Project Details
Description
Antipsychotic medications have been frequently and widely prescribed for patients with mental illnesses or other non-psychiatric diseases in recent decades. However, concerns regarding patient safety (e.g., the risk of myocardial infarction) associated with the use of antipsychotic medications have been raised. Still, the association between antipsychotic use and the risk of myocardial infarction is still controversial. Hence, the current research project aims to re-visit this important clinical and academic issue.Specifically speaking, the current research project aims to probe the following questions: (1). if the risk of MI is associated with antipsychotic medications; (2). if there are significant differences in the risks of MI between typical and atypical antipsychotic drug use; and (3). if dosage as well as duration of antipsychotic treatment exert significant impact upon the odds of MI.With respect to the research methods and procedures, instrumental variable analysis has been suggested as a powerful technique to deal with possible bias caused by unmeasured confounders in observational research. Therefore, the research design of the current project will be a nested case-control study design with an instrumental variable analysis. Data used in this study will be collected for the period 2009-2016 from the Health and Welfare Data Science Center (HWDC), Ministry of Health and Welfare, Taiwan. Data files in the National Health Insurance Research Database (NHIRD) and will be linked by unique national identification numbers of the study subjects. Furthermore, statistical methods will include the followings: Pearson's χ2 test and/or Fisher's exact test, Cochran-Mantel-Haenszel χ2 test, and conditional logistic regression analysis. Lastly, a couple of sensitivity analyses will also be performed to examine the robustness of the results of the regression model.
Status | Finished |
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Effective start/end date | 8/1/18 → 7/1/19 |
Keywords
- Antipsychotics
- Myocardial Infarction
- Instrumental variable analysis
- Propensity score matching
- Nested case-control study design
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