Implications of non-response and loss to follow up and some possible solutions.
James Doidge PhD Candidate, University of South Australia
Prof Leonie Segal, Supervisor, University of South Australia
A. Prof Paul Delfabbro, Supervisor, University of Adelaide
ATP Collaborators
Dr Ben Edwards, AIFS
Prof John Toumbourou, Deakin
Dr Daniel Higgins, AIFS
Ms Suzanne Vassall
1. Missing data in research on
child maltreatment
Implications of non-response and loss to follow-up,
and some possible solutions
PhD Candidate James Doidge, University of South
Australia
Supervisors Prof Leonie Segal, University of South
Australia
A/Prof Paul Delfabbro , University of
Adelaide
ATP
Collaborators
Dr Ben Edwards, AIFS
Prof John Toumbourou, Deakin
Dr Darryl Higgins, AIFS
Ms Suzanne Vassallo, AIFS
British Association for the Study and Prevention of Child Abuse and Neglect | Congress 2015
James.Doidge@unisa.edu.au
3. The Australian Temperament Project
2,443 participants and their families/teachers/nurses
15 waves of data collection over 29 years from birth
from:
Parents (all waves)
Cohort members (since age 11)
Nurses (1st wave)
Teachers (3 waves)
From the available data, 369 fields were selected,
representing ~130 variables of interest, reflecting10
underlying domains:
Indicators of child maltreatment
Risk factors for child maltreatment (economic factors, social
factors, parental mental health and substance use, child health,
child temperament)
Consequences of child maltreatment (economic, social,
physical health and mental health)
BASPCAN Congress 2015 | James Doidge | University of South Australia
4. Loss to follow-up and non-response in the
Australian Temperament Project
2443 responded
~3000 selected for recruitment
~557 did not participate
~1500 still in contact ~943 lost to follow-up
980 responded ~557 did not respond
953 completed all questions about
child maltreatment
27 did not complete all questions
about child maltreatment
140 with complete information on
all risk factors and outcomes
813 with missing information on
some risk factors or outcomes
Wave 1
Wave 14
(all
waves)
BASPCAN Congress 2015 | James Doidge | University of South Australia
5. Child maltreatment and non-response by parents and
children in waves prior and subsequent to self-report
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
2 3 4 5 6 7 8 9 10 11 12 13 14 15
Relative odds
of any
maltreatment
w.r.t. non-
response
(95% CI)
Wave of follow-up
BASPCAN Congress 2015 | James Doidge | University of South Australia
6. Stepped inverse-probability weighting
with non-monotone missing data
BASPCAN Congress 2015 | James Doidge6
Predict retention in wave 2 using variables from
wave 1
Wave
1
Wave
2
Wave
3
Wave
4
Wave
5
Wave
6
Wave
7
Wave
8
Pattern 1
Pattern 2
Pattern 3
Pattern 4
Pattern 5
Pattern 6
Pattern 7
Pattern 8
7. Stepped inverse-probability weighting
with non-monotone missing data
BASPCAN Congress 2015 | James Doidge7
Predict retention in wave 3 using variables from
waves 1 and 2 amongst respondents to wave 2.
Wave
1
Wave
2
Wave
3
Wave
4
Wave
5
Wave
6
Wave
7
Wave
8
Pattern 1
Pattern 2
Pattern 3
Pattern 4
Pattern 5
Pattern 6
Pattern 7
Pattern 8
8. Stepped inverse-probability weighting
with non-monotone missing data
BASPCAN Congress 2015 | James Doidge8
Predict retention in wave 4 using variables from
waves 1 and 3 amongst respondents to wave 3.
Wave
1
Wave
2
Wave
3
Wave
4
Wave
5
Wave
6
Wave
7
Wave
8
Pattern 1
Pattern 2
Pattern 3
Pattern 4
Pattern 5
Pattern 6
Pattern 7
Pattern 8
9. Stepped inverse-probability weighting
with non-monotone missing data
BASPCAN Congress 2015 | James Doidge9
Predict retention in wave 5 using variables from
waves 1 and 4 amongst respondents to wave 4.
Wave
1
Wave
2
Wave
3
Wave
4
Wave
5
Wave
6
Wave
7
Wave
8
Pattern 1
Pattern 2
Pattern 3
Pattern 4
Pattern 5
Pattern 6
Pattern 7
Pattern 8
10. Stepped inverse-probability weighting
with non-monotone missing data
BASPCAN Congress 2015 | James Doidge10
Predict retention in wave 6 using variables from
waves 1 and 5 amongst respondents to wave 5.
Wave
1
Wave
2
Wave
3
Wave
4
Wave
5
Wave
6
Wave
7
Wave
8
Pattern 1
Pattern 2
Pattern 3
Pattern 4
Pattern 5
Pattern 6
Pattern 7
Pattern 8
11. Stepped inverse-probability weighting
with non-monotone missing data
BASPCAN Congress 2015 | James Doidge11
Predict retention in wave 7 using variables from
waves 1 and 6 amongst respondents to wave 6.
Wave
1
Wave
2
Wave
3
Wave
4
Wave
5
Wave
6
Wave
7
Wave
8
Pattern 1
Pattern 2
Pattern 3
Pattern 4
Pattern 5
Pattern 6
Pattern 7
Pattern 8
12. Stepped inverse-probability weighting
with non-monotone missing data
BASPCAN Congress 2015 | James Doidge12
Predict retention in final wave using variables from
waves 1 and previous wave (no non-respondents by
definition).
Wave
1
Wave
2
Wave
3
Wave
4
Wave
5
Wave
6
Wave
7
Wave
8
Pattern 1
Pattern 2
Pattern 3
Pattern 4
Pattern 5
Pattern 6
Pattern 7
Pattern 8
13. Wave 1
variable
s
Wave 2
variable
s
Wave 3
variable
s
Wave 4
variable
s
Wave
12
variable
s
Wave
13
variable
s
Wave
14
variable
s
Wave
15
variable
s
People lost
after wave 1
People lost
after wave 2
People lost
after wave 3
People lost
after wave 4
People lost
after wave
12
People lost
after wave
13
People lost
after wave
14
People not
Combined inverse probability weighting and
multiple imputation (IPW/MI)
Wave 1
variable
s
Wave 2
variable
s
Wave 3
variable
s
Wave 4
variable
s
Wave
12
variable
s
Wave
13
variable
s
Wave
14
variable
s
Wave
15
variable
s
People lost
after wave 1
People lost
after wave 2
People lost
after wave 3
People lost
after wave 4
People lost
after wave
12
People lost
after wave
13
People lost
after wave
14
People not
IPW
Wave 1
variable
s
Wave 2
variable
s
Wave 3
variable
s
Wave 4
variable
s
Wave
12
variable
s
Wave
13
variable
s
Wave
14
variable
s
Wave
15
variable
s
People lost
after wave 1
People lost
after wave 2
People lost
after wave 3
People lost
after wave 4
People lost
after wave
12
People lost
after wave
13
People lost
after wave
14
People not
IPW
MI
BASPCAN Congress 2015 | James Doidge | University of South Australia
14. Implications for estimates of the
prevalence of child maltreatment
Type of child maltreatment Prevalence (%)
Complete
cases
(n = 951)
Weighted
excluding
missingness
Weighted
including
missingness
Participants
with weight
> 5 (n = 58)
Emotional abuse 17.0 18.4 19.7 27.9
Emotional abuse (very true) 3.4 3.3 3.6 3.3
Neglect 2.8 2.8 2.8 1.6
Physical abuse 5.9 6.6 7.2 16.4
Sexual abuse 5.9 6.7 7.1 11.5
Witnessed domestic violence 4.4 4.8 4.7 4.9
Any child abuse or neglect 24.3 25.2 26.9 37.9
Any child abuse or neglect
(emotional = very true)
16.3 16.9 17.6 26.2
Multiple maltreatment 8.3 9.0 9.8 19.0
Multiple maltreatment
(emotional = very true)
4.2 4.7 4.9 6.9
BASPCAN Congress 2015 | James Doidge | University of South Australia
17. Strength of the missing at random
assumption
Modelled covariates Odds ratio for any maltreatment with respect to:
Non-response
by parents
Non-response
by cohort
Partial
response
Overall
missingness
1 (none) 3.96*** 1.55* 4.87** 5.50***
2 Sex 3.98*** 1.56* 4.89** 5.53***
3 Sex and economic factors 2.18* 1.48 1.45 2.58*
4 Sex, economic and other social
factors
2.09 1.49 0.90 2.27
5 Sex, economic and other social
factors, parental mental health and
substance use
1.97 1.46 0.71 2.06
6 Sex, economic and other social
factors, parental mental health and
substance use, child health and
temperament
2.38* 1.48 0.59 2.39
BASPCAN Congress 2015 | James Doidge | University of South Australia
18. Summary
BASPCAN Congress 2015 | James Doidge | University of South Australia
Prevalence is likely to be underestimated because of
missing data, but not greatly.
The assumption that missing data will ‘conservatively’
bias estimates of risk or consequences cannot be
justified. Most estimates were fairly robust to missing
data, but there are some patterns and some strong
biases (in both directions) that warrant consideration.
The missing at random assumption is unlikely to hold in
research on child maltreatment. Sensitivity analysis
and/or follow-up of non-respondents is usually warranted.
When possible, incorporate measures of missingness
into analyses.
These findings are likely to be generalisable to other
types of population-based research, and possibly to other
types of missingness (e.g. non-participation).
Editor's Notes
Accepted title: “Missing data in child maltreatment: implications of sample selection, non-response and loss to follow-up, and some possible solutions”
INTRODUCE YOURSELF
ASK ABOUT BACKGROUNDS
While it looks like the association was low to begin with, this is actually obscured by the fact that in waves 2 and 3 the researchers surveyed only 2/3 of the cohort, and I was unable to distinguish between parents who chose not to respond and parents who were never asked.
Wave 1 data to predict loss to follow-up at wave 2
Non-respondents to wave 2 get a partial weight of 1 for this wave.