Multivariate Logistic Regression Clause Samples

Multivariate Logistic Regression. A weighted multivariate logistic regression was performed on the final reduced model to assess the relationship between social support and PPD, adjusting for covariates of interest. As recommended for PRAMS data, procedures that accommodate the study design and complex survey methods (e.g. PROC SURVEYLOGISTIC) were used to analyze this data. Adjusted ORs and 95% CIs were reported.
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Multivariate Logistic Regression. The multivariate logistic regression model using all cases confirmed significant independent associations for male gender, white race vs. black/other race, BMI, HIV positive status, and 25(OH)D deficiency (Table VII). Patients with MRSA infections were more likely to be underweight and least likely to be overweight. The odds ratio for serum 25(OH)D deficiency (<20 ng/mL) vs. non-deficiency (≥20 ng/mL) was 1.63 (95% confidence interval (CI): 1.31, 2.03). Subgroup analyses using only SSTIs and only outpatient cases also identified serum 25(OH)D level as an independent risk factor for MRSA infection, although in the former model the association only trended towards significance (Table VII). The odds ratios for serum 25(OH)D deficiency vs. non-deficiency were 1.28 (95% CI: 0.97, 1.70) and 1.67 (95% CI: 1.33, 2.09) for SSTIs only and outpatient cases only, respectively.
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Multivariate Logistic Regression. Table 2 shows the results of the pooled logistic regression to examine change over time in the relationship between couples’ fertility preferences, couples’ relative education and couples’ current contraceptive use. Table 2 has four models. Model 1 includes couples’ fertility preferences along with a dummy for survey years (Hypothesis 1); in Model 2, I added the wife’s education (Hypothesis 2); Model 3 includes all individual and couple-level shared characteristics as control variables. In Model 4, I include the interaction of wife’s education with survey year (Hypothesis 3). -Table 2- Model 1 shows that the odds of using contraception increased significantly between 1990 and 2012. The odds of contraceptive use are 5.6 times as high in 2012 than in 1990. Looking at the results for fertility preferences, the findings largely support the expectation that contraceptive use is significantly higher among couples in which both husband and wife agree to have no more children and lower when both want another child compared to couples in which wife wants another child but husband does not. The analysis does not support Hypothesis 1 of male dominance (in the case of disagreement, contraceptive use will be higher when the husband wants no more children but the wife does compare to when the wife wants no more children but husband does). The relationship between couple’s joint fertility preferences and contraceptive use does not depend on which partner wants another child. Rather, the preferences of both spouses exert equal influence on contraceptive use when conflict arises. In other words, the odds of contraceptive use when only the wife wants another child are not significantly different from when only the husband wants another child. Model 2 includes wife’s education to examine whether a positive education gradient still exists in contraceptive use. The relationship between a couple’s fertility preferences and contraceptive use essentially remains the same. The results show that women’s own education has significant influence on couple’s contraceptive use. The findings support the positive educational gradient hypothesis (Hypothesis 2) that educated women are more likely to use contraception than women with no formal education, particularly among women with secondary and above education. The odds of contraceptive use are 3.3 times as high for women with secondary and higher educated women and 2.4 times as high for women with primary education than women with no for...
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Multivariate Logistic Regression. The regression was conducted on stratified data based on gender. Sleep and demographic factors were studied separately for males and females. The variables included in the full model were statistically significant (p < 0.1) in chi square Table18: Results of Multivariate Logistic Regression of Sleep Factors for Sleep Quality Among Males Table 19: Results of Multivariate Logistic Regression of Demographic Factors for Sleep Quality Among Females Table 20: Results of Multivariate Logistic Regression of Sleep Factors for Sleep Quality Among Males Table 21: Results of Multivariate Logistic Regression of Sleep Factors for Sleep Quality Among Females
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Related to Multivariate Logistic Regression

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