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ObjectivesTo examine reactions to the proposed improvements to standard Kaplan-Meier plots, the standard way to present time-to-event data, and to understand which (if any) facilitated better depiction of (1) the state of patients over time, and (2) uncertainty over time in the estimates of survival.DesignA survey of stakeholders' opinions on the proposals.SettingA web-based survey, open to international participation, for those with an interest in visualisation of time-to-event data.Participants1174 people participated in the survey over a 6-week period. Participation was global (although primarily Europe and North America) and represented a wide range of researchers (primarily statisticians and clinicians).Main outcome measuresTwo outcome measures were of principal importance: (1) participants' opinions of each proposal compared with a 'standard' Kaplan-Meier plot; and (2) participants' overall ranking of the proposals (including the standard).ResultsMost proposals were more popular than the standard Kaplan-Meier plot. The most popular proposals in the two categories, respectively, were an extended table beneath the plot depicting the numbers at risk, censored and having experienced an event at periodic timepoints, and CIs around each Kaplan-Meier curve.ConclusionsThis study produced a high response number, reflecting the importance of graphics for time-to-event data. Those producing and publishing Kaplan-Meier plots-both authors and journals-should, as a starting point, consider using the combination of the two favoured proposals.

Juan Carlos Bazo-Alvarez,1,2 Tim P Morris,3 James R Carpenter,3,4 Irene Petersen1,5 1Research Department of Primary Care and Population Health, University College London (UCL), London, UK; 2School of Medicine, Universidad Cesar Vallejo, Trujillo, Peru; 3MRC Clinical Trials Unit at UCL, London, UK; 4Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK; 5Department of Clinical Epidemiology, Aarhus University, Aarhus, DenmarkCorrespondence: Juan Carlos Bazo-AlvarezResearch Department of Primary Care and Population Health, University College London (UCL), Rowland Hill Street, London, NW3 2PF, UKTel +44 7376076260Email juan.alvarez.16@ucl.ac.ukObjective: Missing data can produce biased estimates in interrupted time series (ITS) analyses. We reviewed recent ITS investigations on health topics for determining 1) the data management strategies and statistical analysis performed, 2) how often missing data were considered and, if so, how they were evaluated, reported and handled.Study Design and Setting: This was a scoping review following standard recommendations from the PRISMA Extension for Scoping Reviews. We included a random sample of all ITS studies that assessed any intervention relevant to health care (eg, policies or programmes) with individual-level data, published in 2019, with abstracts indexed on MEDLINE.Results: From 732 studies identified, we finally reviewed 60. Reporting of missing data was rare. Data aggregation, statistical tools for modelling population-level data and complete case analyses were preferred, but these can lead to bias when data are missing at random. Seasonality and other time-dependent confounders were rarely accounted for and, when they were, missing data implications were typically ignored. Very few studies reflected on the consequences of missing data.Conclusion: Handling and reporting of missing data in recent ITS studies performed for health research have many shortcomings compared with best pra

Juan Carlos Bazo-Alvarez,1,2 Tim P Morris,3 Tra My Pham,3 James R Carpenter,3,4 Irene Petersen1,5 1Research Department of Primary Care and Population Health, University College London (UCL), London, UK; 2Instituto de Investigación, Universidad Católica Los Ángeles de Chimbote, Chimbote, Peru; 3MRC Clinical Trials Unit at UCL, London, UK; 4Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK; 5Department of Clinical Epidemiology, Aarhus University, Aarhus, DenmarkCorrespondence: Juan Carlos Bazo-AlvarezResearch Department of Primary Care and Population Health, University College London (UCL), Rowland Hill Street, London NW3 2PF, UKTel +44 7376076260Email juan.alvarez.16@ucl.ac.ukBackground: In the interrupted time series (ITS) approach, it is common to average the outcome of interest at each time point and then perform a segmented regression (SR) analysis. In this study, we illustrate that such ‘aggregate-level’ analysis is biased when data are missing at random (MAR) and provide alternative analysis methods.Methods: Using electronic health records from the UK, we evaluated weight change over time induced by the initiation of antipsychotic treatment. We contrasted estimates from aggregate-level SR analysis against estimates from mixed models with and without multiple imputation of missing covariates, using individual-level data. Then, we conducted a simulation study for insight about the different results in a controlled environment.Results: Aggregate-level SR analysis suggested a substantial weight gain after initiation of treatment (average short-term weight change: 0.799kg/week) compared to mixed models (0.412kg/week). Simulation studies confirmed that aggregate-level SR analysis was biased when data were MAR. In simulations, mixed models gave less biased estimates than SR analysis and, in combination with multilevel multiple imputation, provided unbiased estimates. Mixed

Tra My Pham,1,2 James R Carpenter,1,3 Tim P Morris,1 Manuj Sharma,2 Irene Petersen2,4 1MRC Clinical Trials Unit at UCL, London WC1V 6LJ, UK; 2Department of Primary Care and Population Health, University College London, London NW3 2PF, UK; 3Department of Medical Statistics, London School of Hygiene & Tropical Medicines, London WC1E 7HT, UK; 4Department of Clinical Epidemiology, Aarhus University, Aarhus N 8200, DenmarkCorrespondence: Tra My PhamMRC Clinical Trials Unit at UCL, 90 High Holborn, London WC1V 6LJ, UKTel +44207 670 4626Email tra.pham.09@ucl.ac.ukAims/Hypothesis: Type 2 diabetes mellitus is associated with high levels of disease burden, including increased mortality risk and significant long-term morbidity. The prevalence of diabetes differs substantially among ethnic groups. We examined the prevalence of type 2 diabetes diagnoses in the UK primary care setting.Methods: We analysed data from 404,318 individuals in The Health Improvement Network database, aged 0–99 years and permanently registered with general practices in London. The association between ethnicity and the prevalence of type 2 diabetes diagnoses in 2013 was estimated using a logistic regression model, adjusting for effect of age group, sex, and social deprivation. A multiple imputation approach utilising population-level information about ethnicity from the UK census was used for imputing missing data.Results: Compared with those of White ethnicity (5.04%, 95% CI 4.95 to 5.13), the crude percentage prevalence of type 2 diabetes was higher in the Asian (7.69%, 95% CI 7.46 to 7.92) and Black (5.58%, 95% CI 5.35 to 5.81) ethnic groups, while lower in the Mixed/Other group (3.42%, 95% CI 3.19 to 3.66). After adjusting for differences in age group, sex, and social deprivation, all minority ethnic groups were more likely to have a diagnosis of type 2 diabetes compared with the White group (OR Asian versus White 2.36, 95% CI 2.26 to 2.47; OR Black versus White 1.65, 95% CI 1.56 to

Irene Petersen,1,2 Catherine A Welch,3 Irwin Nazareth,1 Kate Walters,1 Louise Marston,1 Richard W Morris,4 James R Carpenter,5,6 Tim P Morris,5 Tra My Pham1 1Department of Primary Care and Population Health, University College London, London NW3 2PF, UK; 2Department of Clinical Epidemiology, Aarhus University, 8200 Aarhus N, Denmark; 3Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK; 4Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK; 5MRC Clinical Trials Unit at UCL, London WC1V 6LJ, UK; 6Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK Background: Clinical databases are increasingly used for health research; many of them capture information on common health indicators including height, weight, blood pressure, cholesterol level, smoking status, and alcohol consumption. However, these are often not recorded on a regular basis; missing data are ubiquitous. We described the recording of health indicators in UK primary care and evaluated key implications for handling missing data.Methods: We examined the recording of health indicators in The Health Improvement Network (THIN) UK primary care database over time, by demographic variables (age and sex) and chronic diseases (diabetes, myocardial infarction, and stroke). Using weight as an example, we fitted linear and logistic regression models to examine the associations of weight measurements and the probability of having weight recorded with individuals’ demographic characteristics and chronic diseases.Results: In total, 6,345,851 individuals aged 18–99 years contributed data to THIN between 2000 and 2015. Women aged 18–65 years were more likely than men of the same age to have health indicators recorded; this gap narrowed after age 65. About 60–80% of individuals had their height, weight, blood pressure, smoking status, and alcohol consumptio

ObjectiveTo assess the adequacy of reporting of non-inferiority trials alongside the consistency and utility of current recommended analyses and guidelines.DesignReview of randomised clinical trials that used a non-inferiority design published between January 2010 and May 2015 in medical journals that had an impact factor >10 (JAMA Internal Medicine, Archives Internal Medicine, PLOS Medicine, Annals of Internal Medicine, BMJ, JAMA, Lancet and New England Journal of Medicine).Data sourcesOvid (MEDLINE).MethodsWe searched for non-inferiority trials and assessed the following: choice of non-inferiority margin and justification of margin; power and significance level for sample size; patient population used and how this was defined; any missing data methods used and assumptions declared and any sensitivity analyses used.ResultsA total of 168 trial publications were included. Most trials concluded non-inferiority (132; 79%). The non-inferiority margin was reported for 98% (164), but less than half reported any justification for the margin (77; 46%). While most chose two different analyses (91; 54%) the most common being intention-to-treat (ITT) or modified ITT and per-protocol, a large number of articles only chose to conduct and report one analysis (65; 39%), most commonly the ITT analysis. There was lack of clarity or inconsistency between the type I error rate and corresponding CIs for 73 (43%) articles. Missing data were rarely considered with (99; 59%) not declaring whether imputation techniques were used.ConclusionsReporting and conduct of non-inferiority trials is inconsistent and does not follow the recommendations in available statistical guidelines, which are not wholly consistent themselves. Authors should clearly describe the methods used and provide clear descriptions of and justifications for their design and primary analysis. Failure to do this risks misleading conclusions being drawn, with consequent effects on clinical practice.

The purpose of this study was to determine the cerebrovascular risk stratification potential of baseline degree of stenosis, clinical features, and ultrasonic plaque characteristics in patients with asymptomatic internal carotid artery (ICA) stenosis.

The purpose of this study was to determine the cerebrovascular risk stratification potential of baseline degree of stenosis, clinical features, and ultrasonic plaque characteristics in patients with asymptomatic internal carotid artery (ICA) stenosis.