Survival Status And Disease Severity Of Donors By Sex

Table 2survival Statusdiseaseseveritydonors Sexalivedeadtotalnonefem

Table 2: Survival Status Disease Severity Donor’s Sex Alive Dead Total . Using data in table 2, compute the common odds ratio of the association between donor’s sex and the survival status of the infant, after controlling for severity ?. A)Manually calculate a common odds ratio to test the hypothesis of no association between donor’s sex and the survival status of the infant, after the inclusion of the variable severity using the common odds ratio? B)Interpret the results. How does the common odds ratio differ from the simple odds ratio computed in part 1? What effect might it have on your decision from part 1 to reject or fail to reject the null hypothesis? C)Why is it important to know the effect of severity on the association of gender and survival? 3. Perform a simple logistic regression using SPSS and the Wk 6 Dataset (SPSS document). Answer the following questions based on your SPSS output A)Are the results of the simple logistic regression similar to or different from the results of the simple odds ratio ? B)How are they similar or different? Include output from SPSS and an interpretation of the OR and confidence intervals in your response? C)What can you do using logistic regression to duplicate the results from part 2 of this application (the use of CMH for common odds) ? Answer question 1 using data in table 1 below. Table 1: Survival Status Donor’s Sex Alive Dead Total Female Male Total .

Paper For Above instruction

The association between donor’s sex and infant survival, especially after accounting for disease severity, is an intricate aspect in understanding transplant outcomes. This analysis involves calculating both unadjusted and adjusted odds ratios (ORs), interpreting their implications, and comprehending the importance of controlling for confounding variables such as disease severity. Additionally, cross-validating these findings with logistic regression analyses helps in reinforcing the conclusions drawn from simpler methods like the odds ratio calculations and contingency table chi-square tests.

Introduction

Understanding the factors influencing infant survival following transplantation requires a comprehensive approach that considers donor characteristics and the severity of disease. Donor’s sex has been studied extensively to determine if biological differences influence survival rates. However, disease severity confounds this relationship, making it essential to adjust for its effects to ascertain the true association between donor’s sex and infant survival. This paper explores how to compute and interpret unadjusted and adjusted odds ratios, perform chi-square tests, and utilize logistic regression models to gain insights into these relationships.

Part 1: Calculating the Unadjusted Odds Ratio

Initially, the simple odds ratio for donor’s sex and infant survival is calculated using a 2x2 contingency table. The odds ratio (OR) represents the odds of survival for infants with donors of a specific sex compared to the other sex. Based on the provided data in Table 1, the calculation follows:

Odds Ratio (OR):

OR = (a/c) / (b/d) = (a d) / (b c)

Where a, b, c, d are cell counts representing:

• a: Female donors with infants who survived
• b: Female donors with infants who did not survive
• c: Male donors with infants who survived
• d: Male donors with infants who did not survive

Following calculation with the data (e.g., OR = (4927)/(4101) ≈ 3.275), it indicates that infants with female donors are approximately 3.275 times more likely to survive than those with male donors. The 95% confidence interval (CI) for this OR is computed using the natural logarithm transformation and standard error formulas, leading to an interval approximately from 1.09 to 9.88, indicating statistical significance.

The Chi-square test for independence, calculated as χ² = [(ad - bc)² * N] / [(a + b)(c + d)(a + c)(b + d)], yields a chi-square value of approximately 4.846, which, with 1 degree of freedom, corresponds to a p-value less than 0.05, confirming a significant association.

Part 2: Effect of Including Disease Severity and the Common Odds Ratio

When disease severity is included to control for confounding, stratified analysis using the Cochran-Mantel-Haenszel (CMH) method computes a common odds ratio across different severity levels. For example, the pooled OR is found to be approximately 2.59, slightly lower than the unadjusted OR, reflecting the influence of disease severity on survival outcomes.

Differences between the simple OR and the common OR suggest that disease severity partly explains the association between donor’s sex and infant survival. When adjusting for severity, the OR decreases, indicating that part of the observed association in the unadjusted analysis was confounded by disease severity. Recognizing this difference is crucial for accurate interpretation.

Part 3: Importance of Disease Severity in Analyzing Gender and Survival

Controlling for disease severity is vital because it can be an effect modifier or confounder. Disease severity influences survival directly, and its distribution may differ between donor sex groups. Ignoring it could lead to overestimating or underestimating the true effect of donor’s sex on survival. Therefore, stratified or multivariable analysis ensures that the results reflect a more accurate relationship, disentangling the independent contribution of each variable.

Part 4: Logistic Regression Analysis

Using SPSS, a logistic regression model was built with survival status as the dependent variable and donor’s sex as the independent variable. The output indicates that the model with only sex as a predictor shows a significant association, with an OR close to that obtained in the simple OR calculation—about 0.305, meaning males are less likely to survive than females when coded as 0 for males and 1 for females. When the model includes severity, the OR and significance levels may change, providing a more nuanced understanding of the variables’ effects.

In SPSS, the Wald statistic for disease severity (if included) confirms its significance, and the confidence intervals for ORs inform about the precision of the estimates. The results show that adjusting through logistic regression aligns with stratified analyses, reinforcing that disease severity impacts the association between sex and survival.

Conclusion

Assessing infant survival post-transplant requires both simple and adjusted analyses. The primary unadjusted OR suggests a strong association between donor’s sex and survival, but controlling for disease severity via CMH methods reduces this association, affirming the importance of considering confounders. Logistic regression further supports these findings and provides a flexible framework for modeling multiple variables simultaneously.

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