Using laplace regression to model and predict percentiles of age at death when age is the primary time scale

American Journal of Epidemiology

Volumn 182, Issue 3, 2015, Pages 271-277

  Bellavia, Andrea ^a   Discacciati, Andrea ^a   Bottai, Matteo ^a   Wolk, Alicja ^a   Orsini, Nicola ^a  

^a KAROLINSKA INSTITUTE   (Sweden)

Author keywords

age; Laplace regression; survival analysis; survival percentiles; time scale

Indexed keywords

AGE DETERMINATION; COHORT ANALYSIS; LAPLACE TRANSFORM; MODEL TEST; MORTALITY; PREDICTION; REGRESSION ANALYSIS; RISK ASSESSMENT; SURVIVAL; TIMESCALE;

AGE; ARTICLE; DEATH; HUMAN; LAPLACE REGRESSION; PREDICTION; REGRESSION ANALYSIS; SIMULATION; SURVIVAL TIME; AGED; COMORBIDITY; DEATH CERTIFICATE; EPIDEMIOLOGY; FEMALE; FOLLOW UP; KAPLAN MEIER METHOD; LIFE EXPECTANCY; MALE; MIDDLE AGED; SMOKING; STATISTICAL MODEL; SURVIVAL; SURVIVAL RATE; SWEDEN; VERY ELDERLY;

AGE FACTORS; AGED; AGED, 80 AND OVER; COMORBIDITY; DEATH CERTIFICATES; FEMALE; FOLLOW-UP STUDIES; HUMANS; KAPLAN-MEIER ESTIMATE; LIFE EXPECTANCY; LINEAR MODELS; MALE; MIDDLE AGED; MODELS, STATISTICAL; SMOKING; SURVIVAL ANALYSIS; SURVIVAL RATE; SWEDEN;

EID: 84938816248     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwv033     Document Type: Article

References (44)

1. Gong Z, Holly EA, Bracci PM. Survival in populationbased pancreatic cancer patients: San Francisco Bay Area, 1995-1999. Am J Epidemiol. 2011; 174(12): 1373-1381
2. Tan A, Kuo YF, Goodwin JS. Predicting life expectancy for community-dwelling older adults from Medicare claims data. Am J Epidemiol. 2013; 178(6): 974-983
3. Bellavia A, Åkerstedt T, Bottai M, et al. Sleep duration and survival percentiles across categories of physical activity. Am J Epidemiol. 2014; 179(4): 484-491
4. Herńn MA. The hazards of hazard ratios. Epidemiology. 2010; 21(1): 13-15
5. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958; 53(282): 457-481
6. Nieto FJ, Coresh J. Adjusting survival curves for confounders: a review and a new method. Am J Epidemiol. 1996; 143(10): 1059-1068
7. GhaliWA, Quan H, Brant R, et al. Comparison of 2 methods for calculating adjusted survival curves from proportional hazards models. JAMA. 2001; 286(12): 1494-1497
8. Cole SR, Herńn MA. Adjusted survival curves with inverse probability weights. Comput Methods Programs Biomed. 2004; 75(1): 45-49
9. MacKenzie TA, Brown JR, Likosky DS, et al. Review of case-mix corrected survival curves. Ann Thorac Surg. 2012; 93(5): 1416-1425
10. Orsini N, Wolk A, Bottai M. Evaluating percentiles of survival. Epidemiology. 2012; 23(5): 770-771
11. Cheung YB, Gao F, Khoo KS. Age at diagnosis and the choice of survival analysis methods in cancer epidemiology. J Clin Epidemiol. 2003; 56(1): 38-43
12. Thiébaut AC, Bénichou J. Choice of time-scale in Cox's model analysis of epidemiologic cohort data: a simulation study. Stat Med. 2004; 23(24): 3803-3820
13. Cologne J, Hsu WL, Abbott RD, et al. Proportional hazards regression in epidemiologic follow-up studies: an intuitive consideration of primary time scale. Epidemiology. 2012; 23(4): 565-573
14. Westreich D, Cole SR, Tien PC, et al. Time scale and adjusted survival curves for marginal structural Cox models. Am J Epidemiol. 2010; 171(6): 691-700
15. Lamarca R, Alonso J, Gómez G, et al. Left-Truncated data with age as time scale: an alternative for survival analysis in the elderly population. J Gerontol A Biol Sci Med Sci. 1998; 53(5): M337-M343
16. Lawless JF. Statistical Models and Methods for Lifetime Data. Vol. 362. Hoboken, NJ: John Wiley & Sons, Inc.; 2011
17. Royston P, Lambert PC. Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model. College Station, TX: Stata Press Books; 2011
18. Korn EL, Graubard BI, Midthune D. Time-To-event analysis of longitudinal follow-up of a survey: choice of the time-scale. Am J Epidemiol. 1997; 145(1): 72-80
19. Liestøl K, Andersen PK. Updating of covariates and choice of time origin in survival analysis: problems with vaguely defined disease states. Stat Med. 2002; 21(23): 3701-3714
20. Mackenzie T. Survival curve estimation with dependent left truncated data using Cox's model. Int J Biostat. 2012; 8(1): Article 29
21. Beyerlein A. Quantile regression-opportunities and challenges from a user's perspective. Am J Epidemiol. 2014; 180(3): 330-331
22. Andersson TM, Dickman PW, Eloranta S, et al. Estimating the loss in expectation of life due to cancer using flexible parametric survival models. Stat Med. 2013; 32(30): 5286-5300
23. Bottai M, Zhang J. Laplace regression with censored data. Biom J. 2010; 52(4): 487-503
24. Bottai M, Orsini N A command for Laplace regression. Stata J. 2013; 13(2): 302-314
25. Gigante B, Leander K, Vikstrom M, et al. Elevated ApoB serum levels strongly predict early cardiovascular events. Heart. 2012; 98(16): 1242-1245
26. Johannessen A, Skorge TD, Bottai M, et al. Mortality by level of emphysema and airway wall thickness. Am J Respir Crit Care Med. 2013; 187(6): 602-608
27. Miniati M, Monti S, Bottai M, et al. Prognostic value of alveolar volume in systolic heart failure: a prospective observational study. BMC Pulm Med. 2013; 13: 69
28. Bellavia A, Larsson SC, Bottai M, et al. Fruit and vegetable consumption and all-cause mortality: a dose-response analysis. Am J Clin Nutr. 2013; 98(2): 454-459
29. Rizzuto D, Orsini N, Qiu C, et al. Lifestyle, social factors, and survival after age 75: population based study. BMJ. 2012; 345: e5568
30. Harris H, Håkansson N, Olofsson C, et al. The Swedish Mammography Cohort and the Cohort of Swedish Men: study design and characteristics of two population-based longitudinal cohorts. OA Epidemiol. 2013; 1(2): 16
31. Woolf SH. The meaning of translational research and why it matters. JAMA. 2008; 299(2): 211-213
32. Khoury MJ, Gwinn M, Ioannidis JP. The emergence of translational epidemiology: from scientific discovery to population health impact. Am J Epidemiol. 2010; 172(5): 517-524
33. Jha P, Ramasundarahettige C, Landsman V, et al. 21st-century hazards of smoking and benefits of cessation in the United States. N Engl J Med. 2013; 368(4): 341-350
34. Downing SR, Oyetunji TA, Greene WR, et al. The impact of insurance status on actuarial survival in hospitalized trauma patients: when do they die? J Trauma. 2011; 70(1): 130-134
35. Schnohr P, Marott JL, Lange P, et al. Longevity in male and female joggers: the Copenhagen City Heart Study. Am J Epidemiol. 2013; 177(7): 683-689
36. Jiang H, Symanowski J, Qu Y, et al. Covariate-Adjusted non-parametric survival curve estimation. Stat Med. 2011; 30(11): 1243-1253
37. Brenner H, Gefeller O, Greenland S. Risk and rate advancement periods as measures of exposure impact on the occurrence of chronic diseases. Epidemiology. 1993; 4(3): 229-236
38. Gardner JW, Sanborn JS. Years of potential life lost (YPLL)-what does it measure? Epidemiology. 1990; 1(4): 322-329
39. Bang KM, Mazurek JM, Wood JM, et al. Diseases attributable to asbestos exposure: years of potential life lost, United States, 1999-2010. Am J Ind Med. 2014; 57(1): 38-48
40. Rubak TS, Svendsen SW, Søballe K, et al. Risk and rate advancement periods of total hip replacement due to primary osteoarthritis in relation to cumulative physical workload. Scand J Work Environ Health. 2013; 39(5): 486-494
41. Gellert C, Schöttker B, Holleczek B, et al. Using rate advancement periods for communicating the benefits of quitting smoking to older smokers. Tob Control. 2013; 22(4): 227-230
42. Bellavia A, Bottai M, Discacciati A, et al. Adjusted survival curves with multivariable Laplace regression. Epidemiology. 2015; 26(2): e17-e18
43. Bellavia A, Bottai M, Wolk A, et al. Alcohol consumption and mortality: a dose-response analysis in terms of time. Ann Epidemiol. 2014; 24(4): 291-296
44. Koenker R, Bassett G Jr. Regression quantiles. Econometrica. 1978; 46(1): 33-50