Associations, Predictions, Causes, and Interventions

Table of Contents

Associations, Predictions, Causes, and Interventions
Initial notes
Annotations on common readings
Annotated additions by students
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 class)?

Initial notes

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 of shortcomings of observational studies.

Krieger and Davey-Smith (2016) show how two leading social epidemiologists are rethinking the reliance on RCTs for thinking about causality (and resisting the reliance on DAGs for thinking about confounding--see session 6).

Mini-lecture
Notes and annotations from 2007 course, 2009
Common readings and cases: Ridker 2007 (Cardiac risk factors), Stampfer 1991, 2004 (Hormone replacement therapy)
Supplementary Reading: Alzheimer Research Forum 2004, Davey-Smith & Ebrahim 2007,pp2-8, Jick 2000, Petitti 2004, Shunkert 2008

Annotations on common readings



Annotated additions by students

(In alphabetical order by author's name with contributor's initials and date at the end.)

Diana Petitti, Commentary: Hormone replacement therapy and coronary heart disease: four lessons, International Journal of Epidemiology 2004;33:461–463
The current prevailing epidemiological stance (bolstered by the results of large randomized, placebo-controlled trials) is that there is no categorical beneficial effect of combined hormone replacement therapy on morbidity or mortality from CHD in individuals with established coronary disease. In this commentary, Diana Petitti espouses four lessons garnered from the epidemiological narrative of the effects of hormone replacement therapy on coronary heart disease. Petitti dissects the 1991 findings of Stampfer and Colditz meta-analytic study of the effects of postmenopausal oestrogen on coronary heart disease; a paper widely cited as encapsulating a body of epidemiologic evidence (the derivation of sound experimental studies) that overwhelmingly endorsed the capacity of homorne replacemnet therapy to prevent coronary heart disease. Firstly, the author dissuades the researcher from turning a blind eye to contradiction. Petitti contends that by not exploring extant results of crossed effects relating to sex, dose and age present in the available literature, Stampfer and Colditz effectively ignored contradictions. Secondly, the author cautions the researcher of not being seduced by a particular mecahnism. Stampfer and Colditz promoted the effects of oestrogen on the lipid profile as being the likely mechanism for lowering CHD risk. Petitti argues that in 1991, there was available knowledge that hormone replacement therapy increased triglyceride levels (a negative trend) and furthermore it exhibited complex effects on a number of factors involved in coagulation and atherosclerosis. Moreover, Petitti reasons that “even if all the mechanistic data had pointed in the same direction”, exhaustive understanding of mechanisms is never achieved. Thirdly, the author advises the researcher to suspend belief. Petitti surmises that Stampfer and Colditz failure to acknowledge socioeconomic status as a potential uncontrolled confounder influencing coronary heart disease risk (a well-studied phenomenon) may perhaps be indicative of their belief that prospective designs could overcome the confounding. Petitti also speculates that Stampfer and Colditz’s belief may have been driven by the misleading narrowness of their confidence interval. Fourthly, Petitti suggests that the researcher maintain scepticism. The author recognizes the remnant of legitimate questions about hormone replacement therapy mediated effects on CHD risk. Thus, Petitti asserts that it is not outside the realm of possibilities for science to renew its pursuit of a favorable mechanism to prevent coronary heart disease utilizing hormone replacement therapy. (SY)