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A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study

Publication ,  Journal Article
Ngwa, JS; Cabral, HJ; Cheng, DM; Pencina, MJ; Gagnon, DR; LaValley, MP; Cupples, LA
Published in: BMC medical research methodology
November 3, 2016

BACKGROUND: Typical survival studies follow individuals to an event and measure explanatory variables for that event, sometimes repeatedly over the course of follow up. The Cox regression model has been used widely in the analyses of time to diagnosis or death from disease. The associations between the survival outcome and time dependent measures may be biased unless they are modeled appropriately. METHODS: In this paper we explore the Time Dependent Cox Regression Model (TDCM), which quantifies the effect of repeated measures of covariates in the analysis of time to event data. This model is commonly used in biomedical research but sometimes does not explicitly adjust for the times at which time dependent explanatory variables are measured. This approach can yield different estimates of association compared to a model that adjusts for these times. In order to address the question of how different these estimates are from a statistical perspective, we compare the TDCM to Pooled Logistic Regression (PLR) and Cross Sectional Pooling (CSP), considering models that adjust and do not adjust for time in PLR and CSP. RESULTS: In a series of simulations we found that time adjusted CSP provided identical results to the TDCM while the PLR showed larger parameter estimates compared to the time adjusted CSP and the TDCM in scenarios with high event rates. We also observed upwardly biased estimates in the unadjusted CSP and unadjusted PLR methods. The time adjusted PLR had a positive bias in the time dependent Age effect with reduced bias when the event rate is low. The PLR methods showed a negative bias in the Sex effect, a subject level covariate, when compared to the other methods. The Cox models yielded reliable estimates for the Sex effect in all scenarios considered. CONCLUSIONS: We conclude that survival analyses that explicitly account in the statistical model for the times at which time dependent covariates are measured provide more reliable estimates compared to unadjusted analyses. We present results from the Framingham Heart Study in which lipid measurements and myocardial infarction data events were collected over a period of 26 years.

Duke Scholars

Published In

BMC medical research methodology

EISSN

1471-2288

Publication Date

November 3, 2016

Volume

16

Issue

1

Start / End Page

148

Related Subject Headings

  • General & Internal Medicine
  • 4206 Public health
  • 4202 Epidemiology
  • 1117 Public Health and Health Services
 

Citation

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Ngwa, J. S., Cabral, H. J., Cheng, D. M., Pencina, M. J., Gagnon, D. R., LaValley, M. P., & Cupples, L. A. (2016). A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study. BMC Medical Research Methodology, 16(1), 148.
Ngwa, J. S., H. J. Cabral, D. M. Cheng, M. J. Pencina, D. R. Gagnon, M. P. LaValley, and L. A. Cupples. “A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study.” BMC Medical Research Methodology 16, no. 1 (November 3, 2016): 148.
Ngwa JS, Cabral HJ, Cheng DM, Pencina MJ, Gagnon DR, LaValley MP, et al. A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study. BMC medical research methodology. 2016 Nov 3;16(1):148.
Ngwa JS, Cabral HJ, Cheng DM, Pencina MJ, Gagnon DR, LaValley MP, Cupples LA. A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study. BMC medical research methodology. 2016 Nov 3;16(1):148.
Journal cover image

Published In

BMC medical research methodology

EISSN

1471-2288

Publication Date

November 3, 2016

Volume

16

Issue

1

Start / End Page

148

Related Subject Headings

  • General & Internal Medicine
  • 4206 Public health
  • 4202 Epidemiology
  • 1117 Public Health and Health Services