1. Handling Missing Data on ALSPAC
Paul Clarke
(CMPO, University of Bristol)
ALSPAC Social Science User Group meeting
21 May 2008

2. Outline What causes missing data? ‘Types’ of missing data
Methods for missing data: quick overview
ALSPAC ‘Blitz’ on non-respondents
Investigating MNAR data in ALSPAC

3. Example ‘ALSPAC’ analysis
At age 11
Outcome: Mood (ordinal, 3 categories)
Depressive symptoms, maternally rated
Main exposure: Physical activity (score)
Measured on actigraph, 3 days
Adjustment:
BMI (score)
Sex, Age at screening
Ordinal logistic regression

4. Missing Value (MV) pattern1
Sex
Mood
BMI
Physical Activity
Age
4220 █
673 ?
1204
5397
11679
Monotone
90%
858
12352
Non-monotone
95%
1All MV patterns < 200 cases ignored

5. What causes missing data?
Interviewer effectiveness
Incentive for participant
Loyalty
Letter
Telephone calls
Interviewer visits
Incomplete questionnaire
Refusal
Follow-up
Non-contact
Fail to attend clinic
Parent characteristics
Parent & child characteristics

6. Result of processes leading to:
Refusal to answer questions (item)
Refusal to participate (unit)
No contact (unit)
Longitudinal-specific: attrition & drop-out
Non-response mechanism(s) - NRM

7. Rubin’s definitions1 Missing Completely At Random (MCAR)
Independent of observed variables
Missing At Random (MAR)
NRM depends only on observed variables
Missing Not At Random (MNAR)
NRM depends on missing variables too
1Little & Rubin (2002) Statistical Analysis with Missing Data

8. Directed Acyclic Graph (DAG)X R C
R independent  data MCAR

9. MAR data Y X R C
R indirectly related to Y through X and C

10. Methods for MAR data Complete cases analysis/Listwise deletion
Weighting
Weighting classes, post-stratification
(Single) imputation methods
e.g. regression, hot-deck/nearest-neighbour
Multiple imputation methods
e.g. Norm, MICE
Semiparametric estimators

11. Imputation in practice: pitfalls1
Omitting the outcome
Imputing non-normal variables
MAR completely implausible
Convergence of iterative procedures
1Sterne et al. (2008) British Medical Journal

12. Complex methods Analysis model
e.g. Ordinal logistic regression
Imputation model: Missing given Observed
ALL assume MAR data

13. MAR data in reality Unknown factors drive non-response? Y X R C
Unknown factors drive non-response
…correlated with model predictors
…but not with Y

14. Why is this important? Weakness of MAR: How do we know?
Central problem: missing data is missing!
MAR is a “leap of faith”

15. MNAR data ? Y X R C
Unknowns directly correlated with Y?

16. Physical activity example?
Mood
Phys Act R
BMI, Sex, Age
NRM is mother-driven (child age 11)
Child must wear actigraph for 3 days
Mother must assess her child’s mood

17. ALSPAC ‘Blitz’ Co-ordinated by Family Liaison Unit
4 tranches: Nov 2007-May 2008
Target 5000 teenagers not in last 2 waves
Mini-clinic for difficult to persuade

18. Proposed analysis MAR is context dependent
Risky behaviours (Glyn Lewis, et al)
Outcomes: Cannabis use, sexual practices, etc
Risk factors: mental health, sensation seeking, etc
Basic analysis:
Compare follow-up with main sample
Still differences after adjustment?

19. Unit non-response 100% follow-up rate unlikely! Directly model NRM
Continuum of non-response
Hard to contact less like main sample
Weighting scheme (Alho 1990; Wood et al. 2006)
Lower bound for MNAR bias

20. Item non-response Parallel qualitative post
Items: questions on risky behaviours
What mechanisms drive non-response?
Test hypotheses from this project