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Objective: To develop a prioritisation framework to support priority setting for elective surgeries after COVID-19 based on the impact on patient well-being and cost., Design: We developed decision analytical models to estimate the consequences of delayed elective surgical procedures (eg, total hip replacement, bariatric surgery or septoplasty)., Setting: The framework was applied to a large hospital in the Netherlands., Outcome Measures: Quality measures impacts on quality of life and costs were taken into account and combined to calculate net monetary losses per week delay, which quantifies the total loss for society expressed in monetary terms. Net monetary losses were weighted by operating times., Results: We studied 13 common elective procedures from four specialties. Highest loss in quality of life due to delayed surgery was found for total hip replacement (utility loss of 0.27, ie, 99 days lost in perfect health); the lowest for arthroscopic partial meniscectomy (utility loss of 0.05, ie, 18 days lost in perfect health). Costs of surgical delay per patient were highest for bariatric surgery (€31/pp per week) and lowest for arthroscopic partial meniscectomy (-€2/pp per week). Weighted by operating room (OR) time bariatric surgery provides most value (€1.19/pp per OR minute) and arthroscopic partial meniscectomy provides the least value (€0.34/pp per OR minute). In a large hospital the net monetary loss due to prolonged waiting times was €700 840 after the first COVID-19 wave, an increase of 506% compared with the year before., Conclusions: This surgical prioritisation framework can be tailored to specific centres and countries to support priority setting for delayed elective operations during and after the COVID-19 pandemic, both in and between surgical disciplines. In the long-term, the framework can contribute to the efficient distribution of OR time and will therefore add to the discussion on appropriate use of healthcare budgets. The online framework can be accessed via: https://stanwijn.shinyapps.io/priORitize/., Competing Interests: Competing interests: MMR had financial support by means of a VICI grant from NWO (Dutch Research Council) for the submitted work, the other authors had no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To adjust for confounding in observational data, researchers use propensity score matching (PSM), but more advanced methods might be required when dealing with longitudinal data and time-varying treatments as PSM might not include possible changes that occurred over time. This study aims to explore which confounding adjustment methods have been used in longitudinal observational data to estimate a treatment effect and identify potential inappropriate use of PSM., Design: Mapping review., Data Sources: We searched PubMed, from inception up to January 2021, for studies in which a treatment was evaluated using longitudinal observational data., Eligibility Criteria: Methodological, non-medical and cost-effectiveness papers were excluded, as were non-English studies and studies that did not study a treatment effect., Data Extraction and Synthesis: Studies were categorised based on time of treatment: at baseline (interventions performed at start of follow-up) or time-varying (interventions received asynchronously during follow-up) and sorted based on publication year, time of treatment and confounding adjustment method. Cumulative time series plots were used to investigate the use of different methods over time. No risk-of-bias assessment was performed as it was not applicable., Results: In total, 764 studies were included that met the eligibility criteria. PSM (165/201, 82%) and inverse probability weighting (IPW; 154/502, 31%) were most common for studies with a treatment at baseline (n=201) and time-varying treatment (n=502), respectively. Of the 502 studies with a time-varying treatment, 123 (25%) used PSM with baseline covariates, which might be inappropriate. In the past 5 years, the proportion of studies with a time-varying treatment that used PSM over IPW increased., Conclusions: PSM is the most frequently used method to correct for confounding in longitudinal observational data. In studies with a time-varying treatment, PSM was potentially inappropriately used in 25% of studies. Confounding adjustment methods designed to deal with a time-varying treatment and time-varying confounding are available, but were only used in 45% of the studies with a time-varying treatment., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Involving end-users and patients in the development of surgical devices, even when patients are not end-users, is deemed important in policy and in academia since it could improve strategic choices in research and development (R&D). Nonetheless, research into innovators' views on end-user and patient involvement is rare. This study explores what end-users and patients are being involved by innovators during development, what methods for involvement are being used and what topics are being discussed with these end-users and patients., Design: A qualitative study featuring semi-structured interviews with innovators of surgical devices. Interviews were recorded and a thematic analysis was performed on verbatim transcripts., Participants: 15 interviews were conducted with 19 innovators of 14 surgical devices., Setting: Innovation practices of surgical devices in the Netherlands and Belgium., Results: End-users were engaged in R&D with formal methods and in unsystematic ways. These users all work in the clinical domain, for example, as surgeons or nurses. The innovators engaged users to analyse problems for which a device could be a solution, define functionalities, make design choices, analyse usability, ensure safety and improve aesthetics. Patients were rarely involved. Innovators stated that patients are not considered to be end-users, that physicians can represent patient interests and that involving patients is unethical as false expectations could be raised., Conclusion: Innovators involve end-users with methods and unsystematic ways in the development of surgical devices. Despite governmental calls for patient involvement in the development of medical devices and surgical devices, innovators do not generally involve patients., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Acute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials., Methods and Analysis: This is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8-15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal-external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk., Ethics and Dissemination: In this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals., Prospero Registration Number: CRD42020220108., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Arthroscopic partial meniscectomy (APM) after degenerative meniscus tears is one of the most frequently performed surgeries in orthopaedics. Although several randomised controlled trials (RCTs) have been published that showed no clear benefit compared with sham treatment or non-surgical treatment, the incidence of APM remains high. The common perception by most orthopaedic surgeons is that there are subgroups of patients that do need APM to improve, and they argue that each study sample of the existing trials is not representative for the day-to-day patients in the clinic. Therefore, the objective of this individual participant data meta-analysis (IPDMA) is to assess whether there are subgroups of patients with degenerative meniscus lesions who benefit from APM in comparison with non-surgical or sham treatment., Methods and Analysis: An existing systematic review will be updated to identify all RCTs worldwide that evaluated APM compared with sham treatment or non-surgical treatment in patients with knee symptoms and degenerative meniscus tears. Time and effort will be spent in contacting principal investigators of the original trials and encourage them to collaborate in this project by sharing their trial data. All individual participant data will be validated for missing data, internal data consistency, randomisation integrity and censoring patterns. After validation, all datasets will be combined and analysed using a one-staged and two-staged approach. The RCTs' characteristics will be used for the assessment of clinical homogeneity and generalisability of the findings. The most important outcome will be the difference between APM and control groups in knee pain, function and quality of life 2 years after the intervention. Other outcomes of interest will include the difference in adverse events and mental health., Ethics and Dissemination: All trial data will be anonymised before it is shared with the authors. The data will be encrypted and stored on a secure server located in the Netherlands. No major ethical concerns remain. This IPDMA will provide the evidence base to update and tailor diagnostic and treatment protocols as well as (international) guidelines for patients for whom orthopaedic surgeons consider APM. The results will be submitted for publication in a peer-reviewed journal., Prospero Registration Number: CRD42017067240., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: With the increasing interest in personalised medicine, the use of subgroup analyses is likely to increase. Subgroup analyses are challenging and often misused, possibly leading to false interpretations of the effect. It remains unclear to what extent key organisations warn for such pitfalls and translate current methodological research to detect these effects into research guidelines. The aim of this scoping review is to determine and evaluate the current guidance used by organisations for exploring, confirming and interpreting subgroup effects., Design: Scoping review., Eligibility Criteria: We identified four types of key stakeholder organisations: industry, health technology assessment organisations (HTA), academic/non-profit research organisations and regulatory bodies. After literature search and expert consultation, we identified international and national organisations of each type. For each organisation that was identified, we searched for official research guidance documents and contacted the organisation for additional guidance., Results: Twenty-seven (45%) of the 60 organisations that we included had relevant research guidance documents. We observed large differences between organisation types: 18% (n=2) of the industry organisations, 64% (n=9) of the HTA organisations, 38% (n=8) of academic/non-profit research organisations and 57% (n=8) of regulatory bodies provided guidance documents. The majority of the documents (n=33, 63%) mentioned one or more challenges in subgroup analyses, such as false positive findings or ecological bias with variations across the organisation types. Statistical recommendations were less common (n=19, 37%) and often limited to a formal test of interaction., Conclusions: Almost half of the organisations included in this scoping review provided guidance on subgroup effect research in their guidelines. However, there were large differences between organisations in the amount and level of detail of their guidance. Effort is required to translate and integrate research findings on subgroup analysis to practical guidelines for decision making and to reduce the differences between organisations and organisation types., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: The objective of this exploratory study was to see how the Patient-Reported Apnea Questionnaire (PRAQ) may impact the daily clinical practice of sleep centres, and why it may or may not work as expected. The hypotheses were tested that this patient-reported outcome measure makes patients more aware of which of their health complaints may be related to obstructive sleep apnoea (OSA), and that it improves patient-centredness of care by shifting the focus of care away from (only) medical problems towards the individual burden of disease and quality of life., Design: Mixed methods. The quantitative study (surveys, patient records) was a before-and-after study., Setting: Three sleep centres in The Netherlands (secondary care)., Participants: 27 patients and 14 healthcare professionals were interviewed. 487 patients completed surveys pre-implementation, and 377 patients completed surveys post-implementation of the PRAQ. For the health records, 125 patients were included in the pre-implementation group, and 124 other patients in the post-implementation group., Interventions: The PRAQ was used in clinical practice for six successive months., Outcome Measures: Scores on individual survey items, number of patients receiving non-medical treatment, adjustment of treatment at first follow-up, compliance with treatment., Results: Patients were generally positive about the usefulness of the PRAQ before and during the consultation, as they felt more informed. Healthcare providers did not consider the PRAQ very useful, and they reported minor impact on their consultations. The surveys and health record study did not show an impact of the PRAQ on clinical practice., Conclusions: Implementing the PRAQ may be beneficial to patients, but this study does not show much impact with regard to patient-centredness of care. New Dutch guidelines for OSA care may lead to a greater emphasis on quality of life and value of care for patients, making its integration in clinical care potentially more useful., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like τ(2) or I(2), but their clinical interpretation is not straightforward. A prediction interval is less complicated: it presents the expected range of true effects in similar studies. We aimed to show the advantages of having the prediction interval routinely reported in meta-analyses., Design: We show how the prediction interval can help understand the uncertainty about whether an intervention works or not. To evaluate the implications of using this interval to interpret the results, we selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009-2013 with a dichotomous (n=2009) or continuous (n=1254) outcome, and generated 95% prediction intervals for them., Results: In 72.4% of 479 statistically significant (random-effects p<0.05) meta-analyses in the Cochrane Database 2009-2013 with heterogeneity (I(2)>0), the 95% prediction interval suggested that the intervention effect could be null or even be in the opposite direction. In 20.3% of those 479 meta-analyses, the prediction interval showed that the effect could be completely opposite to the point estimate of the meta-analysis. We demonstrate also how the prediction interval can be used to calculate the probability that a new trial will show a negative effect and to improve the calculations of the power of a new trial., Conclusions: The prediction interval reflects the variation in treatment effects over different settings, including what effect is to be expected in future patients, such as the patients that a clinician is interested to treat. Prediction intervals should be routinely reported to allow more informative inferences in meta-analyses., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)