Kaplan-Meier survival analysis
Kaplan-Meier survival analysis
Biostatistics and Public Health
Biostatistics and Public Health
Introduction to biostatistics
Mean, median, and mode
Probability
Range, variance, and standard deviation
Types of data
Normal distribution and z-scores
Standard error of the mean (Central limit theorem)
Paired t-test
Two-sample t-test
Hypothesis testing: One-tailed and two-tailed tests
One-way ANOVA
Two-way ANOVA
Repeated measures ANOVA
Correlation
Linear regression
Logistic regression
Type I and type II errors
Chi-squared test
Fisher's exact test
Kaplan-Meier survival analysis
Kappa coefficient
Mann-Whitney U test
Spearman's rank correlation coefficient
Positive and negative predictive value
Test precision and accuracy
Odds ratio
DALY and QALY
Direct standardization
Ecologic study
Cross sectional study
Case-control study
Cohort study
Randomized control trial
Sample size
Placebo effect and masking
Disease causality
Selection bias
Information bias
Confounding
Interaction
Modes of infectious disease transmission
Outbreak investigations
Disease surveillance
Vaccination and herd immunity
Key Takeaways
Kaplan-Meier survival analysis is a statistical technique used to estimate the chance of survival (or failure) for a group of patients (or other objects) over time. It does this by partitioning the total time into intervals and computing the proportion of subjects who are still alive or still in the study at the end of each interval.