Biostatistics Breakdown Common Statistical tests

1. Biostatistics BreakdownCommon Statistical tests Special thanks to: Christyn Mullen, Pharm.D. Clinical Pharmacy Specialist John Peter Smith Hospital

2. Objectives • Briefly review important terms needed to understand common types of statistical analysis • Review the different types of data and how they determine what type of statistical analysis is appropriate to use • Explore real examples of common statistical analysis and their relevance to that particular study

3. Types of Variables • Independent • Variables that occur regardless of other variables or factors • Intervention in a trial • Dependent • Variables that are dependent upon other variables or factors • Outcome in a trial

5. Central Tendency

6. Distribution

7. Measures of Variability • Range • Interval between lowest and highest values within a data set • Interquartile Range • Describes interval between 25th and 75th percentile (middle 50% of measures) • Standard Deviation • Describes the distribution of values in a data set by comparing each measured value to the mean (continuous data only) • Variance • Deviation from the mean

8. Statistical Significance • P-Value – indicates statistical significance • A p-value < 0.05 means that 5% of the time, the null could be rejected in error • Confidence Interval (typically 95%) • The range in which sample values are likely representative of the true population • Power • The ability of a study to detect specified differences between groups • Increasing sample size can increase power

9. CommonStatistical Analyses

10. Student t-test • Compares means of 2 groups • ANOVA assumptions • Data have normal distribution • Each observation is independent of the others • The variances within the groups being compared are equal

11. Mann-Whitney and Wilcoxan Rank • Non-parametric equivalent to t-test • Kruskal- Wallis with multiple comparison correction • Wilcoxan signed-rank • Alternative to log-rank analysis used in Kaplan Meier Regression

12. Chi-square (X2) • Compares categorical variables to see if there is a difference • Fisher’s exact test • For a smaller sample size (n < 5) • Mantel – Haenszel • Adjusts for confounding variables • McNemar • Analyzes results from studies with related or dependent measures

13. Regression • Predicts the effect of independent variables on the outcome (Framingham Risk Score) • Multiple linear regression • Used when outcome data is continuous • Logistic regression • Used when outcome data is categorical (binary)

14. Relative Risk and Odds Ratio • Relative Risk • Ratio of incidence of disease in exposed group divided by incidence in unexposed group • Cohort Studies • Odds Ratio • Odds of exposure in the group with the disease divided by odds in control group • Case-Control Studies (approximates relative risk b/c patients already have the disease) • If the Confidence Interval includes 1, there is NO statistical difference between groups

16. Survival Analysis • Kaplan- Meier Curve • Assesses time to an event • Log-Rank test will tell if differences between 2 groups are significant • Cox Proportional Hazard Model • Assesses the effects of covariates (2 or more) on survival or time to an event (adjusts for confounders) • Uses Hazard Ratio as a function of relative risk

17. Propensity Matching • Used to decrease selection bias by matching participants based on characteristics • Matching can be done based on a score • Can set number of significant digits depending on how precise you want to be • Allows for a more confident assessment of the intervention • Instrumental variable analysis • Gives each participant a probability of receiving an intervention and then apply it to an entire group (grouped-treatment rate) • Takes away selection bias based on prognosis or prescriber preference

18. Questions?

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