Presenter: Rui Tang Advisor: Dr. Mari Palta Department of Statistics,

1. Shapiro RA Presentation "Trends During Adolescence as Predictors of Later Risk Factor and Complications in Type 1 Diabetes" Presenter: Rui Tang Advisor: Dr. Mari Palta Department of Statistics, University of Wisconsin-Madison Dec., 15, 2006

2. Background Information • Type 1 diabetes: Diabetes means your blood glucose (often called blood sugar) is too high. Formerly called juvenile diabetes or insulin-dependent diabetes, Type 1 diabetes is usually first diagnosed in children, teenagers, or young adults. With this form of diabetes, the beta cells of the pancreas no longer make insulin because the body’s immune system has attacked and destroyed them. • Complication and Risk Factor: After many years, diabetes can lead to serious problems with your eyes (Retinopathy is a complication of Type 1 Diabetes), kidneys, nerves, and gums and teeth. But the most serious problem caused by diabetes is heart disease. Large waist circumference is a risk factor for heart disease.

3. Project Objective • Whole Project:Wisconsin Diabetes Registry Study Followed individuals in Southern Wisconsin from diagnosis with Type 1 diabetes during 1987- 1992. • My Work Part:(a small piece) • What kind of trends during adolescence are significant for predicting the risk factor, i.e. waist, in the future? • What factors are relevant to the chance for a Type 1 Diabetes patient getting retinopathy in the future?

4. Population • The subjects included in this analysis are those who were diagnosed before age 14 and had a follow-up as adults (after age 20) • Participants lived within a geographically defined area of central and southern Wisconsin (28 counties) that comprised 2/3 of the state’s population according to 1990 census.

5. Data and Specimen Collection Data were collected by mailed questionnaires, blood tests on mailed in kits and clinical examinations • Diabetes Management and Demographic Data: Insulin Dose; Numbers of Daily Injections and Blood Glucose Checks; Mother’s Education and parental occupation; • Ghb Testing: Ghb Levels; • Fundus Photographs: Anthropometric Data, including height, weight, waist and hip circum; Blood Pressures; Retinopathy Status.

6. Variables Description • Current Variable: • Body Size variables, e.g., bmi, waist, height_cm; • Health Condition variable, e.g., retinopathy; • Time Variables, e.g., age_exam, dur_retino. • Past Variable: • Mean Variables, e.g, bmim1, ghgbm2, etc; ( means across ages 10-14 and 15-19) • Trend Variables, e.g, insulins1, ghgbs2, etc. ( regression slopes across ages 10-14 and 15-19)

7. Trends Variables Figure 1: Developing Trends of Ghb

8. Statistical Issues • Missing Data: • only those with exam data were included- additional data can be missing because people did not return questionnaires or blood kits. • Trends can be missing if there are less than 2 observations in the age interval for a person. • Model Selection

9. Model Selection With “Waist”-Outline • Stepwise Model Selection with Non-imputed Dataset • Multiple Imputation (MI) • Stepwise Model Selection with MI Datasets • Recognize the “Most Frequent Significant Variables (MFSV)” • Regress on “MFSV” with Imputed Dataset and Combine • Compare the two models

10. Model Selection With “Waist”-(1) Table 1: Summary for Model with Non-imputed Data

11. Model Selection With “Waist”-(2) Table 2: Frequency of “MFSV” for 20 Imputed Dataset Table 3: Summary of “MFSV” for 20 Imputed Dataset Sample Size=177

12. Model Selection With “Waist”-(3) Table 4:MIANALYZE Estimation Table 5: Non-Imputed Estimation Sample Size=96 Sample Size=177

13. Model Selection With “Waist”-(4) Table 6: Final Selected Model Estimation*** ***It is a little different from the model stepwise-selected with non-imputed data because there is missing data in the variables that have not been selected into the model, so the n is different.

14. Diagnostic plots for “Waist” Model Figure 2: Residual vs. Predicted Figure 3: Cook Distance vs. Obs Figure 4: Rstudent vs. Obs Figure 5: Normal PP Plot

15. Diagnostic plots for “Waist” Model (Deleted) Figure 6: Residual vs. Predicted Figure 7: Cook Distance vs. Obs Figure 8: Rstudent vs. Obs Figure 9: Normal PP Plot

16. “Waist” Model Summary & Interpretation ADJRSQ: 0.57 Table 7: Model (Deleted) Summary • Interpretation: New interesting findings for future exploration • Ghgbs1: When the blood sugar rises, it means the person is not metabolizing the sugar, and will tend to lose weight- will affect waist too • Insulin_dosem1: Needs further investigation. Higher insulin dose stimulated growth hormone. Growth hormone has both favorable and unfavorable effects.

17. Two Sample t-test for the Means of dur_retino within retino 14:02 Wednesday, December 7, 2006 Sample Statistics Group N Mean Std. Dev. Std. Error -------------------------------------------------------------- 0 45 9.582904 2.302 0.3432 1 76 11.54224 2.196 0.2519 Hypothesis Test Null hypothesis: Mean 1 - Mean 2 = 0 Alternative: Mean 1 - Mean 2 ^= 0 If Variances Are t statistic Df Pr > t ---------------------------------------------------------- Equal -4.659 119 <.0001 Not Equal -4.603 89.03 <.0001 Two Sample t-test for the Means of ghgbm2 within retino 14:02 Wednesday, December 7, 2006 Sample Statistics Group N Mean Std. Dev. Std. Error -------------------------------------------------------------- 0 44 9.79885 1.8654 0.2812 1 76 11.85487 2.6788 0.3073 Hypothesis Test Null hypothesis: Mean 1 - Mean 2 = 0 Alternative: Mean 1 - Mean 2 ^= 0 If Variances Are t statistic Df Pr > t ------------------------------------------------------------ Equal -4.495 118 <.0001 Not Equal -4.936 113.89 <.0001 First Impression with “Retino” Table 8: T-Test for dur_retino Table 9: T-Test for ghgbm2

18. Logistic Model with “Retinopathy”(1) Table 9: Summary of “MFSV” for 20 Imputed Dataset Table 10: Summary of the Logistic Regression Estimate Sample Size=110

19. Logistic Model with “Retinopathy”(2) Table 11: Predicting Ability for Fitted Model Figure 10: ROC Plot for Fitted Model

20. “Retinopathy” Model Interpretation • Dur_retino (=0.3818): risk of complications always goes up the longer a person has diabetes; • Insulin slope (=4.2087): could be a marker for poor control of blood sugar, or could cause rise on growth hormone- a risk factor; • Ghgbm2 (=0.3857): high sugar is also a main risk factor for diabetes complications.

21. Discussion • MAR Assumption; • Measurement Error Problem.

22. Thank you! Q&A