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Regulatory decision criteria on the suitability of the glycated hemoglobin level (HbA1c) as a surrogate for cardiac events.
Proposal
11801
Title of Proposed Research
Regulatory decision criteria on the suitability of the glycated hemoglobin level (HbA1c) as a surrogate for cardiac events.
Lead Researcher
Malwina Mackowiak
Affiliation
Hochschule Koblenz - RheinAhrCampus Fachbereich Mathematik und Technik
Funding Source
Potential Conflicts of Interest
Data Sharing Agreement Date
27 September 2021
Lay Summary
A well observed fact is the higher incidence of cardiac events in patients suffering from diabetes. In the planned analysis, the suitability of the glycated hemoglobin level (HbA1c), which is a measure for the long-term glucose level, will be used to answer the question of whether HbA1c values can be taken as a predictor for the probability of experiencing a cardiac event. In this context, the following questions will be addressed:- how much does the probability of experiencing a cardiac event rise with an increase in the HbA1c value of 10% (and how crucial is the initial value)?- of what magnitude must the in- or decrease in the HbA1c be to show a significant change in the rate of cardiac events?
Study Data Provided
[{ "PostingID": 4730, "Title": "SANOFI-EFC11319", "Description": "A Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate Cardiovascular Outcomes During Treatment With Lixisenatide in Type 2 Diabetic Patients After an Acute Coronary Syndrome / ELIXA" }]
Statistical Analysis Plan
Analyses as described in "The Evaluation of Surrogate Endpoints" by Burzykowski, Molenbergh and Buyse will be applied on longitudinal measurements of HbA1c values and time-to-event data for the endpoint cardiac event. The main goal is the description of the association between the longitudinal surrogate and the event processes on the treatment Lixisenatide. In the first step, a mixed model will be applied to estimate the treatment effect on the surrogate. Then, the event intensity process will be modelled through a hazard function including trial-specific parameters.In the third step, the association between these two will be investigated through a bivariate Gaussian process. A possible estimation of the processes' components is proposed by the authors as well. The first component will consist of a linear regression with the random intercept and random slope from the model with the longitudinal measurements. The second component will be set in dependence to the random intercept and slope. The iterative EM algorithm as described by Henderson, Diggle and Dobson (2000) in which the log-likelihood is maximised with replacing each functional of the random effects by its expectation, conditional on the observed data, could be used for estimation.As both components are dependent of time, the resulting association will be dependent on time, too.A further method will be opposed to this procedure by applying a frailty model to the data.
Publication Citation
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