Assignment 1 Epi

Week One: Assignment 1 Week One: Assignment Christian Ibe National University

Week One: Assignment Week One: Assignment The analysis of gender differences across various causes of death, as elucidated by the chi-square test, reveals several insights. The chi-square statistic stands at 8.6452 with a corresponding p-value of 0.3731. The likelihood ratio and Mantel-Haenszel chi-square corroborate the lack of statistical significance with p-values of 0.2212 and 0.6184, respectively. Such findings suggest that gender may not play a significant role in determining cause of death within this sample. Despite a moderate phi coefficient and Cramer’s V (both 0.4970), the significance levels prompt caution in asserting a strong relationship. This interpretation is constrained by a key limitation: the small expected counts in the data cells, which can invalidate the chi-square test's assumptions. Notably, all cells have expected counts of less than five, challenging the robustness of the chi-square test in this context. This issue is often mitigated in peer-reviewed research through alternative statistical approaches suitable for small sample sizes, like Fisher's exact test, but our data set is so small that it is difficult to say if there is direct correlation. Additionally, as demonstrated by my cited scholarly literature, most studies have larger datasets and sample sizes to enhance statistical power and reliability. Such research would also explore confounding factors and employ rigorous methodologies to provide a comprehensive analysis of the data. The present analysis does not embody this level of detail, mainly due to the dataset's constraints, thus affecting the conclusiveness of the results. As demonstrated by Dr. Fabijanic in his study, there are sometimes correlation between gender and mortaility rates, “There are conflicting data about gender differences in short-term mortality after acute 2

Week One: Assignment References Fabijanic, D., Culic, V., Bozic, I., Miric, D., Stipic, S. S., Radic, M., & Vucinovic, Z. (2006). Gender differences in in-hospital mortality and mechanisms of death after the first acute myocardial infarction. Annals of Saudi medicine, 26(6), 455–460. https://doi.org/10.5144/0256- 4947.2006.455 4

Week One: Assignment Datasets From SAS The FREQ Procedure Freque ncy Percen t Row Pct Col Pct Table of Gender by CauseofDeath Gender(G ender) CauseofDeath(CauseofDeath) Stro ke HIV/ AIDS Suic ide Hear t Dise ase Injury or Accid ent Can cer Alzhei mer's Diseas e Diabe tes CO PD Tot al Male 6 17.1 4 33.3 3 66.6 7 0 0.00 0.00 0.00 2 5.71 11.1 1 66.6 7 1 2.86 5.56 25.00 4 11.43 22.22 66.67 2 5.71 11.1 1 33.3 3 2 5.71 11.11 100.00 1 2.86 5.56 50.00 0 0.00 0.00 0.00 18 51.4 3 Female 3 8.57 17.6 5 33.3 3 2 5.71 11.76 100.0 0 1 2.86 5.88 33.3 3 3 8.57 17.65 75.00 2 5.71 11.76 33.33 4 11.4 3 23.5 3 66.6 7 0 0.00 0.00 0.00 1 2.86 5.88 50.00 1 2.86 5.88 100. 00 17 48.5 7 Total 9 25.7 1 2 5.71 3 8.57 4 11.43 6 17.14 6 17.1 4 2 5.71 2 5.71 1 2.86 35 100. 00 Statistics for Table of Gender by CauseofDeath Statistic DF Value Prob WARNING: 100% of the cells have expected counts less than 5. Chi-Square may not be a valid test. Chi-Square 8 8.6452 0.3731 Likelihood Ratio Chi-Square 8 10.6678 0.2212 5