1. European Patients’ Academy on Therapeutic Innovation The Purpose and Fundamentals of Statistics in Clinical Trials

2. European Patients’ Academy on Therapeutic Innovation  Basics of hypothesis testing:  Null and alternative hypothesis  Sample size  Bias  Type I and Type II error  Significance  Power  Confidence intervals  Trial design types 2 The purpose and fundamentals of statistics

3. European Patients’ Academy on Therapeutic Innovation  A statistical hypothesis is an assumption about a population parameter (a measurable characteristic of a population).  Hypothesis testing is the evaluation done by a researcher in order to either confirm or disprove a hypothesis.  Hypothesis tests typically examine a random sample from the population. If sample data are not consistent with the statistical hypothesis, the hypothesis is rejected.  Samples should be representative of the population, however, hypothesis testing on samples can never guarantee a hypothesis completely - only say that it has a certain probability to be true or false. 3 What is hypothesis testing?

4. European Patients’ Academy on Therapeutic Innovation  Null hypothesis (H0) - formulated to capture our current situation. A null hypothesis in a clinical trial might be that the new medicine is no better than the current treatment.  Alternative hypothesis (H1) - formulated to capture what we want to show by doing the trial. An alternative hypothesis in a clinical trial might be that the new medicine is better than the current treatment. 4 Null and alternative hypothesis

5. European Patients’ Academy on Therapeutic Innovation Null hypothesis is true Null hypothesis is false Reject the null hypothesis Type I error False positive Correct outcome True positive Fail to reject the null hypothesis Correct outcome True negative Type II error False negative 5 Type I and Type II error

6. European Patients’ Academy on Therapeutic Innovation  Sample size is the total number of participants required for a trial. It is based on the principles of statistical hypothesis testing: 1.Magnitude of the effect expected 2.Variability in the variables being analysed 3.Desired probability 6 Sample size

7. European Patients’ Academy on Therapeutic Innovation  A randomly selected study sample may not be representative of the true population.  By using larger study samples the severity of sampling error can be reduced. 7 Sampling error

8. European Patients’ Academy on Therapeutic Innovation  Bias is the intentional or unintentional adjustment in the design and/or conduct of a clinical trial, and analysis and evaluation of the data that may affect the results.  An example of bias: when examining patients, a doctor looks more favourably towards patients receiving the actual medicine instead of the placebo. 8 Bias

9. European Patients’ Academy on Therapeutic Innovation  Significance level is the probability of committing a type I error.  Factors that affect significance level are:  The power of the test  Size of sample 9 Significance level

10. European Patients’ Academy on Therapeutic Innovation  The probability of not committing a type II error is called the 'power' of the hypothesis test.  Factors that can increase the power:  Increasing sample size  Higher significance level 10 Power

11. European Patients’ Academy on Therapeutic Innovation  The 'confidence interval' is used to express the degree of uncertainty associated with a sample statistic. 11 Confidence interval

12. European Patients’ Academy on Therapeutic Innovation  There are several types of statistical tests that can be used for hypothesis testing:  z-test: used to test hypothesis about a population mean when the population variance is known.  t-test: tells if there is a significant difference between two sets of data.  Chi-squared test: used to determine if two variables are related. 12 Common hypothesis tests