Search

IntroductionThere is a critical need to understand the optimal treatment regimen in patients with potentially resectable stage III-N2 nonsmall cell lung cancer (NSCLC).MethodsA 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).FindingsSix trials were prioritised for evidence synthesis. The fixed-effects network meta-analyses demonstrated an improvement in disease-free survival (DFS) for CRSversusCS and CRSversusCR 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%.InterpretationCRS 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.

Supplemental material, sj-docx-1-mdm-10.1177_0272989X231162069 for General Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations by Mathyn Vervaart, Eline Aas, Karl P. Claxton, Mark Strong, Nicky J. Welton, Torbjørn Wisløff and Anna Heath in Medical Decision Making

Supplemental material, sj-docx-1-mdm-10.1177_0272989X231162069 for General Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations by Mathyn Vervaart, Eline Aas, Karl P. Claxton, Mark Strong, Nicky J. Welton, Torbjørn Wisløff and Anna Heath in Medical Decision Making

Supplemental material, sj-docx-1-mdm-10.1177_0272989X231165985 for Constructing Relative Effect Priors for Research Prioritization and Trial Design: A Meta-epidemiological Analysis by David Glynn, Georgios Nikolaidis, Dina Jankovic and Nicky J. Welton in Medical Decision Making

This protocol describes the 2023 update of the original systematic review and network meta-analysis on treatments for patellofemoral pain. The first version of the protocol was published in 2018:( Winters M, Holden S, Vicenzino B, et al Which treatment is most effective for patients with patellofemoral pain? A protocol for a living systematic review including network meta-analysis BMJ Open 2018;8:e022920. doi: 10.1136/bmjopen-2018-022920) and the first report was published in 2021 (Winters M, Holden S, Lura CB, Welton NJ, Caldwell DM, Vicenzino BT, Weir A, Rathleff MS. Comparative effectiveness of treatments for patellofemoral pain: a living systematic review with network meta-analysis. Br J Sports Med. 2020 Oct 26;55(7))

Additional file 3: Supplementary figures S24-S44. Observed/expected SAEs before and after standardization by multimorbidity count. Figure S24. Observed/expected SAEs before and after standardization by multimorbidity count: asthma trials. Figure S25. Observed/expected SAEs before and after standardization by multimorbidity count: atrial fibrillation trials. Figure S26. observed/expected SAEs before and after standardization by multimorbidity count: axial spondyloarthritis trials. Figure S27. Observed/expected SAEs before and after standardization by multimorbidity count: benign prostatic hyperplasia trials. Figure S28. Observed/expected SAEs before and after standardization by multimorbidity count: dementia trials. Figure S29. observed/expected SAEs before and after standardization by multimorbidity count: type 2 diabetes mellitus trials. Figure S30. Observed/expected SAEs before and after standardization by multimorbidity count: hypertension trials. Figure S31. Observed/expected SAEs before and after standardization by multimorbidity count: pulmonary hypertension trials. Figure S32. Observed/expected SAEs before and after standardization by multimorbidity count: inflammatory bowel disease trials. Figure S33. Observed/expected SAEs before and after standardization by multimorbidity count: migraine trials. Figure S34. Observed/expected SAEs before and after standardization by multimorbidity count: osteoarthritis trials. Figure S35. Observed/expected SAEs before and after standardization by multimorbidity count: osteoporosis trials. Figure S36. Observed/expected SAEs before and after standardization by multimorbidity count: Parkinson’s disease trials. Figure S37. Observed/expected SAEs before and after standardization by multimorbidity count: psoriasis trials. Figure S38. Observed/expected SAEs before and after standardization by multimorbidity count: psoriatic arthropathy trials. Figure S39. Observed/expected SAEs before and after standardization by multimorbidity count: chronic obstructive pulmonary disease trials. Figure S40. Observed/expected SAEs before and after standardization by multimorbidity count: pulmonary fibrosis trials. Figure S41. Observed/expected SAEs before and after standardization by multimorbidity count: restless legs syndrome trials. Figure S42. Observed/expected SAEs before and after standardization by multimorbidity count: rheumatoid arthritis trials. Figure S43. Observed/expected SAEs before and after standardization by multimorbidity count: systemic lupus erythematosus trials. Figure S44. Observed/expected SAEs before and after standardization by multimorbidity count: thromboembolism trials.

Supplemental material, sj-pdf-2-mdm-10.1177_0272989X221117162 for Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis by David M. Phillippo, Sofia Dias, A. E. Ades, Mark Belger, Alan Brnabic, Daniel Saure, Yves Schymura and Nicky J. Welton in Medical Decision Making

Additional file 1: Supplementary figures S1-S21. Trial level estimates of observed/expected SAEs. Figure S1. Observed/expected SAEs in asthma trials. Figure S2. Observed/expected SAEs in atrial fibrillation trials. Figure S3. Observed/expected SAEs in axial spondyloarthritis trials. Figure S4. Observed/expected SAEs in benign prostatic hyperplasia trials. Figure S5. Observed/expected SAEs in dementia trials. Figure S6. Observed/expected SAEs in type 2 diabetes mellitus trials. Figure S7. Observed/expected SAEs in epilepsy trials. Figure S8. Observed/expected SAEs in hypertension trials. Figure S9. Observed/expected SAEs in pulmonary hypertension trials. Figure S10. Observed/expected SAEs in inflammatory bowel disease trials. Figure S11. Observed/expected SAEs in myocardial infarction trials. Figure S12. Observed/expected SAEs in osteoarthritis trials. Figure S13. Observed/expected SAEs in osteoporosis trials. Figure S14. Observed/expected SAEs in Parkinson’s disease trials. Figure S15. Observed/expected SAEs in psoriasis trials. Figure S16. Observed/expected SAEs in psoriatic arthropathy trials. Figure S17. Observed/expected SAEs in chronic obstructive pulmonary disease trials. Figure S18. Observed/expected SAEs in pulmonary fibrosis trials. Figure S19. Observed/expected SAEs in rheumatoid arthritis trials. Figure S20. Observed/expected SAEs in systemic lupus erythematosus trials. Figure S21. Observed/expected SAEs in thromboembolism trials.

Supplemental material, sj-pdf-1-mdm-10.1177_0272989X221117162 for Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis by David M. Phillippo, Sofia Dias, A. E. Ades, Mark Belger, Alan Brnabic, Daniel Saure, Yves Schymura and Nicky J. Welton in Medical Decision Making

Supplemental material, sj-pdf-1-mdm-10.1177_0272989X211068019 for An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial by Mathyn Vervaart, Mark Strong, Karl P. Claxton, Nicky J. Welton, Torbj��rn Wisl��ff and Eline Aas in Medical Decision Making

Additional file 4. Statistical methods.

Supplemental material, sj-docx-3-mdm-10.1177_0272989X221117162 for Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis by David M. Phillippo, Sofia Dias, A. E. Ades, Mark Belger, Alan Brnabic, Daniel Saure, Yves Schymura and Nicky J. Welton in Medical Decision Making

Supplemental material, sj-pdf-2-mdm-10.1177_0272989X221117162 for Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis by David M. Phillippo, Sofia Dias, A. E. Ades, Mark Belger, Alan Brnabic, Daniel Saure, Yves Schymura and Nicky J. Welton in Medical Decision Making

Additional file 2: Supplementary figures S22-23 Comparison of SAEs between trial arms. Figure S22. Trial-level comparison of SAE rate between trial arms. Figure S23. Index condition-level meta-analyses of comparison of SAE rate between trial arms.

Supplemental material, sj-pdf-1-mdm-10.1177_0272989X211068019 for An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial by Mathyn Vervaart, Mark Strong, Karl P. Claxton, Nicky J. Welton, Torbj��rn Wisl��ff and Eline Aas in Medical Decision Making

Supplemental material, sj-docx-3-mdm-10.1177_0272989X221117162 for Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis by David M. Phillippo, Sofia Dias, A. E. Ades, Mark Belger, Alan Brnabic, Daniel Saure, Yves Schymura and Nicky J. Welton in Medical Decision Making

Supplemental material, sj-pdf-3-mdm-10.1177_0272989X20983315 for Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis by Hugo Pedder, Sofia Dias, Meg Bennetts, Martin Boucher and Nicky J. Welton in Medical Decision Making

Supplemental material, sj-pdf-1-mdm-10.1177_0272989X211026305 for Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information by Wei Fang, Zhenru Wang, Michael B. Giles, Chris H. Jackson, Nicky J. Welton, Christophe Andrieu and Howard Thom in Medical Decision Making

Supplemental material, sj-pdf-1-mdm-10.1177_0272989X211026305 for Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information by Wei Fang, Zhenru Wang, Michael B. Giles, Chris H. Jackson, Nicky J. Welton, Christophe Andrieu and Howard Thom in Medical Decision Making

Supplemental material, sj-pdf-3-mdm-10.1177_0272989X20983315 for Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis by Hugo Pedder, Sofia Dias, Meg Bennetts, Martin Boucher and Nicky J. Welton in Medical Decision Making

Supplemental material, sj-docx-1-mdm-10.1177_0272989X211029556 for Improving the Estimation of Subgroup Effects for Clinical Trial Participants with Multimorbidity by Incorporating Drug Class-Level Information in Bayesian Hierarchical Models: A Simulation Study by Laurie J. Hannigan, David M. Phillippo, Peter Hanlon, Laura Moss, Elaine W. Butterly, Neil Hawkins, Sofia Dias, Nicky J. Welton and David A. McAllister in Medical Decision Making

Network meta-analysis (NMA) allows pooling evidence on multiple interventions from a set of randomised controlled trials (RCTs), each of which compare two or more of the interventions of interest. This feature enables to address relevant questions for practitioners and policy makers across many health areas including mental health. Interventions designed to prevent or treat mental health problems tend to be complex, in the sense that they may include several active ingredients or “components”. If each combination of components is considered a separate intervention, then NMA could be used to simultaneously compare the different interventions. However, NMA requires that the comparisons made by the RCTs form a connected network, in other words that there is a path of comparisons between any two included interventions. This is unlikely to be the case with complex interventions, due to the large number of possible component combinations, and even if such a network is connected, the resulting analysis may lead to imprecise estimates. Recently, component-level NMA regression methods have been developed within a Bayesian framework to allow estimation of the additive contribution of components and/or combinations of components of complex interventions while fully respecting the randomised structure of the evidence. This approach allows meaningful conclusions on effectiveness of components of complex interventions, whilst overcoming issues with connected networks and low precision with standard NMA. In this presentation, we will illustrate the use of standard and component-level NMA with two examples in the context of mental health interventions. In the first example, we compared the effectiveness of different types of therapy, different components and combinations of components and aspects of delivery used in cognitive-behavioural therapy (CBT) interventions for adult depression. We included 91 RCTs and found strong evidence that CBT interventions yielded a larger short-term decrease in depression scores compared to treatment-as-usual, with a standardised difference in mean change of -1.11 (95% credible interval -1.62 to -0.60) for face-to-face CBT, -1.06 (-2.05 to -0.08) for hybrid CBT, and -0.59 (-1.20 to 0.02) for multimedia CBT, whereas wait list control showed a detrimental effect of 0.72 (0.09 to 1.35). We found no evidence of specific effects of any content components or combinations of components, and importantly, we found that multimedia and hybrid CBT might be as effective as face-to-face CBT, although results need to be interpreted cautiously. The second application that we will discuss is an ongoing systematic review where the overall aim is to identify the most effective intervention component(s), or combination of components, for universal, selective, and indicated prevention of anxiety and depression problems in children and young people. We will present results based on NMA models both at the therapy and at the component levels. Last, we will conclude the presentation with a summary of the advantages of component-level NMA methods to explore the impact of different components of complex interventions on mental health outcomes, alongside the challenges that researchers might find when implementing this approach.

Additional file 6. Trials-characteristics.pdf: Summary of characteristics of included trials.

Additional file 2. Representativeness-of-community-data-sail.pdf: Analysis of the representativeness of the community sample.

Additional file 11. Explore-relationship-of-trial-mean-comorbidity-counts-to-trial-characteristics.pdf: Analysis of characteristics based on trial meta-data and relationship to comorbidity counts.

Additional file 9. Proportion-with-each-comorbidity-for-trials-and-sail.pdf: Analysis of the prevalence of each comorbidity, within each index condition, in trial participants and the community sample.

Additional file 8. Summary-Statistics-Comparing-Ipd-Trials-To-Wider-Body-Of-Trials-From-Clinicaltrials.Gov.Pdf: Comparison of included trials with registered trials on clinicaltrials.gov for which individual participant data were not available.

Additional file 5. More-detailed-statistical-analysis.pdf: Model description and code required for analyses. Detailed description of statistical methods.

Capture-recapture methods, largely developed in ecology, are now commonly used in epidemiology to adjust for incomplete registries and to estimate the size of difficult-to-reach populations such as problem drug users. Overlapping lists of individuals in the target population, taken from administrative data sources, are considered analogous to overlapping "captures" of animals. Log-linear models, incorporating interaction terms to account for dependencies between sources, are used to predict the number of unobserved individuals and, hence, the total population size. A standard assumption to ensure parameter identifiability is that the highest-order interaction term is 0. We demonstrate that, when individuals are referred directly between sources, this assumption will often be violated, and the standard modeling approach may lead to seriously biased estimates. We refer to such individuals as having been "precaptured," rather than truly recaptured. Although sometimes an alternative identifiable log-linear model could accommodate the referral structure, this will not always be the case. Further, multiple plausible models may fit the data equally well but provide widely varying estimates of the population size. We demonstrate an alternative modeling approach, based on an interpretable parameterization and driven by careful consideration of the relationships between the sources, and we make recommendations for capture-recapture in practice.

BackgroundNetwork meta-analysis may require substantially more resources than does a standard systematic review. One frequently asked question is “how far should I extend the network and which treatments should I include?”ObjectiveTo explore the increase in precision from including additional evidence.MethodsWe assessed the benefit of extending treatment networks in terms of precision of effect estimates and examined how this depends on network structure and relative strength of additional evidence. We introduced a “star”-shaped network. Network complexity is increased by adding more evidence connecting treatments under five evidence scenarios. We also examined the impact of heterogeneity and absence of evidence facilitating a “first-order” indirect comparison.ResultsIn all scenarios, extending the network increased the precision of the A versus B treatment effect. Under a fixed-effect model, the increase in precision was modest when the existing direct A versus B evidence was already strong and was substantial when the direct evidence was weak. Under a random-effects model, the gain in precision was lower when heterogeneity was high. When evidence is available for all “first-order” indirect comparisons, including second-order evidence has limited benefit for the precision of the A versus B estimate. This is interpreted as a “ceiling effect.”ConclusionsIncluding additional evidence increases the precision of a “focal” treatment comparison of interest. Once the comparison of interest is connected to all others via “first-order” indirect evidence, there is no additional benefit in including higher order comparisons. This conclusion is generalizable to any number of treatment comparisons, which would then all be considered “focal.” The increase in precision is modest when direct evidence is already strong, or there is a high degree of heterogeneity.

ObjectivesTo develop outcome measures to assess practical management of primary surgical wounds and patient experience.DesignMixed methods, including qualitative interviews and data extraction from published randomised controlled trials (RCTs).SettingTwo university-teaching NHS hospitals and three district NHS hospitals in the South West and Midlands regions of England.ParticipantsSixty-four patients and 15 healthcare professionals from abdominal general surgical specialities and obstetrics (caesarean section).MethodsMeasures were developed according to standard guidelines to identify issues relevant to patients’ experiences of surgical wounds and dressings, including analysis of existing RCT outcomes and semi-structured interviews. These were written into provisional questionnaire items for a single outcome measure. Cognitive interviews with patients and healthcare professionals assessed face validity, acceptability and relevance. Findings from interviews were regularly shared with the study team who suggested amendments to modify and reword items to improve understanding before further iterative testing with patients and healthcare professionals.ResultsAnalyses of existing RCT outcomes and interviews produced a total of 69 issues. Pretesting and iterative revision established the need for two separate measures. One measure addresses healthcare professionals’ experience of wound management in two key areas: exudate and its impact, and allergic reactions to the dressing. The other measure addresses patients’ experience of wounds in seven key areas: wound comfort, dressing removal, dressings to protect the wound, impact on daily activities, ease of movement, anxiety about the wound and satisfaction with dressing. Each measure took less than five min to complete and both were understood and acceptable to patients and healthcare professionals.ConclusionThis in-depth study has developed two measures to assess practical management of primary surgical wounds and patient experience. Further work to test their validity, reliability and application to other settings is now required.Trial registration numberHTA - 12/200/04; Pre-results.

SPIRIT 2013 Checklist: recommended items to address in a clinical trial protocol and related documents. (DOC 122 kb)

Concentrations of metabolites of illicit drugs in sewage water can be measured with great accuracy and precision, thanks to the development of sensitive and robust analytical methods. Based on assumptions about factors including the excretion profile of the parent drug, routes of administration and the number of individuals using the wastewater system, the level of consumption of a drug can be estimated from such measured concentrations. When presenting results from these ‘back-calculations’, the multiple sources of uncertainty are often discussed, but are not usually explicitly taken into account in the estimation process. In this paper we demonstrate how these calculations can be placed in a more formal statistical framework by assuming a distribution for each parameter involved, based on a review of the evidence underpinning it. Using a Monte Carlo simulations approach, it is then straightforward to propagate uncertainty in each parameter through the back-calculations, producing a distribution for instead of a single estimate of daily or average consumption. This can be summarised for example by a median and credible interval. To demonstrate this approach, we estimate cocaine consumption in a large urban UK population, using measured concentrations of two of its metabolites, benzoylecgonine and norbenzoylecgonine. We also demonstrate a more sophisticated analysis, implemented within a Bayesian statistical framework using Markov chain Monte Carlo simulation. Our model allows the two metabolites to simultaneously inform estimates of daily cocaine consumption and explicitly allows for variability between days. After accounting for this variability, the resulting credible interval for average daily consumption is appropriately wider, representing additional uncertainty. We discuss possibilities for extensions to the model, and whether analysis of wastewater samples has potential to contribute to a prevalence model for illicit drug use.

Additional file 1: Search strategy.

Additional file 1. Selection-of-trials-protocol.pdf: Protocol for selection of clinical trials individual participant data and search of wider body of registered trials from clinicaltrials.gov.

SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol. (PDF 132 kb)

Participant Consent Form. (PDF 30 kb)

Participant Information Leaflet. (PDF 273 kb)

Supplemental material, Appendices_-_rev1_-_clean.rjf_online_supp for Directly Acting Oral Anticoagulants for the Prevention of Stroke in Atrial Fibrillation in England and Wales: Cost- Effectiveness Model and Value of Information Analysis by Howard H. Z. Thom, Will Hollingworth, Reecha Sofat, Zhenru Wang, Wei Fang, Pritesh N. Bodalia, Peter A. Bryden, Philippa A. Davies, Deborah M. Caldwell, Sofia Dias, Diane Eaton, Julian P. T. Higgins, Aroon D. Hingorani, Jose A. Lopez-Lopez, George N. Okoli, Alison Richards, Chris Salisbury, Jelena Savović, Annya Stephens-Boal, Jonathan A. C. Sterne and Nicky J. Welton in MDM Policy & Practice

Additional file 4. Defining-comorbidity-protocol.pdf: Protocol detailing the identification of comorbidities from clinical trial data.

Additional file 3. Selection-of-patients-and-participants-from-primary-care-data-read-codes.pdf: Read codes used to identify index conditions.

Additional file 4. Defining-comorbidity-protocol.pdf: Protocol detailing the identification of comorbidities from clinical trial data.

Participant Consent Form. (PDF 30 kb)

BACKGROUND: This article addresses the choice of state structure in a cost-effectiveness multi-state model. Key model outputs, such as treatment recommendations and prioritisation of future research, may be sensitive to state structure choice. For example, it may be uncertain whether to consider similar disease severities or similar clinical events as the same state or as separate states. Standard statistical methods for comparing models require a common reference dataset but merging states in a model aggregates the data, rendering these methods invalid. METHODS: We propose a method that involves re-expressing a model with merged states as a model on the larger state space in which particular transition probabilities, costs and utilities are constrained to be equal between states. This produces a model that gives identical estimates of cost effectiveness to the model with merged states, while leaving the data unchanged. The comparison of state structures can be achieved by comparing maximised likelihoods or information criteria between constrained and unconstrained models. We can thus test whether the costs and/or health consequences for a patient in two states are the same, and hence if the states can be merged. We note that different structures can be used for rates, costs and utilities, as appropriate. APPLICATION: We illustrate our method with applications to two recent models evaluating the cost effectiveness of prescribing anti-depressant medications by depression severity and the cost effectiveness of diagnostic tests for coronary artery disease. CONCLUSIONS: State structures in cost-effectiveness models can be compared using standard methods to compare constrained and unconstrained models.

SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol. (PDF 132 kb)

Additional file 1. Selection-of-trials-protocol.pdf: Protocol for selection of clinical trials individual participant data and search of wider body of registered trials from clinicaltrials.gov.

Additional file 3. Selection-of-patients-and-participants-from-primary-care-data-read-codes.pdf: Read codes used to identify index conditions.

Supplemental material, Appendices_-_rev1_-_clean.rjf_online_supp for Directly Acting Oral Anticoagulants for the Prevention of Stroke in Atrial Fibrillation in England and Wales: Cost- Effectiveness Model and Value of Information Analysis by Howard H. Z. Thom, Will Hollingworth, Reecha Sofat, Zhenru Wang, Wei Fang, Pritesh N. Bodalia, Peter A. Bryden, Philippa A. Davies, Deborah M. Caldwell, Sofia Dias, Diane Eaton, Julian P. T. Higgins, Aroon D. Hingorani, Jose A. Lopez-Lopez, George N. Okoli, Alison Richards, Chris Salisbury, Jelena Savović, Annya Stephens-Boal, Jonathan A. C. Sterne and Nicky J. Welton in MDM Policy & Practice

Participant Information Leaflet. (PDF 273 kb)