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.

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.

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.

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.

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.

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.

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.

Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis.

BACKGROUND: Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life. METHODS AND FINDINGS: Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631. CONCLUSIONS: Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity. TRIAL REGISTRATION: A prespecified protocol was registered on PROSPERO (CRD42018048202).

Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis.

BACKGROUND: Network meta-analysis (NMA) and indirect comparisons combine aggregate data (AgD) from multiple studies on treatments of interest but may give biased estimates if study populations differ. Population adjustment methods such as multilevel network meta-regression (ML-NMR) aim to reduce bias by adjusting for differences in study populations using individual patient data (IPD) from 1 or more studies under the conditional constancy assumption. A shared effect modifier assumption may also be necessary for identifiability. This article aims to demonstrate how the assumptions made by ML-NMR can be assessed in practice to obtain reliable treatment effect estimates in a target population. METHODS: We apply ML-NMR to a network of evidence on treatments for plaque psoriasis with a mix of IPD and AgD trials reporting ordered categorical outcomes. Relative treatment effects are estimated for each trial population and for 3 external target populations represented by a registry and 2 cohort studies. We examine residual heterogeneity and inconsistency and relax the shared effect modifier assumption for each covariate in turn. RESULTS: Estimated population-average treatment effects were similar across study populations, as differences in the distributions of effect modifiers were small. Better fit was achieved with ML-NMR than with NMA, and uncertainty was reduced by explaining within- and between-study variation. We found little evidence that the conditional constancy or shared effect modifier assumptions were invalid. CONCLUSIONS: ML-NMR extends the NMA framework and addresses issues with previous population adjustment approaches. It coherently synthesizes evidence from IPD and AgD studies in networks of any size while avoiding aggregation bias and noncollapsibility bias, allows for key assumptions to be assessed or relaxed, and can produce estimates relevant to a target population for decision-making. HIGHLIGHTS: Multilevel network meta-regression (ML-NMR) extends the network meta-analysis framework to synthesize evidence from networks of studies providing individual patient data or aggregate data while adjusting for differences in effect modifiers between studies (population adjustment). We apply ML-NMR to a network of treatments for plaque psoriasis with ordered categorical outcomes.We demonstrate for the first time how ML-NMR allows key assumptions to be assessed. We check for violations of conditional constancy of relative effects (such as unobserved effect modifiers) through residual heterogeneity and inconsistency and the shared effect modifier assumption by relaxing this for each covariate in turn.Crucially for decision making, population-adjusted treatment effects can be produced in any relevant target population. We produce population-average estimates for 3 external target populations, represented by the PsoBest registry and the PROSPECT and Chiricozzi 2019 cohort studies.

Assessing trial representativeness using serious adverse events: an observational analysis using aggregate and individual-level data from clinical trials and routine healthcare data.

BACKGROUND: The applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. We explore an approach assessing trial representativeness by comparing rates of trial serious adverse events (SAE) to rates of hospitalisation/death in routine care. METHODS: This was an observational analysis of individual (125 trials, n=122,069) and aggregate-level drug trial data (483 trials, n=636,267) for 21 index conditions compared to population-based routine healthcare data (routine care). Trials were identified from ClinicalTrials.gov . Routine care comparison from linked primary care and hospital data from Wales, UK (n=2.3M). Our outcome of interest was SAEs (routinely reported in trials). In routine care, SAEs were based on hospitalisations and deaths (which are SAEs by definition). We compared trial SAEs in trials to expected SAEs based on age/sex standardised routine care populations with the same index condition. Using IPD, we assessed the relationship between multimorbidity count and SAEs in both trials and routine care and assessed the impact on the observed/expected SAE ratio additionally accounting for multimorbidity. RESULTS: For 12/21 index conditions, the pooled observed/expected SAE ratio was <1, indicating fewer SAEs in trial participants than in routine care. A further 6/21 had point estimates <1 but the 95% CI included the null. The median pooled estimate of observed/expected SAE ratio was 0.60 (95% CI 0.55-0.64; COPD) and the interquartile range was 0.44 (0.34-0.55; Parkinson's disease) to 0.87 (0.58-1.29; inflammatory bowel disease). Higher multimorbidity count was associated with SAEs across all index conditions in both routine care and trials. For most trials, the observed/expected SAE ratio moved closer to 1 after additionally accounting for multimorbidity count, but it nonetheless remained below 1 for most. CONCLUSIONS: Trial participants experience fewer SAEs than expected based on age/sex/condition hospitalisation and death rates in routine care, confirming the predicted lack of representativeness. This difference is only partially explained by differences in multimorbidity. Assessing observed/expected SAE may help assess the applicability of trial findings to older populations in whom multimorbidity and frailty are common.

Cost-effectiveness of topical pharmacological, oral pharmacological, physical and combined treatments for acne vulgaris.

BACKGROUND: Acne vulgaris is a common skin condition that may cause psychosocial distress. There is evidence that topical treatment combinations, chemical peels and photochemical therapy (combined blue/red light) are effective for mild-to-moderate acne, while topical treatment combinations, oral antibiotics combined with topical treatments, oral isotretinoin and photodynamic therapy are most effective for moderate-to-severe acne. Effective treatments have varying costs. The National Institute for Health and Care Excellence (NICE) in England considers cost-effectiveness when producing national clinical, public health and social care guidance. AIM: To assess the cost-effectiveness of treatments for mild-to-moderate and moderate-to-severe acne to inform relevant NICE guidance. METHODS: A decision-analytical model compared costs and quality-adjusted life-years (QALYs) of effective topical pharmacological, oral pharmacological, physical and combined treatments for mild-to-moderate and moderate-to-severe acne, from the perspective of the National Health Service in England. Effectiveness data were derived from a network meta-analysis. Other model input parameters were based on published sources, supplemented by expert opinion. RESULTS: All of the assessed treatments were more cost-effective than treatment with placebo (general practitioner visits without active treatment). For mild-to-moderate acne, topical treatment combinations and photochemical therapy (combined blue/red light) were most cost-effective. For moderate-to-severe acne, topical treatment combinations, oral antibiotics combined with topical treatments, and oral isotretinoin were the most cost-effective. Results showed uncertainty, as reflected in the wide confidence intervals around mean treatment rankings. CONCLUSION: A range of treatments are cost-effective for the management of acne. Well-conducted studies are needed to examine the long-term clinical efficacy and cost-effectiveness of the full range of acne treatments.

A systematic review and network meta-analysis of topical pharmacological, oral pharmacological, physical and combined treatments for acne vulgaris.

BACKGROUND: Various treatments for acne vulgaris exist, but little is known about their comparative effectiveness in relation to acne severity. OBJECTIVES: To identify best treatments for mild-to-moderate and moderate-to-severe acne, as determined by clinician-assessed morphological features. METHODS: We undertook a systematic review and network meta-analysis of randomized controlled trials (RCTs) assessing topical pharmacological, oral pharmacological, physical and combined treatments for mild-to-moderate and moderate-to-severe acne, published up to May 2020. Outcomes included percentage change in total lesion count from baseline, treatment discontinuation for any reason, and discontinuation owing to side-effects. Risk of bias was assessed using the Cochrane risk-of-bias tool and bias adjustment models. Effects for treatments with ≥ 50 observations each compared with placebo are reported below. RESULTS: We included 179 RCTs with approximately 35 000 observations across 49 treatment classes. For mild-to-moderate acne, the most effective options for each treatment type were as follows: topical pharmacological - combined retinoid with benzoyl peroxide (BPO) [mean difference 26·16%, 95% credible interval (CrI) 16·75-35·36%]; physical - chemical peels, e.g. salicylic or mandelic acid (39·70%, 95% CrI 12·54-66·78%) and photochemical therapy (combined blue/red light) (35·36%, 95% CrI 17·75-53·08%). Oral pharmacological treatments (e.g. antibiotics, hormonal contraceptives) did not appear to be effective after bias adjustment. BPO and topical retinoids were less well tolerated than placebo. For moderate-to-severe acne, the most effective options for each treatment type were as follows: topical pharmacological - combined retinoid with lincosamide (clindamycin) (44·43%, 95% CrI 29·20-60·02%); oral pharmacological - isotretinoin of total cumulative dose ≥ 120 mg kg-1 per single course (58·09%, 95% CrI 36·99-79·29%); physical - photodynamic therapy (light therapy enhanced by a photosensitizing chemical) (40·45%, 95% CrI 26·17-54·11%); combined - BPO with topical retinoid and oral tetracycline (43·53%, 95% CrI 29·49-57·70%). Topical retinoids and oral tetracyclines were less well tolerated than placebo. The quality of included RCTs was moderate to very low, with evidence of inconsistency between direct and indirect evidence. Uncertainty in findings was high, in particular for chemical peels, photochemical therapy and photodynamic therapy. However, conclusions were robust to potential bias in the evidence. CONCLUSIONS: Topical pharmacological treatment combinations, chemical peels and photochemical therapy were most effective for mild-to-moderate acne. Topical pharmacological treatment combinations, oral antibiotics combined with topical pharmacological treatments, oral isotretinoin and photodynamic therapy were most effective for moderate-to-severe acne. Further research is warranted for chemical peels, photochemical therapy and photodynamic therapy for which evidence was more limited. What is already known about this topic? Acne vulgaris is the eighth most common disease globally. Several topical, oral, physical and combined treatments for acne vulgaris exist. Network meta-analysis (NMA) synthesizes direct and indirect evidence and allows simultaneous inference for all treatments forming an evidence network. Previous NMAs have assessed a limited range of treatments for acne vulgaris and have not evaluated effectiveness of treatments for moderate-to-severe acne. What does this study add? For mild-to-moderate acne, topical treatment combinations, chemical peels, and photochemical therapy (combined blue/red light; blue light) are most effective. For moderate-to-severe acne, topical treatment combinations, oral antibiotics combined with topical treatments, oral isotretinoin and photodynamic therapy (light therapy enhanced by a photosensitizing chemical) are most effective. Based on these findings, along with further clinical and cost-effectiveness considerations, National Institute for Health and Care Excellence (NICE) guidance recommends, as first-line treatments, fixed topical treatment combinations for mild-to-moderate acne and fixed topical treatment combinations, or oral tetracyclines combined with topical treatments, for moderate-to-severe acne.