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

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: The commentary employs 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 employing RWE, including techniques like target trial emulation and quantitative bias analysis methods to address biases inherent in RWE. RESULTS: The commentary highlights that the IRA introduces a unique approach to value assessment in the US by evaluating drug value several years post-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, the 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.

Adapting Health Technology Assessment agency standards for surrogate outcomes in early stage cancer trials: what needs to happen?

INTRODUCTION: An avalanche of early stage cancer clinical trials is coming. The majority of these solely use surrogate outcomes that have not been validated against a target outcome of interest (e.g. overall survival). Current HTA guidance on surrogate outcome validation are not methodologically or practically conducive to this scenario. AREAS COVERED: We provide a high-level overview of methods, approaches, and conceptual thinking for making better use of limited evidence within early stage cancer HTA submissions. We outline regulatory and HTA issues and emphasize how evidence transitions from one to another, what major gaps currently exist, and how these may be bridged. We summarize current methodologies and practices, their pros and cons. We outline how complementary measurements strengthen evaluations and address fallacies and biases of conventional statistical methods for surrogate outcomes validation. The value of real-world data to support some of the necessary validity components is discussed. Lastly, we address the importance of the patient voice for better understanding which surrogate outcomes may appropriately inform HTA. EXPERT OPINION: Conventional surrogate outcome validation represents a fraught and sub-optimal framework for HTA purposes, particularly for early stage cancer. Tools for optimizing use of limited evidence exist. Education of stakeholders is highly needed.

The importance of appropriate selection of clinical endpoints in outpatient COVID-19 clinical trials.

Clinical trial endpoints must be carefully and intentionally selected so that the results of the trial can be used to inform policy- and decision-making. The relative importance of potential endpoints often depends on the stakeholder, with patients having different preferences to policymakers and regulators. The set up of clinical trials for COVID-19 was problematic, as endpoints that could be reasonably measured did not always match the efficacy endpoints usually required by guideline panels. Thus, different endpoints were used, which made the timely comparison and evaluation of interventions difficult. Here we discuss the evolution of the COVID-19 landscape and the effect this is having on the selection of consistent and measurable clinical trial endpoints. Using appropriate endpoints is crucial for researchers to offer the most reliable, valid, and interpretable results possible.

Early Treatment with Fluvoxamine among Patients with COVID-19: A Cost-Consequence Model.

To date, two published randomized trials have indicated a clinical benefit of early treatment with fluvoxamine versus placebo for adults with symptomatic COVID-19. Using the results of the largest of these trials, the TOGETHER trial, we conducted a cost-consequence analysis to assess the health system benefits of preventing progression to severe COVID-19 in outpatient populations in the United States. A decision-analytic model in the form of a decision tree was constructed to evaluate two treatment strategies for high-risk patients with confirmed, symptomatic COVID-19 in the primary analysis: treatment with a 10-day course of fluvoxamine (100 mg twice daily) and current standard-of-care. A secondary analysis comparing a 5-day course of nirmatrelvir-ritonavir was also conducted. We used a time horizon of 28 days. Reported outcomes included cost-savings and hospitalization days avoided. The results of our analysis indicated that administration of fluvoxamine to symptomatic outpatients at high risk of progressing to severe COVID-19 was substantially cost-saving, in the amount of $232 per eligible patient and prevented an average of 0.15 hospital days per patient treated, compared with standard of care. Nirmatrelvir-ritonavir was also shown to be cost-saving despite its higher acquisition cost and provided savings to the healthcare system of $625 per patient treated. These findings suggest that fluvoxamine is likely to be a cost-effective addition to frontline COVID-19 mitigation strategies in many settings, particularly where access to nirmaltrevir-ritonavir or monoclonal antibodies is limited.

Infliximab as a second-line therapy for children with refractory Kawasaki disease: A systematic review and meta-analysis of randomized controlled trials.

AIMS: Infliximab is a tumour necrosis factor-alpha inhibitor that is used to treat children with refractory Kawasaki disease (KD). Our purpose was to evaluate the safety and impact of infliximab versus intravenous immunoglobulins on the incidence of coronary artery aneurysms (CAAs) and treatment resistance in children with refractory KD. METHODS: The Medline/PubMed, Embase, CINAHL, Cochrane Central Register of Controlled Trials and clinical trials registries were searched to December 2021. Randomized controlled trials (RCTs) comparing infliximab as second-line therapy to a second dose of intravenous immunoglobulin (IVIG) in children with refractory KD, reported in abstract or full text, were included. Studies were selected and assessed for risk of bias by two reviewers. Data were extracted and pooled using conventional random-effects meta-analysis. The certainty of evidence was assessed using the GRADE system. RESULTS: A total of 199 participants from four RCTs were included. The pooled risk ratio (RR) for the incidence of treatment resistance in patients treated with infliximab was 0.40 (95% confidence interval [CI] 0.25-0.64). For incidence of CAAs RR was 1.20 (95% CI 0.54-2.63), the incidence of adverse effect "infusion reactions" RR was 0.48, (95% CI 0.12-1.92) and for "infections" RR was 0.55 (95% CI 0.27-1.12). Overall, the GRADE strength of evidence for the primary outcomes was low. Evidence on the duration of fever and inflammatory biomarkers was sparse, heterogeneous and inconclusive. CONCLUSION: Moderate-certainty evidence indicates that infliximab may reduce the incidence of treatment resistance in children with refractory KD. However, the limited strength of evidence warrants further research.

A Bayesian adaptive design for clinical trials of rare efficacy outcomes with multiple definitions.

INTRODUCTION: Bayesian adaptive designs for clinical trials have gained popularity in the recent years due to the flexibility and efficiency that they offer. We consider the scenario where the outcome of interest comprises events with relatively low risk of occurrence and different case definitions resulting in varying control group risk assumptions. This is a scenario that occurs frequently for infectious diseases in global health research. METHODS: We propose a Bayesian adaptive design that incorporates different case definitions of the outcome of interest that vary in stringency. A set of stopping rules are proposed where superiority and futility may be concluded with respect to different outcome definitions and therefore maintain a realistic probability of stopping in trials with low event rates. Through a simulation study, a variety of stopping rules and design configurations are compared. RESULTS: The simulation results are provided in an interactive web application that allows the user to explore and compare the design operating characteristics for a variety of assumptions and design parameters with respect to different outcome definitions. The results for select simulation scenarios are provided in the article. DISCUSSION: Bayesian adaptive designs offer the potential for maximizing the information learned from the data collected through clinical trials. The proposed design enables monitoring and utilizing multiple composite outcomes based on rare events to optimize the trial design operating characteristics.

Making Statistical Sense of the Molnupiravir MOVe-OUT Clinical Trial.

Oral therapies for the early treatment of COVID-19 may prevent disease progression and health system overcrowding. A new oral therapeutic named molnupiravir has been promoted as providing an approximately 50% reduction in death or the need for hospitalization. The clinical trial evaluating this drug was stopped early at the recommendation of the Data Safety and Monitoring Board after approximately 50% of the sample had been recruited. At the point of discontinuing the trial, approximately 90% of the planned sample had been recruited and had available follow-up data accessible. We discuss issues about the study conduct, analysis, and interpretation, including 1) the authors and sponsors presented the interim analysis as the primary analysis; 2) communication between sponsors and the Data Safety and Monitoring Board was insufficient; 3) the treatment effects reverse when examining only the post-interim analysis population, and are substantially attenuated when examining the full data; 4) the choice of primary analysis is incorrect; 5) analysis of lost-to-follow-up patients favors the study drug; and 6) other known molnupiravir trials were not presented in the primary study findings. As a result of methodological and statistical concerns, it seems that external trials, separate from those supported by the sponsoring company, are required to determine the utility of this drug.

Fluvoxamine for the Early Treatment of COVID-19: A Meta-analysis of Randomized Clinical Trials.

Fluvoxamine is widely prescribed as an antidepressant. Recent studies show the drug may have a clinical benefit in treating COVID-19. We aimed to perform a meta-analysis of the existing randomized trials of fluvoxamine compared with placebo on the early treatment of COVID-19 patients. We included only randomized clinical trials enrolling ambulatory patients with early-stage disease (symptoms > 7 days) for the prevention of hospitalization. We searched MEDLINE, and clinicaltrials.gov databases to identify trials and extract data with clarifications from the study investigators. We performed a fixed-effects meta-analysis and sensitivity analyses via R to evaluate the pooled estimate of hospitalization. We included three randomized trials: STOP COVID 1 and 2, and the TOGETHER Trial. The studies included a total of 2,196 patients. The STOP COVID trials measured clinical deterioration whereas the TOGETHER Trial measured hospitalization as the primary outcome. All trials reported on hospitalization up to day 28. The meta-analysis results show that patients receiving fluvoxamine were 31% less likely to experience clinical deterioration or hospitalization compared with placebo (risk ratio, 0.69; 95% CI, 0.54-0.88). A sensitivity analysis using the definition of hospitalization resulted in a risk reduction of 21% (95% CI, 0.60-1.03). Data from three randomized controlled trials show that fluvoxamine was associated with a reduction in the primary outcome measure (either clinical deterioration or composite outcome of hospitalization or extended emergency setting observation), although analysis of hospitalization-only was not statistically significant. More evidence from future trials is still needed to support the findings of this meta-analysis.

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.

Randomised trials at the level of the individual.

In global health research, short-term, small-scale clinical trials with fixed, two-arm trial designs that generally do not allow for major changes throughout the trial are the most common study design. Building on the introductory paper of this Series, this paper discusses data-driven approaches to clinical trial research across several adaptive trial designs, as well as the master protocol framework that can help to harmonise clinical trial research efforts in global health research. We provide a general framework for more efficient trial research, and we discuss the importance of considering different study designs in the planning stage with statistical simulations. We conclude this second Series paper by discussing the methodological and operational complexity of adaptive trial designs and master protocols and the current funding challenges that could limit uptake of these approaches in global health research.

The role and challenges of cluster randomised trials for global health.

Evaluating whether an intervention works when trialled in groups of individuals can pose complex challenges for clinical research. Cluster randomised controlled trials involve the random allocation of groups or clusters of individuals to receive an intervention, and they are commonly used in global health research. In this paper, we describe the potential reasons for the increasing popularity of cluster trials in low-income and middle-income countries. We also draw on key areas of global health research for an assessment of common trial planning practices, and we address their methodological shortcomings and pitfalls. Lastly, we discuss alternative approaches for population-level intervention trials that could be useful for research undertaken in low-income and middle-income countries for situations in which the use of cluster randomisation might not be appropriate.

Comparing the use of aggregate data and various methods of integrating individual patient data to network meta-analysis and its application to first-line ART.

BACKGROUND: The 2018 World Health Organization HIV guidelines were based on the results of a network meta-analysis (NMA) of published trials. This study employed individual patient-level data (IPD) and aggregate data (AgD) and meta-regression methods to assess the evidence supporting the WHO recommendations and whether they needed any refinements. METHODS: Access to IPD from three trials was granted through ClinicalStudyDataRequest.com (CSDR). Seven modelling approaches were applied and compared: 1) Unadjusted AgD network meta-analysis (NMA) - the original analysis; 2) AgD-NMA with meta-regression; 3) Two-stage IPD-AgD NMA; 4) Unadjusted one-stage IPD-AgD NMA; 5) One-stage IPD-AgD NMA with meta-regression (one-stage approach); 6) Two-stage IPD-AgD NMA with empirical-priors (empirical-priors approach); 7) Hierarchical meta-regression IPD-AgD NMA (HMR approach). The first two were the models used previously. Models were compared with respect to effect estimates, changes in the effect estimates, coefficient estimates, DIC and model fit, rankings and between-study heterogeneity. RESULTS: IPD were available for 2160 patients, representing 6.5% of the evidence base and 3 of 24 edges. The aspect of the model affected by the choice of modeling appeared to differ across outcomes. HMR consistently generated larger intervals, often with credible intervals (CrI) containing the null value. Discontinuations due to adverse events and viral suppression at 96 weeks were the only two outcomes for which the unadjusted AgD NMA would not be selected. For the first, the selected model shifted the principal comparison of interest from an odds ratio of 0.28 (95% CrI: 10.17, 0.44) to 0.37 (95% CrI: 0.23, 0.58). Throughout all outcomes, the regression estimates differed substantially between AgD and IPD methods, with the latter being more often larger in magnitude and statistically significant. CONCLUSIONS: Overall, the use of IPD often impacted the coefficient estimates, but not sufficiently as to necessitate altering the final recommendations of the 2018 WHO Guidelines. Future work should examine the features of a network where adjustments will have an impact, such as how much IPD is required in a given size of network.

When does the use of individual patient data in network meta-analysis make a difference? A simulation study.

BACKGROUND: The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. METHODS: Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. RESULTS: Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. CONCLUSIONS: Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

Reporting and design of randomized controlled trials for COVID-19: A systematic review.

BACKGROUND: The novel coronavirus 2019 (COVID-19) pandemic has mobilized global research at an unprecedented scale. While challenges associated with the COVID-19 trial landscape have been discussed previously, no comprehensive reviews have been conducted to assess the reporting, design, and data sharing practices of randomized controlled trials (RCTs). PURPOSE: The purpose of this review was to gain insight into the current landscape of reporting, methodological design, and data sharing practices for COVID-19 RCTs. DATA SOURCES: We conducted three searches to identify registered clinical trials, peer-reviewed publications, and pre-print publications. STUDY SELECTION: After screening eight major trial registries and 7844 records, we identified 178 registered trials and 38 publications describing 35 trials, including 25 peer-reviewed publications and 13 pre-prints. DATA EXTRACTION: Trial ID, registry, location, population, intervention, control, study design, recruitment target, actual recruitment, outcomes, data sharing statement, and time of data sharing were extracted. DATA SYNTHESIS: Of 178 registered trials, 112 (62.92%) were in hospital settings, median planned recruitment was 100 participants (IQR: 60, 168), and the majority (n = 166, 93.26%) did not report results in their respective registries. Of 35 published trials, 31 (88.57%) were in hospital settings, median actual recruitment was 86 participants (IQR: 55.5, 218), 10 (28.57%) did not reach recruitment targets, and 27 trials (77.14%) reported plans to share data. CONCLUSIONS: The findings of our study highlight limitations in the design and reporting practices of COVID-19 RCTs and provide guidance towards more efficient reporting of trial results, greater diversity in patient settings, and more robust data sharing.

Comparative efficacy, tolerability and safety of dolutegravir and efavirenz 400mg among antiretroviral therapies for first-line HIV treatment: A systematic literature review and network meta-analysis.

BACKGROUND: To inform World Health Organization (WHO) global guidelines, we updated and expanded the evidence base to assess the comparative efficacy, tolerability, and safety of first-line antiretroviral therapy (ART) regimens. METHODS: We searched Embase, Medline and CENTRAL on 28 February 2020 to update the systematic literature review of clinical trials comparing recommended first-line ART that informed previous WHO guidelines. Outcomes included viral suppression, change in CD4 cell counts, mortality, serious and overall adverse events (AEs), discontinuation, discontinuations due to AEs (DAEs); and new outcomes: drug-resistance, neuropsychiatric AEs, early viral suppression, weight gain and birth outcomes. Comparative effects were assessed through network meta-analyses and certainty in the evidence was assessed using the GRADE framework. FINDINGS: We identified 156 publications pertaining to 68 trials for the primary population. Relative to efavirenz, dolutegravir had improved odds of viral suppression across all time points (odds ratio [OR]: 1·94; 95% credible interval [CrI]: 1·48-2·56 at 96 weeks); was protective of drug-resistance (OR: 0·13; 95%CrI: 0·04-0·48); and led to fewer discontinuations (OR: 0·58; 95%CrI: 0·48-0·70). Evidence supported dolutegravir use among TB-HIV co-infected persons and pregnant women. Adverse birth outcomes were observed in 33.2% of dolutegravir-managed pregnancies and 35.0% of efavirenz-managed pregnancies. Low-dose efavirenz had comparable efficacy and safety to standard-dose efavirenz, but led to fewer DAEs (OR: 0·70; 95%CrI: 0·50-0·92). INTERPRETATION: The evidence supports choosing dolutegravir in combination with lamivudine/emtricitabine and tenofovir disoproxil fumarate as the preferred first-line regimen and low-dose efavirenz-based regimens as an alternative. Dolutegravir can be considered to be effective, safe and tolerable. FUNDING: WHO.