12 research outputs found
Competing Risks Methodology in the Evaluation of Cardiovascular and Cancer Mortality as a Consequence of Albuminuria in Type 2 Diabetes
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 3: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus
Figure S1. Proportional cause-specific hazards assessment cardiovascular mortality. (PDF 1874 kb
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
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
Additional file 6: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus
Figure S4. Proportional subdistribution hazards assessment for cardiovascular mortality. (PDF 1100 kb
Additional file 4: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus
Figure S2. Proportional cause-specific hazards assessment for cancer mortality. (PDF 1233 kb
Additional file 5: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus
Figure S3. Proportional cause-specific hazards assessment for other mortality. (PDF 1695 kb
Additional file 2: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus
Table S2. Adjusted estimates for the effect of baseline risk factors on cancer mortality from Cox-PH, Lunn-McNeil and Fine-Gray Models. (DOCX 15 kb
Additional file 7: of Standard and competing risk analysis of the effect of albuminuria on cardiovascular and cancer mortality in patients with type 2 diabetes mellitus
Figure S5. Proportional subdistribution hazards assessment for cancer mortality. (PDF 865 kb
Administrateur provisoire-Dissension entre associés-Absence de péril pour l'entreprise sociale-Nomination (non) ; Note sous Cour de cassation, Chambre commerciale, 18 mai 2010, pourvoi numéro 09-14.838
National audienceno abstrac
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]
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