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1. Header Subhead Biostatistics and Epidemiology Lillian Sung, MD PhD

2. Disclosure Information • Lillian Sung – No disclosures

3. Outline • Principles of Use of Biostatistics in Research • Principles of Epidemiology and Clinical Research Design • Applying Research to Clinical Practice

4. Principles of Use of Biostatistics in Research

5. Principles of Use of Biostatistics in Research • 1. Types of variables • Distinguish types of variables (eg, continuous, categorical, ordinal, nominal) (slide 7) • Understand how the type of variable (eg, continuous, categorical, nominal) affects the choice of statistical test (slides 7, 12-16) • 2. Distribution of Data • Understand how distribution of data affects the choice of statistical test (slides 8, 12, 13) • Differentiate normal from skewed distribution of data (slide 8) • Understand the appropriate use of the mean, median, and mode (slide 8) • Understand the appropriate use of standard deviation (slide 9) • Understand the appropriate use of standard error (slide 9) • 3. Hypothesis testing • Distinguish the null hypothesis from an alternative hypothesis (slides 10, 11) • Interpret the results of hypothesis testing (slides 10, 11, 18) • 4. Statistical tests • Understand the appropriate use of the chi-square test versus a t-test (slides 12-16) • Understand the appropriate use of analysis of variance (ANOVA) (slides 12, 13) • Understand the appropriate use of parametric (eg, t-test, ANOVA) versus non-parametric (eg, Mann-Whitney U, Wilcoxon) statistical tests (slides 12, 13) • Interpret the results of chi-square tests (slides 15, 16) • Interpret the results of t-tests (slide 14) • Understand the appropriate use of a paired and non-paired t-test (slides 14, 17) • Determine the appropriate use of a 1- versus 2-tailed test of significance (slides 19, 20) • Interpret a p-value (slides 18-21) • Interpret a p-value when multiple comparisons have been made (slide 21) • Interpret a confidence interval (slide 22) • Identify a type I error (slide 23) • Identify a type II error (slide 23)

6. 5. Measurement of association Differentiate relative risk reduction from absolute risk reduction (slide 24) Calculate and interpret a relative risk (slide 25) Calculate and interpret an odds ratio (slide 25) Interpret a hazard ratio (slide 25) Understand the uses and limitations of a correlation coefficient (slide 26) 6. Regression Identify when to apply regression analysis (eg, linear, logistic) (slide 27) Interpret a regression analysis (eg, linear, logistic) (slide 27) Identify when to apply survival analysis (eg, Kaplan-Meier) (slides 28-30) Interpret a survival analysis (eg, Kaplan-Meier) (slides 28-30) 7. Diagnostic tests Recognize the importance of an independent "gold standard" in evaluating a diagnostic test (slides 31, 32) Calculate and interpret sensitivity and specificity (slides 31, 32) Calculate and interpret positive and negative predictive values (slide 31) Understand how disease prevalence affects the positive and negative predictive value of a test (slide 32) Calculate and interpret likelihood ratios (slide 33) Interpret a receiver operator characteristic curve (slide 34) Interpret and apply a clinical prediction rule (slide 34) 8. Systematic reviews and meta-analysis Understand the purpose of a systematic review (slide 35) Understand the advantages of adding a meta-analysis to a systematic review (slide 35) Interpret the results of a meta-analysis (slide 35) Identify the limitations of a systematic review (slide 35) Identify the limitations of a meta-analysis (slide 35)

7. Types of Variables Nature of outcome variable (dichotomous, categorical, ordinal, survival) drives the choice of statistical tests

8. Distribution of DataCentral Tendency Skewed Normal Mode Median Mean=Median=Mode Mean Mean – average value (use if normal) Median – middle value (use if skewed) Mode – most common value Distribution drives the choice of statistical tests

9. Standard Deviation and Standard Error • Standard deviation – average spread from the mean • Wider the spread, the larger the SD • Standard error – SD/sqrt(n)

10. Hypothesis Testing • Define null hypothesis (treatment does not work) • Alternate hypothesis – treatment works • Determine probability of observing data as or more extreme assuming the treatment does not work • If this probability is sufficiently small – reject null hypothesis

11. Rejection of Null Hypothesis Z=+1.96 Z=-1.96 Rejection of the null hypothesis

12. Statistical Tests Nature of outcome variable (dichotomous, categorical, ordinal, survival) drives the choice of statistical tests Distribution drives the choice of statistical tests

13. Outcome is Continuous

14. Student’s T-test Two groups with a continuous outcome measure t = mean(gp1) – mean(gp2) Variance Larger t ~ smaller p value Assumptions: Data normally distributed Observations independent If data are matched (eg blood pressure before and after) should use paired T-test

15. Outcome is Dichotomous

16. Chi Square Test Compares proportions in 2 or more groups: Calculate expected values for each cell X2 =∑ (O-E)2/E Larger X2~ smaller p value

17. Matched/Paired VersusIndependent Are the exposure groups independent of one another? Ways to induce matching: Compare outcome within an individual Eg. Pre-post intervention, cross-over trial Can create match by how you select subjects Eg. In case-control study, can match cases and controls

18. P Values • P value: probability of obtaining a test statistic at least as extreme as the one actually observed assuming the null hypothesis is true • Translation: Chance of getting the results you saw assuming that the treatment doesn’t work • P=0.05: 5% chance of seeing data as extreme assuming null hypothesis • Translation: Assuming that the treatment doesn’t work, there is a 5% chance of observing a difference by chance alone

19. Two vs One Sided P Value Two-sided P value will evaluate both that the treatment is better and that the treatment is worse than control Typically use two-sided P values Z=-1.96 Z=+1.96 2.5% 2.5% Rejection of the null hypothesis Two-sided P value

20. Two vs One Sided P Value One-sided P value will only evaluate that the treatment is better than control Easier to show “statistical significance” Less commonly used Z=+1.645 5% Rejection of the null hypothesis One-sided P value

21. Multiple testing: if you do many tests, increase the chance of finding P < 0.05 just by chance alone, therefore need to adjust P value for multiple comparisons

22. Confidence Intervals Confidence interval: probability that the interval contains the true parameter For example, 95% CI around the mean – 95% probability that the interval contains the true mean

23. Type I and Type II Errors

24. Measures of Association Risk of outcome in treatment group = 0.10 (A/A+B) Risk of outcome in control group = 0.40 (C/(C+D) • Absolute risk reduction: decrease in risk of an outcome associated with an intervention ARR = C/(C+D)- A/(A+B) = 0.40 – 0.10 = 0.30 • Relative risk reduction: absolute risk reduction divided by event rate in the control arm RRR = 0.30/0.40=0.75 • Number needed to treat = 1/ARR = 1/0.30 = 3.3

25. Odds Ratio and Relative Risk • Risk in treatment = A/(A+B); Risk in control = C/(C+D) Relative risk = A/(A+B) C/(C+D) RR 2.5 = 2.5 times risk of outcome if treated • Odds in treatment = A/B; Odds in control = C/D Odds ratio = A/B = AD/BC C/D OR 2.5 = 2.5 times odds of outcome if treated • Hazard ratio: analogous to a relative risk used in survival analysis

26. Correlation Coefficient • Strength of the linear relationship between two numbers • - 1 ≤ r ≤ +1 r = -1.0 r = 1.0 • Measure of correlation • Not measure of concordance

27. Regression Used to define relationships or to predict an outcome based on one or more exposure variables • Univariate: single exposure variable, single outcome • Multivariable: multiple exposure variables, single outcome Type of regression depends on nature of outcome variable • Dichotomous – logistic regression • Continuous – linear regression • Survival – Cox proportional hazards model

28. Survival Analysis • Outcome is time to event • Censor patients who don’t have an event when last observed • Most data is right censored censored START STUDY STOP STUDY

29. Kaplan-Meier Method • Example of how to display survival data • Calculates survival probability whenever an event occurs Survival Months • Use to describe survival at a given time eg survival at 30 months is 40%

30. When to Use Survival Analysis • Time to event data • Each individual - different length of follow-up • Patients may be lost to follow-up • Patients may be censored

31. Diagnostic Tests Need gold standard • Sensitivity = A/(A+C) – proportion of those with the disease who have a positive test • Specificity – D/(B+D) – proportion of those without the disease who have a negative test • Positive predictive value = A/(A+B) – proportion of those who test positive who have the disease • Negative predictive value = D/(C+D) – proportion of those who test negative who do not have the disease

32. Influence of Prevalence on Diagnostic Tests • Because (A+C) is prevalence of disease: • Prevalence influences PPV and NPV • Does not influence sensitivity and specificity

33. Likelihood Ratios • LR+: How much the odds of a disease increase when the test is positive = sensitivity/(1-specificity) • LR-: How much the odds of a disease decrease when the test is negative = (1-sensitivity)/specificity

34. Other Diagnostic Test Issue • Receiver operator curve: to evaluate the optimal threshold for a diagnostic test • Clinical prediction rule: using signs, symptoms and tests to predict a clinical outcome Sensitivity (1-Specificity)

35. Systematic Reviews • Identify studies that address a similar question – and synthesize the data either qualitatively or quantitatively • Quantitative review – meta-analysis • Limitations • Heterogeneity in treatment effect – may not be appropriate to combine • Publication bias

36. Principles of Epidemiology and Clinical Research Design and Applying Research to Clinical Practice

37. B. Principles of Epidemiology and Clinical Research Design 1. Study types Distinguish between Phase I, II, III, and IV clinical trials (slide 39) Recognize a retrospective study (slide 40) Understand the strengths and limitations of retrospective studies (slide 40) Recognize a case series (slide 41) Understand the strengths and limitations of case series (slide 41) Recognize a cross-sectional study (slide 42) Understand the strengths and limitations of cross-sectional studies (slide 42) Recognize a case-control study (slide 43) Understand the strengths and limitations of case-control studies (slide 44) Recognize a longitudinal study (slide 48) Understand the strengths and limitations of longitudinal studies (slide 48) Recognize a cohort study (slide 45) Understand the strengths and limitations of cohort studies (slide 46) Recognize a randomized-controlled study (slide 47) Understand the strengths and limitations of randomized-controlled studies (slide 47) Recognize a before-after study (slide 48) Understand the strengths and limitations of before-after studies (slide 48) Recognize a crossover study (slide 48) Understand the strengths and limitations of crossover studies (slide 48) Recognize an open-label study (slide 49) Understand the strengths and limitations of open-label studies (slide 49) Recognize a post-hoc analysis (slide 49) Understand the strengths and limitations of post-hoc analyses (slide 49) Recognize a subgroup analysis (slide 49) Understand the strengths and limitations of subgroup analyses (slide 49) 2. Bias and Confounding Understand how bias affects the validity of results (slide 50) Understand how confounding affects the validity of results (slide 50) Identify common strategies in study design to avoid or reduce bias (slide 51) Identify common strategies in study design to avoid or reduce confounding (slide 51) Understand how study results may differ between distinct sub-populations (effect modification) (slide 52)

38. 3. Causation Understand the difference between association and causation (slide 53) Identify factors that strengthen causal inference in observational studies (eg, temporal sequence, dose response, repetition in a different population, consistency with other studies, biologic plausibility) (slide 54) 4. Incidence and Prevalence Distinguish disease incidence from disease prevalence (slide 55) 5. Screening Understand factors that affect the rationale for screening for a condition or disease (eg, prevalence, test accuracy, risk-benefit, disease burden, presence of a presymptomatic state) (slide 56) 6. Decision analysis Understand the strengths and limitations of decision analyses (slide 57) Interpret a decision analysis 7. Cost-benefit, cost-effectiveness, and outcomes Differentiate cost-benefit from cost-effectiveness analysis (slide 58) Understand how quality-adjusted life years are used in cost analyses (slide 58) Understand the multiple perspectives (eg, of an individual, payor, society) that influence interpretation of cost-benefit and cost-effectiveness analyses (slide 58) 8. Sensitivity analysis Understand the strengths and limitations of sensitivity analysis (slide 59) Interpret the results of sensitivity analysis (slide 59) 9. Measurement Understand the types of validity that relate to measurement (eg, face, construct, criterion, predictive, content) (slide 61) Distinguish validity from reliability (slides 60, 61) Distinguish internal from external validity (slide 61) Distinguish accuracy from precision (slide 60) Understand and interpret measurements of interobserver reliability (eg, kappa) (slide 60) Understand and interpret Cronbach's alpha (slide 60)

39. Study TypesPhases of Drug Studies

40. Retrospective Study • Exposures and outcomes have already occurred • Strengths: • Feasible and inexpensive • Limitations: • Limited availability of confounders • No control over when or how exposure or outcome measured • Recall bias

41. Case Series • Describing similar cases, treatments or outcomes • Strengths: • Feasible and inexpensive • Limitations: • Cannot test hypotheses • Selection bias

42. Cross-Sectional Study • All measures obtained on a single occasion • Strengths: • Fast/inexpensive • No lost to follow-up • Limitations: • Difficult to establish causal relationships • Can measure prevalence – not incidence

43. Case-Control Studies • Identify those with and without outcome • Look BACK to see how many had potential predictor CASES Predictor Present/Absent? CONTROLS

44. Case-Control Studies • Strengths: • Good for rare outcomes or long latency between predictor and outcome • Limitations: • Cannot estimate incidence or prevalence of disease • Can only study one outcome • Prone to bias: • Sampling – selection of controls is critical • Recall bias

45. Cohort Studies • Identify those with and without potential predictor • Look FORWARD to see how many have outcome • Can be prospective or retrospective PRED YES Outcome Yes/No? PRED NO

46. Cohort Studies • Strengths: • Time sequence strengthens inference • Absence of recall bias • Can calculate incidence • Limitations: • Expensive

47. Randomization: Ensures that known and unknown potential confounders are equally distributed among the treatment and control groups Avoid allocation bias Strengths: Strongest design to make inferences about therapy Limits influence of confounders, allocation bias Limitations: Expensive Usually lack generalizability Randomized Controlled Trials

48. Other Study Types • Longitudinal study: track same individuals over a period of time and repeat measurements • Strengths: natural history • Limitations: hard to determine causation, lost to follow-up • Before and after study: evaluate an outcome before and following institution of an intervention • Strengths: feasible • Limitations: confounders, regression to the mean • Crossover study: evaluate an outcome in two time periods within the same individual – typically randomize order • Strengths: reduces variability, improves power • Limitations: need chronic stable conditions, short onset of action, condition cannot be “cured” by intervention

49. Other Study Types • Open label study: not blinded • Strengths: feasible, ethics • Limitations: co-interventions (may treat groups differently), contamination, observer bias • Post-hoc analysis: examining data after a study has been completed for relationships not hypothesized a priori • Limitations: multiple testing • Sub-group analysis: examining patterns in a sub-group of patients • Strengths: may be sub-groups of patients who respond differently to treatment • Limitations: multiple testing, limited power

50. Bias and Confounding • Bias: systematic error • Selection bias • Measurement bias • Confounder: third variable that is associated with exposure and outcome variables and not in the causal pathway Both bias and confounders are major threats to validity of any study