In order to look at the studies and judge for yourself, you have to learn about the terminology and what they mean.
(RR)Risk ratio In statistics and mathematical epidemiology, relative risk (RR) is the risk of an event (or of developing a disease) relative to exposure. Relative risk is a ratio of the probability of the event occurring in the exposed group versus a non-exposed group.
1 is considered no risk.
1.25 would indicate a 25% increased risk.
0.75 would indicate a negative effect or protective
2.0 would indicate a doubling of risk or 100% increase
No to put this into perspective if you bought 2 lottery tickets instead of one you doubled your chances of winning or RR=2.0.
Now There is a lot of contention on what is considered conclusive proof at what level proves cause. The courts considers a RR of 2 to be the bare minimum to establish cause.(From Reference Manual on Scientific Evidence)
The anti-smoking activist contend that if the results are repeated often enough that very low RR’s are perfectly acceptable and the science is in and conclusive. The problem with this statement they can’t name another cause of a disease with equally low RR’s that is considered conclusive. As a matter of fact an award winning article From Epidemiology faces its limits asked this very question,
That’s the rationale for meta-analysis — a technique for combining many ambiguous studies to see whether they tend in the same direction (Science, 3 August 1990, p. 476).
But when Science asked epidemiologists to identify weak associations that are now considered convincing because they show up repeatedly, opinions were divided — consistently. . . . Bias times 12 is still bias.” What’s more, the epidemiologists interviewed by Science point out that an apparently consistent body of published reports showing a positive association between a risk factor and a disease may leave out other, negative findings that never saw the light of day.
Problems with low RR’s
A confounding variable confounders are behavioral patterns or biological conditions which may be a risk factor for the disease under investigation. To be an actual confounder, however, these patterns and/or conditions must be associated with the exposure under study in that study. This pattern and/or condition also must be present in sufficient strength to be a plausible source of the excess risk in the situation under study. A third test of a candidate confounder can be made using dose-response observations.46 Any confounder that is to explain that risk likely would have to become stronger if and as the integrated ETS exposure increases.
To put this in plain english, it is possibility that other things are the actual cause and the statistical link is just coincidence.
Now the number of confounder’s for ETS studies are too numerous to name but here are some examples,radon,diet,heredity,Occupational exposure to chemicals or other toxins,viruses, etc etc etc.
Bias are the possibility of numerous errors within the study itself, with studies like ETS there are numerous possibility’s for errors, misclassification(classifying exsmokers as non-smokers),unavailability of comparison groups that have not been exposed to ETS,exposure misclassification and recall bias. For Meta-analysis, Publication bias is a real problem. We will talk about Meta-analysis in another installment.
Because of the confounder’s and bias problems, studies with low relative risk should be viewed with very sceptically.
Now for your first look at the studies.
EPIDEMIOLOGICALSTUDIES ON SHS
Next time we will get into the (CI) Confidence Intervals