%0 Journal Article
%T Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon ¨C the reversal paradox
%A Yu-Kang Tu
%A David Gunnell
%A Mark S Gilthorpe
%J Emerging Themes in Epidemiology
%D 2008
%I BioMed Central
%R 10.1186/1742-7622-5-2
%X This article discusses three statistical paradoxes that pervade epidemiological research: Simpson's paradox, Lord's paradox, and suppression. These paradoxes are not just tantalising puzzles of purely academic interest; potentially, they have serious implications for the interpretation of evidence from observational studies. Scenarios which are associated with and can be explained by these paradoxes are discussed. A concise explanation of these paradoxes and an historical overview is also provided. Simulated data based upon the foetal origins of adult diseases hypothesis [1,2] are used to illustrate how the three paradoxes are different manifestations of one phenomenon ¨C the reversal paradox ¨C depending on whether the outcome and explanatory variables are categorical, continuous or a combination of both; this renders the issues and remedies for any one to be similar for all three. All statistical analyses were performed within SPSS 15.0 (SPSS Inc, Chicago, USA).The 'foetal origins of adult disease' hypothesis (FOAD), which has evolved into the 'developmental origins of health and disease' (DOHaD) hypothesis [1,2], was proposed to explain the associations observed between low birth weight and a range of diseases in later life. These associations have been interpreted as evidence that growth retardation in utero has adverse long-term effects on the development of vital organ systems which predispose the individuals to a range of metabolic and related disorders in later life. Nevertheless, although an inverse association between birth weight and disease in later life was found in some studies, this relationship was only established in many studies after the current body size variables such as body mass index (BMI), body weight and/or body height were adjusted for in the regression analysis. As body sizes may be in the causal pathway from birth weight to health outcomes in later life, the justification of this adjustment of current body sizes has been questioned recentl
%U http://www.ete-online.com/content/5/1/2