Conservation and production responses vary by disturbance intensity in a long-term forest management experiment.

Reductions in management intensity are often proposed to support a broader range of beneficial ecosystem responses than traditional management approaches. However, few studies evaluate ecosystem responses across approaches. Also, managers lack information about how species traits mediate responses across management approaches, a potentially substantial source of spatial and temporal variation in population and community responses that if ignored may hinder effectiveness of management programs. We used data collected over eight years from a manipulative experiment to test how four forest management strategies influenced avian community composition and wood production. After harvesting, we evaluated responses to three levels of plant cover suppression (Light, Moderate, and Intensive herbicide applications) in relation to a control without herbicide. We predicted the Moderate and Intensive treatments would exert strong negative effects on leaf-gleaning insectivores, including species of conservation concern due to long-term population declines. However, given high forest productivity, we expected temporal duration of effects to be short. Richness of leaf-gleaning bird species was reduced by 20-50% during the first four years post-harvest (when herbicide treatments were on-going), but the effect size declined over the next four years once treatments were completed (13-20% reduction). Effect sizes were substantially smaller for the non-leaf-gleaner group during years 1-4 (19-27%) and disappeared during years 5-8 (2-3%). However, in our final year of observation, we did find an average of five fewer non-leaf-gleaner species on Light vs. Control units. In the last two years of observation, turnover probabilities for the leaf-gleaner species remained higher on all treatments compared to the Control (0.11-0.21), indicating that new species continued to colonize treatments. Planted conifers were 40-44% taller and 74-81% larger in diameter in the Moderate and Intensive treatments compared to the Control, leading to substantial gains in wood biomass. Current practices provided more balance between two ecosystem responses, avian diversity and wood production, compared to less intensive alternatives. When short-term negative effects occur, the spatial distribution of harvesting and regeneration regionally indicates that habitat is often available locally to support leaf-gleaning and non-leaf-gleaning bird populations while releasing other portions of the region for high priority conservation objectives including late-successional forest reserves.

Augmenting external control arms using Bayesian borrowing: a case study in first-line non-small cell lung cancer.

Aim: This study aimed to improve comparative effectiveness estimates and discuss challenges encountered through the application of Bayesian borrowing (BB) methods to augment an external control arm (ECA) constructed from real-world data (RWD) using historical clinical trial data in first-line non-small-cell lung cancer (NSCLC). Materials & methods: An ECA for a randomized controlled trial (RCT) in first-line NSCLC was constructed using ConcertAI Patient360™ to assess chemotherapy with or without cetuximab, in the bevacizumab-inappropriate subpopulation. Cardinality matching was used to match patient characteristics between the treatment arm (cetuximab + chemotherapy) and ECA. Overall survival (OS) was assessed as the primary outcome using Cox proportional hazards (PH). BB was conducted using a static power prior under a Weibull PH parameterization with borrowing weights from 0.0 to 1.0 and augmentation of the ECA from a historical control trial. Results: The constructed ECA yielded a higher overall survival (OS) hazard ratio (HR) (HR = 1.53; 95% CI: 1.21-1.93) than observed in the matched population of the RCT (HR = 0.91; 95% CI: 0.73-1.13). The OS HR decreased through the incorporation of BB (HR = 1.30; 95% CI: 1.08-1.54, borrowing weight = 1.0). BB was applied to augment the RCT control arm via a historical control which improved the precision of the observed HR estimate (1.03; 95% CI: 0.86-1.22, borrowing weight = 1.0), in comparison to the matched population of the RCT alone. Conclusion: In this study, the RWD ECA was unable to successfully replicate the OS estimates from the matched population of the selected RCT. The inability to replicate could be due to unmeasured confounding and variations in time-periods, follow-up and subsequent therapy. Despite these findings, we demonstrate how BB can improve precision of comparative effectiveness estimates, potentially aid as a bias assessment tool and mitigate challenges of traditional methods when appropriate external data sources are available.

Evaluating treatments in rare indications warrants a Bayesian approach.

Evaluating efficacy and real-world effectiveness for novel therapies targeting rare mutations or patient subpopulations with unmet needs is a growing challenge in health economics and outcomes research (HEOR). In these settings it may be difficult to recruit enough patients to run adequately powered randomized clinical trials, resulting in greater reliance on single-arm trials or basket trial designs. Additionally, evidence networks for performing network meta-analysis may be sparse or disconnected when comparing available treatments in narrower patient populations. These challenges create an increased need for use of appropriate methods for handling small sample sizes, structural modelling assumptions and more nuanced decision rules to arrive at "best-available evidence" on comparative and non-comparative efficacy/effectiveness. We advocate for greater use of Bayesian methods to address these challenges as they can facilitate efficient and transparent borrowing of information across varied data sources under flexible modelling assumptions, probabilistic sensitivity analysis to assess model assumptions, and more nuanced decision-making where limited power reduces the utility of classical frequentist hypothesis testing. We illustrate how Bayesian methods have been recently used to overcome several challenges of rare indications in HEOR, including approaches to borrowing information from external data sources, evaluation of efficacy in basket trials, and incorporating non-randomized studies into network meta-analysis. Lastly, we provide several recommendations for HEOR practitioners on appropriate use of Bayesian methods to address challenges in the rare disease setting.

Totality of evidence of the effectiveness of repurposed therapies for COVID-19: Can we use real-world studies alongside randomized controlled trials?

Rapid and robust strategies to evaluate the efficacy and effectiveness of novel and existing pharmacotherapeutic interventions (repurposed treatments) in future pandemics are required. Observational "real-world studies" (RWS) can report more quickly than randomized controlled trials (RCTs) and would have value were they to yield reliable results. Both RCTs and RWS were deployed during the coronavirus disease 2019 (COVID-19) pandemic. Comparing results between them offers a unique opportunity to determine the potential value and contribution of each. A learning review of these parallel evidence channels in COVID-19, based on quantitative modeling, can help improve speed and reliability in the evaluation of repurposed therapeutics in a future pandemic. Analysis of all-cause mortality data from 249 observational RWS and RCTs across eight treatment regimens for COVID-19 showed that RWS yield more heterogeneous results, and generally overestimate the effect size subsequently seen in RCTs. This is explained in part by a few study factors: the presence of RWS that are imbalanced for age, gender, and disease severity, and those reporting mortality at 2 weeks or less. Smaller studies of either type contributed negligibly. Analysis of evidence generated sequentially during the pandemic indicated that larger RCTs drive our ability to make conclusive decisions regarding clinical benefit of each treatment, with limited inference drawn from RWS. These results suggest that when evaluating therapies in future pandemics, (1) large RCTs, especially platform studies, be deployed early; (2) any RWS should be large and should have adequate matching of known confounders and long follow-up; (3) reporting standards and data standards for primary endpoints, explanatory factors, and key subgroups should be improved; in addition, (4) appropriate incentives should be in place to enable access to patient-level data; and (5) an overall aggregate view of all available results should be available at any given time.