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A Comparison of Modeling Scales in Flexible Parametric Models

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  • Noori Akhtar-Danesh

    (McMaster University)

Abstract

Background: Cox-regression and parametric survival models are quite common in the analysis of survival data. Recently, Flexible Parametric Models (FPM), have been introduced which are extensions of the parametric models such as Weibull (hazard- scale) model, loglogistic (odds-scale) model, and lognormal (probit-scale) model. In this presentation we aim to statistically compare between these modeling scales. Methods: We used Stata code stpm2 to compare flexible parametric models based on these three different scales. We used two subsets of the U.S. National Cancer Institute's Surveillance, Epidemiology and End Results (SEER) dataset; Ovarian cancer diagnosed between 1991 and 2010 and colorectal cancer diagnosed in men between 2001 and 2010 for this illustration. Results: The ovarian and colorectal dataset included data from 13810 and 42002 patients, respectively. Patients were classified into different age groups. The results will be presented using graphs to compare survival curves, trends in one-year and five-year survival rates, and mortality rates. Conclusion: In general, there were no substantial differences between the three modeling scales, although the probit-scale showed better fit based on the Akaike information criterion (AIC) for both datasets.

Suggested Citation

  • Noori Akhtar-Danesh, 2015. "A Comparison of Modeling Scales in Flexible Parametric Models," 2015 Stata Conference 15, Stata Users Group.
  • Handle: RePEc:boc:scon15:15
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    References listed on IDEAS

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    1. Patrick Royston & Paul C. Lambert, 2011. "Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model," Stata Press books, StataCorp LP, number fpsaus, August.
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