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Design and Analysis of Crossover Study Designs

Design and Analysis of Crossover Study Designs. Bhargava Kandala Department of Pharmaceutics College of Pharmacy , UF. 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.

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Design and Analysis of Crossover Study Designs

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  1. Design and Analysis of Crossover Study Designs Bhargava Kandala Department of Pharmaceutics College of Pharmacy , UF

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

  4. Model for Crossover Design

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

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

  7. Baseline

  8. Baseline - Covariate • Average baseline values not significantly different • Presence of significant carryover effects (p-value < 0.05)

  9. Carryover Effect

  10. * Covariates tested for carryover; procmixeddata = allperiodanaly; class sequence subject period trt; modelfenoav = sequence period fenobtrt carry1 carry2; random subject(sequence); lsmeanstrt/ pdiffcle; run;

  11. Results • β cannot be forced to be 1

  12. Results

  13. Results • Reduced impact of the baseline values while using ANCOVA can explain the absence of carryover effects

  14. Conclusions • Day 5 data suitable for analysis • 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

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