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We describe the command artbin, which offers various new facilities for the calculation of sample size for binary outcome variables that are not otherwise available in Stata. While artbin has been available since 2004, it has not been previously described in the Stata Journal. artbin has been recently updated to include new options for different statistical tests, methods and study designs, improved syntax, and better handling of noninferiority trials. In this article, we describe the updated version of artbin and detail the various formulas used within artbin in different settings.

The widespread use of skin sensitizing preservatives is well-known. Contact allergy to preservatives is often caused by their presence in cosmetic products. Preservative use in non-cosmetic products is less well-known. We have reviewed European Union (EU) legislations on classification and labelling, biocides and cosmetics, concerning conditions for use of the most used sensitizing preservatives (including formaldehyde-releasing substances, isothiazolinones and parabens). We have analysed temporal trends in their use in non-cosmetic products (tonnes, number of products, concentrations), based on annual reports to the Swedish Products Register 1995-2018; and we discuss implications for stakeholders. Major changes over time are that the use of most of the preservatives has increased by tonnes and/or by number of products, and that several use concentrations have declined following harmonized classification as a skin sensitizer with low concentration limits for this classification. We conclude that the massive increase in use of preservatives is alarming, and that urgent action is needed for protection of health. Their use in non-cosmetic products is broad, increasing and often undisclosed. In the EU, legislations concerning chemicals can provide relevant restrictions to reduce their use and associated health risks, monitored by efficient surveillance. Prevention would be benefited by better coordination between legislations.

Often patients may require treatment on multiple occasions. The re-randomisation design can be used in such multi-episode settings, as it allows patients to be re-enrolled and re-randomised for each new treatment episode they experience. We propose a set of estimands that can be used in multi-episode settings, focusing on issues unique to multi-episode settings, namely how each episode should be weighted, how the patient's treatment history in previous episodes should be handled, and whether episode-specific effects or average effects across all episodes should be used. We then propose independence estimators for each estimand, and show the manner in which many re-randomisation trials have been analysed in the past (a simple comparison between all intervention episodes vs. all control episodes) corresponds to a per-episode added-benefit estimand, that is, the average effect of the intervention across all episodes, over and above any benefit conferred from the intervention in previous episodes. We show this estimator is generally unbiased, and describe when other estimators will be unbiased. We conclude that (i) consideration of these estimands can help guide the choice of which analysis method is most appropriate; and (ii) the re-randomisation design with an independence estimator can be a useful approach in multi-episode settings.

We describe a new command, artcat, that calculates sample size or power for a randomized controlled trial or similar experiment with an ordered categorical outcome, where analysis is by the proportional-odds model. artcat implements the method of Whitehead (1993, Statistics in Medicine 12: 2257–2271). We also propose and implement a new method that 1) allows the user to specify a treatment effect that does not obey the proportional-odds assumption, 2) offers greater accuracy for large treatment effects, and 3) allows for noninferiority trials. We illustrate the command and explore the value of an ordered categorical outcome over a binary outcome in various settings. We show by simulation that the methods perform well and that the new method is more accurate than Whitehead’s method.

Background: Network meta-analysis (NMA) is increasingly used in guideline development and other aspects of evidence-based decision-making. We aimed to develop a risk of bias (RoB) tool to assess NMAs (RoB NMA tool). An international steering committee intended the RoB NMA tool to be used in combination with ROBIS (recommend as it was designed to assess biases only) or other similar quality appraisal tools (e.g., AMSTAR 2) to assess quality of systematic reviews. The RoB NMA tool will assess NMA biases and limitations regarding how the analysis was planned, data were analysed, and results were presented, including the way in which the evidence was assembled and interpreted. Objectives: Conduct (a) a Delphi process to determine expert opinion on an item’s inclusion, and (b) a knowledge user survey to widen its impact. Design: Cross sectional survey and Delphi process Methods: Delphi panellists were asked to rate whether items should be included. All agreed-upon item were included in a second round of the survey (defined as 70% agreement). We surveyed knowledge users’ views and preferences about the importance, utility, and willingness to use the RoB NMA tool to evaluate evidence in practice and in policymaking. We included 12 closed and 10 open-ended questions, and we followed a knowledge translation plan to disseminate the survey through social media and professional networks. Results: 22 items were entered into a Delphi survey of which 28 respondents completed round 1, and 22 completed round 2.Seven items did not reach consensus in round 2. A total of 298 knowledge users participated in the survey (14% respondent rate). 75% indicated that their organization produced NMAs, and 78% showedhigh interest in the tool, especially if they had received adequate training (84%). Most knowledge users and Delphi panellists preferred a tool to assess both bias in individual NMA results and authors’ conclusions. Response bias in our sample is a major limitation as knowledge users working in higher income countries were more represented. One of the limitations of the Delphi process is that it depends on the purposive selection of experts and their availability, thus limiting the variability in perspectives and scientific disciplines. Conclusions: This Delphi process and knowledge user survey informs the development of the RoB NMA tool. Registration: We uploaded our study protocol on the Open Science Framework at https://osf.io/da4uy/

Simulation studies are a powerful tool in epidemiology and biostatistics, but they can be hard to conduct successfully. Sometimes unexpected results are obtained. We offer advice on how to check a simulation study when this occurs, and how to design and conduct the study to give results that are easier to check. Simulation studies should be designed to include some settings where answers are already known. They should be coded sequentially, with data generating mechanisms checked before simulated data are analysed. Results should be explored carefully, with scatterplots of standard error estimates against point estimates a powerful tool. Failed estimation and outlying estimates should be identified and avoided by changing data generating mechanisms or coding realistic hybrid analysis procedures. Finally, surprising results should be investigated by methods including considering whether sources of variation are correctly included. Following our advice may help to prevent errors and to improve the quality of published simulation studies.

ImportanceThe common use of isothiazolinones as preservatives is a global cause of allergic contact dermatitis. Differences in allowable concentrations of methylisothiazolinone (MI) exist in Europe, Canada, and the US.ObjectiveTo compare the prevalence of positive patch test reactions to the methylchloroisothiazolinone/methylisothiazolinone (MCI/MI) combination and MI alone in North America and Europe from 2009 to 2018.Design, Setting, and ParticipantsThis retrospective analysis of North American Contact Dermatitis Group, European Surveillance System on Contact Allergies (ESSCA), and the Information Network of Departments of Dermatology (IVDK) databases included data from patients presenting for patch testing at referral patch test clinics in North America and Europe.ExposuresPatch tests to MCI/MI and MI.Main Outcomes and MeasuresPrevalence of allergic contact dermatitis to MCI/MI and MI.ResultsFrom 2009 to 2018, participating sites in North America and Europe patch tested a total of 226 161 individuals to MCI/MI and 118 779 to MI. In Europe, positivity to MCI/MI peaked during 2013 and 2014 at 7.6% (ESSCA) and 5.4% (IVDK) before decreasing to 4.4% (ESSCA) and 3.2% (IVDK) during 2017 and 2018. Positive reactions to MI were 5.5% (ESSCA) and 3.4% (IVDK) during 2017 and 2018. In North America, the frequency of positivity to MCI/MI increased steadily through the study period, reaching 10.8% for MCI/MI during 2017 and 2018. Positive reactions to MI were 15.0% during 2017 and 2018.Conclusions and RelevanceThe study results suggest that in contrast to the continued increase in North America, isothiazolinone allergy is decreasing in Europe. This trend may coincide with earlier and more stringent government regulation of MI in Europe.

Bayesian analysis of a non-inferiority trial is advantageous in allowing direct probability statements to be made about the relative treatment difference rather than relying on an arbitrary and often poorly justified non-inferiority margin. When the primary analysis will be Bayesian, a Bayesian approach to sample size determination will often be appropriate for consistency with the analysis. We demonstrate three Bayesian approaches to choosing sample size for non-inferiority trials with binary outcomes and review their advantages and disadvantages. First, we present a predictive power approach for determining sample size using the probability that the trial will produce a convincing result in the final analysis. Next, we determine sample size by considering the expected posterior probability of non-inferiority in the trial. Finally, we demonstrate a precision-based approach. We apply these methods to a non-inferiority trial in antiretroviral therapy for treatment of HIV-infected children. A predictive power approach would be most accessible in practical settings, because it is analogous to the standard frequentist approach. Sample sizes are larger than with frequentist calculations unless an informative analysis prior is specified, because appropriate allowance is made for uncertainty in the assumed design parameters, ignored in frequentist calculations. An expected posterior probability approach will lead to a smaller sample size and is appropriate when the focus is on estimating posterior probability rather than on testing. A precision-based approach would be useful when sample size is restricted by limits on recruitment or costs, but it would be difficult to decide on sample size using this approach alone.

Background. In a non-inferiority trial, the choice of margin depends on the expected control event risk. If the true risk differs from expected, power and interpretability of results can be affected. A non-inferiority frontier pre-specifies an appropriate non-inferiority margin for each value of control event risk. D3 is a non-inferiority trial comparing two treatment regimens in children living with HIV, designed assuming a control event risk of 12%, a non-inferiority margin of 10%, 80% power, significance level (α) 0.025. We consider approaches to choosing and implementing a frontier for this already funded trial, where changing the sample size substantially would be difficult. Methods. In D3 we fix the non-inferiority margin at 10%, 8% and 5% for control event risks of ≥ 9%, 5% and 1% respectively. We propose four frontiers which fit these fixed points, including a Smooth Away From Expected (SAFE) frontier. Analysis approaches considered are: using the pre-specified significance level (α = 0.025); always using a reduced significance level (to achieve α ≤ 0.025 across control event risks); reducing significance levels only when the control event risk differs significantly from expected (control event risk Results. Changing the significance level only when the control event risk is Conclusions. The SAFE non-inferiority frontier will be used in D3, and the non-inferiority margin and significance level will be modified if the control event risk is lower than expected. This ensures results will remain interpretable if design assumptions are incorrect, while achieving similar power. A similar approach could be considered for other non-inferiority trials where the control event risk is uncertain.

To what extent the COVID-19 pandemic and its containment measures influenced mental health in the general population is still unclear.To assess the trajectory of mental health symptoms during the first year of the pandemic and examine dose-response relations with characteristics of the pandemic and its containment.Relevant articles were identified from the living evidence database of the COVID-19 Open Access Project, which indexes COVID-19-related publications from MEDLINE via PubMed, Embase via Ovid, and PsycInfo. Preprint publications were not considered.Longitudinal studies that reported data on the general population's mental health using validated scales and that were published before 31 March 2021 were eligible.An international crowd of 109 trained reviewers screened references and extracted study characteristics, participant characteristics, and symptom scores at each timepoint. Data were also included for the following country-specific variables: days since the first case of SARS-CoV-2 infection, the stringency of governmental containment measures, and the cumulative numbers of cases and deaths.In a total of 43 studies (331 628 participants), changes in symptoms of psychological distress, sleep disturbances, and mental well-being varied substantially across studies. On average, depression and anxiety symptoms worsened in the first 2 months of the pandemic (standardized mean difference at 60 days, -0.39 [95% credible interval, -0.76 to -0.03]); thereafter, the trajectories were heterogeneous. There was a linear association of worsening depression and anxiety with increasing numbers of reported cases of SARS-CoV-2 infection and increasing stringency in governmental measures. Gender, age, country, deprivation, inequalities, risk of bias, and study design did not modify these associations.The certainty of the evidence was low because of the high risk of bias in included studies and the large amount of heterogeneity. Stringency measures and surges in cases were strongly correlated and changed over time. The observed associations should not be interpreted as causal relationships.Although an initial increase in average symptoms of depression and anxiety and an association between higher numbers of reported cases and more stringent measures were found, changes in mental health symptoms varied substantially across studies after the first 2 months of the pandemic. This suggests that different populations responded differently to the psychological stress generated by the pandemic and its containment measures.Swiss National Science Foundation. (PROSPERO: CRD42020180049).

Background/aims Tuberculosis remains one of the leading causes of death from an infectious disease globally. Both choices of outcome definitions and approaches to handling events happening post-randomisation can change the treatment effect being estimated, but these are often inconsistently described, thus inhibiting clear interpretation and comparison across trials. Methods Starting from the ICH E9(R1) addendum’s definition of an estimand, we use our experience of conducting large Phase III tuberculosis treatment trials and our understanding of the estimand framework to identify the key decisions regarding how different event types are handled in the primary outcome definition, and the important points that should be considered in making such decisions. A key issue is the handling of intercurrent (i.e. post-randomisation) events (ICEs) which affect interpretation of or preclude measurement of the intended final outcome. We consider common ICEs including treatment changes and treatment extension, poor adherence to randomised treatment, re-infection with a new strain of tuberculosis which is different from the original infection, and death. We use two completed tuberculosis trials (REMoxTB and STREAM Stage 1) as illustrative examples. These trials tested non-inferiority of new tuberculosis treatment regimens versus a control regimen. The primary outcome was a binary composite endpoint, ‘favourable’ or ‘unfavourable’, which was constructed from several components. Results We propose the following improvements in handling the above-mentioned ICEs and loss to follow-up (a post-randomisation event that is not in itself an ICE). First, changes to allocated regimens should not necessarily be viewed as an unfavourable outcome; from the patient perspective, the potential harms associated with a change in the regimen should instead be directly quantified. Second, handling poor adherence to randomised treatment using a per-protocol analysis does not necessarily target a clear estimand; instead, it would be desirable to develop ways to estimate the treatment effects more relevant to programmatic settings. Third, re-infection with a new strain of tuberculosis could be handled with different strategies, depending on whether the outcome of interest is the ability to attain culture negativity from infection with any strain of tuberculosis, or specifically the presenting strain of tuberculosis. Fourth, where possible, death could be separated into tuberculosis-related and non-tuberculosis-related and handled using appropriate strategies. Finally, although some losses to follow-up would result in early treatment discontinuation, patients lost to follow-up before the end of the trial should not always be classified as having an unfavourable outcome. Instead, loss to follow-up should be separated from not completing the treatment, which is an ICE and may be considered as an unfavourable outcome. Conclusion The estimand framework clarifies many issues in tuberculosis trials but also challenges trialists to justify and improve their outcome definitions. Future trialists should consider all the above points in defining their outcomes.

Background: Missing data compromise the internal and external validity of trial findings, however there is limited evidence on how best to reduce missing data in palliative care trials. Aim: To assess the association between participant and site level factors and missing data in palliative care trials. Design and setting: Individual participant-level data analysis of 10 phase 3 palliative care trials using multi-level cross-classified models. Results: Participants with missing data at the previous time-point and poorer performance status were more likely to have missing data for the primary outcome and quality of life outcomes, at the primary follow-up point and end of follow-up. At the end of follow-up, the number of site randomisations and number of study site personnel were significantly associated with missing data. Trial duration and the number of research personnel explained most of the variance at the trial and site-level respectively, except for the primary outcome where the amount of data requested was most important at the trial-level. Variance at the trial level was more substantial than at the site level across models and considerable variance remained unexplained for all models except quality of life at the end of follow-up. Conclusion: Participants with a poorer performance status are at higher risk of missing data in palliative care trials and require additional support to provide complete data. Performance status is a potential auxiliary variable for missing data imputation models. Reducing trial variability should be prioritised and further factors need to be identified and explored to explain the residual variance.

Introduction: Network meta-analyses (NMAs) have gained popularity and grown in number due to their ability to provide estimates of comparative effectiveness of multiple treatments for the same condition. The aim of this study is to conduct a methodological review to compile a preliminary list of concepts related to bias in NMAs. Methods and Analysis: We included papers that present items related to bias, reporting, or methodological quality, papers assessing the quality of NMAs, or methods papers. We searched MEDLINE, the Cochrane library, and unpublished literature. We extracted items related to bias in NMAs. An item was excluded if it was an item related to general systematic review quality or bias, and included in currently available tools such as ROBIS or AMSTAR 2. We reworded items, typically structured as questions, into concepts (i.e. general notions). Results: 181 articles were assessed at full text and 57 were included. Of these articles, 12 were tools, checklists or journal standards, 12 were guidance documents for NMAs, 26 were articles related to bias or methods, and 7 were papers that assessed the quality of NMAs. These studies yielded 99 items of which the majority related to general systematic review quality and biases and were therefore excluded. The items we included were reworded into 22 concepts specific to bias in NMAs. Conclusions: A list of 22 concepts were included. This list is not intended to be used to assess biases in NMAs, but to inform the development of items to be included in our tool.

Effective decision making from randomised controlled clinical trials relies on robust interpretation of the numerical results. However, the language we use to describe clinical trials can cause confusion both in trial design and in comparing results across trials. ACceptability Curve Estimation using Probability Above Threshold (ACCEPT) aids comparison between trials (even where of different designs) by harmonising reporting of results, acknowledging different interpretations of the results may be valid in different situations, and moving the focus from comparison to a pre-specified value to interpretation of the trial data. ACCEPT can be applied to historical trials or incorporated into statistical analysis plans for future analyses. An online tool enables ACCEPT on up to three trials simultaneously.

Antimicrobial resistance is impacting treatment decisions for, and patient outcomes from, bacterial infections worldwide, with particular threats from infections with carbapenem-resistant Enterobacteriaceae, Acinetobacter baumanii, or Pseudomonas aeruginosa. Numerous areas of clinical uncertainty surround the treatment of these highly resistant infections, yet substantial obstacles exist to the design and conduct of treatment trials for carbapenem-resistant bacterial infections. These include the lack of a widely acceptable optimised standard of care and control regimens, varying antimicrobial susceptibilities and clinical contraindications making specific intervention regimens infeasible, and diagnostic and recruitment challenges. The current single comparator trials are not designed to answer the urgent public health question, identified as a high priority by WHO, of what are the best regimens out of the available options that will significantly reduce morbidity, costs, and mortality. This scenario has an analogy in network meta-analysis, which compares multiple treatments in an evidence synthesis to rank the best of a set of available treatments. To address these obstacles, we propose extending the network meta-analysis approach to individual randomisation of patients. We refer to this approach as a Personalised RAndomised Controlled Trial (PRACTical) design that compares multiple treatments in an evidence synthesis, to identify, overall, which is the best treatment out of a set of available treatments to recommend, or how these different treatments rank against each other. In this Personal View, we summarise the design principles of personalised randomised controlled trial designs. Specifically, of a network of different potential regimens for life-threatening carbapenem-resistant infections, each patient would be randomly assigned only to regimens considered clinically reasonable for that patient at that time, incorporating antimicrobial susceptibility, toxicity profile, pharmacometric properties, availability, and physician assessment. Analysis can use both direct and indirect comparisons across the network, analogous to network meta-analysis. This new trial design will maximise the relevance of the findings to each individual patient, and enable the top-ranked regimens from any personalised randomisation list to be identified, in terms of both efficacy and safety.

Background: A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. Many trials use a modified intention-to-treat (mITT) approach, whereby participants who do not initiate treatment are excluded from the analysis. However, it is not clear the estimand being targeted by such an approach or the assumptions necessary for it to be unbiased. Methods: We demonstrate that a mITT analysis which excludes participants who do not begin treatment is estimating a principal stratum estimand (i.e. the treatment effect in the subpopulation of participants who would begin treatment, regardless of which arm they were assigned to). The mITT estimator is unbiased for the principal stratum estimand under the assumption that the intercurrent event is not affected by the assigned treatment arm, that is, participants who initiate treatment in one arm would also do so in the other arm. Results: We identify two key criteria in determining whether the mITT estimator is likely to be unbiased: first, we must be able to measure the participants in each treatment arm who experience the intercurrent event, and second, the assumption that treatment allocation will not affect whether the participant begins treatment must be reasonable. Most double-blind trials will satisfy these criteria, and we provide an example of an open-label trial where these criteria are likely to be satisfied as well. Conclusions: A modified intention-to-treat analysis which excludes participants who do not begin treatment can be an unbiased estimator for the principal stratum estimand. Our framework can help identify when the assumptions for unbiasedness are likely to hold, and thus whether modified intention-to-treat is appropriate or not., Changes to Introduction and Abstract, minor changes to Methods

Background Factorial designs and multi-arm multi-stage (MAMS) platform designs have many advantages, but the practical advantages and disadvantages of combining the two designs have not been explored. Methods We propose practical methods for a combined design within the platform trial paradigm where some interventions are not expected to interact and could be given together. Results We describe the combined design and suggest diagrams that can be used to represent it. Many properties are common both to standard factorial designs, including the need to consider interactions between interventions and the impact of intervention efficacy on power of other comparisons, and to standard multi-arm multi-stage designs, including the need to pre-specify procedures for starting and stopping intervention comparisons. We also identify some specific features of the factorial-MAMS design: timing of interim and final analyses should be determined by calendar time or total observed events; some non-factorial modifications may be useful; eligibility criteria should be broad enough to include any patient eligible for any part of the randomisation; stratified randomisation may conveniently be performed sequentially; and analysis requires special care to use only concurrent controls. Conclusion A combined factorial-MAMS design can combine the efficiencies of factorial trials and multi-arm multi-stage platform trials. It allows us to address multiple research questions under one protocol and to test multiple new treatment options, which is particularly important when facing a new emergent infection such as COVID-19.

Background: It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. Methods: Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. Results: The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions, and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually-transmitted-infection testing and results service. Conclusions: No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely., 1 figure, 5 main tables, 2 appendices, 2 appendix tables

ObjectiveDepressed patients rate social support as important for prognosis, but evidence for a prognostic effect is lacking. We aimed to test the association between social support and prognosis independent of treatment type, and the severity of depression, and other clinical features indicating a more severe illness.MethodsIndividual patient data were collated from all six eligible RCTs (n = 2858) of adults seeking treatment for depression in primary care. Participants were randomized to any treatment and completed the same baseline assessment of social support and clinical severity factors. Two‐stage random effects meta‐analyses were conducted.ResultsSocial support was associated with prognosis independent of randomized treatment but effects were smaller when adjusting for depressive symptoms and durations of depression and anxiety, history of antidepressant treatment, and comorbid panic disorder: percentage decrease in depressive symptoms at 3–4 months per z‐score increase in social support = −4.14(95%CI: −6.91 to −1.29). Those with a severe lack of social support had considerably worse prognoses than those with no lack of social support: increase in depressive symptoms at 3–4 months = 14.64%(4.25% to 26.06%).ConclusionsOverall, large differences in social support pre‐treatment were associated with differences in prognostic outcomes. Adding the Social Support scale to clinical assessments may be informative, but after adjusting for routinely assessed clinical prognostic factors the differences in prognosis are unlikely to be of a clinically important magnitude. Future studies might investigate more intensive treatments and more regular clinical reviews to mitigate risks of poor prognosis for those reporting a severe lack of social support.

Kontaktallergie (Sensibilisierung) und daraus resultierende allergische Kontaktdermatitis (ACD) sind von erheblicher Tragweite aus Sicht des Gesundheitswesens. Fur die vorliegende Ubersichtsarbeit hierzu wurden alle passenden Artikel aus dem Zeitraum 2018–2019 per systematischer Suche in Medline und Web of ScienceTM ermittelt; auserdem wurden alle verfugbaren Ausgaben der Fachzeitschriften „Contact Dermatitis“ und „Dermatitis“ manuell durchsucht und so eine fruhere, ahnliche Ubersichtsarbeit fortgeschrieben und neu fokussiert. Neue Allergene ebenso wie bekannte Allergene, die in neuem Expositionskontext gefunden wurden oder deren Bedeutung in sonstiger Hinsicht neu bewertet wurde, werden in Abschnitten nach Substanzklasse gegliedert beschrieben (z. B. Metalle, Konservierungsmittel, Duftstoffe). Als gemeinsames Ergebnis zahlreicher Untersuchungen fallt sowohl ein Mangel an Informationen uber die Produktzusammensetzung auf, z. B. in Bezug auf ein neu beschriebenes Allergen in Leinenturnschuhen (Dimethylthiocarbamylbenzothiazolsulfid), als auch vor allem die mangelnde Kooperativitat von Herstellern von Blutzuckermessgeraten bzw. Insulinpumpen. Zu den letztgenannten Geraten hat eine internationale Zusammenarbeit zwischen Dermatologen und Chemikern gezeigt, dass sie bei zahlreichen Diabetikern schwere ACD verursachen, indem sie Isobornylacrylat bzw. N,N’-Dimethylacrylamid freisetzen. Eine verbesserte und vollstandige Deklaration der Inhaltsstoffe fur Produkte alle Art – und nicht nur, wie wir es derzeit in Europa haben, bei Kosmetika (abgesehen von der vollstandigen Auflistung der Duftstoffe) – muss auf die legislative Agenda gesetzt werden.

Introduction: Network meta-analysis (NMA) is increasingly used in guideline development and other aspects of evidence-based decision-making. We engaged with stakeholders in early phases of the development of a new tool to assess risk of bias (RoB) in NMA to facilitate its later use and potentially widen its impact. Methods: We surveyed stakeholders’ views and preferences about the importance, utility, and willingness to use the RoB NMA tool to evaluate evidence in practice and in policymaking. We included 12 closed and 10 open-ended questions, and followed a knowledge translation plan to disseminate the survey through social media and professional networks. Results: A total of 298 stakeholders participated in the survey (14% respondent rate) which took on average 2 minutes to complete. 75% of stakeholders indicated that their organization produced NMAs, and 78% of respondents showed high interest in the tool. Most stakeholders (84%) reported they would use the tool to assess an NMA if they had received adequate training, while half reported they preferred a tool to assess both bias in individual NMA results and authors’ conclusions. Twelve (8%) stakeholders said they would use the tool to conduct an overview of reviews, while 24 (17%) respondents indicated that they would use the tool to distinguish between NMAs at high and low risk of bias. Conclusions: This survey informs the development of a RoB tool for NMAs and is a pillar in an integrated knowledge translation approach. Stakeholders preferred a tool to assess both bias in individual NMA results and authors’ conclusions.

Aims To assess the effects of bed rest duration on short-term complications following transfemoral catheterization. Methods and results A systematic search was carried out in MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, Scopus, SciELO and in five registries of grey literature. Randomized controlled trials and quasi-experimental studies comparing different durations of bed rest after transfemoral catheterization were included. Primary outcomes were haematoma and bleeding near the access site. Secondary outcomes were arteriovenous fistula, pseudoaneurysm, back pain, general patient discomfort and urinary discomfort. Study findings were summarized using a network meta-analysis (NMA). Twenty-eight studies and 9217 participants were included (mean age 60.4 years). In NMA, bed rest duration was not consistently associated with either primary outcome, and this was confirmed in sensitivity analyses. There was no evidence of associations with secondary outcomes, except for two effects related to back pain. A bed rest duration of 2–2.9 h was associated with lower risk of back pain [risk ratio (RR) 0.33, 95% confidence interval (CI) 0.17–0.62] and a duration over 12 h with greater risk of back pain (RR 1.94, 95% CI 1.16–3.24), when compared with the 4–5.9 h interval. Post hoc analysis revealed an increased risk of back pain per hour of bed rest (RR 1.08, 95% CI 1.04–1.11). Conclusion A short bed rest was not associated with complications in patients undergoing transfemoral catheterization; the greater the duration of bed rest, the more likely the patients were to experience back pain. Ambulation as early as 2 h after transfemoral catheterization can be safely implemented. Registration PROSPERO: CRD42014014222.

Background Clinical trial investigators may need to evaluate treatment effects in a specific subgroup (or subgroups) of participants in addition to reporting results of the entire study population. Such subgroups lack power to detect a treatment effect, but there may be strong justification for borrowing information from a larger patient group within the same trial, while allowing for differences between populations. Our aim was to develop methods for eliciting expert opinions about differences in treatment effect between patient populations, and to incorporate these opinions into a Bayesian analysis. Methods We used an interaction parameter to model the relationship between underlying treatment effects in two subgroups. Elicitation was used to obtain clinical opinions on the likely values of the interaction parameter, since this parameter is poorly informed by the data. Feedback was provided to experts to communicate how uncertainty about the interaction parameter corresponds with relative weights allocated to subgroups in the Bayesian analysis. The impact on the planned analysis was then determined. Results The methods were applied to an ongoing non-inferiority trial designed to compare antiretroviral therapy regimens in 707 children living with HIV and weighing ≥ 14 kg, with an additional group of 85 younger children weighing Conclusions Borrowing information from a larger subgroup or subgroups can facilitate estimation of treatment effects in small subgroups within a clinical trial, leading to improved power and precision. Informative prior distributions for interaction parameters are required to inform the degree of borrowing and can be informed by expert opinion. We demonstrated accessible methods for obtaining opinions.

Background: Missing data can introduce bias and reduce the power, precision and generalisability of study findings. Guidelines on how to address missing data are limited in scope and detail, and poorly implemented. Aim: To develop guidelines on how best to (i) reduce, (ii) handle and (iii) report missing data in palliative care clinical trials. Design: Modified nominal group technique. Setting/participants: Patient and public research partners, palliative care clinicians, trialists, methodologists and statisticians attended a 1-day workshop, following which a multi-stakeholder development group drafted the guidelines. Results: Seven main recommendations for reducing missing data, nine for handling missing data and twelve for reporting missing data were developed. The top five recommendations were: (i) train all research staff on missing data, (ii) prepare for missing data at the trial design stage, (iii) address missing data in the statistical analysis plan, (iv) collect the reasons for missing data and (v) report descriptive statistics comparing the baseline characteristics of those with missing and observed data. Reducing missing data, preparing for missing data and understanding the reasons for missing data were greater priorities for stakeholders than how to deal with missing data once they had occurred. Conclusion: Comprehensive guidelines on how to address missing data were developed by stakeholders involved in palliative care trials. Implementation of the guidelines will require endorsement of research funders and research journals.