Katherine M Flegal1, Barry I Graubard2, Sang-Wook Yi3. 1. Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. 2. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA. 3. Department of Preventive Medicine and Public Health, Catholic Kwandong University College of Medicine, Gangneung, Korea.
Abstract
BACKGROUND: A method applied in some large studies of weight and mortality is to begin with a well-defined analytic cohort and use successive restrictions to control for methodologic bias and arrive at final analytic results. MATERIALS AND METHODS: Two observational studies of body mass index and mortality allow a comparative assessment of these restrictions in very large data sets. One was a meta-analysis of individual participant data with a sample size of 8 million. The second was a study of a South Korean cohort with a sample size of 12 million. Both presented results for participants without pre-existing disease before and after restricting the sample to never-smokers and deleting the first 5 years of follow-up. RESULTS: Initial results from both studies were generally similar, with hazard ratios (HRs) below 1 for overweight and above 1 for underweight and obesity. The meta-analysis showed higher HRs for overweight and obesity after the restrictions, including a change in the direction of the HR for overweight from 0·99 (95% CI: 0·98-1·01) to 1·11 (95% CI: 1·10, 1·11). The South Korean data showed little effect of the restrictions and the HR for overweight changed from 0·85 (95% CI: 0·84-0·86) to 0·91 (95% CI: 0·90, 0·91). The summary effect size for overweight was 0·90 (95% CI: 0·89-0·91) before restrictions and 1·02 (95% CI: 1·02, 1·03) after restrictions. CONCLUSIONS: The effect of the restrictions is not consistent across studies, weakening the argument that analyses without such restrictions lack validity.
BACKGROUND: A method applied in some large studies of weight and mortality is to begin with a well-defined analytic cohort and use successive restrictions to control for methodologic bias and arrive at final analytic results. MATERIALS AND METHODS: Two observational studies of body mass index and mortality allow a comparative assessment of these restrictions in very large data sets. One was a meta-analysis of individual participant data with a sample size of 8 million. The second was a study of a South Korean cohort with a sample size of 12 million. Both presented results for participants without pre-existing disease before and after restricting the sample to never-smokers and deleting the first 5 years of follow-up. RESULTS: Initial results from both studies were generally similar, with hazard ratios (HRs) below 1 for overweight and above 1 for underweight and obesity. The meta-analysis showed higher HRs for overweight and obesity after the restrictions, including a change in the direction of the HR for overweight from 0·99 (95% CI: 0·98-1·01) to 1·11 (95% CI: 1·10, 1·11). The South Korean data showed little effect of the restrictions and the HR for overweight changed from 0·85 (95% CI: 0·84-0·86) to 0·91 (95% CI: 0·90, 0·91). The summary effect size for overweight was 0·90 (95% CI: 0·89-0·91) before restrictions and 1·02 (95% CI: 1·02, 1·03) after restrictions. CONCLUSIONS: The effect of the restrictions is not consistent across studies, weakening the argument that analyses without such restrictions lack validity.
Authors: I Baik; A Ascherio; E B Rimm; E Giovannucci; D Spiegelman; M J Stampfer; W C Willett Journal: Am J Epidemiol Date: 2000-08-01 Impact factor: 4.897
Authors: Umed A Ajani; Paulo A Lotufo; J Michael Gaziano; I-Min Lee; Angela Spelsberg; Julie E Buring; Walter C Willett; Joann E Manson Journal: Ann Epidemiol Date: 2004-11 Impact factor: 3.797
Authors: Hailey R Banack; Jennifer W Bea; Jay S Kaufman; Andrew Stokes; Candyce H Kroenke; Marcia L Stefanick; Shirley A Beresford; Chloe E Bird; Lorena Garcia; Robert Wallace; Robert A Wild; Bette Caan; Jean Wactawski-Wende Journal: Am J Epidemiol Date: 2019-10-01 Impact factor: 4.897
Authors: Sebastian E Baumeister; Inga Schlecht; Christa Meisinger; Michael F Leitzmann; Britton Trabert; Michael Nolde Journal: Cancer Causes Control Date: 2021-01-22 Impact factor: 2.506
Authors: Mathew Vithayathil; Paul Carter; Siddhartha Kar; Amy M Mason; Stephen Burgess; Susanna C Larsson Journal: PLoS Med Date: 2021-07-29 Impact factor: 11.613