Idea: Relationships among associations, predictions, causes, and interventions run through all the cases and controversies in this course. The idea introduced in this session is that epidemiology has two faces: One from which the thinking about associations, predictions, causes, and interventions are allowed to cross-fertilize, and the other from which the distinctions among them are vigorously maintained, as in “Correlation is not causation!” The second face views Randomized Control Trial (RCTs) as the “gold-standard” for testing treatments in medicine. The first face recognizes that many hypotheses about treatment and other interventions emerge from observational studies and often such studies provide the only data we have to work with. What are the shortcomings of observational studies we need to pay attention to (e.g., systematic sampling errors leading to unmeasured confounders-see next post)?
Ridker et al. show that the conventional risk factors for heart disease in women (as combined in the Framingham score) identify many women as of intermediate risk who are higher or lower risk. The new Reynolds Risk Score does a much better job, primarily it seems by including the risk marker cReactive Protein. Both scores are based on observations not randomized trials. (But see Shunkert for recent assessment of the role of CRP.)
The case of hormone replacement therapy as a protection against heart disease (Stampfer 1990) is another, more significant instance of mismatch of observational results and RCTs — see Stampfer 2004 & Pettiti for analyses of the discrepancy. It is important to get a handle on the different kinds of explanation for this and other discrepancies, including physician bias in who gets prescribed a treatment, residual confounders, and reverse causation.
Jick presents evidence that statin treatment was associated with lowered risk of dementia but the Alzheimer Research Forum presents the more recent assessment (using RCTs) that statins are not protective against dementia. The discrepancy seems to be undetected bias in which patients get prescribed statins.
Davey-Smith & Ebrahim (2007, pp.2-8) provide a quick review of a number of cases.
(This post continues a series laying out a sequence of basic ideas in thinking like epidemiologists, especially epidemiologists who pay attention to possible social influences on the development and unequal distribution of diseases and behaviors in populations [see first post in series and contribute to open-source curriculum http://bit.ly/EpiContribute].)
Alzheimer_Research_Forum (2004). “Philadelphia: All Is Not Well with the Statin Story.” http://www.alzforum.org/new/detailprint.asp?id=1046.
Davey-Smith, G. and S. Ebrahim (2007). “Mendelian randomization: Genetic variants as instruments for strengthening causal influences in observational studies. Pp 336-366 in Weinstein, M., Vaupel, J. W., Wachter, K.W. (eds) Biosocial Surveys. Washington, DC, National Academies Press.
Jick, H., G. L. Zomberg, et al. (2000). “Statins and the risk of dementia.” Lancet 356: 1627-1631.
Petitti, D. B. and D. A. Freedman (2005). “Invited Commentary: How Far Can Epidemiologists Get with Statistical Adjustment?” American Journal of Epidemiology 162: 415-418.
Ridker, P. M., J. E. Buring, et al. (2007). “Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women: The Reynolds Risk Score.” Journal of the American Medical Association 297: 611-619.
Schunkert, H. and N. J. Samani (2008). “Elevated C-Reactive Protein in Atherosclerosis – Chicken or Egg?” New England Journal of Medicine 359(18): 1953-1955.
Stampfer, M. J. and G. A. Colditz (1991). “Estrogen replacement therapy and coronary heart disease: a quantitative assessment of the epidemiologic evidence.” Preventive Medicine 20: 47-63.
Stampfer, M. J.(2004) “Commentary: Hormones and heart disease: do trials and observational studies address different questions?” International Journal of Epidemiology 33: 545-455.