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

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 2

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 3

4. Types of Data Continuous Interval Arbitrary 0 Ex: Temperature (°F) Ratio Absolute 0 Ex: Blood glucose Categorical (Discrete) Nominal Categories of data that do not have a rank Ex: Sex, Smoking Status, Race Ordinal Data measured by a finite number of ranked categories Ex: NYHA Classes I-IV 4

5. Central Tendency Mean Continuous Median Ordinal Mode Nominal 5

6. Distribution Parametric Normal Distribution Continuous Data Nonparametric Non-Normal Distribution Ordinal or Nominal Data 6

7. Measures of Variability  Range  Interval between lowest and highest values within a data set  Interquartile Range  Describes interval between 25 th and 75 th 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 7

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 8

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10. Student t-test ▫Compares means of 2 groups ANOVA assumptions 1.Data have normal distribution 2.Each observation is independent of the others 3.The variances within the groups being compared are equal 10

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 11

12. Chi-square (X 2 ) ▫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 12

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) 13

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 14

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16. 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 Survival Analysis 16

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 17

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19. References Allen, J. Applying study results to patient care: Glossary of study design and statistical terms. Pharmacists Letter.. 2004;20:3-14. Gaddis, GM and Gaddis, ML. Introduction to biostatistics: Parts 1-6. Annals of Emergency Medicine. 1990; 19. Israni, RK. ‘Guide to Biostatistics.” MedPageToday. 2007. http://medpagetoday.com DeYoung GR. Understanding statistics: An approach for the clinician. Pharmacotherapy Self-Assessment Program, 5 th Edition. Pg 1-15. Al-Qadheeb NS, et al. Impact of enteral methadone on the ability to wean off continuously infused opioids in critically ill, mechanically ventilated adults: A case control study. The Annals of Pharmacotherapy. 2012;46:1160- 1166. Marcus M, et al. Kinematic shoulder MRI: The diagnostic value in acute shoulder dislocations. European Radiology. 2012;1-6. Stefan MS, Rothberg MB, Priyaa, et al. Association between B-blocker therapy and outcomes in patients hospitalized with acute exacerbations in chronic obstructive lung disease with underlying ishaemic heart disease, heart failure or hypertension. Thorax. (2012): DOI:10.1136/Thorax.JNL-2012-201945 http://stat.ethz.ch/education/semesters/ss2011/seminar/contents/presentation_2.pdf. Accessed 20 Sept 2012. http://www.gog.org/sdcstaff/MikeSill/Classes/STA575/Lectures/LectureNotesChp5.pdf. Accessed 25 Sept 2012. https://statistics.laerd.com/spss-tutorials/mann-whitney-u-test-using-spss-statistics.php. Accessed 24 Sept 2012. http://www.experiment-resources.com/mann-whitney-u-test.html. Accessed 26 Sept 2012. 19