Noah, A. (2024). Bias in Pharmacoepidemiology: Understanding and Mitigating Challenges. Journal of the Medical Research Institute, 45(1), 40-44. doi: 10.21608/jmalexu.2024.279832.1016
Ashraf Noah. "Bias in Pharmacoepidemiology: Understanding and Mitigating Challenges". Journal of the Medical Research Institute, 45, 1, 2024, 40-44. doi: 10.21608/jmalexu.2024.279832.1016
Noah, A. (2024). 'Bias in Pharmacoepidemiology: Understanding and Mitigating Challenges', Journal of the Medical Research Institute, 45(1), pp. 40-44. doi: 10.21608/jmalexu.2024.279832.1016
Noah, A. Bias in Pharmacoepidemiology: Understanding and Mitigating Challenges. Journal of the Medical Research Institute, 2024; 45(1): 40-44. doi: 10.21608/jmalexu.2024.279832.1016
Bias in Pharmacoepidemiology: Understanding and Mitigating Challenges
Alexandria Clinical Research Administration, Directorate of Health Affairs, Egyptian Ministry of Health and Population, Alexandria, Egypt
Abstract
Bias exerts a significant influence on pharmaco-epidemiological research across various phases, affecting study planning, execution, and interpretation. This article comprehensively explores different types of bias in each study phase, encompassing the planning phase (pre-trial), the in-process phase (during trial), and the interpretation phase (post-trial). Bias in the study planning phase, which involves study design and selection bias, can distort the research outcomes due to nuances in research outlining and participant selection. Biases in the in-process phase, including interviewer bias, chronology bias, recall bias, transfer bias, and performance bias, present challenges in interpreting findings. Understanding these biases reveals their intricate impact on study validity. Additionally, interpretation phase biases such as publication bias, selective reporting bias, spin bias, ghostwriting bias, and publication delay bias further complicate interpretations. Recognizing and mitigating bias through rigorous study design, transparent reporting, and proactive measures are essential for robust pharmaco-epidemiological research, ensuring validity and reliability in findings.