A regression model for risk difference estimation in population-based case–control studies clarifies gender differences in lung cancer risk of smokers and never smokers

BMC Medical Research Methodology

Sholom Wacholder⁵  Hormuzd A Katki⁵  Andrew W Bergen²  Dario Consonni⁴  Ravi Varadhan¹  Neil E Caporaso⁵  Maria Teresa Landi⁵  Sara De Matteis⁴  Stephanie A Kovalchik³ 

[1] Division of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA;Molecular Genetics Program, Center for Health Sciences, SRI International, Menlo Park, CA, USA;Economics, Sociology, and Statistics Department, RAND Corporation, Santa Monica, CA, USA;Unit of Epidemiology, Department of Preventive Medicine, Fondazione IRCCS Ca’ Granda - Ospedale Maggiore Policlinico, Milan, Italy;Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

关键词: Smoking;    Sex factors;    Risk assessment;    Lung cancer;    EAGLE;    Case–control study;    Absolute risk;    Additive risk;   

Others  :  866566
DOI  :  10.1186/1471-2288-13-143

 received in 2013-07-25, accepted in 2013-11-07,  发布年份 2013

【 摘 要 】

Background

Additive risk models are necessary for understanding the joint effects of exposures on individual and population disease risk. Yet technical challenges have limited the consideration of additive risk models in case–control studies.

Methods

Using a flexible risk regression model that allows additive and multiplicative components to estimate absolute risks and risk differences, we report a new analysis of data from the population-based case–control Environment And Genetics in Lung cancer Etiology study, conducted in Northern Italy between 2002–2005. The analysis provides estimates of the gender-specific absolute risk (cumulative risk) for non-smoking- and smoking-associated lung cancer, adjusted for demographic, occupational, and smoking history variables.

Results

In the multiple-variable lexpit regression, the adjusted 3-year absolute risk of lung cancer in never smokers was 4.6 per 100,000 persons higher in women than men. However, the absolute increase in 3-year risk of lung cancer for every 10 additional pack-years smoked was less for women than men, 13.6 versus 52.9 per 100,000 persons.

Conclusions

In a Northern Italian population, the absolute risk of lung cancer among never smokers is higher in women than men but among smokers is lower in women than men. Lexpit regression is a novel approach to additive-multiplicative risk modeling that can contribute to clearer interpretation of population-based case–control studies.

【 授权许可】

   
2013 Kovalchik et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Breslow NE, Day NE: Statistical Methods in Cancer Research, Vol. I. The Design and Analysis of Case–control Studies, IARC Scientific Publication No. 32. New York, NY: Oxford University Press; 1980.
• [2]Greenland S: Interpretation and choice of effect measures in epidemiologic analyses. Am J Epidemiol 1987, 125(5):761-768.
• [3]Sackett DL, Deeks JJ, Altman DG: Down with odds ratios! Evid Based Med 1996, 1:164-166.
• [4]Skrondal A: Interaction as departure from additivity in case–control studies: a cautionary note. Am J Epidemiol 2003, 158:251-258.
• [5]Rothman KJ: Causes. Am J Epidemiol 1976, 104:587-593.
• [6]Knol MJ, VanderWeele TJ: Recommendations for presenting analyses of effect modification and interaction. Int J Epidemiol 2012, 41:514-520.
• [7]Wacholder S: The case–control study as data missing by design: estimating risk differences. Epidemiol 1996, 7(2):144-150.
• [8]Wacholder S: Binomial regression in GLIM: estimating risk ratios and risk differences. Am J Epidemiol 1986, 123:174-184.
• [9]Spiegelman D, Hertzmark E: Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol 2005, 162(3):199-200.
• [10]Landi MT, Consonni D, Rotunno M, Bergen AW, Goldstein AM, Lubin JH, Goldin L, Alavanja M, Morgan G, Subar AF, Linnoila I, Previdi F, Corno M, Rubagotti M, Marinelli B, Albetti B, Colombi A, Tucker M, Wacholder S, Pesatori AC, Caporaso NE, Bertazzi PA: Environment and genetics in lung cancer etiology (EAGLE) study: an integrative population-based case–control study of lung cancer. BMC Public Health 2008, 8:203. BioMed Central Full Text
• [11]De Matteis S, Consonni D, Pesatori AC, Bergen AW, Bertazzi PA, Caporaso NE, Lubin JH, Wacholder SW, Landi MT: Are women who smoke at higher risk for lung cancer than men who smoke? Am J Epidemiol 2013, 177(7):601-612.
• [12]Alberg AJ, Wallace K, Silvestri GA, Brock MV: Invited commentary: the etiology of lung cancer in men compared with women. Am J Epidemiol 2013, 177(7):613-616.
• [13]Kovalchik SA, Varadhan R, Fetterman B, Poitras NE, Wacholder S, Katki HA: A general binomial regression model to estimate standardized risk differences from binary response data. Stat Med 2013, 32:808-821.
• [14]Consonni D, De Matteis S, Lubin JH, Wacholder S, Tucker M, Pesatori AC, Caporaso NE, Bertazzi PA, Landi MT: Lung cancer and occupation in a population-based case–control study. Am J Epidemiol 2010, 171(3):323-333.
• [15]Horvitz DG, Thompson DJ: A generalization of sampling without replacement from a finite universe. J Am Stat Assoc 1952, 47:663-685.
• [16]Wacholder S, Silverman DT, McLaughlin JK, Mandel JS: Selection of controls in case–control studies: 2. Types of controls. Am J Epidemiol 1992, 135(9):1029-1041.
• [17]Benichou J, Wacholder S: A comparison of 3 approaches to estimate exposure-specific incidence rates from population-based case–control data. Stat Med 1994, 13:651-661.
• [18]Graubard BI, Fears TR: Standard errors for attributable risk for simple and complex sample designs. Biometrics 2005, 61(3):847-855.
• [19]R Development Core Team: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2012.
• [20]Kovalchik SA, Varadhan R: Fitting additive binomial regression models with the R package blm. J Stat Softw 2013, 54(1):1-18.
• [21]Martinussen T, Scheike TH: A flexible additive-multiplicative hazard model. Biometrika 2002, 89(2):283-298.
• [22]Cortese G, Scheike TH, Martinussen T: Felxible survival regression modelling. Stat Methods Med Res 2010, 19(1):5-28.
• [23]Blot WJ, McLaughlin JK: Are women more susceptible to lung cancer? J Natl Cancer Inst 2004, 96(11):812-813.
• [24]Khuder SA: Effect of cigarette smoking on major histological types of lung cancer: a meta-analysis. Lung Cancer 2001, 31(2–3):139-148.
• [25]Bain C, Feskanich D, Speizer FE, Thun M, Hertzmark E, Rosner BA, Colditz GA: Lung cancer rates in men and women with comparable histories of smoking. J Natl Cancer Inst 2004, 96(11):826-834.
• [26]Gandini S, Botteri E, Iodice S, Boniol M, Lowenfels AB, Maisonneuve P, Boyle P: Tobacco smoking and cancer: a meta-analysis. Int J Cancer 2008, 122(1):155-164.
• [27]Boiselle PM: Computed tomography screening for lung cancer. JAMA 2013, 309:1163-1170.
• [28]Katki HA, Schiffman M, Castle PE, Fetterman B, Poitras NE, Lorey T, Cheung LC, Raine-Bennett T, Gage JC, Kinney WK: Five-year risks of CIN 2+ and CIN 3+ among women with HPV-positive and HPV-negative LSIL pap results. J Low Genit Tract Dis 2013, 17:S43-S49.
• [29]Wakelee H, Chang E, Gomez S, Keegan T, Feskanich D, Clarke C, Holmberg L, Yong L, Kolonel L, Gould M, et al.: Lung cancer incidence in never smokers. J Clin Oncol 2007, 25(5):472-478.
• [30]Greenland S, Holland P: Estimating standardized risk differences from odds ratios. Biometrics 1991, 47(1):319-322.
• [31]Greenland S: Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case–control studies. Am J Epidemiol 2004, 160(4):301-305.
• [32]Benichou J, Gail MH: Methods of inference for estimates of absolute risk derived from population-based case–control studies. Biometrics 1995, 51(1):182-194.
• [33]Marschner IC, Gillett AC, O’Connell RL: Stratified additive Poisson models: computational methods and applications in clinical epidemiology. Comput Stat Data Anal 2012, 56(5):1115-1130.
• [34]Knol MJ, van der Tweel I, Grobbee DE, Numans ME, Geerlings MI: Estimating interaction on an additive scale between continuous determinants in a logistic regression model. Int J Epidemiol 2007, 36:1111-1118.
• [35]Richardson DB, Kaufman JS: Estimation of the relative excess risk due to interaction and associated confidence bounds. Am J Epidemiol 2009, 169(6):756-760.
• [36]Wacholder S, McLaughlin JK, Silverman DT: Selection of controls in case–control studies: 1. Principles. Am J Epidemiol 1992, 135(9):1019-1028.