2004
DOI: 10.1016/s0140-6736(04)16260-0
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Those confounded vitamins: what can we learn from the differences between observational versus randomised trial evidence?

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Cited by 404 publications
(278 citation statements)
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“…Blood samples were analysed in this subsample to provide objective validation of effects established in the analysis of FFQ data for two antioxidant vitamins (C and E), and two mineral cofactors (selenium and copper) defined a priori to be of primary interest in the original study protocol. While we are unable to exclude the possibility of confounding by lifestyle factors (Lawlor et al, 2004), these observations warrant detailed consideration.…”
Section: Discussionmentioning
confidence: 74%
“…Blood samples were analysed in this subsample to provide objective validation of effects established in the analysis of FFQ data for two antioxidant vitamins (C and E), and two mineral cofactors (selenium and copper) defined a priori to be of primary interest in the original study protocol. While we are unable to exclude the possibility of confounding by lifestyle factors (Lawlor et al, 2004), these observations warrant detailed consideration.…”
Section: Discussionmentioning
confidence: 74%
“…For example, there have been many claims about the benefits of various vitamin supplements based on observational studies 128,129 that have been reliably refuted by large-scale randomized trials. [130][131][132] Similarly, when compared with the results from randomized trials of the effects of treatments for several different cancers, observational studies have generated improbable results despite controlling for comorbidity, extent of disease and many other characteristics that were recorded in detailed databases [133][134][135] (as is also the case for reported associations of statins with lower rates of cancer 90,[136][137][138] ). These findings are consistent with empirical studies in which biases in observational studies were shown to be large enough to conclude falsely that treatment produced benefit or harm, with none of a range of statistical strategies (such as regression analysis or propensity matching) capable of adjusting adequately or predictably for bias.…”
Section: Biases Due To Differences In Underlying Risks Of Health Outcmentioning
confidence: 99%
“…Confounding is not a new phenomenon in the world of vitamin epidemiology. We and others have shown that social class and other lifestyle factors influence circulating levels of vitamins C and E and carotenoids, and as such confound associations of circulating vitamin levels with risk of chronic disease (37,38). Many similar confounding factors are potentially relevant in the field of vitamin D epidemiology, including studies on its relationship to inflammatory arthritis (Figure 1).…”
Section: Are Reverse Causality and Residual Confounding Potential Limmentioning
confidence: 97%