further used a 2 km resolution regional ocean simulation coupled with a larval dispersal model to predict the flow of coral larvae between reef sites. Patterns of admixture and gene flow are broadly supported by model predictions, but the realised connectivity is greater than that predicted from model simulations. Both methods detected a biogeographic dispersal barrier between the Inner and Outer Islands of Seychelles. However, this barrier is permeable and substantial larval transport is possible across Seychelles, particularly for one of two putative species found in our genomic study. The broad agreement between predicted connectivity and observed genetic patterns supports the use of such larval dispersal simulations in reef system management in Seychelles and the wider region.