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.

Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence.

It is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication-use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group-based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K-means. A time-varying treatment was generated as a quadratic function of time, baseline, and time-varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K-means using the absolute bias, the variance, the c-statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K-means.

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.

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.

Transportability of Overall Survival Estimates From US to Canadian Patients With Advanced Non-Small Cell Lung Cancer With Implications for Regulatory and Health Technology Assessment.

Importance: The external validity of survival outcomes derived from clinical practice data from US patients with advanced non-small cell lung cancer (NSCLC) is not known and is of potential importance because it may be used to support regulatory decision-making and health technology assessment outside of the US. Objective: To evaluate whether overall survival (OS) estimates for a selected group of patients with advanced NSCLC from a large US clinical practice database are transportable to Canadian patients receiving the same systemic therapies. Design, Setting, and Participants: This retrospective multicenter cohort study used transportability analysis to assess whether adjustment for pretreatment characteristics of eligible patient cohorts could reliably approximate OS estimated from US-based samples to Canadian populations. A total of 17 432 eligible adult patients who were diagnosed de novo with advanced NSCLC on or after January 1, 2011, were included in the analysis and followed up until September 30, 2020. Because data on race and ethnicity were available in the US database but not the Canadian database and because racial and ethnic distribution was likely to be similar between US and Canadian patients, these characteristics were not analyzed. Exposures: Initiation of platinum-doublet chemotherapy or pembrolizumab monotherapy as first-line systemic treatment for advanced NSCLC. Main Outcomes and Measures: OS measured from the time of initiation of the respective treatment regimen. Results: Among 17 432 eligible patients, 15 669 patients from the US and 1763 patients from Canada were included in the analysis. Of those, 11 863 patients (sample size-weighted estimates of mean [SD] age, 68.0 [9.3] years; 6606 [55.7%] male; 10 100 from the US and 1763 from Canada) were included in the subset of patients with complete data for baseline covariates. A total of 13 532 US patients received first-line chemotherapy, and 2137 received first-line pembrolizumab monotherapy. Of those, 8447 patients (62.4%) in the first-line chemotherapy group and 1653 patients (77.3%) in the first-line pembrolizumab group had complete data on baseline covariates for outcome model estimation. A total of 1476 Canadian patients who received first-line chemotherapy and 287 patients who received first-line pembrolizumab monotherapy were identified from the target population. After standardization to baseline patient covariates in the Canadian cohorts, transported OS estimates revealed a less than 5% mean absolute difference from the observed OS in the target population (0.56% over 60 months of follow-up in the first-line chemotherapy group and 4.54% over 30 months of follow-up in the first-line pembrolizumab group). Negative control analysis using a mismatched outcome model revealed a 6.64% discrepancy and an incompatible survival curve shape. The results were robust to assumptions of random missingness for baseline covariates, to unadjusted differences in baseline metastases and comorbidities, and to differences in the standard of care between the US and Canada related to administration of second-line anti-programmed cell death 1 ligand 1 immunotherapy for patients who initiated first-line chemotherapy. Conclusions and Relevance: The results of this cohort study suggest that, under specific circumstances, OS estimates from US clinical practice data can be adjusted using baseline clinical characteristics to closely approximate OS in selected groups of Canadian patients with advanced NSCLC. These results may have implications for regulatory decision-making and health technology assessment in target populations outside of the US.

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.

Association Between Smoking History and Overall Survival in Patients Receiving Pembrolizumab for First-Line Treatment of Advanced Non-Small Cell Lung Cancer.

Importance: There is a need to tailor treatments to patients who are most likely to derive the greatest benefit from them to improve patient outcomes and enhance cost-effectiveness of cancer therapies. Objective: To compare overall survival (OS) between patients with a current or former history of smoking with patients who never smoked and initiated pembrolizumab monotherapy as first-line (1L) treatment for advanced non-small lung cancer (NSCLC). Design, Setting, and Participants: This retrospective cohort study compared patients diagnosed with advanced NSCLC aged 18 or higher selected from a nationwide real-world database originating from more than 280 US cancer clinics. The study inclusion period was from January 1, 2011, to October 1, 2019. Exposures: Smoking status at the time of NSCLC diagnosis. Main Outcomes and Measures: OS measured from initiation of 1L pembrolizumab monotherapy. Results: In this retrospective cohort study, a total of 1166 patients (median [IQR] age, 72.9 [15.3] years; 581 [49.8%] men and 585 [50.2%] women) were assessed in the primary analysis, including 91 patients [7.8%] with no history of smoking (ie, never-smokers) and 1075 patients [92.2%] who currently or formerly smoked (ie, ever-smokers). Compared with ever-smokers, never-smokers were older (median age [IQR] of 78.2 [12.0] vs 72.7 [15.5] years), more likely to be female (61 [67.0%] vs 524 [48.7%]) and to have been diagnosed with nonsquamous tumor histology (70 [76.9%] vs 738 [68.7%]). After adjustment for baseline covariates, ever-smokers who initiated 1L pembrolizumab had significantly prolonged OS compared to never-smokers (median OS: 12.8 [10.9-14.6] vs 6.5 [3.3-13.8] months; hazard ratio (HR): 0.69 [95% CI, 0.50-0.95]). This trend was observed across all sensitivity analyses for the 1L pembrolizumab cohort, but not for initiators of 1L platinum chemotherapy, for which ever-smokers showed significantly shorter OS compared with never-smokers (HR, 1.2 [95% CI, 1.07-1.33]). Conclusions and Relevance: In patients with advanced NSCLC who received 1L pembrolizumab monotherapy in routine clinical practices in the US, patients who reported a current or former history of smoking at the time of diagnosis had consistently longer OS than never-smokers. This finding suggests that in never-smoking advanced NSCLC, 1L pembrolizumab monotherapy may not be the optimal therapy selection, and genomic testing for potential genomically matched therapies should be prioritized over pembrolizumab in never-smokers.

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.

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.

Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection.

BACKGROUND: Body weight has been implicated as a risk factor for latent tuberculosis infection (LTBI) and the active disease. DESIGN AND METHODS: This study aimed to develop artificial neural network (ANN) models for predicting LTBI from body weight and other host-related disease risk factors. We used datasets from participants of the US-National Health and Nutrition Examination Survey (NHANES; 2012; n=5,156; 514 with LTBI and 4,642 controls) to develop three ANNs employing body mass index (BMI, Network I), BMI and HbA1C (as a proxy for diabetes; Network II) and BMI, HbA1C and education (as a proxy for socioeconomic status; Network III). The models were trained on n=1018 age- and sex-matched subjects equally distributed between the control and LTBI groups. The endpoint was the prediction of LTBI. RESULTS: When data was adjusted for age, sex, diabetes and level of education, odds ratio (OR) and 95% confidence intervals (CI) for risk of LTBI with increased BMI was 0.85 (95%CI: 0.77 - 0.96, p=0.01). The three ANNs had a predictive accuracy varied from 75 to 80% with sensitivities ranged from 85% to 94% and specificities of approximately 70%. Areas under the receiver operating characteristic curve (AUC) were between 0.82 and 0.87. Optimal ANN performance was noted using BMI as a risk indicator. CONCLUSION: Body weight can be employed in developing artificial intelligence-based tool to predict LTBI. This can be useful in precise decision making in clinical and public health practices aiming to curb the burden of tuberculosis, e.g., in the management and monitoring of the tuberculosis prevention programs and to evaluate the impact of healthy weight on tuberculosis risk and burden.

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.

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.

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.

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine.

OBJECTIVE: The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction to a knowledge representation and machine-learning tool for risk estimation in medical science known as Bayesian networks (BNs). STUDY DESIGN: In this article we review how BNs are compact and intuitive graphical representations of joint probability distributions (JPDs) that can be used to conduct causal reasoning and risk estimation analysis and offer several advantages over regression-based methods. We discuss how BNs represent a different approach to risk estimation in that they are graphical representations of JPDs that take the form of a network representing model random variables and the influences between them, respectively. METHODS: We explore some of the challenges associated with traditional risk prediction methods and then describe BNs, their construction, application, and advantages in risk prediction based on examples in cancer and heart disease. RESULTS: Risk modeling with BNs has advantages over regression-based approaches, and in this article we focus on three that are relevant to health outcomes research: (1) the generation of network structures in which relationships between variables can be easily communicated; (2) their ability to apply Bayes's theorem to conduct individual-level risk estimation; and (3) their easy transformation into decision models. CONCLUSIONS: Bayesian networks represent a powerful and flexible tool for the analysis of health economics and outcomes research data in the era of precision medicine.

Phosphatidylinositol 4,5-bisphosphate regulates cilium transition zone maturation in Drosophila melanogaster.

Cilia are cellular antennae that are essential for human development and physiology. A large number of genetic disorders linked to cilium dysfunction are associated with proteins that localize to the ciliary transition zone (TZ), a structure at the base of cilia that regulates trafficking in and out of the cilium. Despite substantial effort to identify TZ proteins and their roles in cilium assembly and function, processes underlying maturation of TZs are not well understood. Here, we report a role for the membrane lipid phosphatidylinositol 4,5-bisphosphate (PIP2) in TZ maturation in the Drosophila melanogaster male germline. We show that reduction of cellular PIP2 levels through ectopic expression of a phosphoinositide phosphatase or mutation of the type I phosphatidylinositol phosphate kinase Skittles induces formation of longer than normal TZs. These hyperelongated TZs exhibit functional defects, including loss of plasma membrane tethering. We also report that the onion rings (onr) allele of DrosophilaExo84 decouples TZ hyperelongation from loss of cilium-plasma membrane tethering. Our results reveal a requirement for PIP2 in supporting ciliogenesis by promoting proper TZ maturation.