Purpose of statistics

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.
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Common hypothesis tests