Download PDF
Methods Inf Med 2006; 45(02): 186-190
DOI: 10.1055/s-0038-1634065
Original Article
Schattauer GmbH

Gaining Insight from Flexible Models

Assessment of the Secondary Prevention Trial of CHD in the Czech Male Population with MI History
Z. Valenta
1   EuroMISE Center, Institute of Computer Science AS CR, Prague, Czech Republic
2   Institute for Clinical and Experimental Medicine, Prague, Czech Republic
,
J. Pitha
2   Institute for Clinical and Experimental Medicine, Prague, Czech Republic
,
I. Podrapska
3   Diabetology Department, Litomerice, Czech Republic
,
R. Poledne
2   Institute for Clinical and Experimental Medicine, Prague, Czech Republic
› Author Affiliations
Further Information

Publication History

Publication Date:
06 February 2018 (online)

    Permissions and Reprints

Summary

Objectives: We present results from a secondary prevention trial of coronary heart disease (CHD) in the Czech male population from northern Bohemia with the history of myocardial infarction (MI) and high prevalence of metabolic syndrome. We compare several approaches to analyzing survival data from our study in terms of respective model assumptions.

Methods: While both the Cox and Weibull survival regression models assume proportionality of the hazard functions over time, in many instances this assumption appears incompatible with the data at hand. Gray’s implementation of flexible models using penalized splines allows for a more realistic assessment of the covariate effects which may vary over time.

Results: Gray’s model results revealed a steady decline in the age-adjusted intervention effect over time, which remained significant until about 2.7 years of follow-up. This was in contrast with the results obtained from the Cox and Weibull models which suggested an overall risk reduction due to intervention during the total follow-up of 6.7 years. Survival estimates based on the Cox and Gray models are shown for the two treatment groups and selected sample quantiles of the age distribution for illustration.

Conclusions: Gray’s time-varying coefficients model facilitated a more realistic assessment of the intervention effect. Using suitable historical controls with MI history the effect of intervention was found to gradully diminish over time.

Keywords

Coronary heart disease - non-proportional hazards - secondary prevention - survival
 

  • References

  • 1 Cox DR. Regression Models and Life Tables (with discussion). JRSS 1972; 34 (Ser. B) 187-220.
    PubMedGoogle Scholar
  • 2 Gray RJ. Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis. JASA 1992; 87 (420) 942-51.
    CrossrefPubMedGoogle Scholar
  • 3 Valenta Z, Weissfeld L. Estimation of the Survival Function for Gray’s Piecewise-Constant Time- Varying Coefficients Model. Statistics in Medicine 2002; 21: 717-27.
    CrossrefPubMedGoogle Scholar
  • 4 Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals (Corr: 95V82 p668). Biometrika 1994; 81: 515-26.
    CrossrefPubMedGoogle Scholar
  • 5 Gray RJ. Spline-based tests in survival analysis. Biometrics 1994; 50: 640-52.
    CrossrefPubMedGoogle Scholar
  • 6 Ihaka R, Gentleman R. R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics 1996; 5: 299-314.
    PubMedGoogle Scholar
  • 7 Kasal J. et al. Comparison of Cox and Gray’s survival models in severe sepsis. Critical Care Medicine 2004; 32 (03) 700-7.
    CrossrefPubMedGoogle Scholar