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Challenges in survival analysis with large datasetsNoori
Akhtar-Danesh, PhDMcMaster University,Hamilton,
[email protected]
San Francisco, USA
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BackgroundThe Canadian Community Health Survey, Cycle 3.1
(CCHS-3.1) is a large cross-sectional survey which includes
information on over 130000 Canadians. Preliminary results show that
63% of Canadians (age>=12 years) ever smoked a whole
cigarette.
San Francisco, USA
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Objectives & ChallengesThe main objective was to investigate
the age of smoking initiation based on the variables of gender and
place of birth. We compared different survival analysis techniques
including Cox regression and the available parametric methods. To
highlight some challenges that we encountered in search for an
appropriate model.
San Francisco, USA
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Challenge: PH AssumptionIn large datasets, test-based assessment
of PH assumption is challenging because Schoenfeld test would be
significant for even very small rhos due to large dataset.For the
CCHS-3.1 dataset, Schoenfeld test for both Sex and Birth Place
variables is significant with small rhos.
San Francisco, USA
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Challenge: PH Assumption
San Francisco, USA
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Challenge: PH AssumptionHowever, the log(-log) graph showed
quite parallel lines for these variables which indicates that PH
assumption is satisfied.
San Francisco, USA
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Challenge: PH Assumption
San Francisco, USA
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Challenge: PH Assumption Perhaps we need to specify a minimum
value for correlation, for instance r=0.33, to be accepted as
significant (as it is common in fields such as factor
analysis).
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Challenge: PH Assumption However, if we incorporate the survey
design into the analysis, the PH test would work fine for these
variables but the global test is still significant (in Stata).
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Challenge: PH Assumption
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Challenge: Appropriate parametric model
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Challenge: Parametric models We used different parametric models
incorporating the survey design and sampling weight (using svy:
option).A Weibull model with frailty appeared to be the best model.
But, we were not able to draw diagnostic graphs or have an overall
GOF test due to the big sample size.
San Francisco, USA
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Using Cure Fraction Models One main assumption in survival
analysis is that eventually everyone will experience the
event.However, we have a large proportion (37%) of censored
individuals (those who never started smoking) in the CCHS-3.1
dataset.
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Using Cure Fraction Models
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Using Cure Fraction Models Therefore, it is more appropriate to
use a cure fraction model (Lambert 2007; Stata Journal, 7:(3), pp.
1-25).Using this model, both the cure fraction (the proportion who
did not experience the event) and the time to failure (age of
smoking initiation) depend (separately) on the explanatory
variables.
San Francisco, USA
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Using Cure Fraction Models We used the strxnmix code in Stata
for a non-mixture model (Lambert 2007; Stata Journal, 7:(3), pp.
1-25).Challenge: sampling weight cannot be incorporated in
estimation.
San Francisco, USA
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ConclusionSurvival analysis for large datasets with sampling
weight cannot be conducted easily.Common challenges:Assessment of
PH assumptionModel diagnosticsUse of cure fraction models may not
be appropriate because sampling weight cannot be incorporated in
the estimation.
San Francisco, USA
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