American Journal of Epidemiology Advance Access published online on July 21, 2007
American Journal of Epidemiology, doi:10.1093/aje/kwm145
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Biases in the Identification of Risk Factor Thresholds and J-Curves
1 Asia Biometrics Center, Pfizer Australia, Sydney, Australia
2 National Health and Medical Research Council Clinical Trials Center, University of Sydney, Sydney, Australia
Correspondence to Dr. Ian C. Marschner, Asia Biometrics Center, Pfizer Australia, 38-42 Wharf Road, West Ryde, NSW 2114, Australia (e-mail: ian.marschner{at}pfizer.com).
Received for publication March 15, 2006. Accepted for publication April 9, 2007.
For some diseases, there has been controversy about whether key risk factors are related linearly to the occurrence of disease events. This issue has important implications for strategies to modify risk factors, since nonlinear threshold or J-curve associations imply that risk factor modification is not beneficial beyond a certain level. This paper considers whether nonlinear risk factor associations can arise spuriously from selection mechanisms common in prospective cohort studies. Using theory, simulation, and cohort data, the authors show that selecting individuals based on their prior disease status leads to the primary risk factor being negatively confounded with other residual risk factors. If this confounding combines with effect modification between the primary and residual risk factors, as exists in cardiovascular disease, then the aggregate effect is nonlinear distortion of the risk factor relation. Such distortion can produce an apparent threshold or J-curve relation, even if the true underlying relation is linear. The authors conclude that nonlinear risk factor associations observed in primary or secondary prevention cohorts should be interpreted with caution because they may be consistent with an underlying linear lower-is-better relation. Randomized studies provide an important complement to prospective cohort studies when choosing between intensive and moderate risk factor modification strategies in high-risk populations.
bias (epidemiology); cardiovascular diseases; confounding factors (epidemiology); effect modifiers (epidemiology); incidence; primary prevention; risk factors
Abbreviations: LIPID, Long-Term Intervention with Pravastatin in Ischemic Disease
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  J. Cui, N. de Klerk, M. Abramson, A. Del Monaco, G. Benke, M. Dennekamp, A. W. Musk, and M. Sim Fractional Polynomials and Model Selection in Generalized Estimating Equations Analysis, With an Application to a Longitudinal Epidemiologic Study in Australia Am. J. Epidemiol., January 1, 2009; 169(1): 113 - 121. [Abstract] [Full Text] [PDF]
|
|