The Impact of Lifestyle and Demographic Factors on thyroid Cancer Incidence Using a Probit Regression Model
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Abstract
This research studies the impact of demographic and lifestyle variables on the occurrence of thyroid cancer. The data has been collected from Smart Health Tower in Sulaimani, encompassing 100 instances of cancer and thyroid disorders. The Probit regression analysis incorporated the subsequent covariates. These parameters included in the model age, gender, tumor size, multifocality, vascular invasion, capsular invasion (CI), extrathyroidal extension (ETE), involvement of lymph nodes (LN), and distant metastases. In order to create a bivariate probit model, researchers often characterize the joint distribution of the unobservable as a bivariate normal distribution.
The Probit model was employed to investigate the relationship between these parameters and cancer incidence for risk estimation. The results specify that certain factors, such as tumor laterality and thyroid-stimulating hormone (TSH) levels, were deemed less significant, but others, including gender, lymph node invasion, and preoperative ATPO levels, exhibited a robust association with an increased cancer risk. The results indicated that gender, lymph node invasion, and preoperative ATPO levels are substantially correlated with an elevated risk of thyroid cancer.
Although certain parameters, such as TSH levels and tumor laterality, were shown to be less significant, the results demonstrate robust correlations between cancer incidence and other covariates. The findings underscore the imperative for holistic models to evaluate the diverse interacting elements in cancer risk assessment.
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