# Confounding in epidemiological studies, is SCCS the answer?

```08/08/2013
Confounding in epidemiological studies, is
SCCS for censored, perturbed, or curtailed postevent exposures
Linda Wijlaars
PhD student
Department of Primary Care and Population Health
UCL
Why should I use a complicated little-used
method when I could also just do a
cohort/case control study?
Main limitation of SCCS method:
exposure distribution + observation period ⊥ event times
can’t use SCCS:
→ when event alters subsequent exposure risk
→ for terminal events
→ if observation of exposure is censored/disrupted by event
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08/08/2013
Solution using counterfactuals
Counterfactuals:
What would have
happened if patient 1
would not have
Patient
Treatment Death
group
1
No
Yes
2
Yes
No
3
Yes
No
4
Yes
Yes
5
No
No
3
Patient 1
X=1, Y=1
C
Y
X
C
Patient 1
X*=0, Y*=?
X*
Y*
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Star Trek II: counterfactuals
Wrath of Khan
Khan=marooned
Khan
Into Darkness
Khan≠marooned
Khan
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Counterfactuals &amp; the SCCS
What if there were no subsequent exposures?
→ counterfactual used to estimate effects as if
there were no subsequent exposures
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Counterfactuals &amp; the SCCS
Even if observation is censored, potential end of
observation period often known
 bi (factual or counterfactual) is known
 Event=non-recurrent so no events in unobserved period
Potential exposure = unknown
Event
Start of
observation period
ai
Start of risk
period ci1
Start of risk
period ci2
End of
observation period
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bi
Counterfactuals &amp; the SCCS
No assumptions about event-free exposure process
Analyse data for each exposure as if there could be
no subsequent exposures → counterfactual
Recursive principle: start at last pre-event exposure,
work back through previous exposures
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Counterfactuals &amp; the SCCS
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Assumptions SCCS for censored post-event
exposures
Exposure is binary
Risk period is (relatively) short
Event of interest is uncommon &amp; non-recurrent
Risk returns to baseline level after each risk period
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Example: OPV and intussusception
Farrington CP et al. Biostatistics (2009)
Oral Polio Vaccine (OPV) – given in 3 doses
Intussusception = side effect &amp; contraindication
HES data from 1991-1997
207 children aged 28-365 days
Up to 3 doses, risk period: 14-41 days after
vaccination
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OPV and intussusception
4 analyses: standard model = standard SCCS
Analysis 1: gold standard
Analysis 2-4: censoring simulated
Dose
Analysis 1:
original data
Analysis 2:
observation
ends at event
Analysis 3:
censored data
Analysis 4:
censored data
Standard model
Standard model
Standard model
Censoring model
RI (95% CI)
RI (95% CI)
RI (95% CI)
RI (95% CI)
1
0.71 (0.33-1.41)
0.58 (0.05-3.80)
0.85 (0.37-1.65)
0.58 (0.26-1.17)
2
0.92 (0.50-1.64)
0.48 (0.17-1.17)
1.43 (0.73-2.66)
0.88 (0.44-1.63)
3
1.63 (1.04-2.59)
1.35 (0.49-5.10)
2.91 (1.69-5.02)
1.57 (1.00-2.52)
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Example 2: RV1 and intussusception
Patel MM et al. NEJM (2011)
Rotavirus cause of severe gastroenteritis in infants
and young children
– Worldwide 95% of children will be infected before age 5
Old rotavirus vaccine association with
intussusception: 1 : 10,000 recipients
New vaccine (RV1): added to national immunization
program in Mexico (2007)
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RV1 and intussusception
Cases characteristics
N = 285
Age in months (range)
5 (1.5 – 8.0)
Boys
61%
Death
1%
Surgical treatment
87%
RV1 vaccination
Dose 1
95%
Dose 2
13%
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RV1 and intussusception
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RV1 and intussusception
Dose and risk
Period
Cases
285
Controls
739
SCCS
(IRR)
CC
(OR)
91%
91%
-
1.0 (0.6-1.7)
1-7 days
9%
2%
5.3 (3.0-9.3)
5.8 (2.6-13.0)
8-14 days
2%
2%
1.1 (0.5-2.7)
1.0 (0.4-2.9)
15-21 days
2%
3%
0.9 (0.3-2.2)
0.8 (0.3-2.1)
5%
5%
1.8 (0.9-3.8)
1.1 (0.6-2.2)
Either dose
First dose
Second dose
1-7 days
8-14 days
7%
4%
2.2 (1.1-4.2)
2.3 (1.2-4.4)
15-21 days
7%
4%
2.2 (1.2-4.0)
2.0 (1.0-3.8)
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RV1 and intussusception
Very similar results between CC and SCCS
Slight increase in intussusception, but:
– Many more rotavirus deaths/hospitalizations averted
than intussusception d/h caused
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Example 3: Antidepressants and suiciderelated behaviour
2003/04: MHRA warning against SSRIs
Possible association antidepressants ↔ ‘suicidality’
Trials: small and short
 Suicide-study needs large and long
Problem: confounding by (severity of) indication
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Cohort characteristics
Total
Girls
# taking
Completed
suicide
Attempted
suicide
Suicidal
ideation
Self-harm
General
population
81
1,496
1,178
2,361
952,892
29.6%
72.8%
60.1%
74.2%
48.4%
23.1%
36.6%
52.8%
5.7%
2.9%
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Day zero effect
Prescription day effect: recording artefact?
 Patient discloses earlier suicide-related event at GP
visit
 GP decides to prescribe antidepressant
 Both are recorded on same day
Prescription might be started by psychiatrist
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Why should I use a complicated little-used
method when I could also just do a
cohort/case control study?
• (unmeasured) confounding + selection bias
• Case-only eliminates time-invariant confounders
• Efficient compared to cohort studies
• Code (also for extensions) available online
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Presentations available on UCL THIN page
bit.ly/UCLTHINteam
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References
Farrington CP, Whithaker HJ, Hocine MN. Case series for analysis for censored, perturbed,
or curtailed post-event exposures. Biostatistics (2009), 10(1)3-16.
Nordmann S, Biard L, Ravaud P, Esposito-Far&egrave;se M. Case-only designs in
pharmacoepidemiology: a systematic review. PLoS one (2012).
Patel MM et al. Intussusception risk and health benefits of rotavirus vaccination in Mexico
and Brazil. New England Journal of Medicine (2011), 364(24)2283-92.
Pearl J. Causality: Models, Reasoning and Inference. Cambridge University Press (2000).
Whitaker H. (2010) The Self-Controlled Case Series Method. URL:
http://statistics.open.ac.uk/sccs
The Health Improvement Network (THIN) Research Team. URL:
http://www.ucl.ac.uk/pcph/research-groups-themes/thin-pub
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