Literature DB >> 9345122

Examination of "early mortality exclusion" as an approach to control for confounding by occult disease in epidemiologic studies of mortality risk factors.

D B Allison1, M Heo, D W Flanders, M S Faith, D F Williamson.   

Abstract

Methods for the estimation of the effects of chronic disease risk factors on mortality continue to be an area that generates confusion and controversy. In response to the frequently observed U- or J-shaped relations between risk factors and mortality, some authors suggest that subjects dying during the first k years of follow-up (where k is some positive number less than the total length of follow-up) be excluded from statistical analyses. By excluded, the authors mean completely removed from the data set. The rationale is that persons dying during the first k years are likely to have a preexisting occult disease that confounds the relation between the risk factor under study and mortality. Excluding persons dying during the first k years of follow-up purportedly reduces this confounding. However, the authors are aware of no demonstration that this procedure effectively accomplishes its goal. They show that excluding subjects who die during the first k years of follow-up does not necessarily lead to a reduction in bias in the estimated effect of a risk factor on mortality when this relation is confounded by the presence of occult disease. Moreover, it is possible for such exclusion to exacerbate the confounding due to preexisting disease. Thus, excluding subjects dying during the first k years of follow-up is not necessarily an effective strategy for dealing with confounding due to occult disease. Investigators are encouraged to pursue alternative methods.

Entities:  

Keywords:  Causes Of Death; Demographic Factors; Methodological Studies; Mortality; Population; Population Dynamics; World

Mesh:

Year:  1997        PMID: 9345122     DOI: 10.1093/oxfordjournals.aje.a009334

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  15 in total

1.  Overweight adults may have the lowest mortality--do they have the best health?

Authors:  Anna Zajacova; Jennifer Beam Dowd; Sarah A Burgard
Journal:  Am J Epidemiol       Date:  2011-01-12       Impact factor: 4.897

2.  Body mass index and mortality rate among Hispanic adults: a pooled analysis of multiple epidemiologic data sets.

Authors:  K R Fontaine; R McCubrey; T Mehta; N M Pajewski; S W Keith; S S Bangalore; C J Crespo; D B Allison
Journal:  Int J Obes (Lond)       Date:  2011-10-11       Impact factor: 5.095

3.  Association of body mass index and weight change with all-cause mortality in the elderly.

Authors:  María M Corrada; Claudia H Kawas; Farah Mozaffar; Annlia Paganini-Hill
Journal:  Am J Epidemiol       Date:  2006-04-26       Impact factor: 4.897

4.  Does exclusion of extreme reporters of energy intake (the "Goldberg cutoffs") reliably reduce or eliminate bias in nutrition studies? Analysis with illustrative associations of energy intake with health outcomes.

Authors:  Keisuke Ejima; Andrew W Brown; Dale A Schoeller; Steven B Heymsfield; Erik J Nelson; David B Allison
Journal:  Am J Clin Nutr       Date:  2019-11-01       Impact factor: 7.045

5.  Revealing the burden of obesity using weight histories.

Authors:  Andrew Stokes; Samuel H Preston
Journal:  Proc Natl Acad Sci U S A       Date:  2016-01-04       Impact factor: 11.205

6.  Validity of the WHO cutoffs for biologically implausible values of weight, height, and BMI in children and adolescents in NHANES from 1999 through 2012.

Authors:  David S Freedman; Hannah G Lawman; Asheley C Skinner; Lisa C McGuire; David B Allison; Cynthia L Ogden
Journal:  Am J Clin Nutr       Date:  2015-09-16       Impact factor: 7.045

7.  Defining cutoffs to diagnose obesity using the relative fat mass (RFM): Association with mortality in NHANES 1999-2014.

Authors:  Orison O Woolcott; Richard N Bergman
Journal:  Int J Obes (Lond)       Date:  2020-01-07       Impact factor: 5.095

8.  Shape of the BMI-mortality association by cause of death, using generalized additive models: NHIS 1986-2006.

Authors:  Anna Zajacova; Sarah A Burgard
Journal:  J Aging Health       Date:  2011-05-10

9.  Body mass index and mortality: results of a cohort of 184,697 adults in Austria.

Authors:  Jochen Klenk; Gabriele Nagel; Hanno Ulmer; Alexander Strasak; Hans Concin; Günter Diem; Kilian Rapp
Journal:  Eur J Epidemiol       Date:  2009-01-29       Impact factor: 8.082

10.  The prevalence and validity of high, biologically implausible values of weight, height, and BMI among 8.8 million children.

Authors:  David S Freedman; Hannah G Lawman; Liping Pan; Asheley C Skinner; David B Allison; Lisa C McGuire; Heidi M Blanck
Journal:  Obesity (Silver Spring)       Date:  2016-03-17       Impact factor: 5.002

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.