1 / 25

Amelia Haviland Bobby Jones Daniel S. Nagin

Extending Group-Based Trajectory Modeling to Account for Subject Attrition (Sociological Methods & Research, 2011). Amelia Haviland Bobby Jones Daniel S. Nagin. 4%. 28%. 52%. 16%. Trajectories Based on 1979 Dutch Conviction Cohort. The Likelihood Function. Missing Data. Two Types

rusty
Download Presentation

Amelia Haviland Bobby Jones Daniel S. Nagin

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. Extending Group-Based Trajectory Modeling to Account for Subject Attrition(Sociological Methods & Research, 2011) Amelia Haviland Bobby Jones Daniel S. Nagin

  2. 4% 28% 52% 16%

  3. Trajectories Based on 1979 Dutch Conviction Cohort

  4. The Likelihood Function

  5. Missing Data • Two Types • Intermittent missing assessments (y1, y2 , . ,y4, . ,y6) • Subject attrition where assessments cease starting in period τ (y1 , y2 , y3 , . , . , .) • Both types assumed to be missing at random • Model extension designed to account for potentially non-random subject attrition • No change in the model for intermittent missing assessments

  6. Some Notation T=number of assessment periods τi =period t in which subject i drops out = Probability of Drop out in group j in period t

  7. The Dropout Extended Likelihood for Group j

  8. Specification of • Binary Logit Model • Predictor Variables • Fixed characteristics of i, • Prior values of outcome, • If trajectory group was known within trajectory group j dropout would be “exogenous” or “ignorable conditional on observed covariates” • Because trajectory group is latent, at population level, dropout is “non-ignorable”

  9. Simulation Objectives • Examine effects of differential attrition rate across groups that are not initially well separated • Examine the effects of using model estimates to make population level projections

  10. Simulation 1: Two Group Model With Different Drop Probabilities and Small Initial Separation 10 10 E(y) E(y) No dropout Slope=.5 Time Time 10 10 E(y) E(y) Time Time

  11. Simulation Results: Group 1 and Group 2 Initially not Well Separated

  12. An Important Distinction from Zhang and Rubin (2003) • Dropout due death • Subject exits population of interest-the living • Data said to be “truncated” • Dropout due termination of study participation • Subject exits the sample but remains in the population • Data said to be censored

  13. Simulation 2: Projecting to the Population Level from Model Parameter Estimates

  14. Simulation 2 Continued 12.5 10 Dropout=.2 per period No Dropout

  15. Chinese Longitudinal Healthy Longevity Survey (CLHLS) • Random selected counties and cities in 22 provinces • 4 waves 1998 to 2005 • 80 to 105 years old at baseline • 8805 individual at baseline • 68.9% had died by 2005 • Analyzed 90-93 years old cohort in 1998

  16. Activities of Daily Living • On your own and without assistance can you: • Bath • Dress • Toilet • Get up from bed or chair • Eat • Disability measured by count of items where assistance is required

  17. Adding Covariates to Model to Test the Morbidity Compression v. Expansion Hypothesis • Will increases in longevity compress or expand disability level in the population of the elderly? • “Had a life threatening disease” at baseline or prior is positively correlated with both ADL counts at baseline and subsequent mortality rate. • Question: Would a reduction in the incidence of life threatening diseases at baseline increase or decrease the population level ADL count?

  18. Testing Strategy and Results • Specify group membership probability (πj) and dropout probability ( ) to be a function of life threatening disease variable • Both also functions of sex and dropout probability alone of ADL count in prior period • Life threatening disease significantly related to group membership in expected way but has no relationship with dropout due to death • Thus, unambiguous support for compression

  19. Projecting the reduction in population average ADL count from a 25% reduction in the incidence of the life threatening disease at baseline Projected % Reduction in Population Average ADL Count

  20. Conclusions and Future Research • Large differences in dropout rates across trajectory groups matter • Future research • Investigate effects of endogenous selection • Compare results in data sets with more modest dropout rates • Further research morbidity expansion and contraction

More Related