Medical Statistical Analysis: Surgical Menopause

Overview

Co-authored a Scopus-indexed peer-reviewed paper published in the Revista da Associação Médica Brasileira (2023). The study retrospectively analyzed the sociodemographic characteristics of 704 women with indications for surgical menopause between 2010 and 2020 to evaluate surgical indications and associated health risks.

Methodology

Statistical Modeling: Utilized R Version 4.1.1 to clean, process, and analyze 10 years of patient archive records. Executed multinomial and binary logistic regression analysis to evaluate complex relationships between sociodemographic data, chronic disease presence, and surgical indications.

Predictive Analysis: Quantified the exact impact of lifestyle and demographic factors on surgical menopause indications originating from cancer. Calculated specific odds ratios demonstrating how smoking, regular drug use, and chronic diseases increased surgical menopause risks at a 95% confidence interval.

Data Visualization: Leveraged the Plotly and ggplot2 libraries in R to build high-quality, publication-ready visual models (including demographic distribution plots) that effectively communicated statistical findings to the medical community.

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