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    The importance of the chosen statistical methods in medical research: study over modelling in estimation superficial varicose vein risk factors in young male population
    (2020) Aydin, Feray; Jun, Young Bae; Albuz, Özgür
    Objective: To clarify the under which conditions univariate analysis is insufficient and the importance of multiple logistic regression in medical research is the primary objective. For this purpose, lower extremity superficial venous risk factors were selected as a model in a young male population retrospectively planned. Material and Method: All patients who presented to the General Surgery Clinic of 29 Mayıs State Hospital with symptoms of pruritus, edema, leg pain and swelling between May 30, 2016 and January 1, 2019 were retrospectively analyzed for this study. Results: The results of univariate analysis showed that the development of varicose veins was significantly higher in terms of variables than in the control group. Family history [34 (61.8%) vs. 21 (38.2%)], p<0.008), smoking [37 (59.7%) and 25 (40.3), p<0.04], long-term standing, respectively [90 (53.6%) vs. 78 (46.4%), p<0.01], constipation [64 (55.2%) vs. 52 (44.8%), p<0.001] and the urban lifestyle [49 (56.7%) - 22 ( 43.3), p<0.04] were found to be effective on the development of varicose veins. However, when we do binary logistic regression for important univariate results; We found different results in terms of risk factors of varicose veins. The results showed that family history, smoking, long standing and constipation contributed to the development of varicose veins [(61.8% vs. 38.2%, AOR: 2.62 (1.35, 5.07), p (0.04)], [(59.7% vs. 40.3%, AOR: 2.08 (1.13, 3.8) p<0.02)], [(53.6%) 46.4%, AOR: 1.83 (1.06, 3.16) p<0.03)], [(55.2% and 44.8%) AOR: 1.74 (1.03, 2.92) p<0.04) ]. On the other hand, urban lifestyle was found to be insignificant [(56.7% and 43.3%, AOR: 1.73 (0.96, 3.15) p<0.07)*]. Conclusion: In cases where there is more than one independent variable affecting the dependent variable; In determining how and in what way the dependent variable is affected by independent variables; multiple logistic regression tests are more successful than univariate analysis. False or incomplete statistical studies selected in medical research may lead to very important false clinical interpretations.