12 research outputs found

    Competing Risks Methodology in the Evaluation of Cardiovascular and Cancer Mortality as a Consequence of Albuminuria in Type 2 Diabetes

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    Background: ‘Competing risks’ are events that either preclude or alter the probability of experiencing the primary study outcome(s). Many standard survival models fail to account for competing risks, introducing an unknown level of bias in their measures of absolute and relative risk. Individuals with type 2 diabetes mellitus (T2DM) and albuminuria are at increased risk of multiple competing causes of mortality, including cardiovascular disease (CVD), cancer and renal disease, yet studies to date have not implemented competing risks methodology. Aim: Using albuminuria in T2DM as a case study, this Thesis set out to quantify differences between standard- and competing-risks-adjusted survival analysis estimates of absolute and relative risk for the outcomes of cardiovascular and cancer mortality. Methods: 86,962 patients aged ≥35 years with T2DM present on or before 2005 were identified in the Clinical Practice Research Datalink. To quantify differences in measures of absolute risk, cumulative risk estimates for cardiovascular and cancer mortality from standard survival analysis methods (Kaplan-Meier estimator) were compared to those from competing-risks-adjusted methods (cumulative incidence competing risk estimator). Cumulative risk estimates were stratified by patient albuminuria level (normoalbuminuria vs albuminuria). To quantify differences in measures of relative risk, estimates for the effect of albuminuria on the relative hazards of cardiovascular and cancer mortality were compared between standard cause-specific hazard (CSH) models (Cox-proportional-hazards regression), competing risk CSH models (unstratified Lunn-McNeil model), and competing risk subdistribution hazard (SDH) models (Fine-Gray model). Results: Patients with albuminuria, compared to those with normoalbuminuria, were older (p<0.001), had higher systolic blood pressure (p<0.001), had worse glycaemic control (p<0.001), and were more likely to be current or ex-smokers (p<0.001). Over the course of nine years of follow-up 22,512 patients died; 8,800 from CVD, 5,239 from cancer, and 8,473 from other causes. Median follow-up was 7.7 years. In patients with normoalbuminuria, nine-year standard and competing-risks-adjusted cumulative risk estimates for cardiovascular mortality were 11.1% (95% confidence interval (CI): 10.8-11.5%) and 10.2% (95% CI: 9.9-10.5%), respectively. For cancer mortality, these figures were 8.0% (95% CI: 7.7-8.3%) and 7.2% (95% CI: 6.9-7.5%). In patients with albuminuria, standard and competing-risks-adjusted estimates for cardiovascular mortality were 21.8% (95% CI: 20.9-22.7%) and 18.5% (95% CI: 17.8-19.3%), respectively. For cancer mortality, these figures were 10.7% (95% CI: 10.0-11.5%) and 8.6% (8.1-9.2%). For the effect of albuminuria on cardiovascular mortality, hazard ratios from multivariable standard CSH, competing risks CSH, and subdistribution hazard ratios from competing risks SDH models were 1.75 (95% CI: 1.63-1.87), 1.75 (95% CI: 1.64-1.87), and 1.58 (95% CI: 1.48-1.69), respectively. For the effect of albuminuria on cancer mortality, these values were 1.27 (95% CI: 1.16-1.39), 1.28 (95% CI: 1.17-1.40), and 1.11 (95% CI: 1.01-1.21). Conclusions: When evaluating measures of absolute risk, differences between standard and competing-risks-adjusted methods were small in absolute terms, but large in relative terms. For the investigation of epidemiological relationships using relative hazards models, standard survival analysis methods produced near-identical risk estimates to the CSH competing risks methods for the clinical associations evaluated in this Thesis. For the evaluation of risk prediction using relative hazards models, CSH models produced consistently higher risk estimates than SDH models, and their use may lead to over-estimation of the predictive effect of albuminuria on either outcome. Where outcomes are less common (like cancer) CSH models provide poor estimates of risk prediction, and SDH models should be used. This research demonstrates that differences can be present between risk estimates derived using CSH and SDH methods, and that the two are not necessarily interchangeable. Moreover, such differences may be present in other clinical areas.</p

    Additional file 1: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus

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    Table S1. Adjusted estimates for the effect of baseline risk factors on cardiovascular mortality from Cox-PH, Lunn-McNeil and Fine-Gray Models. (DOCX 15 kb

    RetractoBot: a protocol for a randomised controlled trial to assess the impact of notifying authors that they have cited a retracted paper [Registered Report Stage 1 Protocol]

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    Background: There is a problem with the visibility of retractions, and many retracted papers continue to be cited as if they were still valid. It has been suggested that authors citing retracted papers should be contacted about it, but this has been deemed too challenging and has never been attempted.Design: This is a randomised controlled trial.Methods: All eligible retracted papers will be randomised either to the intervention (an email notifying authors of the citing papers about the fact that they have cited a retracted publication) or the control group (no notification email).Outcomes: The primary outcome will be the rate of citation of retracted papers during 12 months follow-up. The secondary outcomes will be a 24-month follow-up and the qualitative analysis of feedback from the authors in response to the intervention email.Conclusions: Our trial will investigate whether the number of citations of retracted papers can be reduced by notifying researchers that they cited a retracted paper.<br
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