1 / 28

Identifying Predictors of Cognitive Change When the Outcome Is Measured With a Ceiling

Identifying Predictors of Cognitive Change When the Outcome Is Measured With a Ceiling. Gerontological Society of America 2004 Annual Meeting Maria Glymour, Jennifer Weuve, Lisa F. Berkman, James M. Robins Harvard School of Public Health. Outline. The question Why it’s difficult to answer

vance-hunter
Download Presentation

Identifying Predictors of Cognitive Change When the Outcome Is Measured With a Ceiling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Identifying Predictors of Cognitive Change When the Outcome Is Measured With a Ceiling Gerontological Society of America 2004 Annual Meeting Maria Glymour, Jennifer Weuve, Lisa F. Berkman, James M. Robins Harvard School of Public Health

  2. Outline • The question • Why it’s difficult to answer • How CLAD regression helps • An example with HRS data

  3. The Question • Does education affect cognitive change in old age? • Earl attended 10 years of school and declined by 2 points on a cognitive test score from age 70 to 75. Would Earl have experienced more or less cognitive change if he had, counter to fact, completed more schooling?

  4. Indirect Measurement of Cognition • Test is an indirect measure of our primary interest (cognitive function): Test Score=g(cognition) + e • But the test has a maximum possible score: Test Score=min(15, g(cognition) + e)

  5. Scaling Challenges True Cognitive Status Values Low High Maximum text score Measured Test Score

  6. Measurement Ceilings A ceiling on the dependent variable will bias the regression coefficient away from the coefficient for the true outcome variable.

  7. 36 36 34 34 32 32 30 30 28 28 26 26 24 24 22 22 20 20 18 18 16 16 0 1 Time Ceilings with Longitudinal Data Difference in True D = 0 Observed = -3

  8. 36 36 34 34 32 32 30 30 28 28 26 26 24 24 22 22 20 20 18 18 16 16 0 1 Time Ceilings with Longitudinal Data Difference in True D = 0 Observed = 3

  9. 800 Test Score 600 400 200 0 Median Mean Medians vs Means 800 600 400 Cognitive Status 200 0 Mean, Median

  10. CLAD Regression • The median is more robust to ceiling effects than the mean, so contrast medians by level of exposure • Use CLAD if believe the relationship between X and Y does not differ above (vs below) the ceiling • Calculate the median regression coefficients • Drop observations with a predicted value of Y over ceiling • Repeat steps 1 and 2 until all predicted values are below the ceiling. • Standard errors are messy: bootstrap. • Can use any quantile

  11. Data Set • AHEAD cohort of HRS • Enrolled in 1993 • National sample of non-institutionalized survivors born pre-1924 • n=7,542, Observations=23,752 • Self-report years of education: dichotomized at <12 years • Telephone Interview for Cognitive Status (modified) • Possible range 0 (bad) -15 (good) • ~20% scored max at each interview • Assessed 1-5 times

  12. TICSti = b00 + b1Timeti + b2Educationi + b3Timeti*Educationi + bkOther Covariatesti + ei Bootstrap (500 resamples) for standard errors, resampling on the individual (rather than the observation) Analysis

  13. Other covariates: Age at enrollment, mother’s education, father’s education, Hispanic ethnicity Stratify by sex and race (black vs all other) Up to 5 cognitive assessments Initial models treat time flexibly Impose a linear model of time Analysis

  14. Summary of AHEAD Data

  15. Predicted Median TICS Score From CLAD models, adjusted for sex, race, age at baseline, Hispanic ethnicity, mother’s and father’s education

  16. Baseline Education Effect Estimates

  17. Slope Education Effect Estimates

  18. Loss to Follow-Up

  19. Effect at Alternative Quantiles

  20. (Less Desirable) Alternatives • Baseline adjustment • Introduces new (and larger) biases • Add the scales • Hides the ceiling • Hides the bias • Tobit models • Stronger assumptions about the distribution

  21. Conclusions • More educated respondents had much higher average cognitive scores for the duration of the study. • Education associated with better evolution of cognitive function for white women. • Ceilings introduced bias of unknown direction.

  22. Limitations & Future Work • Discrete outcomes • Missing data • Complex sampling design • Unequal scale intervals not due to ceilings

  23. Acknowledgements • Dean Jolliffe, CLAD ado • Funding: • National Institute of Aging • Office for Behavioral and Social Science Research • “Causal Effects of Education on Elder Cognitive Decline” • AG023399 • NIA Training grant: AG00138

  24. END

  25. True Cognitive Status Values Low High Measured MMSE Unequal Scale Intervals

  26. 6 4.0 3.5 5 3.0 4 2.5 function=square root of errors Function=ln(score) 3 2.0 1.5 2 1.0 1 0.5 0 - 0 5 10 15 20 25 30 Test Score Unequal Scale Intervals Do similar size increments have the same “meaning” across all levels of the test?

  27. Unequal Scale Intervals Do similar size increments have the same “meaning” across all levels of the test?

  28. Unequal Scale Intervals Do similar size increments have the same “meaning” across all levels of the test?

More Related