crossover design is a repeated/longitudinal measurements design. | Patients (experimental units) cross over from one treatment to another during the trial course. | In contrast to a parallel design where patients are randomized to a treatment and remain on that treatment throughout the trial duration

1. Overview on crossover trials
Statistical illustration “SPSS”
Continues outcome
Nouran Hamza
Independent Biostatistics Consultant

2. • Crossover study design
• Study participant receives all treatments that are being
investigated, but at different times.
• With this type of study, every patient acts as his or her own control.

3. crossover design
• is a repeated/longitudinal measurements design.
• Patients (experimental units) cross over from one
treatment to another during the trial course.
• In contrast to a parallel design where patients are
randomized to a treatment and remain on that
treatment throughout the trial duration

4. Statistical analysis for continues outcome

5. Illustration using SPSS
First: Code the variables as following
Drug group
variable:
Nominal
variable
(e.g.: A and
B groups)
Treatment outcome variable:
Scale variable
to be reported as numbers and
decimals

6. Second: Test the normality of the variables
Kolmogorov-Smirnov
test
• Click on “Analyze” > “ Descriptive Statistics “ >
“ Explore “

10. • Kolmogorov test of significance should be
more than 0.05 to assume that the data is
normally distributed
• Here it is more than 0.05 (0.200) so it is
normally distributed data
• We shall perform t test in this case
• But >>>>>>

11. • If Kromogrov test of significant is less than
0.05 so it is skewed data
• In this case, Wilcoxon rank-sum test (Mann-
Whitney-Wilcoxon rank-sum test) is the test of
choice.

12. Mann-Whitney-Wilcoxon rank-sum
test on SPSS
• Click on “Analyze” > “Non parametric” >
“legacy Dialogs” > “ 2 independent samples “

14. Then click on define group

15. Finally: click “continue” > “ok”

16. Please don’t hesitate to