Some challenges in survival analysis with large datasets
In this presentation some common challenges in survival analysis with large datasets are demonstrated. We investigate the relationship between the age of smoking initiation and some demographic factors in the Canadian Community Health Survey, Cycle 3.1 (CCHS-3.1) dataset. CCHS-3.1 is a large dataset which includes information for over 130000 individuals. We used different techniques for model fitting and model checking. Test-based techniques for the assessment of PH assumption are not very useful as small deviation from the theoretical model leads to the rejection of PH assumption. In contrast graphical approaches seem to be more helpful. However, not every diagnostic graph can be drawn due to large dataset. Preliminary results show that 63% of Canadians ever smoked a whole cigarette. Therefore, it seems more appropriate to use a cure fraction model (Lambert 2007; Stata Journal, 7:(3), pp. 1-25) to handle the large proportion of censored data. However, sampling weights cannot be used in this model. In conclusion, survival analysis for large datasets cannot be done easily. Some challenges include assessment of PH assumption and drawing diagnostic graphs. Besides, use of cure fraction model may not be appropriate if sampling weights cannot be incorporated in the model estimation.
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