Scientific Figures - Revision history

Petunia: /* Epidemiologic studies of passive smoke (ETS) – not the same as science */ punctuation

2012-06-26T12:20:45Z

Epidemiologic studies of passive smoke (ETS) – not the same as science: punctuation

Petunia: /* Recommended Reading */ punctuation

2012-06-26T12:16:45Z

Petunia: /* How to interpret scientific epidemiology reports? */ hyphens to en-dashes

2012-06-26T12:16:16Z

How to interpret scientific epidemiology reports?: hyphens to en-dashes

Petunia: /* Relative Risk/Odds Ratio */ spaces added

2012-06-26T12:13:13Z

Relative Risk/Odds Ratio: spaces added

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2012-06-26T12:12:21Z

Relative Risk/Odds Ratio: spaces added

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2012-06-26T12:08:51Z

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2012-06-26T12:07:19Z

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Petunia: minor edits

2012-06-26T12:06:15Z

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2012-06-26T12:00:44Z

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Petunia at 11:56, 26 June 2012

2012-06-26T11:56:17Z

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 <blockquote>[I]t is commonly, but mistakenly, supposed that multiple regression, logistic regression, or various forms of standardization can routinely be used to answer the question: “Is the correlation of exposure (E) with disease (D) due merely to a common correlation of both with some confounding factor (or factors)?”</blockquote>
-<blockquote>... Moreover, it is obvious that multiple regressions cannot correct for important variables that have not been recorded at all. "…[T]hese disadvantages limit the value of observations in humans, but ... until we know exactly how cancer is caused and how some factors are able to modify the effects of others, the need to observe imaginatively what actually happens to various different categories of people will remain."<ref>Doll R, Peto R, The causes of cancer, JNCI 66:1192–1312, 1981, p. 1281</ref></blockquote>
+<blockquote>... Moreover, it is obvious that multiple regressions cannot correct for important variables that have not been recorded at all. "…[T]hese disadvantages limit the value of observations in humans, but ... until we know exactly how cancer is caused and how some factors are able to modify the effects of others, the need to observe imaginatively what actually happens to various different categories of people will remain."<ref>Doll R, Peto R, The causes of cancer, JNCI 66:1192–1312, 1981, p. 1281.</ref></blockquote>
 (The “multiple regression” and “logical regression” referred to in the quotes above are techniques used in the statistics of epidemiology.)

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 ===Recommended Reading===
-*Above selections are primarily extracts taken from [[Media:ETS4Dummies.pdf]].
+*Above selections are primarily extracts taken from [[Media:ETS4Dummies.pdf]]
 *Also read [http://www.olivernorvell.com/ThePlainTruthAboutTobacco.pdf The Plain Truth About Tobacco]
 *Search the evidence in [http://www.forces.org/Scientific_Portal/ the Forces Scientific Portal]
 *[http://www.henrysturman.com/articles/passivesmoking.html The making of ETS: Lying about passive smoking]
 *[http://www.henrysturman.com/articles/passivesmokinglies.html Lies About Secondhand Smoke]

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 [[File:RR_CI.png|right|Example 4]]So let's have a look at some examples:
-#RR or OR = 1.9 (95%CI 1.2-4.6) means that the best estimate of the risk may be 1.9, but that its true value could be anywhere between 1.2 and 4.6, with a probability of 95% that it is indeed in that range, and a 5% possibility that the true value is actually outside that range! It also means that within that range all values are statistically significant at the 95% level, because all would mean an increase of risk, the lowest value still being >1.
+#RR or OR = 1.9 (95%CI 1.2–4.6) means that the best estimate of the risk may be 1.9, but that its true value could be anywhere between 1.2 and 4.6, with a probability of 95% that it is indeed in that range, and a 5% possibility that the true value is actually outside that range! It also means that within that range all values are statistically significant at the 95% level, because all would mean an increase of risk, the lowest value still being >1.
 #RR or OR = 1.9 (95%CI 0.7–2.3) ) means that the best estimate of the risk may be 1.9, but that its true value could be between 0.7 and 2.3, with a probability of 95%. It also means that some values could be <1 and could mean protection, others could be >1 and could mean risk. As a consequence the result is said to be equivocal and not statistically significant.
-#RR or OR = 0.7 (95%CI 0.2-0.9) ) means that the best estimate of the risk may be 0.7, but that its true value could be anywhere between 0.2 and 0.9, with a probability of 95%. It also means that within that range all values are statistically significant at the 95% level, because all would mean a reduction of risk, the highest value still being <1.
+#RR or OR = 0.7 (95%CI 0.2–0.9) ) means that the best estimate of the risk may be 0.7, but that its true value could be anywhere between 0.2 and 0.9, with a probability of 95%. It also means that within that range all values are statistically significant at the 95% level, because all would mean a reduction of risk, the highest value still being <1.
-#RR or OR = 0.7 (95%CI 0.3-1.9) means that the best estimate of the risk may be 0.7, but that its true value can only be said to be between 0.3 and 1.9, with a probability of 95%. It also means that some values could be <1 and could mean protection, others could be >1 and could mean risk. As a consequence the result is said to be equivocal and not statistically significant.
+#RR or OR = 0.7 (95%CI 0.3–1.9) means that the best estimate of the risk may be 0.7, but that its true value can only be said to be between 0.3 and 1.9, with a probability of 95%. It also means that some values could be <1 and could mean protection, others could be >1 and could mean risk. As a consequence the result is said to be equivocal and not statistically significant.
 ===Confounders or co-factors===

@@ Line 70: / Line 70: @@
 [[File:OR.png]]
-In the above ratio, if the odds are the same in exposed and non-exposed subjects, the OR=1 and there is no inference of  difference in risk. If OR is greater than 1, there is an inference of increased risk in the cases. If OR is smaller than 1, there is an inference of decreased risk for the cases, indicating that the exposure may possibly protect against the disease under study.
+In the above ratio, if the odds are the same in exposed and non-exposed subjects, the OR = 1 and there is no inference of  difference in risk. If OR is greater than 1, there is an inference of increased risk in the cases. If OR is smaller than 1, there is an inference of decreased risk for the cases, indicating that the exposure may possibly protect against the disease under study.
 Both cohort and case-control studies are affected by similar difficulties of design, data collection, and interpretation — difficulties that are far worse for case-control studies that uniquely rely on vague recollections of exposure.

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 Thus, the disease incidence rate in the exposed subjects is simply divided by the incidence rate in non-exposed subjects. The RR ratio reflects that a certain incidence of disease is observed in both non-exposed and exposed subjects, due to multiple background causes operating in conjunction with, or entirely separate from the exposure under study. Therefore, risk in the exposed is said to be an increment or decrement of incidence, relative to the basic incidence of the non-exposed subjects.
-In the RR ratio above, if the rates are the same in exposed and non-exposed subjects, the RR=1 and therefore there is no risk differential.  If RR is greater than 1, the risk is said to be increased in the exposed subjects. If RR is smaller than 1, the risk is said to be decreased in the exposed subjects, indicating that the exposure under study might be possibly protective.
+In the RR ratio above, if the rates are the same in exposed and non-exposed subjects, the RR = 1 and therefore there is no risk differential.  If RR is greater than 1, the risk is said to be increased in the exposed subjects. If RR is smaller than 1, the risk is said to be decreased in the exposed subjects, indicating that the exposure under study might be possibly protective.
 Because case-control studies infer but do not directly estimate possible risk, their results are expressed as odds ratios (OR), namely the ratio between the odds (expressed as % or other rate) of being exposed for the cases and the controls:

@@ Line 11: / Line 11: @@
 The epidemiology of ETS (Environmental Tobacco Smoke) claims correlations of ETS exposure with cancer, cardiovascular, and other diseases that are not caused by single entities such as viruses or bacteria, but depend on a constellation of possible causes, none either necessary or sufficient. Laboratory and clinical studies have proven unable to determine specific causal mechanism for such diseases. In this regard, Doll and Peto, arguably the most prominent epidemiologists today, have concluded that:
-<blockquote>[E]pidemiological observations...have serious disadvantages... [T]hey can seldom be made according to the strict requirements of experimental science and therefore may be open to a variety of interpretations. A particular factor may be associated with some disease merely because of its association with some other factor that causes the disease, or the association may be an artefact due to some systematic bias in the information collection…</blockquote>
+<blockquote>[E]pidemiological observations ... have serious disadvantages ... [T]hey can seldom be made according to the strict requirements of experimental science and therefore may be open to a variety of interpretations. A particular factor may be associated with some disease merely because of its association with some other factor that causes the disease, or the association may be an artefact due to some systematic bias in the information collection …</blockquote>
 <blockquote>[I]t is commonly, but mistakenly, supposed that multiple regression, logistic regression, or various forms of standardization can routinely be used to answer the question: “Is the correlation of exposure (E) with disease (D) due merely to a common correlation of both with some confounding factor (or factors)?”</blockquote>
-<blockquote>... Moreover, it is obvious that multiple regressions cannot correct for important variables that have not been recorded at all. "…[T]hese disadvantages limit the value of observations in humans, but...until we know exactly how cancer is caused and how some factors are able to modify the effects of others, the need to observe imaginatively what actually happens to various different categories of people will remain."<ref>Doll R, Peto R, The causes of cancer, JNCI 66:1192–1312, 1981, p. 1281</ref></blockquote>
+<blockquote>... Moreover, it is obvious that multiple regressions cannot correct for important variables that have not been recorded at all. "…[T]hese disadvantages limit the value of observations in humans, but ... until we know exactly how cancer is caused and how some factors are able to modify the effects of others, the need to observe imaginatively what actually happens to various different categories of people will remain."<ref>Doll R, Peto R, The causes of cancer, JNCI 66:1192–1312, 1981, p. 1281</ref></blockquote>
 (The “multiple regression” and “logical regression” referred to in the quotes above are techniques used in the statistics of epidemiology.)

@@ Line 3: / Line 3: @@
 The basic foundational drive behind modern day smoking bans can be traced to former British Chief Medical Officer Sir George Godber and his chairing of a World Health Organization conference in 1975:
-The overall guideline from the panels and speakers at that conference was one advising Antismokers that to successfully eliminate smoking it would first be essential to foster a perception that would [http://tobaccodocuments.org/pm/2046323437-3484.html  “emphasize that active cigarette smokers injure those around them, including their families and, especially, any infants that might be exposed involuntarily to ETS"]
+The overall guideline from the panels and speakers at that conference was one advising Antismokers that to successfully eliminate smoking it would first be essential to foster a perception that would [http://tobaccodocuments.org/pm/2046323437-3484.html  “emphasize that active cigarette smokers injure those around them, including their families and, especially, any infants that might be exposed involuntarily to ETS"].
 This marked the beginning of the ETS Fraud and started the true downward spiral of the quality of science as concerns smoking and health issues overall.
@@ Line 11: / Line 11: @@
 The epidemiology of ETS (Environmental Tobacco Smoke) claims correlations of ETS exposure with cancer, cardiovascular, and other diseases that are not caused by single entities such as viruses or bacteria, but depend on a constellation of possible causes, none either necessary or sufficient. Laboratory and clinical studies have proven unable to determine specific causal mechanism for such diseases. In this regard, Doll and Peto, arguably the most prominent epidemiologists today, have concluded that:
-<blockquote>[E]pidemiological observations...have serious disadvantages... [T]hey can seldom be made according to the strict requirements of experimental science and therefore may be open to a variety of interpretations. A particular factor may be associated with some disease merely because of its association with some other factor that causes the disease, or the association may be an artefact due to some systematic bias in the information collection……..</blockquote>
+<blockquote>[E]pidemiological observations...have serious disadvantages... [T]hey can seldom be made according to the strict requirements of experimental science and therefore may be open to a variety of interpretations. A particular factor may be associated with some disease merely because of its association with some other factor that causes the disease, or the association may be an artefact due to some systematic bias in the information collection…..</blockquote>
 <blockquote>[I]t is commonly, but mistakenly, supposed that multiple regression, logistic regression, or various forms of standardization can routinely be used to answer the question: “Is the correlation of exposure (E) with disease (D) due merely to a common correlation of both with some confounding factor (or factors)?”</blockquote>
-<blockquote>... Moreover, it is obvious that multiple regressions cannot correct for important variables that have not been recorded at all. "…….[T]hese disadvantages limit the value of observations in humans, but...until we know exactly how cancer is caused and how some factors are able to modify the effects of others, the need to observe imaginatively what actually happens to various different categories of people will remain."<ref>Doll R, Peto R, The causes of cancer, JNCI 66:1192–1312, 1981, p. 1281</ref></blockquote>
+<blockquote>... Moreover, it is obvious that multiple regressions cannot correct for important variables that have not been recorded at all. "…[T]hese disadvantages limit the value of observations in humans, but...until we know exactly how cancer is caused and how some factors are able to modify the effects of others, the need to observe imaginatively what actually happens to various different categories of people will remain."<ref>Doll R, Peto R, The causes of cancer, JNCI 66:1192–1312, 1981, p. 1281</ref></blockquote>
 (The “multiple regression” and “logical regression” referred to in the quotes above are techniques used in the statistics of epidemiology.)

@@ Line 99: / Line 99: @@
 <blockquote>"In epidemiologic research, [increases in risk of less than 100 percent] are considered small and are usually difficult to interpret. Such increases may be due to chance, statistical bias, or the effects of confounding factors that are sometimes not evident." National Cancer Institute, Press Release, October 26, 1994</blockquote>
 <blockquote>"As a general rule of thumb, we are looking for a relative risk of 3 or more before accepting a paper for publication." Marcia Angell, editor of the New England Journal of Medicine</blockquote>
-<blockquote>"My basic rule is if the relative risk isn't at least 3 or 4, forget it." Robert Temple, director of drug evaluation at the Food and Drug Administration.</blockquote>
+<blockquote>"My basic rule is if the relative risk isn't at least 3 or 4, forget it." Robert Temple, director of drug evaluation at the Food and Drug Administration</blockquote>
 <blockquote>"An association is generally considered weak if the odds ratio [relative risk] is under 3.0 and particularly when it is under 2.0, as is the case in the relationship of ETS and lung cancer." Dr. Kabat, IAQC epidemiologist</blockquote>