Biologicals for the treatment of lupus nephritis: a Bayesian network meta-regression analysis.

Background: Previous studies comparing the efficacy and safety of different treatment regimens for lupus nephritis are scarce. Moreover, confounding factors such as the duration of follow-up were hardly adjusted in those studies, potentially compromising the results and their extents to clinical settings. Objective: To rigorously investigate the efficacy and safety of biologics in patients with lupus nephritis using Bayesian network meta-regression analyses that adjust for the follow-up period, in order to provide more robust evidence for clinicians. Methods: Databases comprising PubMed, Embase, MedlinePlus, Cochrane Library, Google Scholars, and Scopus were retrieved for eligible articles from inception to February 29, 2024. The primary endpoint was the complete response rate, the secondary endpoint was the partial response rate, the tertiary endpoints were the adverse events, and infection-related adverse events. Napierian Logarithm of hazard ratio (lnHR) and the standard error of lnHR (selnHR) were generated for dichotomous variants by STATA 18.0 MP and then put into Rstudio 4.3.2 to conduct Bayesian network meta-analysis as well as network meta-regression analysis to yield hazard ratio (HR) as pairwise effect size. Results: Ten studies involving 2138 patients and 11 treatment regimens were ultimately included. In the original analysis, for the primary endpoint, compared to the control group, obinutuzumab (22.6 months), abatacept-30mg (20.5 months), abatacept-10mg (17.8 months), and belimumab (23.3 months) demonstrated significant superiority (HR ranged from 1.6 to 2.5), more ever, their significance regarding relative efficacy was correlated with follow up period, namely "time window" (shown in parentheses above). For the secondary endpoint, compared to the control group, obinutuzumab and abatacept-30mg showed conspicuous preponderance (HR ranged from 1.6 to 2.4), "time window" was also detected in abatacept-30mg (20.5 months), whereas obinutuzumab remained consistently obviously effective regardless of the follow-up period (shown in parentheses above). For the tertiary endpoint, there were no differences among active regimens and control. Conclusions: Considering the efficacy and safety and "time window" phenomenon, we recommend obinutuzumab as the preferred treatment for LN. Certainly, more rigorous head-to-head clinical trials are warranted to validate those findings.

crossnma: An R package to synthesize cross-design evidence and cross-format data using network meta-analysis and network meta-regression.

BACKGROUND: Although aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) can be informative about relative treatment effects. The individual participant data (IPD) of the study, when available, are preferred to AD for adjusting for important participant characteristics and to better handle heterogeneity and inconsistency in the network. RESULTS: We developed the R package crossnma to perform cross-format (IPD and AD) and cross-design (RCT and NRS) NMA and network meta-regression (NMR). The models are implemented as Bayesian three-level hierarchical models using Just Another Gibbs Sampler (JAGS) software within the R environment. The R package crossnma includes functions to automatically create the JAGS model, reformat the data (based on user input), assess convergence and summarize the results. We demonstrate the workflow within crossnma by using a network of six trials comparing four treatments. CONCLUSIONS: The R package crossnma enables the user to perform NMA and NMR with different data types in a Bayesian framework and facilitates the inclusion of all types of evidence recognising differences in risk of bias.

Implementing Multilevel Network Meta-Regression for Time-To-Event Outcomes: A Case Study in Relapsed Refractory Multiple Myeloma.

OBJECTIVES: Multilevel network meta-regression (ML-NMR) leverages individual patient data (IPD) and aggregate data from a network of randomized controlled trials (RCTs) to assess the comparative efficacy of multiple treatments, while adjusting for between-study differences. We provide an overview of ML-NMR for time-to-event outcomes and apply it to an illustrative case study, including example R code. METHODS: The case study evaluated the comparative efficacy of idecabtagene vicleucel (ide-cel), selinexor+dexamethasone (Sd), belantamab mafodotin (BM), and conventional care (CC) for patients with triple-class exposed relapsed/refractory multiple myeloma in terms of overall survival. Single-arm clinical trials and real-world data were naively combined to create an aggregate data artificial RCT (aRCT) (MAMMOTH-CC versus DREAMM-2-BM versus STORM-2-Sd) and an IPD aRCT (KarMMa-ide-cel versus KarMMa-RW-CC). With some assumptions, we incorporated continuous covariates with skewed distributions, reported as median and range. The ML-NMR models adjusted for number of prior lines, triple-class refractory status, and age and were compared using the leave-one-out information criterion. We summarized predicted hazard ratios and survival (95% credible intervals) in the IPD aRCT population. RESULTS: The Weibull ML-NMR model had the lowest leave-one-out information criterion. Ide-cel was more efficacious than Sd, BM, and CC in terms of overall survival. Effect modifiers had minimal impact on the model, and only triple-class refractory was a prognostic factor. CONCLUSIONS: We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code that can be used to aid implementation. Given its benefits, we encourage practitioners to utilize ML-NMR when population adjustment is necessary for comparisons of multiple treatments.

Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis.

OBJECTIVES: We aimed to develop a network meta-analytic model for the evaluation of treatment effectiveness within predictive biomarker subgroups, by combining evidence from individual participant data (IPD) from digital sources (in the absence of randomized controlled trials) and aggregate data (AD). STUDY DESIGN AND SETTING: A Bayesian framework was developed for modeling time-to-event data to evaluate predictive biomarkers. IPD were sourced from electronic health records, using a target trial emulation approach, or digitized Kaplan-Meier curves. The model is illustrated using two examples: breast cancer with a hormone receptor biomarker, and metastatic colorectal cancer with the Kirsten Rat Sarcoma (KRAS) biomarker. RESULTS: The model allowed for the estimation of treatment effects in two subgroups of patients defined by their biomarker status. Effectiveness of taxanes did not differ in hormone receptor positive and negative breast cancer patients. Epidermal growth factor receptor inhibitors were more effective than chemotherapy in KRAS wild type colorectal cancer patients but not in patients with KRAS mutant status. Use of IPD reduced uncertainty of the subgroup-specific treatment effect estimates by up to 49%. CONCLUSION: Utilization of IPD allowed for more detailed evaluation of predictive biomarkers and cancer therapies and improved precision of the estimates compared to use of AD alone.

Comparison of indirect treatment methods in migraine prevention to address differences in mode of administration.

Aim: Indirect treatment comparisons (ITCs) are anchored on a placebo comparator, and the placebo response may vary according to drug administration route. Migraine preventive treatment studies were used to evaluate ITCs and determine whether mode of administration influences placebo response and the overall study findings. Materials & methods: Change from baseline in monthly migraine days produced by monoclonal antibody treatments (subcutaneous, intravenous) was compared using fixed-effects Bayesian network meta-analysis (NMA), network meta-regression (NMR), and unanchored simulated treatment comparison (STC). Results: NMA and NMR provide mixed, rarely differentiated results between treatments, whereas unanchored STC strongly favors eptinezumab over other preventive treatments. Conclusion: Further investigations are needed to determine which ITC best reflects the impact of mode of administration on placebo.

Comparing efficacy and safety of first-line treatment of metastatic renal cell carcinoma: A Bayesian network meta-regression analysis.

Background: This Bayesian network meta-regression analysis provides a head-to-head comparison of first-line therapeutic immune checkpoint inhibitors (ICI) and tyrosine kinase inhibitors (TKI) combinations for metastatic renal cell carcinoma (mRCC) using median follow-up time as covariate. Methods: We searched Six databases for a comprehensive analysis of randomised clinical trials (RCTs). Comparing progression free survival (PFS) and overall survival (OS) of different interventions at the same time node by Bayesian network meta-analysis. Bayesian network meta-regression analysis was performed on objective response rate (ORR), adverse events (AEs) (grade ≥ 3) and the hazard ratios (HR) associated with PFS and OS, with the median follow-up time as the covariate. Results: Eventually a total of 22 RCTs reporting 11,090 patients with 19 interventions. Lenvatinib plus Pembrolizumab (LenPem) shows dominance of PFS, and Pembrolizumab plus Axitinib (PemAxi) shows superiority in OS at each time point. After meta-regression analysis, for HRs of PFS, LenPem shows advantages; for HRs of OS, PemAxi shows superiority; For ORR, LenPem provides better results. For AEs (grade ≥ 3), Atezolizumab plus Bevacizumab (AtezoBev) is better. Conclusion: Considering the lower toxicity and the higher quality of life, PemAxi should be recommended as the optimal therapy in treating mRCC. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD4202236775.

An Indirect Treatment Comparison of Semaglutide 2.0 mg vs Dulaglutide 3.0 mg and 4.5 mg Using Multilevel Network Meta-regression.

AIMS: Currently, no head-to-head data are available comparing semaglutide 2.0 mg with dulaglutide 3.0 mg or 4.5 mg. We conducted an indirect treatment comparison (ITC) of their effects on glycated hemoglobin (HbA1c) and body weight in patients with type 2 diabetes. MATERIALS AND METHODS: Multilevel network meta-regression was conducted, based on a connected evidence network of published results from the A Study of the Efficacy and Safety of Dulaglutide (LY2189265) in Participants With Type 2 Diabetes 11 trial and individual patient data from the A Research Study to Compare Two Doses of Semaglutide Taken Once Weekly in People With Type 2 Diabetes (SUSTAIN) and SUSTAIN 7 trials. RESULTS: Semaglutide 2.0 mg significantly reduced HbA1c vs dulaglutide 3.0 mg and 4.5 mg, with estimated treatment differences (ETDs) of -0.44% points (95% credible interval [CrI], -0.68 to -0.19) and -0.28% points (95% CrI, -0.52 to -0.03), respectively. Semaglutide 2.0 mg also significantly reduced body weight vs dulaglutide 3.0 mg and 4.5 mg with ETDs of -3.29 kg (95% CrI, -4.62 to -1.96) and -2.57 kg (95% CrI, -3.90 to -1.24), respectively. Odds of achieving HbA1c < 7.0% were significantly greater for semaglutide 2.0 vs dulaglutide 3.0 mg (odds ratio [OR]: 2.23 [95% CrI, 1.15-3.90]), whereas this did not reach significance for semaglutide 2.0 mg vs dulaglutide 4.5 mg (OR: 1.58 [95% CrI, 0.82-2.78]). Sensitivity analyses supported the main analysis findings. CONCLUSIONS: This ITC demonstrated significantly greater reductions from baseline in HbA1c and body weight with semaglutide 2.0 mg vs dulaglutide 3.0 mg and 4.5 mg. The findings of this study provide important comparative effectiveness information until randomized head-to-head studies become available.

Comparative efficacy of subcutaneous (CT-P13) and intravenous infliximab in adult patients with rheumatoid arthritis: a network meta-regression of individual patient data from two randomised trials.

BACKGROUND: A subcutaneous (SC) formulation of infliximab biosimilar CT-P13 is approved in Europe for the treatment of adult patients with rheumatoid arthritis (RA). It may offer improved efficacy versus intravenous (IV) infliximab formulations. METHODS: A network meta-regression was conducted using individual patient data from two randomised trials in patients with RA, which compared CT-P13 SC with CT-P13 IV, and CT-P13 IV with reference infliximab IV. In this analysis, CT-P13 SC was compared with CT-P13 IV, reference infliximab IV and pooled data for both reference infliximab IV and CT-P13 IV. Outcomes included changes from baseline in 28-joint Disease Activity Score based on C-reactive protein (DAS28-CRP), Simplified Disease Activity Index (SDAI) and Clinical Disease Activity Index (CDAI), and rates of remission, low disease activity or clinically meaningful improvement in functional disability per Health Assessment Questionnaire-Disability Index (HAQ-DI). RESULTS: The two studies enrolled 949 patients with RA; pooled data for 840 and 751 patients were evaluable at weeks 30 and 54, respectively. For the CT-P13 SC versus pooled IV treatment arm comparison, differences in changes from baseline in DAS28-CRP (- 0.578; 95% confidence interval [CI] - 0.831, - 0.325; p < 0.0001), CDAI (- 3.502; 95% CI - 5.715, - 1.289; p = 0.002) and SDAI (- 4.031; 95% CI - 6.385, - 1.677; p = 0.0008) scores at 30 weeks were statistically significant in favour of CT-P13 SC. From weeks 30 to 54, the magnitude of the differences increased and remained statistically significant in favour of CT-P13 SC. Similar results were observed for the comparison of CT-P13 SC with CT-P13 IV and with reference infliximab IV. Statistically significant differences at week 30 favoured CT-P13 SC over the pooled IV treatment arms for the proportions of patients achieving EULAR-CRP good response, American College of Rheumatology (ACR) 50 and ACR70 responses, DAS28-CRP-defined remission, low disease activity (DAS28-CRP, CDAI and SDAI criteria) and clinically meaningful HAQ-DI improvement. CONCLUSIONS: CT-P13 SC was associated with greater improvements in DAS28-CRP, CDAI and SDAI scores and higher rates of clinical response, low disease activity and clinically meaningful improvement in functional disability, compared with CT-P13 IV and reference infliximab IV. TRIAL REGISTRATION: EudraCT, 2016-002125-11 , registered 1 July 2016; EudraCT 2010-018646-31 , registered 23 June 2010.

Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study.

Standard network meta-analysis and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any factors that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods such as multilevel network meta-regression (ML-NMR), matching-adjusted indirect comparison (MAIC), and simulated treatment comparison (STC) relax this assumption using individual patient data from one or more studies, and are becoming increasingly prevalent in health technology appraisals and the applied literature. Motivated by an applied example and two recent reviews of applications, we undertook an extensive simulation study to assess the performance of these methods in a range of scenarios under various failures of assumptions. We investigated the impact of varying sample size, missing effect modifiers, strength of effect modification and validity of the shared effect modifier assumption, validity of extrapolation and varying between-study overlap, and different covariate distributions and correlations. ML-NMR and STC performed similarly, eliminating bias when the requisite assumptions were met. Serious concerns are raised for MAIC, which performed poorly in nearly all simulation scenarios and may even increase bias compared with standard indirect comparisons. All methods incur bias when an effect modifier is missing, highlighting the necessity of careful selection of potential effect modifiers prior to analysis. When all effect modifiers are included, ML-NMR and STC are robust techniques for population adjustment. ML-NMR offers additional advantages over MAIC and STC, including extending to larger treatment networks and producing estimates in any target population, making this an attractive choice in a variety of scenarios.

Assessing the consistency assumptions underlying network meta-regression using aggregate data.

When numerous treatments exist for a disease (Treatments 1, 2, 3, etc), network meta-regression (NMR) examines whether each relative treatment effect (eg, mean difference for 2 vs 1, 3 vs 1, and 3 vs 2) differs according to a covariate (eg, disease severity). Two consistency assumptions underlie NMR: consistency of the treatment effects at the covariate value 0 and consistency of the regression coefficients for the treatment by covariate interaction. The NMR results may be unreliable when the assumptions do not hold. Furthermore, interactions may exist but are not found because inconsistency of the coefficients is masking them, for example, when the treatment effect increases as the covariate increases using direct evidence but the effect decreases with the increasing covariate using indirect evidence. We outline existing NMR models that incorporate different types of treatment by covariate interaction. We then introduce models that can be used to assess the consistency assumptions underlying NMR for aggregate data. We extend existing node-splitting models, the unrelated mean effects inconsistency model, and the design by treatment inconsistency model to incorporate covariate interactions. We propose models for assessing both consistency assumptions simultaneously and models for assessing each of the assumptions in turn to gain a more thorough understanding of consistency. We apply the methods in a Bayesian framework to trial-level data comparing antimalarial treatments using the covariate average age and to four fabricated data sets to demonstrate key scenarios. We discuss the pros and cons of the methods and important considerations when applying models to aggregated data.

A design-by-treatment interaction model for network meta-analysis and meta-regression with integrated nested Laplace approximations.

Network meta-analysis (NMA) is gaining popularity for comparing multiple treatments in a single analysis. Generalized linear mixed models provide a unifying framework for NMA, allow us to analyze datasets with dichotomous, continuous or count endpoints, and take into account multiarm trials, potential heterogeneity between trials and network inconsistency. To perform inference within such NMA models, the use of Bayesian methods is often advocated. The standard inference tool is Markov chain Monte Carlo (MCMC), which is computationally expensive and requires convergence diagnostics. A deterministic approach to do fully Bayesian inference for latent Gaussian models can be achieved by integrated nested Laplace approximations (INLA), which is a fast and accurate alternative to MCMC. We show how NMA models fit in the class of latent Gaussian models and how NMA models are implemented using INLA and demonstrate that the estimates obtained by INLA are in close agreement with the ones obtained by MCMC. Specifically, we emphasize the design-by-treatment interaction model with random inconsistency parameters (also known as the Jackson model). Also, we have proposed a network meta-regression model, which is constructed by incorporating trial-level covariates to the Jackson model to explain possible sources of heterogeneity and/or inconsistency in the network. A publicly available R package, nmaINLA, is developed to automate the INLA implementation of NMA models, which are considered in this paper. Three applications illustrate the use of INLA for a NMA.