Gaining insight from flexible models--assessment of the secondary prevention trial of CHD in the Czech male population with MI history
Language English Country Germany Media print
Document type Journal Article, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.
PubMed
16538286
PII: 06020186
Knihovny.cz E-resources
- MeSH
- Survival Analysis MeSH
- Adult MeSH
- Myocardial Infarction complications MeSH
- Coronary Disease complications prevention & control MeSH
- Middle Aged MeSH
- Humans MeSH
- Metabolic Syndrome complications epidemiology MeSH
- Prevalence MeSH
- Proportional Hazards Models MeSH
- Aged MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
- Geographicals
- Czech Republic epidemiology MeSH
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.