Multilevel network meta-regression: methods and implementation:<i>in workshop </i>Time to implement multilevel network meta-regression rather than matching adjusted indirect comparisons

PURPOSE: Multi-level network meta-regression (ML-NMR) extends the standard network meta-analysis framework to leverage individual patient data and aggregate data when comparing multiple treatments while adjusting for differences in populations between trials. Unlike previous population adjustment approaches, ML-NMR is applicable in networks of any size, avoids aggregation bias and issues with non-collapsible effect measures, and crucially for decision-making produces estimates in any target population.DESCRIPTION: Workshop attendees will obtain a working knowledge of the ML-NMR method, its advantages, and considerations for implementation. Dr. Jansen will chair the session and introduce ML-NMR in the context of the challenges with existing methods (10 min.). Dr. Phillippo will explain the statistical methods for ML-NMR, highlight advantages relative to existing methods, and provide an overview of how to implement the method using the multinma R package in terms of the syntax and features (15 min.). Ms. Cope will illustrate how these methods can be applied in a case study regarding the comparative efficacy of alternative interventions for triple-class exposed relapsed refractory multiple myeloma. This will include audience participation regarding selection of covariates, alternative time-to-event models, conditional vs. marginal estimates, and target populations for prediction (15 min). Mr. Klijn will describe lessons learned and recommendations for implementation of ML-NMR (15 min). Questions from the audience will be addressed (5 min) and this interactive workshop will be valuable to researchers and industry analysts interested in comparative efficacy research for health technology assessments

Assessing the robustness of recommendations made in a guideline on specialist neonatal respiratory care in babies born preterm with threshold analysis

View from Obiri (Ubirr) Rock, Kakadu National Park, shows rocky ourcrops in distance and plains in mid distance.Crawford, Pauline

Using individual participant data to improve network meta-analysis projects.

A network meta-analysis combines the evidence from existing randomised trials about the comparative efficacy of multiple treatments. It allows direct and indirect evidence about each comparison to be included in the same analysis, and provides a coherent framework to compare and rank treatments. A traditional network meta-analysis uses aggregate data (eg, treatment effect estimates and standard errors) obtained from publications or trial investigators. An alternative approach is to obtain, check, harmonise and meta-analyse the individual participant data (IPD) from each trial. In this article, we describe potential advantages of IPD for network meta-analysis projects, emphasising five key benefits: (1) improving the quality and scope of information available for inclusion in the meta-analysis, (2) examining and plotting distributions of covariates across trials (eg, for potential effect modifiers), (3) standardising and improving the analysis of each trial, (4) adjusting for prognostic factors to allow a network meta-analysis of conditional treatment effects and (5) including treatment-covariate interactions (effect modifiers) to allow relative treatment effects to vary by participant-level covariate values (eg, age, baseline depression score). A running theme of all these benefits is that they help examine and reduce heterogeneity (differences in the true treatment effect between trials) and inconsistency (differences in the true treatment effect between direct and indirect evidence) in the network. As a consequence, an IPD network meta-analysis has the potential for more precise, reliable and informative results for clinical practice and even allows treatment comparisons to be made for individual patients and targeted populations conditional on their particular characteristics

Antidepressants for pain management in adults with chronic pain:a network meta-analysis

Background: Chronic pain is common in adults, and often has a detrimental impact upon physical ability, well-being, and quality of life. Previous reviews have shown that certain antidepressants may be effective in reducing pain with some benefit in improving patients’ global impression of change for certain chronic pain conditions. However, there has not been a network meta-analysis (NMA) examining all antidepressants across all chronic pain conditions. Objectives: To assess the comparative efficacy and safety of antidepressants for adults with chronic pain (except headache). Search methods: We searched CENTRAL, MEDLINE, Embase, CINAHL, LILACS, AMED and PsycINFO databases, and clinical trials registries, for randomised controlled trials (RCTs) of antidepressants for chronic pain conditions in January 2022. Selection criteria: We included RCTs that examined antidepressants for chronic pain against any comparator. If the comparator was placebo, another medication, another antidepressant, or the same antidepressant at different doses, then we required the study to be double-blind. We included RCTs with active comparators that were unable to be double-blinded (e.g. psychotherapy) but rated them as high risk of bias. We excluded RCTs where the follow-up was less than two weeks and those with fewer than 10 participants in each arm. Data collection and analysis: Two review authors separately screened, data extracted, and judged risk of bias. We synthesised the data using Bayesian NMA and pairwise meta-analyses for each outcome and ranked the antidepressants in terms of their effectiveness using the surface under the cumulative ranking curve (SUCRA). We primarily used Confidence in Meta-Analysis (CINeMA) and Risk of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN) to assess the certainty of the evidence. Where it was not possible to use CINeMA and ROB-MEN due to the complexity of the networks, we used GRADE to assess the certainty of the evidence. Our primary outcomes were substantial (50%) pain relief, pain intensity, mood, and adverse events. Our secondary outcomes were moderate pain relief (30%), physical function, sleep, quality of life, Patient Global Impression of Change (PGIC), serious adverse events, and withdrawal. Main results: This review and NMA included 176 studies with a total of 28,664 participants. The majority of studies were placebo-controlled (83), and parallel−armed (141). The most common pain conditions examined were fibromyalgia (59 studies); neuropathic pain (49 studies) and musculoskeletal pain (40 studies). The average length of RCTs was 10 weeks. Seven studies provided no useable data and were omitted from the NMA. The majority of studies measured short-term outcomes only and excluded people with low mood and other mental health conditions. Across efficacy outcomes, duloxetine was consistently the highest-ranked antidepressant with moderate- to high-certainty evidence. In duloxetine studies, standard dose was equally efficacious as high dose for the majority of outcomes. Milnacipran was often ranked as the next most efficacious antidepressant, although the certainty of evidence was lower than that of duloxetine. There was insufficient evidence to draw robust conclusions for the efficacy and safety of any other antidepressant for chronic pain. Primary efficacy outcomes. Duloxetine standard dose (60 mg) showed a small to moderate effect for substantial pain relief (odds ratio (OR) 1.91, 95% confidence interval (CI) 1.69 to 2.17; 16 studies, 4490 participants; moderate-certainty evidence) and continuous pain intensity (standardised mean difference (SMD) −0.31, 95% CI −0.39 to −0.24; 18 studies, 4959 participants; moderate-certainty evidence). For pain intensity, milnacipran standard dose (100 mg) also showed a small effect (SMD −0.22, 95% CI −0.39 to 0.06; 4 studies, 1866 participants; moderate-certainty evidence). Mirtazapine (30 mg) had a moderate effect on mood (SMD −0.5, 95% CI −0.78 to −0.22; 1 study, 406 participants; low-certainty evidence), while duloxetine showed a small effect (SMD −0.16, 95% CI −0.22 to −0.1; 26 studies, 7952 participants; moderate-certainty evidence); however it is important to note that most studies excluded participants with mental health conditions, and so average anxiety and depression scores tended to be in the 'normal' or 'subclinical' ranges at baseline already. Secondary efficacy outcomes. Across all secondary efficacy outcomes (moderate pain relief, physical function, sleep, quality of life, and PGIC), duloxetine and milnacipran were the highest-ranked antidepressants with moderate-certainty evidence, although effects were small. For both duloxetine and milnacipran, standard doses were as efficacious as high doses. Safety. There was very low-certainty evidence for all safety outcomes (adverse events, serious adverse events, and withdrawal) across all antidepressants. We cannot draw any reliable conclusions from the NMAs for these outcomes. Authors' conclusions: Our review and NMAs show that despite studies investigating 25 different antidepressants, the only antidepressant we are certain about for the treatment of chronic pain is duloxetine. Duloxetine was moderately efficacious across all outcomes at standard dose. There is also promising evidence for milnacipran, although further high-quality research is needed to be confident in these conclusions. Evidence for all other antidepressants was low certainty. As RCTs excluded people with low mood, we were unable to establish the effects of antidepressants for people with chronic pain and depression. There is currently no reliable evidence for the long-term efficacy of any antidepressant, and no reliable evidence for the safety of antidepressants for chronic pain at any time point.</p

Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal

Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies such as NICE. These use individual patient data from a subset of trials to form population-adjusted indirect comparisons between treatments, in a specific target population. Recently proposed population adjustment methods include the Matching-Adjusted Indirect Comparison (MAIC) and the Simulated Treatment Comparison (STC). Despite increasing popularity, MAIC and STC remain largely untested. Furthermore, there is a lack of clarity about exactly how and when they should be applied in practice, and even whether the results are relevant to the decision problem. There is therefore a real and present risk that the assumptions being made in one submission to a reimbursement agency are fundamentally different to – or even incompatible with – the assumptions being made in another for the same indication. We describe the assumptions required for population-adjusted indirect comparisons, and demonstrate how these may be used to generate comparisons in any given target population. We distinguish between anchored and unanchored comparisons according to whether a common comparator arm is used or not. Unanchored comparisons make much stronger assumptions which are widely regarded as infeasible. We provide recommendations on how and when population adjustment methods should be used, and the supporting analyses that are required, in order to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal. Simulation studies are needed to examine the properties of population adjustment methods and their robustness to breakdown of assumptions

Calibrating a network meta-analysis of diabetes trials of sodium glucose co-transporter 2 inhibitors, glucagon-like peptide-1 receptor analogues and dipeptidyl peptidase-4 inhibitors to a representative routine population : a systematic review protocol

Introduction: Participants in randomised controlled trials (trials) are generally younger and healthier than many individuals encountered in clinical practice. Consequently, the applicability of trial findings is often uncertain. To address this, results from trials can be calibrated to more representative data sources. In a network meta-analysis, using a novel approach which allows the inclusion of trials whether or not individual-level participant data (IPD) is available, we will calibrate trials for three drug classes (sodium glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP1) receptor analogues and dipeptidyl peptidase-4 (DPP4) inhibitors) to the Scottish diabetes register. Methods and analysis: Medline and EMBASE databases, the US clinical trials registry (clinicaltrials.gov) and the Chinese Clinical Trial Registry (chictr.org.cn) will be searched from 1 January 2002. Two independent reviewers will apply eligibility criteria to identify trials for inclusion. Included trials will be phase 3 or 4 trials of SGLT2 inhibitors, GLP1 receptor analogues or DPP4 inhibitors, with placebo or active comparators, in participants with type 2 diabetes, with at least one of glycaemic control, change in body weight or major adverse cardiovascular event as outcomes. Unregistered trials will be excluded. We have identified a target population from the population-based Scottish diabetes register. The chosen cohort comprises people in Scotland with type 2 diabetes who either (1) require further treatment due to poor glycaemic control where any of the three drug classes may be suitable, or (2) who have adequate glycaemic control but are already on one of the three drug classes of interest or insulin. Ethics and dissemination: Ethical approval for IPD use was obtained from the University of Glasgow MVLS College Ethics Committee (Project: 200160070). The Scottish diabetes register has approval from the Scottish A Research Ethics Committee (11/AL/0225) and operates with Public Benefit and Privacy Panel for Health and Social Care approval (1617-0147). PROSPERO registration number: CRD42020184174