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Crossover Trials

Crossover Trials. Useful when runs are blocked by human subjects or large animals To increase precision of treatment comparisons all treatments are administered to each subject or animal in a sequence Primary purpose is to compare effect of treatments

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Crossover Trials

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  1. Crossover Trials • Useful when runs are blocked by human subjects or large animals • To increase precision of treatment comparisons all treatments are administered to each subject or animal in a sequence • Primary purpose is to compare effect of treatments • Secondary purpose is to protect against bias from carryover effects and to estimate carryover effects • Used extensively in pharmaceutical research, sensory evaluation of food products, animal feeding trials and psychological research

  2. Crossover Designs COD • Useful for comparing a limited number of treatments (from 2 to 6) • Usually not used for factorial designs (other than simple 22 factorial) • Since treatments are applied sequentially in time, COD are only useful for comparing temporary treatments of chronic conditions • Special designs and models for testing and estimating carryover effects are required

  3. Compartment Model

  4. Treatment effect is confounded with the carryover effect

  5. · Design choice dependent on assumptions · Assumption of first-order carryover effects · Variance balance as a design criteria - variance (or standard error of difference in direct treatment means or carryover means) is the same regardless of the pair of treatments - this balance is achieved if every treatment is preceded by every treatment

  6. A is preceded by B in second group, but never by A B is preceded by A in first group, but never by B A is preceded by B in second group second period and by A in third period B is preceded by A in first group second period and by B in third period

  7. Designs for t treatments and p periods where p = t · If no carryover effects are assumed a balanced design for direct treatment effects can be created using any tt Latin square · If carryover effects are assumed Williams showed a balanced design for direct and carryover treatment effects can be created using one particular tt Latin square if t is an even number, and two particular Latin squares if t is an odd number

  8. 1 2 3 2 3 1 3 1 2 1 3 2 1 1 3 2 2 1 3 2 3 1 3 2 2 3 1 2 1 3 3 1 2 3 2 1 1 2 3

  9. 1 2 3 4 2 3 4 1 3 4 1 2 4 1 2 3 1 4 3 2 1 1 4 3 2 2 1 4 3 3 2 1 4 2 1 4 2 3 2 1 3 4 3 2 4 1 4 3 1 2 3 2 4 1 4 3 1 2 1 4 2 3 2 1 3 4 3

  10. Jonathan Chipman 2006 Blocks: Subjects Factor: Running Surface - BYU rubberized track - grass - asphalt Response: Time to sprint 40 yards Latin square column factor: Trial (to account for exhaustion effect) Willams’ design used to account for carryover effects

  11. procglm; class subject period treat carry; model y=subject period treat carry; means treat; lsmeans treat; run;

  12. Nonorthogonality of direct and treatment effects The GLM Procedure Least Squares Means treat y LSMEAN asphalt Non-est grass Non-est track Non-est

  13. procglm; class subject period treat carry; model y=subject period carry treat ; run; Source DF Type I SS Mean Square F Value Pr > F subject 1124.208388892.2007626385.85 <.0001 period 23.206505561.6032527862.54 <.0001 carry 20.021681110.010840560.420.6615 treat 20.639163330.3195816712.470.0004 Source DF Type III SS Mean Square F Value Pr > F subject 1124.254710002.2049736486.01 <.0001 period 10.001837500.001837500.070.7920 carry 20.233205560.116602784.550.0252 treat 20.639163330.3195816712.470.0004

  14. Solution Lucas - Add an extra period • Group • 1 1 3 2 • 2 1 3 • 3 2 1 • 4 2 3 1 • 5 3 1 2 • 6 1 2 3 • Group • 1 1 3 2 2 • 2 1 3 3 • 3 2 1 1 • 4 2 3 1 1 • 5 3 1 2 2 • 6 1 2 3 3

  15. The objective is to compare the trend over time in the response between treatment groups.

  16. Diet fixed effect week fixed effect Cow random effect

  17. Usual Assumptions of Univariate Model • Experimental Error is independent, has equal variance across treatment×time combinations, and is normally distributed with mean zero • Similar assumptions for random effects • Independence assumption is justified by randomization • In Split-Plot Type Experiments, sub-plots are not independent because they are measured within the same whole-plot. • Randomization of subplot treatments to subplots equalizes the correlation between all possible pairs of subplots – this creates a condition called compound symmetry, which justifies the normal univariate analysis

  18. Usual Assumptions of Univariate Model • In repeated measures designs, you can’t randomize levels of time within a subject or cow! • Huyuh and Feldt (1970) showed that if σ2(yi-yj) = 2λ for i ≠ j (Huyuh-Feldt condition) then univariate analysis is justified. • The Mauchly (1940) sphericity test can be used to determine if the Huyuh-Feldt condition holds. This can be performed by proc glm

  19. Subject time 1 time 2 time 3 time 4 time 5 summary 1 y11y12y13y14y15 f(y11, …,y15) 2 y21y22y23y24y25 f(y21, …,y25) · · · · · · · · · · · · · · · · · · · · · nyn1yn2yn3yn4yn5 f(yn1, …,yn5) Summarizing with function over time removes correlation “Growth Curve Approach”

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