Type 2 diabetes is one of the most common chronic diseases. Yet, there are uncertainties in the choice of medication for the treatment of type 2 diabetes. Metformin, if tolerated, is the preferred first-line medication. However, it fails to control the symptoms in most patients and there is currently no consensus on what medications to recommend next. It is recognized that the decisions on which treatment to recommend after another medication has failed should be tailored to the patient's characteristics and should prevent or delay the development of diabetes complications. Health policy makers lack evidence to formalize recommendations on sequences of treatments to treat type 2 diabetes. The purpose of this study is methodological: it aims to demonstrate the use of a simple, accessible and theoretically robust method to identify a sequence of treatment decision rules for type 2 diabetes. Findings will be important in the field of biostatistics and will generate evidence on the individualized treatment of type 2 diabetes.
The objectives of this study are primarily methodological. We aim to develop a novel method to identify a sequence of individualized treatment decisions when the outcome of interest is survival time. Theoretical derivations will be used to formalize the novel method, hereafter referred to as dWSurv, and its performance will be assessed via computer simulations. The analysis of type 2 diabetes treatment pathways will be conducted to showcase the newly developed method while answering an important clinical question: what is the optimal sequence of individualized treatment decisions for maximizing the overall complication-free survival time when metformin initially fails to control the symptoms in patients with type 2 diabetes? The study population consists of all patients who were treated with a first-ever prescription of metformin. The study cohort will be selected from the CPRD data linked with the HES database. The endpoint will be defined from a list of ICD-10 codes characterizing cardiovascular events and death. Specific drug classes will be compared, and possible confounders will be identified. Optimal individualized treatment decisions will be estimated with dWSurv, which require specifying several log-linear and logistic models.
Cardiovascular disease
- Myocardial infarction
- Death Cerebral infarction
- Stroke
- Coronary heart disease
- Heart failure
- Death cerebral infarction
- Heart attack
- Subarachnoid hemmorage
- Congestive heart failure
Samy Suissa - Chief Investigator - Sir Mortimer B Davis Jewish General Hospital
Samy Suissa - Corresponding Applicant - Sir Mortimer B Davis Jewish General Hospital
Erica Moodie - Collaborator - McGill University
Gabrielle Simoneau - Collaborator - McGill University
Laurent Azoulay - Collaborator - McGill University
Robert Platt - Collaborator - McGill University
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation