1. Design and Analysis of Crossover Study Designs
Bhargava Kandala
Department of Pharmaceutics
College of Pharmacy , UF
Bioequivalence (BE) refers to the absence of a significant difference in the rate and extent at which the drug becomes available at the site of action i.e. how much drug reaches the site of action and at what rate. Traditionally, pharmacokinetic studies have been the method of choice to carry out bioequivalence testing of a test and a reference product for systemically acting drugs. Where in the test and the reference are administered to the subjects and the amount of drug in their blood samples collected at specific time points are calculated. Metrics such as area under the curve and the maximum concentration achieved are used to test bioequivalence. The same approach is not used to establish bioequivalence of locally (lung) acting drugs such as Inhaled Corticosteroids (ICS). The reason behind it becomes clear when we look at the fate of an inhaled drug.

2. Crossover Study
Treatments administered in a sequence to each experimental unit over a set of time periods.
Comparison of treatments on a within-subject level.
Increased precision of treatment comparisons.
A treatment given in one period might influence the response in the following treatment period – residual/carryover effect
Baseline values – Can be included as covariates to increase the precision

3. Study Design
Single center, double blind, randomized, 3 period, 3 treatment, 3 sequence crossover study
Randomization
Low
Medium
High
Washout
Subjects = 10
Baseline 1
Period 1 (q.d.)
Period 2 (q.d.)
Period 3 (q.d.)
Baseline 2
Baseline 3
PD Measurements
5 days
1 Week
5 days
1 Week
5 days

4. Model for Crossover Design
Period 1 2 3 4 5 6 I A B C II
III

5. proc glm data = allperiodanaly;
class sequence subject period trt;
model fenoav = sequence subject(sequence) period trt/solution;
random subject(sequence);
run;

6. proc mixed data = allperiodanaly;
class sequence subject period trt;
model fenoav = sequence period trt;
random subject(sequence);
lsmeans trt/ pdiff cl;
run;

7. Baseline

8. Analysis of Covariance (ANCOVA)
Baseline - Covariate
Average baseline values not significantly different
Presence of significant carryover effects (p-value < 0.05)
No Covariate
Analysis of Covariance (ANCOVA)
Baseline – Treatment
β = 0
β = Model Estimate
β =1
Baseline is not used as a covariate
Baseline values are treated as a quantitative variable
By taking the simple difference the value of β is forced to be 1

9. Carryover Effect

10. * Covariates tested for carryover;
proc mixed data = allperiodanaly;
class sequence subject period trt;
model fenoav = sequence period fenob trt carry1 carry2;
random subject(sequence);
lsmeans trt/ pdiff cl e;
run;

11. Analysis of Covariance (ANCOVA)
Results
β cannot be forced to be 1
Parameter
No Covariate
Analysis of Covariance (ANCOVA)
Baseline – Treatment β
0.38 1
Residual Variability
85.39
67.02
180.02
Carryover Effect
Not significant
(p-value >0.05)
Not Significant
(p-value>0.05)
Significant

12. Analysis of Covariance (ANCOVA)
Results
Parameter
No Covariate
Analysis of Covariance (ANCOVA)
Baseline – Treatment β
0.38 1
Residual Variability
85.39
67.02
180.02
Carryover Effect
Not significant
(p-value >0.05)
Not Significant
(p-value>0.05)
Significant

13. Analysis of Covariance (ANCOVA)
Results
Reduced impact of the baseline values while using ANCOVA can explain the absence of carryover effects
Parameter
No Covariate
Analysis of Covariance (ANCOVA)
Baseline – Treatment β
0.38 1
Residual Variability
85.39
67.02
180.02
Carryover Effect
Not significant
(p-value >0.05)
Not Significant
(p-value>0.05)
Significant

14. Conclusions Day 5 data suitable for analysis Baseline adjustment
Maximum dose resolution
No carryover effect
Baseline adjustment
Simple difference increases the variability and introduces carryover effects
ANCOVA is the preferred method
Crossover design model with baseline values as covariates will be used for future simulations