Studies reviewed Randomised controlled trials of two or more months' duration in men and non-pregnant women with type 1 diabetes that compared real time continuous glucose monitoring with self monitoring of blood glucose and where insulin delivery was the same in both arms.Analysis Two step meta-analysis of individual patient data with the primary outcome of final glycated haemoglobin (HbA 1c ) percentage and area under the curve of hypoglycaemia (glucose concentration <3.9 mmol/L) during either treatment, followed by one step metaregression exploring patient level determinants of HbA 1c and hypoglycaemia.Results Six trials were identified, consisting of 449 patients randomised to continuous glucose monitoring and 443 to self monitoring of blood glucose. The overall mean difference in HbA 1c for continuous glucose monitoring versus self monitoring of blood glucose was −0.30% (95% confidence interval −0.43% to −0.17%) (−3.0, −4.3 to −1.7 mmol/mol).A best fit regression model of determinants of final HbA 1c showed that for every one day increase of sensor usage per week the effect of continuous glucose monitoring versus self monitoring of blood glucose increased by 0.150% (95% credibility interval −0.194% to −0.106%) (1.5, −1.9 to −1.1 mmol/mol) and every 1% (10 mmol/mol) increase in baseline HbA 1c increased the effect by 0.126% (−0.257% to 0.0007%) (1.3, −2.6 to 0.0 mmol/mol). The model estimates that, for example, a patient using the sensor continuously would experience a reduction in HbA 1c of about 0.9% (9 mmol/mol) when the baseline HbA 1c is 10% (86 mmol/mol). The overall reduction in area under the curve of hypoglycaemia was −0.28 (−0.46 to −0.09), corresponding to a reduction in median exposure to hypoglycaemia of 23% for continuous glucose monitoring compared with self monitoring of blood glucose. In a best fit regression model, baseline area under the curve of hypoglycaemia was only weakly related to the effect of continuous glucose monitoring compared with self monitoring of blood glucose on hypoglycaemia outcome, and sensor usage was unrelated to hypoglycaemia at outcome.

Identifying which individuals benefit most from particular treatments or other interventions underpins so-called personalised or stratified medicine. However, single trials are typically underpowered for exploring whether participant characteristics, such as age or disease severity, determine an individual’s response to treatment. A meta-analysis of multiple trials, particularly one where individual participant data (IPD) are available, provides greater power to investigate interactions between participant characteristics (covariates) and treatment effects. We use a published IPD meta-analysis to illustrate three broad approaches used for testing such interactions. Based on another systematic review of recently published IPD meta-analyses, we also show that all three approaches can be applied to aggregate data as well as IPD. We also summarise which methods of analysing and presenting interactions are in current use, and describe their advantages and disadvantages. We recommend that testing for interactions using within-trials information alone (the deft approach) becomes standard practice, alongside graphical presentation that directly visualises this.

Background Recommended statistical methods for meta-analysis of diagnostic test accuracy studies require relatively complex bivariate statistical models which can be a barrier for non-statisticians. A further barrier exists in the software options available for fitting such models. Software accessible to non-statisticians, such as RevMan, does not support the fitting of bivariate models thus users must seek statistical support to use R, Stata or SAS. Recent advances in web technologies make analysis tool creation much simpler than previously. As well as accessibility, online tools can allow tailored interactivity not found in other packages allowing multiple perspectives of data to be displayed and information to be tailored to the user’s preference from a simple interface. We set out to: (i) Develop a freely available web-based “point and click” interactive tool which allows users to input their DTA study data and conduct meta-analyses for DTA reviews, including sensitivity analyses. (ii) Illustrate the features and benefits of the interactive application using an existing DTA meta-analysis for detecting dementia. Methods To create our online freely available interactive application we used the existing R packages lme4 and Shiny to analyse the data and create an interactive user interface respectively. Results MetaDTA, an interactive online application was created for conducting meta-analysis of DTA studies. The user interface was designed to be easy to navigate having different tabs for different functions. Features include the ability for users to enter their own data, customise plots, incorporate quality assessment results and quickly conduct sensitivity analyses. All plots produced can be exported as either .png or .pdf files to be included in report documents. All tables can be exported as .csv files. Conclusions MetaDTA, is a freely available interactive online application which meta-analyses DTA studies, plots the summary ROC curve, incorporates quality assessment results and allows for sensitivity analyses to be conducted in a timely manner. Due to the rich feature-set and user-friendliness of the software it should appeal to a wide audience including those without specialist statistical knowledge. We encourage others to create similar applications for specialist analysis methods to encourage broader uptake which in-turn could improve research quality.

SummaryBackgroundLuteinising-hormone-releasing-hormone agonists (LHRHa) to treat prostate cancer are associated with long-term toxic effects, including osteoporosis. Use of parenteral oestrogen could avoid the long-term complications associated with LHRHa and the thromboembolic complications associated with oral oestrogen.MethodsIn this multicentre, open-label, randomised, phase 2 trial, we enrolled men with locally advanced or metastatic prostate cancer scheduled to start indefinite hormone therapy. Randomisation was by minimisation, in a 2:1 ratio, to four self-administered oestrogen patches (100 μg per 24 h) changed twice weekly or LHRHa given according to local practice. After castrate testosterone concentrations were reached (1·7 nmol/L or lower) men received three oestrogen patches changed twice weekly. The primary outcome, cardiovascular morbidity and mortality, was analysed by modified intention to treat and by therapy at the time of the event to account for treatment crossover in cases of disease progression. This study is registered with ClinicalTrials.gov, number NCT00303784.Findings85 patients were randomly assigned to receive LHRHa and 169 to receive oestrogen patches. All 85 patients started LHRHa, and 168 started oestrogen patches. At 3 months, 70 (93%) of 75 receiving LHRHa and 111 (92%) of 121 receiving oestrogen had achieved castrate testosterone concentrations. After a median follow-up of 19 months (IQR 12–31), 24 cardiovascular events were reported, six events in six (7·1%) men in the LHRHa group (95% CI 2·7–14·9) and 18 events in 17 (10·1%) men in the oestrogen-patch group (6·0–15·6). Nine (50%) of 18 events in the oestrogen group occurred after crossover to LHRHa. Mean 12-month changes in fasting glucose concentrations were 0·33 mmol/L (5·5%) in the LHRHa group and −0·16 mmol/L (−2·4%) in the oestrogen-patch group (p=0·004), and for fasting cholesterol were 0·20 mmol/L (4·1%) and −0·23 mmol/L (−3·3%), respectively (p<0·0001). Other adverse events reported by 6 months included gynaecomastia (15 [19%] of 78 patients in the LHRHa group vs 104 [75%] of 138 in the oestrogen-patch group), hot flushes (44 [56%] vs 35 [25%]), and dermatological problems (10 [13%] vs 58 [42%]).InterpretationParenteral oestrogen could be a potential alternative to LHRHa in management of prostate cancer if efficacy is confirmed. On the basis of our findings, enrolment in the PATCH trial has been extended, with a primary outcome of progression-free survival.FundingCancer Research UK, MRC Clinical Trials Unit.

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Objectives: To apply component network meta-analysis (CNMA) models to an existing Cochrane review of psychological preparation interventions for adults undergoing surgery and to extend the models to account for covariates to identify the most effective components for improving postoperative outcomes.Study Design and Setting: Interventions consisted of between one and four components of psychological preparation: procedural information (P), sensory information (S), behavioral instruction (B), cognitive interventions (C), relaxation (R), and emotion-focused techniques (E). We used CNMA models to assess the effect of each component for three outcomes: length of stay, pain, and negative affect.Results: We found evidence that the most effective component for reducing length of stay depends on the type of surgery and that R may improve pain. There was insufficient evidence that individual components contributed to the overall reduction in negative affect, but P and S emerged as the most likely beneficial components. Overall, we were unable to identify any one component as the most effective across all three outcomes.Conclusion: The CNMA method allowed us to address questions about the effects of specific components that could not be answered using standard Cochrane methodology. Ó

enhancements to summary receiver operating characteristic plots to facilitate the analysis and reporting of meta-analysis of diagnostic test accuracy data.

Non-pharmacological interventions for preventing delirium in hospitalised non-ICU patients.