1. Biostatistics Case Studies 2006 Peter D. Christenson Biostatistician http://gcrc.LAbiomed.org/Biostat Session 6: Discrepancies as Predictors: Discrepancy of Opinion

2. Case Study HGI = Hemoglobin Glycation Index = Observed - Predicted (from Blood Glucose) HbA1c

3. Issues in this Paper Biological Variation (BV) Single quantity such as A1c Discrepancy from population mean ANOVA / Significance of discrepancy / Random Model Adjustment or stratification for another quantity such as blood glucose Similar points as for a single quantity Use of a BV measure as a predictor of other events Not independent of quantities from which it is created. Adjustment for other predictors Multicollinearity

4. Biological Variation - Concept Old, basic concept of true individual differences over measurement error or differences. I.e., separate biological from unexplained. Impossible to measure without replication for individuals or known reproducibility of measuring instrument. Also applies to aggregates of individuals, e.g., centers in a multicenter trial. Next slide --- graphical representation.

5. Biological Variation (BV) - Graphical Four subjects - A,B,C,D - with HbA1c in 3 experiments: A A A B B B C C C D D D A A A B B B C C C D DD HbA1c 1 Rep: ? BV 2 Reps: No BV 3 Reps: Strong BV V VsVs VrVr VrVr VsVs V r is replicate variance component. V s is “biological” component

6. Biological Variation - Methods of Detection ANOVA: Emphasis on specific individuals Find confidence interval for discrepancy of each individual from overall mean. Random models: Emphasis on population and its variance component V s Test whether V s significantly improves model fir to data. Looking ahead to current paper - both methods used; next slide.

7. Biological Variation - Detection in the Paper Random Model ANOVA- like

8. Relevant Data in Paper Publicly available data - not original investigators. DCCT: Diabetes Control and Complications Trial - NIH. N=1439 Type 1 diabetics. Followed up to 9 years. MBG=Mean Blood Glucose of a 1-day, 7-timepoint profile measured once every 3 months. 95% of pre and post-meal and 92% of bedtime values are available for MBG calculations. HbA1c measured at the same quarterly visits. Other relevant covariates: age, DM duration, etc. Case definitions for nephropathy using UAER, and for retinopathy using a validated retina score. Next slide --- first look at HGI from authors’ previous study.

9. Biological Variation: HbA1c Adjusted for Glucose Fig 1 ABCABC 3 Subjects: N=682 in 128 Subjects: HGI at 1 time. If BV exists, and this subject is consistent, he is a “high glycator” A=high, B=normal, C=low “glycator”. If most subjects are consistent, HGI has biologic variation

10. HGI Distribution: Previous Study Fig 4 Should happen; these are residuals.

11. Discussion in Previous Study

12. Current Study: HbA1c vs. MBG by HGI Tertiles HGI as before, except covariates also included in regression Evidence of BV in HGI:

13. Current Study: Risks by HGI Tertiles Are Risks Attributable to HGI? Fig 2

14. Letter from DCCT: Need to Adjust for HbA1c Next slides show this with the data.

15. MBG and HbA1c Patterns: Previous Study Fig 2 C = Low HGI HbA1c: ~6-12 B = Mod HGI HbA1c: ~10-15 A = High HGI HbA1c: ~10-15

16. Correlation of MBG and HbA1c : Previous Study

17. Current Study: HGI correlated with HbA1c

18. Author Reply: Ignore the Problem but not necessarily of HbA1c Reaching to the level of multicollinearity?

19. Author Reply: Conclusion

20. Conclusions HGI may be a useful measure. HGI is definitely correlated with HbA1c. Cannot attribute apparent HGI effect to discrepancy rather than HbA1c without adjustment or stratification on HbA1c. Multicollinearity may preclude adjustment. Recall table leg analogy for multicollinearity. Rule of thumb: HGI-HbA1c correlation > 0.90 indicates multicollinearity. Doubtful a problem according to graphs. “Surrogate” and “tautology” are too strong unless there is multicollinearity. Authors seem aware; don’t claim HGI better than HbA1c.