The Inflation Reduction Act: An Opportunity to Accelerate Confidence in Real-World Evidence in the United States.

OBJECTIVES: The Inflation Reduction Act (IRA), enacted in 2022, brings substantial reforms to the US healthcare system, particularly regarding Medicare. A key aspect includes the introduction of Medicare price negotiation. The objective of this commentary is to explore the implications of the IRA for US pharmaceutical companies, with a specific focus on the role of real-world evidence (RWE) in the context of Medicare reforms. METHODS: This commentary uses a qualitative analysis of the IRA's provisions related to healthcare and pharmaceutical regulation, focusing on how these reforms change the evidence requirements for pharmaceutical companies. It discusses the methodological aspects of generating and using RWE, including techniques such as target trial emulation and quantitative bias analysis methods to address biases inherent in RWE. RESULTS: This commentary highlights that the IRA introduces a unique approach to value assessment in the United States by evaluating drug value several years after launch, as opposed to at launch, similar to health technology assessments in other regions. It underscores the central role of RWE in comparing drug effectiveness across diverse clinical scenarios to improve the accuracy of real-world data comparisons. Furthermore, this article identifies key methodologies for managing the inherent biases in RWE, which are crucial for generating credible evidence for IRA price negotiations. CONCLUSIONS: This article underscores the importance of these methodologies in ensuring credible evidence for IRA price negotiations. It advocates for an integrated approach in evidence generation, positioning RWE as pivotal for informed pricing discussions in the US healthcare landscape.

Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers.

Development of medicines in rare oncologic patient populations are growing, but well-powered randomized controlled trials are typically extremely challenging or unethical to conduct in such settings. External control arms using real-world data are increasingly used to supplement clinical trial evidence where no or little control arm data exists. The construction of an external control arm should always aim to match the population, treatment settings and outcome measurements of the corresponding treatment arm. Yet, external real-world data is typically fraught with limitations including missing data, measurement error and the potential for unmeasured confounding given a nonrandomized comparison. Quantitative bias analysis (QBA) comprises a collection of approaches for modelling the magnitude of systematic errors in data which cannot be addressed with conventional statistical adjustment. Their applications can range from simple deterministic equations to complex hierarchical models. QBA applied to external control arm represent an opportunity for evaluating the validity of the corresponding comparative efficacy estimates. We provide a brief overview of available QBA approaches and explore their application in practice. Using a motivating example of a comparison between pralsetinib single-arm trial data versus pembrolizumab alone or combined with chemotherapy real-world data for RET fusion-positive advanced non-small cell lung cancer (aNSCLC) patients (1-2% among all NSCLC), we illustrate how QBA can be applied to external control arms. We illustrate how QBA is used to ascertain robustness of results despite a large proportion of missing data on baseline ECOG performance status and suspicion of unknown confounding. The robustness of findings is illustrated by showing that no meaningful change to the comparative effect was observed across several 'tipping-point' scenario analyses, and by showing that suspicion of unknown confounding was ruled out by use of E-values. Full R code is also provided.

Association between treatment and improvements in overall survival of patients with advanced/metastatic non-small cell lung cancer since 2011: A study in the United States, Canada, and Germany using retrospective real-world databases.

BACKGROUND: This study aimed to describe treatment patterns and overall survival (OS) in patients with advanced non-small cell lung cancer (aNSCLC) in three countries between 2011 and 2020. METHODS: Three databases (US, Canada, Germany) were used to identify incident aNSCLC patients. OS was assessed from the date of incident aNSCLC diagnosis and, for patients who received at least a first line of therapy (1LOT), from the date of 1LOT initiation. In multivariable analyses, we analyzed the influence of index year and type of prescribed treatment on OS. FINDINGS: We included 51,318 patients with an incident aNSCLC diagnosis. The percentage of patients treated with a 1LOT differed substantially between countries, whereas the number of patients receiving immunotherapies/targeted treatments increased over time in all three countries. Median OS from the date of incident diagnosis was 9.9 months in the United States vs. 4.1 months in Canada. When measured from the start of 1LOT, patients had a median OS of 10.7 months in the United States, 10.9 months in Canada, and 10.9 months in Germany. OS from the start of 1LOT improved in all three countries from 2011 to 2020 by approximately 3 to 4 months. CONCLUSIONS: Observed continuous improvement in OS among patients receiving at least a 1LOT from 2011 to 2020 was likely driven by improved care and changes in the treatment landscape. The difference in the proportion of patients receiving a 1LOT in the observed countries requires further investigation.

Early Treatment with Pegylated Interferon Lambda for Covid-19.

BACKGROUND: The efficacy of a single dose of pegylated interferon lambda in preventing clinical events among outpatients with acute symptomatic coronavirus disease 2019 (Covid-19) is unclear. METHODS: We conducted a randomized, controlled, adaptive platform trial involving predominantly vaccinated adults with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Brazil and Canada. Outpatients who presented with an acute clinical condition consistent with Covid-19 within 7 days after the onset of symptoms received either pegylated interferon lambda (single subcutaneous injection, 180 µg) or placebo (single injection or oral). The primary composite outcome was hospitalization (or transfer to a tertiary hospital) or an emergency department visit (observation for >6 hours) due to Covid-19 within 28 days after randomization. RESULTS: A total of 933 patients were assigned to receive pegylated interferon lambda (2 were subsequently excluded owing to protocol deviations) and 1018 were assigned to receive placebo. Overall, 83% of the patients had been vaccinated, and during the trial, multiple SARS-CoV-2 variants had emerged. A total of 25 of 931 patients (2.7%) in the interferon group had a primary-outcome event, as compared with 57 of 1018 (5.6%) in the placebo group, a difference of 51% (relative risk, 0.49; 95% Bayesian credible interval, 0.30 to 0.76; posterior probability of superiority to placebo, >99.9%). Results were generally consistent in analyses of secondary outcomes, including time to hospitalization for Covid-19 (hazard ratio, 0.57; 95% Bayesian credible interval, 0.33 to 0.95) and Covid-19-related hospitalization or death (hazard ratio, 0.59; 95% Bayesian credible interval, 0.35 to 0.97). The effects were consistent across dominant variants and independent of vaccination status. Among patients with a high viral load at baseline, those who received pegylated interferon lambda had lower viral loads by day 7 than those who received placebo. The incidence of adverse events was similar in the two groups. CONCLUSIONS: Among predominantly vaccinated outpatients with Covid-19, the incidence of hospitalization or an emergency department visit (observation for >6 hours) was significantly lower among those who received a single dose of pegylated interferon lambda than among those who received placebo. (Funded by FastGrants and others; TOGETHER ClinicalTrials.gov number, NCT04727424.).

Head-to-head comparison of FOLFIRINOX versus gemcitabine plus nab-paclitaxel in advanced pancreatic cancer: a target trial emulation using real-world data.

PURPOSE: To emulate a hypothetical target trial assessing the effect of initiating 5-fluorouracil, folinic acid, irinotecan, and oxaliplatin (FOLFIRINOX) versus gemcitabine plus nab-paclitaxel (GN) within 8 weeks of diagnosis on overall survival. METHODS: An observational cohort study was conducted using population-level data from Alberta, Canada. Individuals diagnosed with advanced pancreatic cancer between April 2015 and December 2019 were identified through the provincial cancer registry and followed until March 2021. Records were linked to other administrative databases containing information on relevant variables. Individuals were excluded if they did not have adequate hemoglobin, platelet, white blood cell, and serum creatinine measures or if they received prior therapy. The observational analog of the per-protocol effect was estimated using inverse probability weighted Kaplan-Meier curves with bootstrapped 95% confidence intervals. RESULTS: Four hundred seven individuals were eligible. The weighted median overall survival was 8.3 months (95% CI, 5.7-11.9) for FOLFIRINOX and 5.1 months (95% CI: 4.3 to 5.8) for GN. The adjusted difference in median overall survival was 3.2 months (95% CI, 1.1-7.4) and the mortality hazard ratio was 0.78 (95% CI, 0.61-0.97). CONCLUSIONS: Our estimates favored the initiation of FOLFIRINOX over GN with respect to overall survival.

Data capture and sharing in the COVID-19 pandemic: a cause for concern.

Routine health care and research have been profoundly influenced by digital-health technologies. These technologies range from primary data collection in electronic health records (EHRs) and administrative claims to web-based artificial-intelligence-driven analyses. There has been increased use of such health technologies during the COVID-19 pandemic, driven in part by the availability of these data. In some cases, this has resulted in profound and potentially long-lasting positive effects on medical research and routine health-care delivery. In other cases, high profile shortcomings have been evident, potentially attenuating the effect of-or representing a decreased appetite for-digital-health transformation. In this Series paper, we provide an overview of how facets of health technologies in routinely collected medical data (including EHRs and digital data sharing) have been used for COVID-19 research and tracking, and how these technologies might influence future pandemics and health-care research. We explore the strengths and weaknesses of digital-health research during the COVID-19 pandemic and discuss how learnings from COVID-19 might translate into new approaches in a post-pandemic era.

Comparative Effectiveness of Atezolizumab, Nivolumab, and Docetaxel in Patients With Previously Treated Non-Small Cell Lung Cancer.

Importance: Evidence regarding real-world effectiveness of therapies for patients with advanced non-small cell lung cancer (NSCLC) whose tumors are resistant to platinum-based chemotherapy is lacking. Objective: To compare the effectiveness of the immune checkpoint inhibitors atezolizumab (programmed cell death ligand 1 inhibitor) and nivolumab (programmed cell death 1 inhibitor) and the chemotherapy drug docetaxel in patients with advanced NSCLC resistant to platinum-based chemotherapy. Design, Setting, and Participants: This comparative effectiveness study compared patients aged 18 years or older with advanced NSCLC who initiated atezolizumab, docetaxel, or nivolumab and who had previously been exposed to platinum-based chemotherapy using nationally representative real-world data from more than 280 US cancer clinics. Patients were followed-up from May 2011 to March 2020. Data analysis was performed between April and June 2021. Comparisons of interest were between atezolizumab vs docetaxel and atezolizumab vs nivolumab. Exposures: Initiation of atezolizumab, nivolumab, or docetaxel monotherapy. Main Outcome and Measures: The main outcome was overall survival (OS). Results: A total of 3336 patients (mean [SD] age, 67.1 [9.49] years; 1820 [54.6%] men and 1516 [45.4%] women) were assessed in the main analysis, including 206 patients receiving atezolizumab, 500 receiving docetaxel, and 2630 receiving nivolumab. Patients receiving atezolizumab were older than those treated with docetaxel (mean age [SD], 68.3 [9.4] years vs 65.6 [9.5] years), and were more likely to have been treated in an academic setting (39 patients [18.9%]) than those receiving docetaxel (49 patients [9.8%]) and nivolumab (128 patients [4.9%]). After adjustment for baseline characteristics, atezolizumab was associated with a significantly longer OS compared with docetaxel (adjusted hazard ratio [aHR], 0.79; 95% CI, 0.64-0.97). No significant difference in OS was observed between atezolizumab and nivolumab (aHR, 1.07; 95% CI, 0.89-1.28). These findings were consistent across all patient subgroups tested, and robust to plausible deviations from random missingness for Eastern Cooperative Oncology Group performance status in real-world data (eg, the tipping point for loss of a significantly beneficial effect for atezolizumab vs docetaxel was achieved if patients in the docetaxel group missing baseline Eastern Cooperative Oncology Group performance status had a mean performance status of 1.43 higher than expected). Conclusions and Relevance: In this comparative effectiveness study, atezolizumab was superior to docetaxel and matched nivolumab in prolonging OS in a real-world cohort of patients with advanced NSCLC who previously received platinum-based chemotherapy.

Assessment of Alectinib vs Ceritinib in ALK-Positive Non-Small Cell Lung Cancer in Phase 2 Trials and in Real-world Data.

Importance: Quantitative assessment of bias from unmeasured confounding and missing data can help evaluate uncertainty in findings from indirect comparisons using real-world data (RWD). Objective: To compare the effectiveness of alectinib vs ceritinib in terms of overall survival (OS) in patients with ALK-positive, crizotinib-refractory, non-small cell lung cancer (NSCLC) and to assess the sensitivity of these findings to unmeasured confounding and missing data assumptions. Design, Setting, and Participants: This comparative effectiveness research study compared patients from 2 phase 2 alectinib trials and real-world patients. Patients were monitored from June 2013 to March 2020. Comparisons of interest were between alectinib trial data vs ceritinib RWD and alectinib RWD vs ceritinib RWD. RWD treatment groups were selected from nationally representative cancer data from US cancer clinics, the majority from community centers. Participants were ALK-positive patients aged 18 years or older with advanced NSCLC, prior exposure to crizotinib, and Eastern Cooperative Oncology Group Performance Status (PS) of 0 to 2. Data analysis was performed from October 2020 to March 2021. Exposures: Initiation of alectinib or ceritinib therapy. Main Outcomes and Measures: The main outcome was OS. Results: In total, there were 355 patients: 183 (85 men [46.4%]) in the alectinib trial, 91 (43 men [47.3%]) in the ceritinib RWD group, and 81 (38 men [46.9%]) in the alectinib RWD group. Patients in the alectinib trial were younger (mean [SD] age, 52.53 [11.18] vs 57.97 [11.71] years), more heavily pretreated (mean [SD] number of prior therapy lines, 1.95 [0.72] vs 1.47 [0.81]), and had more favorable baseline ECOG PS (ECOG PS of 0 or 1, 165 patients [90.2%] vs 37 patients [77.1%]) than those in the ceritinib RWD group. The alectinib RWD group (mean [SD] age, 58.69 [11.26] years) had more patients with favorable ECOG PS (ECOG PS of 0 or 1, 49 patients [92.4%] vs 37 patients [77.1%]) and more White patients (56 patients [72.7%] vs 53 patients [62.4%]) compared with the ceritinib group. Compared with ceritinib RWD, alectinib-exposed patients had significantly longer OS in alectinib trials (adjusted hazard ratio [HR], 0.59; 95% CI, 0.44-0.75; P < .001) and alectinib RWD (HR, 0.46; 95% CI, 0.29-0.63; P < .001) after adjustment for baseline confounders. For the worst-case HR estimate of 0.59, residual confounding by a hypothetical confounder associated with mortality and treatment by a risk ratio greater than 2.24 was required to reverse the findings. Conclusions were robust to plausible deviations from random missingness for missing ECOG PS and underrecorded comorbidities and central nervous system metastases in RWD. Conclusions and Relevance: Alectinib exposure was associated with longer OS compared with ceritinib in patients with ALK-positive NSCLC, and only substantial levels of bias examined reversed the findings. These findings suggest that quantitative bias analysis can be a useful tool to address uncertainty of findings for decision-makers considering RWD.

Stability of diagnostic rate in a cohort of 38,813 colorectal polyp specimens and implications for histomorphology and statistical process control.

This work sought to quantify pathologists' diagnostic bias over time in their evaluation of colorectal polyps to assess how this may impact the utility of statistical process control (SPC). All colorectal polyp specimens(CRPS) for 2011-2017 in a region were categorized using a validated free text string matching algorithm. Pathologist diagnostic rates (PDRs) for high grade dysplasia (HGD), tubular adenoma (TA_ad), villous morphology (TVA + VA), sessile serrated adenoma (SSA) and hyperplastic polyp (HP), were assessed (1) for each pathologist in yearly intervals with control charts (CCs), and (2) with a generalized linear model (GLM). The study included 64,115 CRPS. Fifteen pathologists each interpreted > 150 CRPS/year in all years and together diagnosed 38,813. The number of pathologists (of 15) with zero or one (p < 0.05) outlier in seven years, compared to their overall PDR, was 13, 9, 9, 5 and 9 for HGD, TVA + VA, TA_ad, HP and SSA respectively. The GLM confirmed, for the subset where pathologists/endoscopists saw > 600 CRPS each(total 52,760 CRPS), that pathologist, endoscopist, anatomical location and year were all strongly correlated (all p < 0.0001) with the diagnosis. The moderate PDR stability over time supports the hypothesis that diagnostic rates are amendable to calibration via SPC and outcome data.

The Need for a Practical Approach to Evaluate the Effectiveness of COVID-19 Vaccines for Low- and Middle-Income Countries.

The global demand for coronavirus disease 2019 (COVID-19) vaccines currently far outweighs the available global supply and manufacturing capacity. As a result, securing doses of vaccines for low- and middle-income countries has been challenging, particularly for African countries. Clinical trial investigation for COVID-19 vaccines has been rare in Africa, with the only randomized clinical trials (RCTs) for COVID-19 vaccines having been conducted in South Africa. In addition to addressing the current inequities in the vaccine roll-out for low- and middle-income countries, there is a need to monitor the real-world effectiveness of COVID-19 vaccines in these regions. Although RCTs are the superior method for evaluating vaccine efficacy, the feasibility of conducting RCTs to monitor COVID-19 vaccine effectiveness during mass vaccine campaigns will likely be low. There is still a need to evaluate the effectiveness of mass COVID-19 vaccine distribution in a practical manner. We discuss how target trial emulation, the application of trial design principles from RCTs to the analysis of observational data, can be used as a practical, cost-effective way to evaluate real-world effectiveness for COVID-19 vaccines. There are several study design considerations that need to be made in the analyses of observational data, such as uncontrolled confounders and selection biases. Target trial emulation accounts for these considerations to improve the analyses of observational data. The framework of target trial emulation provides a practical way to monitor the effectiveness of mass vaccine campaigns for COVID-19 using observational data.

PRIME-IPD SERIES Part 1. The PRIME-IPD tool promoted verification and standardization of study datasets retrieved for IPD meta-analysis.

OBJECTIVES: We describe a systematic approach to preparing data in the conduct of Individual Participant Data (IPD) analysis. STUDY DESIGN AND SETTING: A guidance paper proposing methods for preparing individual participant data for meta-analysis from multiple study sources, developed by consultation of relevant guidance and experts in IPD. We present an example of how these steps were applied in checking data for our own IPD meta analysis (IPD-MA). RESULTS: We propose five steps of Processing, Replication, Imputation, Merging, and Evaluation to prepare individual participant data for meta-analysis (PRIME-IPD). Using our own IPD-MA as an exemplar, we found that this approach identified missing variables and potential inconsistencies in the data, facilitated the standardization of indicators across studies, confirmed that the correct data were received from investigators, and resulted in a single, verified dataset for IPD-MA. CONCLUSION: The PRIME-IPD approach can assist researchers to systematically prepare, manage and conduct important quality checks on IPD from multiple studies for meta-analyses. Further testing of this framework in IPD-MA would be useful to refine these steps.

Risk Prediction Using Bayesian Networks: An Immunotherapy Case Study in Patients With Metastatic Renal Cell Carcinoma.

PURPOSE: To address the need for more accurate risk stratification models for cancer immuno-oncology, this study aimed to develop a machine-learned Bayesian network model (BNM) for predicting outcomes in patients with metastatic renal cell carcinoma (mRCC) being treated with immunotherapy. METHODS: Patient-level data from the randomized, phase III CheckMate 025 clinical trial comparing nivolumab with everolimus for second-line treatment in patients with mRCC were used to develop the BNM. Outcomes of interest were overall survival (OS), all-cause adverse events, and treatment-related adverse events (TRAE) over 36 months after treatment initiation. External validation of the model's predictions for OS was conducted using data from select centers from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC). RESULTS: Areas under the receiver operating characteristic curve (AUCs) for BNM-based classification of OS using baseline data were 0.74, 0.71, and 0.68 over months 12, 24, and 36, respectively. AUC for OS at 12 months increased to 0.86 when treatment response and progression status in year 1 were included as predictors; progression and response at 12 months were highly prognostic of all outcomes over the 36-month period. AUCs for adverse events and treatment-related adverse events were approximately 0.6 at 12 months but increased to approximately 0.7 by 36 months. Sensitivity analysis comparing the BNM with machine learning classifiers showed comparable performance. Test AUC on IMDC data for 12-month OS was 0.71 despite several variable imbalances. Notably, the BNM outperformed the IMDC risk score alone. CONCLUSION: The validated BNM performed well at prediction using baseline data, particularly with the inclusion of response and progression at 12 months. Additionally, the results suggest that 12 months of follow-up data alone may be sufficient to inform long-term survival projections in patients with mRCC.

Factors Modifying the Associations of Single or Combination Programmed Cell Death 1 and Programmed Cell Death Ligand 1 Inhibitor Therapies With Survival Outcomes in Patients With Metastatic Clear Cell Renal Cell Carcinoma: A Systematic Review and Meta-analysis.

Importance: Programmed cell death 1/programmed cell death ligand 1 (PD-1/PD-L1) inhibitors are immune checkpoint inhibitors widely used in the treatment of metastatic clear cell renal cell carcinoma (ccRCC) and other cancers. There is a lack of understanding regarding which factors are associated with therapeutic response. Objectives: To conduct a systematic literature review of trials reporting on factors associated with differential response to PD-1/PD-L1 inhibitors among patients diagnosed with metastatic ccRCC and quantitatively synthesize the magnitude to which each factor modified the response to PD-1/PD-L1 inhibitors. Data Sources: The MEDLINE and Cochrane Register of Trials databases were searched for studies published in English from 2006 onward. Searches were last run on September 3, 2019. Study Selection: This systematic review and meta-analysis assessed 662 phase 2/3 randomized clinical trials that provided subgroup analyses of any baseline characteristics regarding the treatment response to PD-1/PD-L1 inhibitors, alone or as part of a combination therapy, with respect to overall survival (OS) or progression-free survival (PFS) among patients with metastatic ccRCC. Data Extraction and Synthesis: A novel quantitative approach was used to synthesize subgroup findings across trials. The ratio of the subgroup-specific hazard ratios (HRs) from each study were pooled using a random-effects meta-analysis whereby ratios of 1.00 would indicate that the subgroup-specific HRs were equal in magnitude. Main Outcomes and Measures: Main outcomes were OS and PFS. Results: From an initial 662 reports, 7 trials were considered eligible for inclusion. Meta-analyses suggested the treatment response to PD-1/PD-L1 inhibitors in patients with metastatic ccRCC was significantly associated with age (OS: ratio of HR for age ≥75 years to HR for age <65 years, 1.51; 95% CI, 1.01-2.26), PD-L1 expression (PFS: ratio of HR for PD-L1 < 1% to HR for PD-L1 ≥ 10%, 2.21; 95% CI, 1.14-4.27; ratio of HR for PD-L1 < 1% to HR for PD-L1 ≥ 1%, 1.36; 95% CI, 1.10-1.68), Memorial Sloan Kettering Cancer Center risk score (PFS: ratio of HR for immediate risk score to HR for poor risk score, 1.62; 95% CI, 1.14-2.29; ratio of HR for favorable risk score to HR for poor risk score, 1.53; 95% CI, 1.00-2.34; ratio of HR for favorable risk score to HR for intermediate risk score, 0.96; 95% CI, 0.70-1.30), and sarcomatoid tumor presence (PFS: ratio of HR for no sarcomatoid differentiation to HR for sarcomatoid differentiation, 1.54; 95% CI, 1.07-2.21). Conclusions and Relevance: This analysis suggests that older age, low levels of PD-L1 expression, and the absence of sarcomatoid tumor differentiation are associated with a diminished response to anti-PD-1/PD-L1 immunotherapies with respect to survival outcomes among patients with metastatic ccRCC.

The Landscape of Emerging Randomized Clinical Trial Evidence for COVID-19 Disease Stages: A Systematic Review of Global Trial Registries.

PURPOSE: A multitude of randomized controlled trials (RCTs) have emerged in response to the novel coronavirus disease (COVID-19) pandemic. Understanding the distribution of trials among various settings is important to guide future research priorities and efforts. The purpose of this review was to describe the emerging evidence base of COVID-19 RCTs by stages of disease progression, from pre-exposure to hospitalization. METHODS: We collated trial data across international registries: ClinicalTrials.gov; International Standard Randomised Controlled Trial Number Registry; Chinese Clinical Trial Registry; Clinical Research Information Service; EU Clinical Trials Register; Iranian Registry of Clinical Trials; Japan Primary Registries Network; German Clinical Trials Register (up to 7 October 2020). Active COVID-19 RCTs in international registries were eligible for inclusion. We extracted trial status, intervention(s), control, sample size, and clinical context to generate descriptive frequencies, network diagram illustrations, and statistical analyses including odds ratios and the Mann-Whitney U-test. RESULTS: Our search identified 11503 clinical trials registered for COVID-19 and identified 2388 RCTs. After excluding 45 suspended RCTs and 480 trials with unclear or unreported disease stages, 1863 active RCTs were included and categorized into four broad disease stages: pre-exposure (n=107); post-exposure (n=208); outpatient treatment (n=266); hospitalization, including the intensive care unit (n=1376). Across all disease stages, most trials had two arms (n=1500/1863, 80.52%), most often included (hydroxy)chloroquine (n=271/1863, 14.55%) and were US-based (n=408/1863, 21.90%). US-based trials had lower odds of including (hydroxy)chloroquine than trials in other countries (OR: 0.63, 95% CI: 0.45-0.90) and similar odds of having two arms compared to other geographic regions (OR: 1.05, 95% CI: 0.80-1.38). CONCLUSION: There is a marked difference in the number of trials across settings, with limited studies on non-hospitalized persons. Focus on pre- and post-exposure, and outpatients, is worthwhile as a means of reducing infections and lessening the health, social, and economic burden of COVID-19.

Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection.

OBJECTIVES: The present study evaluates the extent of association between hepatitis C virus (HCV) infection and cardiovascular disease (CVD) risk and identifies factors mediating this relationship using Bayesian network (BN) analysis. DESIGN AND SETTING: A population-based cross-sectional survey in Canada. PARTICIPANTS: Adults from the Canadian Health Measures Survey (n=10 115) aged 30 to 74 years. PRIMARY AND SECONDARY OUTCOME MEASURES: The 10-year risk of CVD was determined using the Framingham Risk Score in HCV-positive and HCV-negative subjects. Using BN analysis, variables were modelled to calculate the probability of CVD risk in HCV infection. RESULTS: When the BN is compiled, and no variable has been instantiated, 73%, 17% and 11% of the subjects had low, moderate and high 10-year CVD risk, respectively. The conditional probability of high CVD risk increased to 13.9%±1.6% (p<2.2×10-16) when the HCV variable is instantiated to 'Present' state and decreased to 8.6%±0.2% when HCV was instantiated to 'Absent' (p<2.2×10-16). HCV cases had 1.6-fold higher prevalence of high-CVD risk compared with non-infected individuals (p=0.038). Analysis of the effect modification of the HCV-CVD relationship (using median Kullback-Leibler divergence; DKL ) showed diabetes as a major effect modifier on the joint probability distribution of HCV infection and CVD risk (DKL =0.27, IQR: 0.26 to 0.27), followed by hypertension (0.24, IQR: 0.23 to 0.25), age (0.21, IQR: 0.10 to 0.38) and injection drug use (0.19, IQR: 0.06 to 0.59). CONCLUSIONS: Exploring the relationship between HCV infection and CVD risk using BN modelling analysis revealed that the infection is associated with elevated CVD risk. A number of risk modifiers were identified to play a role in this relationship. Targeting these factors during the course of infection to reduce CVD risk should be studied further.

BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses.

BACKGROUND: Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially attributable to limitations in current NMA software which do not readily produce all of the output needed to satisfy current guidelines. RESULTS: To better facilitate the conduct and reporting of NMAs, we have created an R package called "BUGSnet" (Bayesian inference Using Gibbs Sampling to conduct a Network meta-analysis). This R package relies upon Just Another Gibbs Sampler (JAGS) to conduct Bayesian NMA using a generalized linear model. BUGSnet contains a suite of functions that can be used to describe the evidence network, estimate a model and assess the model fit and convergence, assess the presence of heterogeneity and inconsistency, and output the results in a variety of formats including league tables and surface under the cumulative rank curve (SUCRA) plots. We provide a demonstration of the functions contained within BUGSnet by recreating a Bayesian NMA found in the second technical support document composed by the National Institute for Health and Care Excellence Decision Support Unit (NICE-DSU). We have also mapped these functions to checklist items within current reporting and best practice guidelines. CONCLUSION: BUGSnet is a new R package that can be used to conduct a Bayesian NMA and produce all of the necessary output needed to satisfy current scientific and regulatory standards. We hope that this software will help to improve the conduct and reporting of NMAs.

Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method.

Objectives. Coronary artery disease (CAD) is the leading cause of death and disease burden worldwide, causing 1 in 7 deaths in the United States alone. Risk prediction models that can learn the complex causal relationships that give rise to CAD from data, instead of merely predicting the risk of disease, have the potential to improve transparency and efficacy of personalized CAD diagnosis and therapy selection for physicians, patients, and other decision makers. Methods. We use Bayesian networks (BNs) to model the risk of CAD using the Z-Alizadehsani data set-a published real-world observational data set of 303 Iranian patients at risk for CAD. We also describe how BNs can be used for incorporation of background knowledge, individual risk prediction, handling missing observations, and adaptive decision making under uncertainty. Results. BNs performed on par with machine-learning classifiers at predicting CAD and showed better probability calibration. They achieved a mean 10-fold area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.04, which was comparable with the performance of logistic regression with L1 or L2 regularization (AUC: 0.92 ± 0.06), support vector machine (AUC: 0.92 ± 0.06), and artificial neural network (AUC: 0.91 ± 0.05). We describe the use of BNs to predict with missing data and to adaptively calculate prognostic values of individual variables under uncertainty. Conclusion. BNs are powerful and versatile tools for risk prediction and health outcomes research that can complement traditional statistical techniques and are particularly useful in domains in which information is uncertain or incomplete and in which interpretability is important, such as medicine.

Comparative accuracy of typhoid diagnostic tools: A Bayesian latent-class network analysis.

BACKGROUND: Typhoid fevers are infections caused by the bacteria Salmonella enterica serovar Typhi (Salmonella Typhi) and Paratyphi A, B and C (Salmonella Paratyphi). Approximately 17.8 million incident cases of typhoid fever occur annually, and incidence is highest in children. The accuracy of current diagnostic tests of typhoid fever is poorly understood. We aimed to determine the comparative accuracy of available tests for the pediatric population. METHODS: We first conducted a systematic literature review to identify studies that compared diagnostic tests for typhoid fever in children (aged ≤15 years) to blood culture results. We applied a Bayesian latent-class extension to a network meta-analysis model. We modelled known diagnostic properties of bone marrow culture and the relationship between bone marrow and blood culture as informative priors in a Bayesian framework. We tested sensitivities for the proportion of negative blood samples that were false as well as bone marrow sensitivity and specificity. RESULTS: We found 510 comparisons from 196 studies and 57 specific to the pediatric population. IgM-based tests outperformed their IgG-based counterparts for ELISA and Typhidot tests. The lateral flow IgG test performed comparatively well with 92% sensitivity (72% to 98% across scenario analyses) and 94% specificity. The most sensitive test of those investigated for the South Asian pediatric population was the Reverse Passive Hemagglutination Assay with 99% sensitivity (98% - 100% across scenario analyses). Adding a Widal slide test to other typhoid diagnostics did not substantially improve diagnostic performance beyond the single test alone, however, a lateral flow-based IgG rapid test combined with the typhoid/paratyphoid (TPT) assay yielded improvements in sensitivity without substantial declines in specificity and was the best performing combination test in this setting. CONCLUSION: In the pediatric population, lateral-flow IgG, TPT and Reverse Passive Hemagglutination tests had high diagnostic accuracy compared to other diagnostics. Combinations of tests may provide a feasible option to increase diagnostic sensitivity. South Asia has the most informed set of data on typhoid diagnostic testing accuracy, and the evidence base in other important regions needs to be expanded.

Characteristics and methods of incorporating randomized and nonrandomized evidence in network meta-analyses: a scoping review.

OBJECTIVES: The objective of the study was to conduct a scoping review of the published literature on methods used to combine randomized and nonrandomized evidence (NRE) in network meta-analyses (NMAs) and their respective characteristics. STUDY DESIGN AND SETTING: We conducted a scoping review using a list of NMAs which incorporated NRE that were identified from a previous review. All NMAs that included NRE in the analysis of main outcomes or sensitivity analyses were eligible for inclusion. Two reviewers independently screened studies for inclusion and performed data abstraction. Data analysis involved quantitative (frequencies and percentages) and qualitative (narrative synthesis) methods. RESULTS: A total of 23 NMAs met the predefined inclusion criteria, of which 74% (n = 17) used naïve pooling, 0% used NRE as informative priors, 9% (n = 2) used the 3-level Bayesian hierarchical model, 9% (n = 2) used all methods, and 9% (n = 2) used other methods. Most NMAs were supplemented with additional analyses to investigate the effect estimates when only randomized evidence was included. CONCLUSION: Although most studies provided justification for the inclusion of NRE, transparent reporting of the method used to combine randomized evidence and NRE was unclear in most published networks. Most NMAs used naïve pooling for combining randomized evidence and NRE.