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MASTERMIND: Assessing the cost effectiveness of a stratified approach to type 2 diabetes therapy








MASTERMIND: Assessing the cost effectiveness of a stratified approach to type 2 diabetes therapy


Professor Chris Jennison


University of Bath


This study work will be incorporated and funded by the MRC as part of the MRC APBI Stratification and Extreme Response Mechanism IN Diabetes ?MASTERMIND? consortium which has been awarded to the University of Exeter to establish a platform for a stratified medicines approach in type 2 diabetes. The pilot award of £2.9 million ran from February 2013 to September 2015, grant reference MR/K005707/1, with the current study running from August 2015 - January 2020, grant reference MR/N00633X/1.


None


01 November 2016


Glycated hemoglobin (HbA1c) is a mainstream target for intervention in patients with type 2 diabetes and is widely monitored as a measure of the risk of complications. These patients respond differently to the available therapies; the same treatment may reduce glucose significantly in one person but have little effect in another. Focusing treatment strategies on patients with specific risk profiles, for example identifying patients who are more or less likely to respond to a treatment or experience side effects, and examining the effects is valuable for both patients and health care organisations.
Predictive models are one of many methods that can be used to evaluate the benefits of these new reduction strategies and are useful tools which help healthcare systems plan for the future. The United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model is one such tool and has been widely used in a range of research, clinical and commercial applications worldwide, as well as by NICE. It is a computer simulation model used to estimate the likely occurrence of the major diabetes-related complications over a lifetime for patients with Type 2 diabetes, in order to calculate health outcomes such as life expectancy, quality-adjusted life expectancy and the costs of complications. It is able to evaluate the interactions of HbA1c with other risk factors making it particularly valuable in facilitating evaluations of patient specific targeted treatment approaches in terms of patient HbA1c response and side effects to treatment.
Using the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model, this project aims to assess the benefits of the predictors of response to 3 types of glucose lowering therapy, thiazolidinediones, sulphonylureas and metformin, identified with in the “MASTERMIND: Stratification of Thiazolidinedione glycaemic response the ADOPT and RECORD studies” study. Reductions in HbA1c will be translated to outcomes such as expected quality adjusted life years (E[QALYs]) and expected cost of complications (E[Costs]) of both individual patient and entire populations, improving the overall population HbA1c and enhancing patient outcomes. We will combine data for those treated with TZDs, SUs and metformin in the ADOPT and RECORD studies. Response to treatment will be based on the change from the first HbA1c ?measure of 3 monthly average blood sugar? prior to starting treatment to the closest HbA1c to 12 months after treatment. We will assess how clinical characteristics, such as gender and weight, at study baseline effect both initial treatment response and longer term (6 year) response in terms of E[QALYs] and costs of diabetes related complications. We will also assess if factors associated with treatment response are associated with progression of diabetes and with other factors such as the number of tablets or changes in weight.
This research is part of an MRC study ?MASTERMIND) examining response to diabetes medications.



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Statistical Analysis Plan


Dennis JM, Henley WE, Weedon MN, Lonergan M, Rodgers LR, Jones AG, Hamilton WT, Sattar N, Janmohamed S, Holman RR, Pearson ER, Shields BM, Hattersley AT; MASTERMIND Consortium. Sex and BMI Alter the Benefits and Risks of Sulfonylureas and Thiazolidinediones in Type 2 Diabetes: A Framework for Evaluating Stratification Using Routine Clinical and Individual Trial Data. Diabetes Care. 2018 Sep;41(9):1844-1853.
DOI: 10.2337/dc18-0344