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Logistic Regression

Dive deep into logistic regression techniques for statistical analysis in various study outcomes. Learn how to select appropriate methods and interpret results effectively.

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Logistic Regression

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  1. Logistic Regression Bandit Thinkhamrop, PhD. (Statistics) Department of Biostatistics and Demography Faculty of Public Health, Khon Kaen University

  2. Begin at the conclusion Begin at the conclusion 7

  3. Type of the study outcome: Key for selecting appropriate statistical methods • Study outcome • Dependent variable or response variable • Focus on primary study outcome if there are more • Type of the study outcome • Continuous • Categorical (dichotomous, polytomous, ordinal) • Numerical (Poisson) count • Event-free duration

  4. Continuous Categorical Count Survival The outcome determine statistics Mean Median Proportion (Prevalence Or Risk) Rate per “space” Median survival Risk of events at T(t) Linear Reg. Logistic Reg. Poisson Reg. Cox Reg.

  5. Parameter estimation [95%CI] Hypothesis testing [P-value] Statistics quantify errors for judgments

  6. Continuous Categorical Count Survival Back to the conclusion Appropriate statistical methods Mean Median Proportion (Prevalence or Risk) Rate per “space” Median survival Risk of events at T(t) Magnitude of effect 95% CI P-value Answer the research question based on lower or upper limit of the CI

  7. Begin at the wrong destination

  8. Forest plot

  9. Another “wrong scale” forest plot

  10. Receiver Operating Characteristic Cure

  11. Optimal cut-off point

  12. Diagnostic performance

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