Clinical and cost-effectiveness of clopidogrel resistance genotype testing after ischaemic stroke or transient ischaemic attack: a systematic review and economic model.

Background: Stroke or transient ischaemic attack patients are at increased risk of secondary vascular events. Antiplatelet medications, most commonly clopidogrel, are prescribed to reduce this risk. Factors including CYP2C19 genetic variants can hinder clopidogrel metabolism. Laboratory-based or point-of-care tests can detect these variants, enabling targeted treatment. Objective: To assess the effectiveness of genetic testing to identify clopidogrel resistance in people with ischaemic stroke or transient ischaemic attack. Specific objectives: Do people tested for clopidogrel resistance, and treated accordingly, have a reduced risk of secondary vascular events? Do people with loss-of-function alleles associated with clopidogrel resistance have a reduced risk of secondary vascular events if treated with alternative interventions compared to clopidogrel? Do people with loss-of-function alleles associated with clopidogrel resistance have an increased risk of secondary vascular events when treated with clopidogrel? What is the accuracy of point-of-care tests for detecting variants associated with clopidogrel resistance? What is the technical performance and cost of CYP2C19 genetic tests? Is genetic testing for clopidogrel resistance cost-effective compared with no testing? Design: Systematic review and economic model. Results: Objective 1: Two studies assessed secondary vascular events in patients tested for loss-of-function alleles and treated accordingly. They found a reduced risk, but confidence intervals were wide (hazard ratio 0.50, 95% confidence interval 0.09 to 2.74 and hazard ratio 0.53, 95% confidence interval 0.24 to 1.18). Objective 2: Seven randomised controlled trials compared clopidogrel with alternative treatment in people with genetic variants. Ticagrelor was associated with a lower risk of secondary vascular events than clopidogrel (summary hazard ratio 0.76, 95% confidence interval 0.65 to 0.90; two studies). Objective 3: Twenty-five studies compared outcomes in people with and without genetic variants treated with clopidogrel. People with genetic variants were at an increased risk of secondary vascular events (hazard ratio 1.72, 95% confidence interval 1.43 to 2.08; 18 studies). There was no difference in bleeding risk (hazard ratio 0.98, 95% confidence interval 0.68 to 1.40; five studies). Objective 4: Eleven studies evaluated Genomadix Cube accuracy; no studies evaluated Genedrive. Summary sensitivity and specificity against laboratory reference standards were both 100% (95% confidence interval 94% to 100% and 99% to 100%). Objective 5: Seventeen studies evaluated technical performance of point-of-care tests. Test failure rate ranged from 0.4% to 19% for Genomadix Cube. A survey of 8/10 genomic laboratory hubs revealed variation in preferred technologies for testing, and cost per test ranging from £15 to £250. Most laboratories expected test failure rate to be < 1%. Additional resources could enhance testing capacity and expedite turnaround times. Objective 6: Laboratory and point-of-care CYP2C19 testing strategies were cost-saving and increase quality-adjusted life-years compared with no testing. Both strategies gave similar costs, quality-adjusted life-years and expected net monetary benefit. Conclusions: Our results suggest that CYP2C19 testing followed by tailored treatment is likely to be effective and cost-effective in both populations. Future work: Accuracy and technical performance of Genedrive. Test failure rate of Genomadix Cube in a National Health Service setting. Value of testing additional loss-of-function alleles. Appropriateness of treatment dichotomy based on loss-of-function alleles. Limitations: Lack of data on Genedrive. No randomised 'test-and-treat' studies of dipyramidole plus aspirin. Study registration: This study is registered as PROSPERO CRD42022357661. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135620) and is published in full in Health Technology Assessment; Vol. 28, No. 57. See the NIHR Funding and Awards website for further award information.

The most common type of stroke occurs when the supply of blood to the brain is cut off. Symptoms of stroke happen suddenly and vary depending on which part of the brain is affected. They usually include problems with movement, speech, vision and the face drooping on one side. A 'transient ischaemic attack' is a milder related condition. There are around 100,000 strokes and 60,000 transient ischaemic attacks every year in the UK. People who have a stroke or transient ischaemic attack are at greater risk of having another stroke. To reduce the chances of this happening, doctors will often prescribe medication. The most common medication used is called 'clopidogrel'. However, clopidogrel does not work for everyone. One reason for this is having specific variations of a gene called the CYP2C19 gene. Around one in three people in the UK have this variation. We wanted to know whether introducing genetic testing to identify variations in the CYP2C19 gene for people who have had a stroke or transient ischaemic attack can help doctors prescribe a treatment that will work for them, reducing the risk of having another stroke. We also wanted to know if doing this test would be a good use of NHS money. Doing a genetic test to identify variations in the CYP2C19 gene, and prescribing an alternative medication for people with these variations, may reduce the chances of having a new stroke. It is likely that a genetic test for variations of the CYP2C19 gene would represent value for money for the NHS.

Accuracy and technical characteristics of CYP2C19 point of care tests: a systematic review.

Aim: To assess the accuracy and technical characteristics of CYP2C19 point of care tests (POCTs).Patients & methods: Systematic review of primary studies, in any population or setting, that evaluated POCTs for detecting CYP2C19 loss of function (LOF) alleles.Results: Eleven studies provided accuracy data (eight Spartan; one Genomadix Cube; one GMEX; one Genedrive). The POCTs had very high sensitivity and specificity for the alleles they tested for. Twenty-two studies reported technical characteristics: POCTs were easy to operate and provided results quickly. Limited data were reported for test failure rate and cost.Conclusion: CYP2C19 POCTs may be a useful alternative to laboratory-based testing to guide antiplatelet therapy. Further data are required on accuracy (GMEX; Genedrive), test failure and cost (all POCT).

[Box: see text].

A systematic review and network meta-analysis of psychological, psychosocial, pharmacological, physical and combined treatments for adults with a new episode of depression.

Background: Various effective treatments for depression exist. We aimed to identify the most effective first-line treatments for new episodes of less and more severe depression (defined by depression scale cut-off scores), to update NICE guidance on the management of Depression in Adults in England. Methods: Systematic review and network meta-analysis of randomised controlled trials (RCTs) published up to June 2020 (PROSPERO registration number CRD42019151328). We analysed interventions by class and individually. The primary efficacy outcome was depressive symptom change (expressed as standardised mean difference [SMD]). The review for this outcome was updated in November 2023. Findings: We included 676 RCTs, 105,477 participants and 63 treatment classes. For less severe depression, group cognitive/cognitive behavioural therapy (CT/CBT) class was efficacious versus treatment as usual [TAU], the reference treatment for this population [SMD -1.01 (95% Credible Interval [CrI] -1.76; -0.06)]. For more severe depression, efficacious classes versus pill placebo (reference treatment for this population) included combined individual CT/CBT with antidepressants [-1.18 (-2.07; -0.44)], individual behavioural therapies [-0.86 (-1.65; -0.16)], combined light therapy with antidepressants [-0.86 (-1.59; -0.12)], combined acupuncture with antidepressants [-0.78 (-1.12; -0.44)], individual CT/CBT [-0.78 (-1.42; -0.33)], mirtazapine [-0.35 (-0.48; -0.22)], serotonin and norepinephrine reuptake inhibitors [-0.32 (-0.43; -0.22)], tricyclic antidepressants [-0.29 (-0.50; -0.05)], and selective serotonin reuptake inhibitors [-0.24 (-0.32; -0.16)]. Additional treatments showed evidence of efficacy at the intervention level. Evidence for less and more severe depression was of low and low-to-moderate quality, respectively. In the 2023 update, group yoga and self-help without support emerged as efficacious for less severe depression. For more severe depression, combined group exercise with antidepressants emerged as efficacious, whereas combined light therapy with antidepressants failed to remain efficacious. Interpretation: Group CT/CBT (and possibly group yoga and self-help) appears efficacious in less severe depression, whereas antidepressants do not show evidence of effect. Combined antidepressants with individual CT/CBT, acupuncture and, possibly, group exercise, individual psychological therapies (behavioural therapies, CT/CBT) alone, and antidepressants alone appear efficacious in more severe depression. Quality of evidence, cost-effectiveness, applicability and implementation issues also need to be considered when formulating clinical practice recommendations. Funding: National Institute for Health and Care Excellence.

MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation-A Tutorial.

Survival extrapolation often plays an important role in health technology assessment (HTA), and there are a range of different approaches available. Approaches that can leverage external evidence (i.e. data or information collected outside the main data source of interest) may be helpful, given the extent of uncertainty often present when determining a suitable survival extrapolation. One of these methods is the multi-parameter evidence synthesis (MPES) approach, first proposed for use in HTA by Guyot et al., and more recently by Jackson. While MPES has potential benefits over conventional extrapolation approaches (such as simple or flexible parametric models), it is more computationally complex and requires use of specialist software. This tutorial presents an introduction to MPES for HTA, alongside a user-friendly, publicly available operationalisation of Guyot's original MPES that can be executed using the statistical software package R. Through two case studies, both Guyot's and Jackson's MPES approaches are explored, along with sensitivity analyses relevant to HTA. Finally, the discussion section of the tutorial details important considerations for analysts considering use of an MPES approach, along with potential further developments. MPES has not been used often in HTA, and so there are limited examples of how it has been used and perceived. However, this tutorial may aid future research efforts exploring the use of MPES further.

Response to discussant comments on "NMA, the first 20 years".

We respond to discussant comments on our paper "Twenty years of network meta-analysis: Continuing controversies and recent developments" (https://doi.org/10.1002/jrsm.1700) and raise some additional points for consideration, including: the way in which methodological guidance is generated; integration of the estimand framework with evidence synthesis; and implications of the European Joint Clinical Assessment. We ask: what properties are required of population adjustment methods to enable transparent and consistent decision-making? We also ask why individual patient data is not routinely made available to re-imbursement authorities and clinical guideline developers.

Participant characteristics and exclusion from phase 3/4 industry funded trials of chronic medical conditions: meta-analysis of individual participant level data.

Objectives: To assess whether age, sex, comorbidity count, and race and ethnic group are associated with the likelihood of trial participants not being enrolled in a trial for any reason (ie, screen failure). Design: Bayesian meta-analysis of individual participant level data. Setting: Industry funded phase 3/4 trials of chronic medical conditions. Participants: Participants were identified using individual participant level data to be in either the enrolled group or screen failure group. Data were available for 52 trials involving 72 178 screened individuals of whom 24 733 (34%) were excluded from the trial at the screening stage. Main outcome measures: For each trial, logistic regression models were constructed to assess likelihood of screen failure in people who had been invited to screening, and were regressed on age (per 10 year increment), sex (male v female), comorbidity count (per one additional comorbidity), and race or ethnic group. Trial level analyses were combined in Bayesian hierarchical models with pooling across condition. Results: In age and sex adjusted models across all trials, neither age nor sex was associated with increased odds of screen failure, although weak associations were detected after additionally adjusting for comorbidity (odds ratio of age, per 10 year increment was 1.02 (95% credibility interval 1.01 to 1.04) and male sex (0.95 (0.91 to 1.00)). Comorbidity count was weakly associated with screen failure, but in an unexpected direction (0.97 per additional comorbidity (0.94 to 1.00), adjusted for age and sex). People who self-reported as black seemed to be slightly more likely to fail screening than people reporting as white (1.04 (0.99 to 1.09)); a weak effect that seemed to persist after adjustment for age, sex, and comorbidity count (1.05 (0.98 to 1.12)). The between-trial heterogeneity was generally low, evidence of heterogeneity by sex was noted across conditions (variation in odds ratios on log scale of 0.01-0.13). Conclusions: Although the conclusions are limited by uncertainty about the completeness or accuracy of data collection among participants who were not randomised, we identified mostly weak associations with an increased likelihood of screen failure for age, sex, comorbidity count, and black race or ethnic group. Proportionate increases in screening these underserved populations may improve representation in trials. Trial registration number: PROSPERO CRD42018048202.

Advancing unanchored simulated treatment comparisons: A novel implementation and simulation study.

Population-adjusted indirect comparisons, developed in the 2010s, enable comparisons between two treatments in different studies by balancing patient characteristics in the case where individual patient-level data (IPD) are available for only one study. Health technology assessment (HTA) bodies increasingly rely on these methods to inform funding decisions, typically using unanchored indirect comparisons (i.e., without a common comparator), due to the need to evaluate comparative efficacy and safety for single-arm trials. Unanchored matching-adjusted indirect comparison (MAIC) and unanchored simulated treatment comparison (STC) are currently the only two approaches available for population-adjusted indirect comparisons based on single-arm trials. However, there is a notable underutilisation of unanchored STC in HTA, largely due to a lack of understanding of its implementation. We therefore develop a novel way to implement unanchored STC by incorporating standardisation/marginalisation and the NORmal To Anything (NORTA) algorithm for sampling covariates. This methodology aims to derive a suitable marginal treatment effect without aggregation bias for HTA evaluations. We use a non-parametric bootstrap and propose separately calculating the standard error for the IPD study and the comparator study to ensure the appropriate quantification of the uncertainty associated with the estimated treatment effect. The performance of our proposed unanchored STC approach is evaluated through a comprehensive simulation study focused on binary outcomes. Our findings demonstrate that the proposed approach is asymptotically unbiased. We argue that unanchored STC should be considered when conducting unanchored indirect comparisons with single-arm studies, presenting a robust approach for HTA decision-making.

Prevalence of opioid dependence in Scotland 2015-2020: A multi-parameter estimation of prevalence (MPEP) study.

BACKGROUND AND AIMS: Drug-related deaths in Scotland more than doubled between 2011 and 2020. To inform policymakers and understand drivers of this increase, we estimated the number of people with opioid dependence aged 15-64 from 2014/15 to 2019/20. DESIGN: We fitted a Bayesian multi-parameter estimation of prevalence (MPEP) model, using adverse event rates to estimate prevalence of opioid dependence jointly from Opioid Agonist Therapy (OAT), opioid-related mortality and hospital admissions data. Estimates are stratified by age group, sex and year. SETTING: Scotland, 2014/15 to 2019/20. PARTICIPANTS: People with opioid dependence and potential to benefit from OAT, whether ever treated or not. Using data from the Scottish Public Health Drug Linkage Programme, we identified a baseline cohort of individuals who had received OAT within the last 5 years, and all opioid-related deaths and hospital admissions (whether among or outside of this cohort). MEASUREMENTS: Rates of each adverse event type and (unobserved) prevalence were jointly modelled. FINDINGS: The estimated number and prevalence of people with opioid dependence in Scotland in 2019/20 was 47 100 (95% Credible Interval [CrI] 45 700 to 48 600) and 1.32% (95% CrI 1.28% to 1.37%). Of these, 61% received OAT during 2019/20. Prevalence in Greater Glasgow and Clyde was estimated as 1.77% (95% CrI 1.69% to 1.85%). There was weak evidence that overall prevalence fell slightly from 2014/15 (change -0.07%, 95% CrI -0.14% to 0.00%). The population of people with opioid dependence is ageing, with the estimated number of people aged 15-34 reducing by 5100 (95% CrI 3800 to 6400) and number aged 50-64 increasing by 2800 (95% CrI 2100 to 3500) between 2014/15 and 2019/20. CONCLUSIONS: The prevalence of opioid dependence in Scotland remained high but was relatively stable, with only weak evidence of a small reduction, between 2014/15 and 2019/20. Increased numbers of opioid-related deaths can be attributed to increased risk among people with opioid dependence, rather than increasing prevalence.

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators.

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

Pharmacological interventions for the prevention of bleeding in people undergoing elective hip or knee surgery: a systematic review and network meta-analysis.

BACKGROUND: Hip and knee replacement surgery is a well-established means of improving quality of life, but is associated with a significant risk of bleeding. One-third of people are estimated to be anaemic before hip or knee replacement surgery; coupled with the blood lost during surgery, up to 90% of individuals are anaemic postoperatively. As a result, people undergoing orthopaedic surgery receive 3.9% of all packed red blood cell transfusions in the UK. Bleeding and the need for allogeneic blood transfusions has been shown to increase the risk of surgical site infection and mortality, and is associated with an increased duration of hospital stay and costs associated with surgery. Reducing blood loss during surgery may reduce the risk of allogeneic blood transfusion, reduce costs and improve outcomes following surgery. Several pharmacological interventions are available and currently employed as part of routine clinical care. OBJECTIVES: To determine the relative efficacy of pharmacological interventions for preventing blood loss in elective primary or revision hip or knee replacement, and to identify optimal administration of interventions regarding timing, dose and route, using network meta-analysis (NMA) methodology. SEARCH METHODS: We searched the following databases for randomised controlled trials (RCTs) and systematic reviews, from inception to 18 October 2022: CENTRAL (the Cochrane Library), MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCOhost), Transfusion Evidence Library (Evidentia), ClinicalTrials.gov and WHO International Clinical Trials Registry Platform (ICTRP). SELECTION CRITERIA: We included RCTs of people undergoing elective hip or knee surgery only. We excluded non-elective or emergency procedures, and studies published since 2010 that had not been prospectively registered (Cochrane Injuries policy). There were no restrictions on gender, ethnicity or age (adults only). We excluded studies that used standard of care as the comparator. Eligible interventions included: antifibrinolytics (tranexamic acid (TXA), aprotinin, epsilon-aminocaproic acid (EACA)), desmopressin, factor VIIa and XIII, fibrinogen, fibrin sealants and non-fibrin sealants. DATA COLLECTION AND ANALYSIS: We performed the review according to standard Cochrane methodology. Two authors independently assessed trial eligibility and risk of bias, and extracted data. We assessed the certainty of the evidence using CINeMA. We presented direct (pairwise) results using RevMan Web and performed the NMA using BUGSnet. We were interested in the following primary outcomes: need for allogenic blood transfusion (up to 30 days) and all-cause mortality (deaths occurring up to 30 days after the operation), and the following secondary outcomes: mean number of transfusion episodes per person (up to 30 days), re-operation due to bleeding (within seven days), length of hospital stay and adverse events related to the intervention received. MAIN RESULTS: We included a total of 102 studies. Twelve studies did not report the number of included participants; the other 90 studies included 8418 participants. Trials included more women (64%) than men (36%). In the NMA for allogeneic blood transfusion, we included 47 studies (4398 participants). Most studies examined TXA (58 arms, 56%). We found that TXA, given intra-articularly and orally at a total dose of greater than 3 g pre-incision, intraoperatively and postoperatively, ranked the highest, with an anticipated absolute effect of 147 fewer blood transfusions per 1000 people (150 fewer to 104 fewer) (53% chance of ranking 1st) within the NMA (risk ratio (RR) 0.02, 95% credible interval (CrI) 0 to 0.31; moderate-certainty evidence). This was followed by TXA given orally at a total dose of 3 g pre-incision and postoperatively (RR 0.06, 95% CrI 0.00 to 1.34; low-certainty evidence) and TXA given intravenously and orally at a total dose of greater than 3 g intraoperatively and postoperatively (RR 0.10, 95% CrI 0.02 to 0.55; low-certainty evidence). Aprotinin (RR 0.59, 95% CrI 0.36 to 0.96; low-certainty evidence), topical fibrin (RR 0.86, CrI 0.25 to 2.93; very low-certainty evidence) and EACA (RR 0.60, 95% CrI 0.29 to 1.27; very low-certainty evidence) were not shown to be as effective compared with TXA at reducing the risk of blood transfusion. We were unable to perform an NMA for our primary outcome all-cause mortality within 30 days of surgery due to the large number of studies with zero events, or because the outcome was not reported. In the NMA for deep vein thrombosis (DVT), we included 19 studies (2395 participants). Most studies examined TXA (27 arms, 64%). No studies assessed desmopressin, EACA or topical fibrin. We found that TXA given intravenously and orally at a total dose of greater than 3 g intraoperatively and postoperatively ranked the highest, with an anticipated absolute effect of 67 fewer DVTs per 1000 people (67 fewer to 34 more) (26% chance of ranking first) within the NMA (RR 0.16, 95% CrI 0.02 to 1.43; low-certainty evidence). This was followed by TXA given intravenously and intra-articularly at a total dose of 2 g pre-incision and intraoperatively (RR 0.21, 95% CrI 0.00 to 9.12; low-certainty evidence) and TXA given intravenously and intra-articularly, total dose greater than 3 g pre-incision, intraoperatively and postoperatively (RR 0.13, 95% CrI 0.01 to 3.11; low-certainty evidence). Aprotinin was not shown to be as effective compared with TXA (RR 0.67, 95% CrI 0.28 to 1.62; very low-certainty evidence). We were unable to perform an NMA for our secondary outcomes pulmonary embolism, myocardial infarction and CVA (stroke) within 30 days, mean number of transfusion episodes per person (up to 30 days), re-operation due to bleeding (within seven days), or length of hospital stay, due to the large number of studies with zero events, or because the outcome was not reported by enough studies to build a network. There are 30 ongoing trials planning to recruit 3776 participants, the majority examining TXA (26 trials). AUTHORS' CONCLUSIONS: We found that of all the interventions studied, TXA is probably the most effective intervention for preventing bleeding in people undergoing hip or knee replacement surgery. Aprotinin and EACA may not be as effective as TXA at preventing the need for allogeneic blood transfusion. We were not able to draw strong conclusions on the optimal dose, route and timing of administration of TXA. We found that TXA given at higher doses tended to rank higher in the treatment hierarchy, and we also found that it may be more beneficial to use a mixed route of administration (oral and intra-articular, oral and intravenous, or intravenous and intra-articular). Oral administration may be as effective as intravenous administration of TXA. We found little to no evidence of harm associated with higher doses of tranexamic acid in the risk of DVT. However, we are not able to definitively draw these conclusions based on the trials included within this review.

Twenty years of network meta-analysis: Continuing controversies and recent developments.

Network meta-analysis (NMA) is an extension of pairwise meta-analysis (PMA) which combines evidence from trials on multiple treatments in connected networks. NMA delivers internally consistent estimates of relative treatment efficacy, needed for rational decision making. Over its first 20 years NMA's use has grown exponentially, with applications in both health technology assessment (HTA), primarily re-imbursement decisions and clinical guideline development, and clinical research publications. This has been a period of transition in meta-analysis, first from its roots in educational and social psychology, where large heterogeneous datasets could be explored to find effect modifiers, to smaller pairwise meta-analyses in clinical medicine on average with less than six studies. This has been followed by narrowly-focused estimation of the effects of specific treatments at specific doses in specific populations in sparse networks, where direct comparisons are unavailable or informed by only one or two studies. NMA is a powerful and well-established technique but, in spite of the exponential increase in applications, doubts about the reliability and validity of NMA persist. Here we outline the continuing controversies, and review some recent developments. We suggest that heterogeneity should be minimized, as it poses a threat to the reliability of NMA which has not been fully appreciated, perhaps because it has not been seen as a problem in PMA. More research is needed on the extent of heterogeneity and inconsistency in datasets used for decision making, on formal methods for making recommendations based on NMA, and on the further development of multi-level network meta-regression.

Variability in meta-analysis estimates of continuous outcomes using different standardization and scale-specific re-expression methods.

OBJECTIVES: To explore the impact of using different data standardization and scale-specific re-expression methods (i.e., processes to convert standardized data into scale-specific units) in meta-analyses using standardized mean differences (SMDs). STUDY DESIGN AND SETTING: We used data assessed by the Short Physical Performance Battery and the Barthel Index from a meta-analysis of randomized controlled trials which synthesized evidence of physical activity effectiveness on the functional capacity of hospitalized older adults. We standardized the data using study-specific pooled standard deviations (SDs), an internal, and an external SD references. Bayesian meta-analyses were performed for each method to compare the posterior distributions of the meta-analysis parameters. Posterior estimates were re-expressed into scale-specific units applying different methods established in the Cochrane guidelines. RESULTS: Meta-analysis estimates depend on the used standardization method. Analyses including data standardized using the largest SD reference presented lower estimates with less uncertainty in both scales. The method applied for re-expressing SMDs into scale-specific units impacted in their posterior clinical interpretation. The most similar results across models were obtained when using the same SD reference to standardize and re-express data. CONCLUSION: Different data standardization methods yielded different meta-analysis estimates on the SMD scale. To avoid the introduction of bias, the use of a single scale-specific SD reference to standardize data is recommended and instead of study-specific pooled sample SDs. Meta-analysis software packages may therefore change their default methods to allow this method by a single scale-specific SD. To re-express the SMDs into scale-specific units, we suggest the application of the same SD reference that was used for data standardization.

Monotreatment With Conventional Antirheumatic Drugs or Glucocorticoids in Rheumatoid Arthritis: A Network Meta-Analysis.

Importance: This is the first network meta-analysis to assess outcomes associated with multiple conventional synthetic disease-modifying antirheumatic drugs and glucocorticoid. Objective: To analyze clinical outcomes after treatment with conventional synthetic disease-modifying antirheumatic drugs and glucocorticoid among patients with rheumatoid arthritis. Data Sources: With no time restraint, English language articles were searched in MEDLINE, Embase, Cochrane Central, ClinicalTrials.gov, and reference lists of relevant meta-analyses until September 15, 2022. Study Selection: Four reviewers in pairs of 2 independently included controlled studies randomizing patients with rheumatoid arthritis to mono-conventional synthetic disease-modifying antirheumatic drugs, glucocorticoid, placebo, or nonactive treatment that recorded at least 1 outcome of tender joint count, swollen joint count, erythrocyte sedimentation rate, and C-reactive protein level. Of 1098 assessed articles, 130 articles (132 interventions) were included. Data Extraction and Synthesis: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline, and data quality was assessed by the Cochrane risk of bias tool RoB 2. Data were extracted by a single author and checked independently by 2 authors. Data were analyzed using a random effect model, and data analysis was conducted from June 2021 to February 2023. Main Outcomes and Measures: A protocol with hypothesis and study plan was registered before data recording. The most complete of recorded outcomes (tender joint count) was used as primary outcome, with imputations based on other outcomes to obtain a full analysis of all studies. Absolute change adjusted for baseline disease activity was assessed. Results: A total of 29 interventions in 275 treatment groups among 132 randomized clinical trials (mean [range], 71.0% [27.0% to 100%] females in studies; mean [range] of ages in studies, 53 [36 to 70] years) were identified, which included 13 260 patients with rheumatoid arthritis. The mean (range) duration of RA was 79 (2 to 243) months, and the mean (range) disease activity score was 6.3 (4.0 to 8.8). Compared with placebo, oral methotrexate was associated with a reduced tender joint count by 5.18 joints (95% credible interval [CrI], 4.07 to 6.28 joints). Compared with methotrexate, glucocorticoid (-2.54 joints; 95% CrI, -5.16 to 0.08 joints) and remaining drugs except cyclophosphamide (6.08 joints; 95% CrI, 0.44 to 11.66 joints) were associated with similar or lower tender joint counts. Conclusions and Relevance: This study's results support the present role of methotrexate as the primary reference conventional synthetic disease-modifying antirheumatic drug.

Data visualisation approaches for component network meta-analysis: visualising the data structure.

BACKGROUND: Health and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other components which differ across trials. Multicomponent interventions can be synthesised using component NMA (CNMA). CNMA is limited by the structure of the available evidence, but it is not always straightforward to visualise such complex evidence networks. The aim of this paper is to develop tools to visualise the structure of complex evidence networks to support CNMA. METHODS: We performed a citation review of two key CNMA methods papers to identify existing published CNMA analyses and reviewed how they graphically represent intervention complexity and comparisons across trials. Building on identified shortcomings of existing visualisation approaches, we propose three approaches to standardise visualising the data structure and/or availability of data: CNMA-UpSet plot, CNMA heat map, CNMA-circle plot. We use a motivating example to illustrate these plots. RESULTS: We identified 34 articles reporting CNMAs. A network diagram was the most common plot type used to visualise the data structure for CNMA (26/34 papers), but was unable to express the complex data structures and large number of components and potential combinations of components associated with CNMA. Therefore, we focused visualisation development around representing the data structure of a CNMA more completely. The CNMA-UpSet plot presents arm-level data and is suitable for networks with large numbers of components or combinations of components. Heat maps can be utilised to inform decisions about which pairwise interactions to consider for inclusion in a CNMA model. The CNMA-circle plot visualises the combinations of components which differ between trial arms and offers flexibility in presenting additional information such as the number of patients experiencing the outcome of interest in each arm. CONCLUSIONS: As CNMA becomes more widely used for the evaluation of multicomponent interventions, the novel CNMA-specific visualisations presented in this paper, which improve on the limitations of existing visualisations, will be important to aid understanding of the complex data structure and facilitate interpretation of the CNMA results.

Resistance training prescription for muscle strength and hypertrophy in healthy adults: a systematic review and Bayesian network meta-analysis.

OBJECTIVE: To determine how distinct combinations of resistance training prescription (RTx) variables (load, sets and frequency) affect muscle strength and hypertrophy. DATA SOURCES: MEDLINE, Embase, Emcare, SPORTDiscus, CINAHL, and Web of Science were searched until February 2022. ELIGIBILITY CRITERIA: Randomised trials that included healthy adults, compared at least 2 predefined conditions (non-exercise control (CTRL) and 12 RTx, differentiated by load, sets and/or weekly frequency), and reported muscle strength and/or hypertrophy were included. ANALYSES: Systematic review and Bayesian network meta-analysis methodology was used to compare RTxs and CTRL. Surface under the cumulative ranking curve values were used to rank conditions. Confidence was assessed with threshold analysis. RESULTS: The strength network included 178 studies (n=5097; women=45%). The hypertrophy network included 119 studies (n=3364; women=47%). All RTxs were superior to CTRL for muscle strength and hypertrophy. Higher-load (>80% of single repetition maximum) prescriptions maximised strength gains, and all prescriptions comparably promoted muscle hypertrophy. While the calculated effects of many prescriptions were similar, higher-load, multiset, thrice-weekly training (standardised mean difference (95% credible interval); 1.60 (1.38 to 1.82) vs CTRL) was the highest-ranked RTx for strength, and higher-load, multiset, twice-weekly training (0.66 (0.47 to 0.85) vs CTRL) was the highest-ranked RTx for hypertrophy. Threshold analysis demonstrated these results were extremely robust. CONCLUSION: All RTx promoted strength and hypertrophy compared with no exercise. The highest-ranked prescriptions for strength involved higher loads, whereas the highest-ranked prescriptions for hypertrophy included multiple sets. PROSPERO REGISTRATION NUMBER: CRD42021259663 and CRD42021258902.

Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials.

BACKGROUND: People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). METHODS AND FINDINGS: We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. CONCLUSIONS: Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

Constructing Relative Effect Priors for Research Prioritization and Trial Design: A Meta-epidemiological Analysis.

BACKGROUND: Bayesian methods have potential for efficient design of randomized clinical trials (RCTs) by incorporating existing evidence. Furthermore, value of information (VOI) methods estimate the value of reducing decision uncertainty, aiding transparent research prioritization. These methods require a prior distribution describing current uncertainty in key parameters, such as relative treatment effect (RTE). However, at the time of designing and commissioning research, there may be no data to base the prior on. The aim of this article is to present methods to construct priors for RTEs based on a collection of previous RCTs. METHODS: We developed 2 Bayesian hierarchical models that captured variability in RTE between studies within disease area accounting for study characteristics. We illustrate the methods using a data set of 743 published RCTs across 9 disease areas to obtain predictive distributions for RTEs for a range of disease areas. We illustrate how the priors from such an analysis can be used in a VOI analysis for an RCT in bladder cancer and compare the results with those using an uninformative prior. RESULTS: For most disease areas, the predicted RTE favored new interventions over comparators. The predicted effects and uncertainty differed across the 9 disease areas. VOI analysis showed that the expected value of research is much lower with our empirically derived prior compared with an uninformative prior. CONCLUSIONS: This study demonstrates a novel approach to generating informative priors that can be used to aid research prioritization and trial design. The methods can also be used to combine RCT evidence with expert opinion. Further work is needed to create a rich database of RCT evidence that can be used to form off-the-shelf priors. HIGHLIGHTS: Bayesian methods have potential to aid the efficient design of randomized clinical trials (RCTs) by incorporating existing evidence. Value-of-information (VOI) methods can be used to aid research prioritization by calculating the value of current decision uncertainty.These methods require a distribution describing current uncertainty in key parameters, that is, "prior distributions."This article demonstrates a methodology to estimate prior distributions for relative treatment effects (odds and hazard ratios) estimated from a collection of previous RCTs.These results may be combined with expert elicitation to facilitate 1) value-of-information methods to prioritize research or 2) Bayesian methods for research design.

What is the optimal management of potentially resectable stage III-N2 NSCLC? Results of a fixed-effects network meta-analysis and economic modelling.

Introduction: There is a critical need to understand the optimal treatment regimen in patients with potentially resectable stage III-N2 nonsmall cell lung cancer (NSCLC). Methods: A systematic review of randomised controlled trials was carried out using a literature search including the CDSR, CENTRAL, DARE, HTA, EMBASE and MEDLINE bibliographic databases. Selected trials were used to perform a Bayesian fixed-effects network meta-analysis and economic modelling of treatment regimens relevant to current-day treatment options: chemotherapy plus surgery (CS), chemotherapy plus radiotherapy (CR) and chemoradiotherapy followed by surgery (CRS). Findings: Six trials were prioritised for evidence synthesis. The fixed-effects network meta-analyses demonstrated an improvement in disease-free survival (DFS) for CRS versus CS and CRS versus CR of 0.34 years (95% CI 0.02-0.65) and 0.32 years (95% CI 0.05-0.58) respectively, over a 5-year period. No evidence of effect was observed in overall survival although point estimates favoured CRS. The probabilities that CRS had a greater mean survival time and greater probability of being alive than the reference treatment of CR at 5 years were 89% and 86% respectively. Survival outcomes for CR and CS were essentially equivalent. The economic model calculated that CRS and CS had incremental cost-effectiveness ratios of £19 000/quality-adjusted life-year (QALY) and £78 000/QALY compared to CR. The probability that CRS generated more QALYs than CR and CS was 94%. Interpretation: CRS provides an extended time in a disease-free state leading to improved cost-effectiveness over CR and CS in potentially resectable stage III-N2 NSCLC.

General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations.

BACKGROUND: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty by collecting additional data. EVSI calculations require simulating plausible data sets, typically achieved by evaluating quantile functions at random uniform numbers using standard inverse transform sampling (ITS). This is straightforward when closed-form expressions for the quantile function are available, such as for standard parametric survival models, but these are often unavailable when assuming treatment effect waning and for flexible survival models. In these circumstances, the standard ITS method could be implemented by numerically evaluating the quantile functions at each iteration in a probabilistic analysis, but this greatly increases the computational burden. Thus, our study aims to develop general-purpose methods that standardize and reduce the computational burden of the EVSI data-simulation step for survival data. METHODS: We developed a discrete sampling method and an interpolated ITS method for simulating survival data from a probabilistic sample of survival probabilities over discrete time units. We compared the general-purpose and standard ITS methods using an illustrative partitioned survival model with and without adjustment for treatment effect waning. RESULTS: The discrete sampling and interpolated ITS methods agree closely with the standard ITS method, with the added benefit of a greatly reduced computational cost in the scenario with adjustment for treatment effect waning. CONCLUSIONS: We present general-purpose methods for simulating survival data from a probabilistic sample of survival probabilities that greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can easily be automated from standard probabilistic decision analyses. HIGHLIGHTS: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty through a given data collection exercise, such as a randomized clinical trial. In this article, we address the problem of computing EVSI when we assume treatment effect waning or use flexible survival models, by developing general-purpose methods that standardize and reduce the computational burden of the EVSI data-generation step for survival data.We developed 2 methods for simulating survival data from a probabilistic sample of survival probabilities over discrete time units, a discrete sampling method and an interpolated inverse transform sampling method, which can be combined with a recently proposed nonparametric EVSI method to accurately estimate EVSI for collecting survival data.Our general-purpose data-simulation methods greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can therefore easily be automated from standard probabilistic decision analyses.

Drugs to reduce bleeding and transfusion in major open vascular or endovascular surgery: a systematic review and network meta-analysis.

BACKGROUND: Vascular surgery may be followed by internal bleeding due to inadequate surgical haemostasis, abnormal clotting, or surgical complications. Bleeding ranges from minor, with no transfusion requirement, to massive, requiring multiple blood product transfusions. There are a number of drugs, given systemically or applied locally, which may reduce the need for blood transfusion. OBJECTIVES: To assess the effectiveness and safety of anti-fibrinolytic and haemostatic drugs and agents in reducing bleeding and the need for blood transfusion in people undergoing major vascular surgery or vascular procedures with a risk of moderate or severe (> 500 mL) blood loss. SEARCH METHODS: We searched: Cochrane Central Register of Controlled Trials; MEDLINE; Embase; CINAHL, and Transfusion Evidence Library. We also searched the WHO ICTRP and ClinicalTrials.gov trial registries for ongoing and unpublished trials. Searches used a combination of MeSH and free text terms from database inception to 31 March 2022, without restriction on language or publication status. SELECTION CRITERIA: We included randomised controlled trials (RCTs) in adults of drug treatments to reduce bleeding due to major vascular surgery or vascular procedures with a risk of moderate or severe blood loss, which used placebo, usual care or another drug regimen as control. DATA COLLECTION AND ANALYSIS: We used standard Cochrane methods. Our primary outcomes were units of red cells transfused and all-cause mortality. Our secondary outcomes included risk of receiving an allogeneic blood product, risk of reoperation or repeat procedure due to bleeding, risk of a thromboembolic event, risk of a serious adverse event and length of hospital stay. We used GRADE to assess certainty of evidence. MAIN RESULTS: We included 22 RCTs with 3393 participants analysed, of which one RCT with 69 participants was reported only in abstract form, with no usable data. Seven RCTs evaluated systemic drug treatments (three aprotinin, two desmopressin, two tranexamic acid) and 15 RCTs evaluated topical drug treatments (drug-containing bioabsorbable dressings or glues), including fibrin, thrombin, collagen, gelatin, synthetic sealants and one investigational new agent. Most trials were conducted in high-income countries and the majority of the trials only included participants undergoing elective surgery. We also identified two ongoing RCTs. We were unable to perform the planned network meta-analysis due to the sparse reporting of outcomes relevant to this review. Systemic drug treatments We identified seven trials of three systemic drugs: aprotinin, desmopressin and tranexamic acid, all with placebo controls. The trials of aprotinin and desmopressin were small with very low-certainty evidence for all of our outcomes. Tranexamic acid versus placebo was the systemic drug comparison with the largest number of participants (2 trials; 1460 participants), both at low risk of bias. The largest of these included a total of 9535 individuals undergoing a number of different higher risk surgeries and reported limited information on the vascular subgroup (1399 participants). Neither trial reported the number of units of red cells transfused per participant up to 30 days. Three outcomes were associated with very low-certainty evidence due to the very wide confidence intervals (CIs) resulting from small study sizes and low number of events. These were: all-cause mortality up to 30 days; number of participants requiring an allogeneic blood transfusion up to 30 days; and risk of requiring a repeat procedure or operation due to bleeding. Tranexamic acid may have no effect on the risk of thromboembolic events up to 30 days (risk ratio (RR) 1.10, 95% CI 0.88 to 1.36; 1 trial, 1360 participants; low-certainty evidence due to imprecision). There is one large ongoing trial (8320 participants) comparing tranexamic acid versus placebo in people undergoing non-cardiac surgery who are at high risk of requiring a red cell transfusion. This aims to complete recruitment in April 2023. This trial has primary outcomes of proportion of participants transfused with red blood cells and incidence of venous thromboembolism (DVT or PE). Topical drug treatments Most trials of topical drug treatments were at high risk of bias due to their open-label design (compared with usual care, or liquids were compared with sponges). All of the trials were small, most were very small, and few reported clinically relevant outcomes in the postoperative period. Fibrin sealant versus usual care was the topical drug comparison with the largest number of participants (5 trials, 784 participants). The five trials that compared fibrin sealant with usual care were all at high risk of bias, due to the open-label trial design with no measures put in place to minimise reporting bias. All of the trials were funded by pharmaceutical companies. None of the five trials reported the number of red cells transfused per participant up to 30 days or the number of participants requiring an allogeneic blood transfusion up to 30 days. The other three outcomes were associated with very low-certainty evidence with wide confidence intervals due to small sample sizes and the low number of events, these were: all-cause mortality up to 30 days; risk of requiring a repeat procedure due to bleeding; and risk of thromboembolic disease up to 30 days. We identified one large trial (500 participants) comparing fibrin sealant versus usual care in participants undergoing abdominal aortic aneurysm repair, which has not yet started recruitment. This trial lists death due to arterial disease and reintervention rates as primary outcomes. AUTHORS' CONCLUSIONS: Because of a lack of data, we are uncertain whether any systemic or topical treatments used to reduce bleeding due to major vascular surgery have an effect on: all-cause mortality up to 30 days; risk of requiring a repeat procedure or operation due to bleeding; number of red cells transfused per participant up to 30 days or the number of participants requiring an allogeneic blood transfusion up to 30 days. There may be no effect of tranexamic acid on the risk of thromboembolic events up to 30 days, this is important as there has been concern that this risk may be increased. Trials with sample size targets of thousands of participants and clinically relevant outcomes are needed, and we look forward to seeing the results of the ongoing trials in the future.