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Biostatistics Case Studies 2005

Biostatistics Case Studies 2005. Session 3: Crossover Designs. Peter D. Christenson Biostatistician http://gcrc.humc.edu/Biostat. Goals. Compare crossover vs. parallel designs. Differentiate effects in crossover studies: Treatment Time or attention Period or order

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Biostatistics Case Studies 2005

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  1. Biostatistics Case Studies 2005 Session 3: Crossover Designs Peter D. Christenson Biostatistician http://gcrc.humc.edu/Biostat

  2. Goals • Compare crossover vs. parallel designs. • Differentiate effects in crossover studies: • Treatment • Time or attention • Period or order • Carryover from previous period.

  3. Case Study 6 weeks 1-2 weeks 6 weeks Design: N=23: DHEA Placebo N=23: Placebo DHEA Week 0 Week 6 Week 8 Week 14 = Outcome (Depression Scales) Measured

  4. Crossover vs. Parallel Designs • Parallel design would be just the first period (0-6 weeks) on the previous slide. • Crossover advantage: subjects are their own controls. • Typically, fewer subjects are needed in crossover than parallel for the same power. • Myth: Subjects who do not complete all treatments are excluded from analysis. [They can be included provided they missed different treatments.] • This study wanted a group of responders, defined as responding to DHEA, but not placebo, so crossover was needed.

  5. Case Study Strategy for DHEA Effect: Step 1 Compare Baseline, Post-DHEA, Post-Placebo in ANOVA: P=NS P<0.01 P<0.01 Note: This does not compare DHEA and Placebo, accounting for differences in baseline values, e.g., post-pre changes.

  6. Case Study Strategy for DHEA Effect: Step 2 Compare week 0 vs. week 8 (pre-Period1 vs. pre- Period2) combined over DHEA and Placebo: 13.5 P<0.001 10.5 HDRS-17 0 8 Week Conclude: Pre-period 2 mean is lower than pre-Period 1 (baseline) mean, so Period 2 data is suspect (why?). Thus, secondary analysis uses only Period 1 as a parallel study – next slide.

  7. Case Study Strategy for DHEA Effect: Step 3 Compare DHEA and Placebo in Period 1: 13.5 11.8 13.3 Placebo p=NS DHEA p<0.01 HDRS-17 7.5 0 6 Week Conclude: Even this less sensitive comparison (N=23+23, not paired) shows DHEA effect at 6 weeks. Adjusting for baseline gives 13.5-11.8=1.7 vs. 5.8=13.3-7.5, also p<0.01.

  8. Conclusions on Case Study Strategy • Fortunate that DHEA effect was strong enough to detect it in the period 1, parallel part, only. • Neither of the analyses compared DHEA vs. placebo effects, i.e., changes from pre-values, or adjusted for pre-values (weeks 0 and 8). The week 0 means are similar for the two treatment groups, but no data is reported on DHEA and placebo separately at week 8, although the combined mean hints that changes in period 2 differed from those in period 1. See next slide.

  9. Hamilton Depression Results at Each Time 13.5 11.8 ? 11.2 13.3 Placebo HDRS-17 10.5 ? DHEA 7.5 7.5 0 6 8 14 Week Adjusting for pre-values: Parallel: 13.5-11.8 = 1.7 vs. 5.8 = 13.3-7.5. Crossover: mean of (13.5-11.8) and (?-11.2) vs. mean of (13.3- 7.5) and (? - 7.5). Depression may be reduced enough by week 8 (both groups?) so that the drug has little effect then. The parallel analysis is then preferable.

  10. Carryover vs. Time Effects • A carryover effect occurs if the treatment effect in a period depends on the treatment in the previous period. • Week 0 vs. week 8 differences do measure effects over time – residual or natural course or due to attention given to subjects, but it is not the typical meaning of carryover, as defined above. • Standard crossover analyses can measure period and (with >2 treatments) carryover, and adjust treatment effects for them and pre-values. We now look at examples.

  11. Consider a 3x3 (Treatment x Period) study with 1 subject in each of the 6 ordered patterns of treatments A,B, and C. Values are made-up post-pre changes in each period. Example: 3 Treatments

  12. The carryover effect is measured as: Example: 3 Treatments (cont’d) * Note that carryover exists only if these differ. Differences need to be adjusted for treatment, e.g., 5&6 vs. 9&5 for A vs. B.

  13. Example: 2 Treatments Consider a 2x2 (Treatment x Period) study with 2 subjects in each of the 4 ordered patterns of treatments A and B. Values are made up post-pre changes in each period.

  14. Example: 2 Treatments The carryover effect cannot be measured: * The 6-3.5 difference is completely confounded with treatment effect, since, e.g., 6 measures both the effect of B (7 and 5 are both under B) and the effect of previous A.

  15. Conclusions General: • Can measure time effects, but not true carryover in 2 treatment-2 period studies. • Carryover can be measured with >2 treatments. Adjusting for it is possible, but can be “model-dependent”, e.g., can give different treatment effects depending on how correlations over time are estimated. This study: • A time effect is seen in this study, but it is unknown if there is any carryover effect. • Fortunate that 2 analyses agree. • Would have been more sensitive if corrected for pre-treatment levels, either as a covariate, or using post-pre changes.

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