STA 102: Commonly Used Statistical Tests in Medical Research (Part I)

1. STA 102: Commonly Used Statistical Tests in Medical Research (Part I) Lecturer: Dr. Daisy Dai Department of Medical Research

2. Ashley Sherman Phone: 816-701-1347 aksherman@cmh.edu Daisy Dai Phone: 816-701-5233 Email: hdai@cmh.edu Consultation Experimental design and sampling plan Collaboration in presentation and publication of studies Education Research Who are biostatisticians?

3. SPSS 201: Using SPSS to perform statistical tests I SPSS 202: Using SPSS to perform statistical tests II SPSS 204: Using SPSS to manage data SPSS 203: Summarize data with tables and graphs STA 101: Properly Setting up and Designing a Clinical Research Study Including Power Analysis for Proper Patient Numbers STA 102: Commonly Used Statistical Tests in Medical Research - Part I STA 103: Commonly Used Statistical Tests in Medical Research - Part II Statistical Courses

4. Core Knowledge in Scholarly Activities recommended by ABP • Hypothesis testing • Distinguish the null hypothesis from an alternative hypothesis. • Interpret the results of hypothesis testing.

5. Core Knowledge in Scholarly Activities recommended by ABP • Statistical tests • Understand the appropriate use of the chi-square test versus t-test • Understand the appropriate use of analysis of variance (ANOVA) • Understand the appropriate use of parametric (eg, t-test, ANOVA) versus non-parametric (eg, Mann-Whitney U, Wilcoxon) statistical tests • Interpret the results of chi-square tests • Interpret the results of t-tests

6. Core Knowledge in Scholarly Activities recommended by ABP • Statistical tests (Continued) • Understand the appropriate use of a paired and non-paired t-test • Determine the appropriate use of a 1- versus 2-tailed test of significance • Interpret a p-value • Interpret a p-value when multiple comparison have been made • Interpret a confidence interval • Indentify a type I error • Identify a type II error.

7. Statistical Testing Procedures • Clarify study objectives. • Establish hypotheses. • Determine the outcome variables, treatment groups, risk factors and covariates. • Perform appropriate statistical testing. • Interpret results.

8. Misinterpretation – Fluoridated water supplies Burke and Yiamouyannis (1975) considered 10 fluoridated and 10 non-fluoridated towns in the USA. In the Fluoridated towns, the cancer mortality rate had increased by 20% between 1950 and 1970, whereas in the non-fluoridated towns the increase was only 10%. From this they concluded that fluoridation caused cancer.

9. Fluoridated water supplies (Continued) However, Oldham and Newell (1977), in a careful analysis of the changes in age-gender-ethnic structure of the 20 cities between 1950 and 1970, showed that in fact the excess cancer rate in the fluoridated cities increased by only 1% over the 20 years, while in the un-fluoridated cities the increase was 4%. They concluded from this that there was no evidence fluoridation caused cancer.

10. Continuous Variables Two or multiple treatment groups

11. Two samples t-test Compare the means of a normally distributed interval dependent variable for two independent groups.

12. Case Study: FEV1 Changes A new compound, ABC-123, is being developed for long-term treatment of patients with chronic asthma. Asthma patients were enrolled in a double-blind study and randomized to receive daily oral or a placebo for 6 weeks. asthmatic patients Placebo Test FEV1 after 6-week treatment

13. FEV1 Data

14. What is the difference between std and std error? P-value P-value

15. Mean and Error Bar Conclusion: As compared to placebo, the new drug did not show any effect on FEV1.

16. Paired t-test Compare the means of a normally distributed interval dependent variable for two related groups.

17. Conclusion: For subjects on the new drug, FEV1 at week 6 is significantly higher than baseline. P-value

18. One-way ANOVA Test for differences of the means for continuous variables in multiple independent treatment groups.

19. Case Study: HAM-A Scores in GAD Patients with GAD A new serotonin-update inhibiting agent, SN-X95, is being studied in subjects with general anxiety disorder (GAD). Fifty-two subjects diagnosed with GAD were enrolled and randomly assigned to one of three treatment groups: three treatment groups: 25mg SN-X95, 100mg SN-X95 or placebo. After 10 weeks of once-daily oral dosing in a double-blind fashion, a test based on the Hamilton Rating Scale for Anxiety (HAM-A) was administered. This test consists of 14 anxiety-related items (e.g. ‘anxious mood’, ‘tension’, ‘insomnia’, ‘fear’, etc.), each rated by the subject as ‘no present’, ‘mild’, ‘moderate’, ‘severe’, or ‘very severe’. HAM-A test scores were founded by summing the coded values of all 14 items using the numeric coding scheme of 0 for “not present”, 1 for …. Are there any differenceds in means HAM-A test score among the three groups? 25mg SN-X95 100 mg SN-X95 Placebo HAM-A Score after 10-week treatment

20. Data

21. P-value

22. Mean and Error Bar Conclusion: There is significant difference in mean HAM-A among three treatment at 95% confidence level.

23. Categorical Variables Two or multiple treatment groups

24. Fisher’s Exact Test A conservative non-parametric test about a relationship between two categorical variables.

25. Case Study: CHF Incidence in CABG after ARA A new adenosine-releasing agent (ARA), thought to reduce side effects in patients undergoing coronary artery bypass surgery (CABG), was studied in a pilot trial. Fisher’s exact test: p=0.0455

26. Chi-square test Test a relationship between two categorical variables. The chi-square test assumes that the expected value for each cell is five or higher.

27. Case Study: ADR Frequency with Antibiotic Treatment A study was conducted to monitor the incidence of GI adverse drug reactions of a new antibiotic used in lower respiratory tract infections. Chi-square test: p=0.0252; Fisher’s exact test: p=0.0385

28. One-way repeated measures ANOVA Repeated measures logistic regression Factorial ANOVA Friedman test Factorial logistic regression Simple Linear Regression Multiple Regression Factor analysis Multiple logistic regression Discriminant analysis One-way MANOVA Multivariate multiple regression Canonical correlation Analysis of covariance Other tests

29. In summary… • Use and abuse of statistics • Five commonly used statistical testing • case studies • Results interpretation

30. Thank You For more information, visit my website http://www.childrensmercy.org/content/view.aspx?id=9740 Or go to Scope ->Research -> Statistics

31. References • Medical Statistics by Campbell et al. • Common Statistical Methods for Clinical Research by Walker