CORRESPONDENCE Bayesian Information Criterion) can be used to select the final model regarding the number of knots, the degree of spline, and COPD status. n Author disclosures are available with the text of this letter at www.atsjournals.org. Gregory Marin, Ph.D. CHU Montpellier Montpellier, France Nicolas Molinari, Ph.D. CHU Montpellier Montpellier, France and Inserm U 1046, UM1 UM2 Montpellier, France Anne Sophie Gamez, M.D. Isabelle Vachier, Ph.D. CHU Montpellier Montpellier, France Arnaud Bourdin, M.D., Ph.D. CHU Montpellier Montpellier, France and Inserm U 1046, UM1 UM2 Montpellier, France
References 1. Sood A, Petersen H, Meek P, Tesfaigzi Y. Spirometry and health status worsen with weight gain in obese but improve in normal-weight smokers. Am J Respir Crit Care Med 2014;189:274–281. 2. Jordan JG Jr, Mann JR. Obesity and mortality in persons with obstructive lung disease using data from the NHANES III. South Med J 2010;103:323–330. 3. Fletcher C, Peto R. The natural history of chronic airflow obstruction. BMJ 1977;1:1645–1648. 4. Vestbo J, Edwards LD, Scanlon PD, Yates JC, Agusti A, Bakke P, Calverley PM, Celli B, Coxson HO, Crim C, et al.; ECLIPSE Investigators. Changes in forced expiratory volume in 1 second over time in COPD. N Engl J Med 2011;365:1184–1192 5. de Boor C. A practical guide to splines. New York: Springer-Verlag; 1978. 6. Bessaoud F, Daures JP, Molinari N. Free knot splines for logistic models and threshold selection. Comput Methods Programs Biomed 2005;77:1–9.
We separately presented our analysis on chronic obstructive pulmonary disease (COPD)-only subjects in the online supplement (Tables E3 and E4). In the manuscript, we advised the readers that our subjects with COPD primarily had GOLD stage I and II disease and that our results were therefore generalizable to relatively milder COPD. (We chose not to focus on advanced COPD because the linear association of respiratory outcomes on body mass index for these subjects had been previously described by Landbo and colleagues [2]). We found that our associations were generally similar between those with COPD and those at-risk for COPD, and, therefore, we believe our approach of combining the two groups is reasonable. We agree that readers may be able to better visualize nonlinear relationships generated by generalized regression spline graphics than by using our traditional tabular statistical approach. The authors of the letter also suggest the use of generalized regression spline modeling to construct data-driven threshold values for defining groups. However, we feel that the use of generalized regression spline modeling is more complicated for the reader to understand, and comparisons for statistical significance are more difficult, as the authors of the letter suggest. We defined our body mass index categories based on well-accepted norms of normal-weight, overweight, and obese (3). This allowed for greater ease of reader understanding. Therefore, we decided against the use of generalized regression spline modeling to define our category thresholds, although we admit that the proposed approach might be interesting. n Author disclosures are available with the text of this letter at www.atsjournals.org. Akshay Sood, M.D., M.P.H. University of New Mexico Albuquerque, New Mexico Hans Petersen, M.S. Lovelace Respiratory Research Institute Albuquerque, New Mexico Paula Meek, RN, Ph.D. University of Colorado-Denver Denver, Colorado Yohannes Tesfaigzi, Ph.D. Lovelace Respiratory Research Institute Albuquerque, New Mexico
References Copyright © 2014 by the American Thoracic Society
Reply From the Authors: We thank Marin and colleagues for their interest in our article (1), for proposing novel nonlinear analytic techniques, and for giving us the opportunity to respond to their letter.
Correspondence
1. Sood A, Petersen H, Meek P, Tesfaigzi Y. Spirometry and health status worsen with weight gain in obese but improve in normal-weight smokers. Am J Respir Crit Care Med 2014;189:274–281. 2. Landbo C, Prescott E, Lange P, Vestbo J, Almdal TP. Prognostic value of nutritional status in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1999;160:1856–1861. 3. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157–163.
Copyright © 2014 by the American Thoracic Society
871
References 1. Sood A, Petersen H, Meek P, Tesfaigzi Y. Spirometry and health status worsen with weight gain in obese but improve in normal-weight smokers. Am J Respir Crit Care Med 2014;189:274–281. 2. Jordan JG Jr, Mann JR. Obesity and mortality in persons with obstructive lung disease using data from the NHANES III. South Med J 2010;103:323–330. 3. Fletcher C, Peto R. The natural history of chronic airflow obstruction. BMJ 1977;1:1645–1648. 4. Vestbo J, Edwards LD, Scanlon PD, Yates JC, Agusti A, Bakke P, Calverley PM, Celli B, Coxson HO, Crim C, et al.; ECLIPSE Investigators. Changes in forced expiratory volume in 1 second over time in COPD. N Engl J Med 2011;365:1184–1192 5. de Boor C. A practical guide to splines. New York: Springer-Verlag; 1978. 6. Bessaoud F, Daures JP, Molinari N. Free knot splines for logistic models and threshold selection. Comput Methods Programs Biomed 2005;77:1–9.
We separately presented our analysis on chronic obstructive pulmonary disease (COPD)-only subjects in the online supplement (Tables E3 and E4). In the manuscript, we advised the readers that our subjects with COPD primarily had GOLD stage I and II disease and that our results were therefore generalizable to relatively milder COPD. (We chose not to focus on advanced COPD because the linear association of respiratory outcomes on body mass index for these subjects had been previously described by Landbo and colleagues [2]). We found that our associations were generally similar between those with COPD and those at-risk for COPD, and, therefore, we believe our approach of combining the two groups is reasonable. We agree that readers may be able to better visualize nonlinear relationships generated by generalized regression spline graphics than by using our traditional tabular statistical approach. The authors of the letter also suggest the use of generalized regression spline modeling to construct data-driven threshold values for defining groups. However, we feel that the use of generalized regression spline modeling is more complicated for the reader to understand, and comparisons for statistical significance are more difficult, as the authors of the letter suggest. We defined our body mass index categories based on well-accepted norms of normal-weight, overweight, and obese (3). This allowed for greater ease of reader understanding. Therefore, we decided against the use of generalized regression spline modeling to define our category thresholds, although we admit that the proposed approach might be interesting. n Author disclosures are available with the text of this letter at www.atsjournals.org. Akshay Sood, M.D., M.P.H. University of New Mexico Albuquerque, New Mexico Hans Petersen, M.S. Lovelace Respiratory Research Institute Albuquerque, New Mexico Paula Meek, RN, Ph.D. University of Colorado-Denver Denver, Colorado Yohannes Tesfaigzi, Ph.D. Lovelace Respiratory Research Institute Albuquerque, New Mexico
References Copyright © 2014 by the American Thoracic Society
Reply From the Authors: We thank Marin and colleagues for their interest in our article (1), for proposing novel nonlinear analytic techniques, and for giving us the opportunity to respond to their letter.
Correspondence
1. Sood A, Petersen H, Meek P, Tesfaigzi Y. Spirometry and health status worsen with weight gain in obese but improve in normal-weight smokers. Am J Respir Crit Care Med 2014;189:274–281. 2. Landbo C, Prescott E, Lange P, Vestbo J, Almdal TP. Prognostic value of nutritional status in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1999;160:1856–1861. 3. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157–163.
Copyright © 2014 by the American Thoracic Society
871
