Ordinal regression models for epidemiologic data

American Journal of Epidemiology

Volumn 129, Issue 1, 1989, Pages 191-204

  Armstrong, Ben G ^a   Sloan, Margaret ^a  

^a Biostatistics/Occupational Health   (Canada)

Author keywords

Asbestosis; Regression analysis; Statistics

Indexed keywords

ADULT; ASBESTOSIS; EPIDEMIOLOGY; HUMAN; LUNG FIBROSIS; METHODOLOGY; MINER; MODEL; NONBIOLOGICAL MODEL; PRIORITY JOURNAL; REGRESSION ANALYSIS; SHORT SURVEY; STATISTICS; THORAX RADIOGRAPHY;

EPIDEMIOLOGIC METHODS; HUMAN; MODELS, THEORETICAL; OCCUPATIONAL DISEASES; PULMONARY FIBROSIS; REGRESSION ANALYSIS; SILICIC ACID; STATISTICS; SUPPORT, NON-U.S. GOV'T;

EID: 0024586199     PISSN: 00029262     EISSN: None     Source Type: Journal    
DOI: 10.1093/oxfordjournals.aje.a115109     Document Type: Article

References (28)

1. McCullagh P. Regression models for ordinal data (with discussion). J R Stat Soc [B] 1980;42:109-27.
2. McCullagh P, Nelder JA. Generalized linear models. London: Chapman Hall, 1983.
3. Walker SH, Duncan DB. Estimation of the probability of an event as a function of several independent variables. Biometrika 1967;54:167-79.
4. Fienberg SE. The analysis of cross-classified data. 2nd ed. Cambridge, MA: MIT Press, 1980.
5. Cox DR. Regression models and life tables (with discussion). J R Stat Soc [B] 1972;74:187-220.
6. Cox DR. Partial likelihood. Biometrika 1975; 62:269-76.
7. Laara E, Matthews JNS. The equivalence of two models for ordinal data. Biometrika 1985;72:206-7.
8. Wacholder S. Binomial regression in GLIM: estimating risk ratios and risk differences. Am J Epidemiol 1986;123:174-84.
9. Walker AM, Rothman KJ. Models of varying parametric form in case-referent studies. Am J Epidemiol 1982;115:129-37.
10. Atkinson AC. A method for discriminating between models (with discussion). J R Stat Soc [BJ 1970;32:323-53.
11. SAS Institute Inc. SUGI supplemental library user’s guide, version 5 edition. Cary, NC: SAS Institute Inc., 1986.
12. Thompson R, Baker RJ. Composite link functions in generalized linear models. Appl Stat 1981; 54:167-79.
13. Payne CD, ed. The GLIM system release 3.77 manual. Oxford: Numerical Algorithms Group, 1985.
14. Hutchison D. Ordinal variable regression using the McCullagh (proportional odds) model. GLIM Newsletter 1985;9:9-17.
15. Iyer R. Continuation-odds model in ordinal variable regression. GLIM Newsletter 1985;10:4-8.
16. McDonald JC, Sebastien P, Armstrong B. Radiological survey of past and present vermiculite miners exposed to amphibole fibres in the tremolite series. Br J Ind Med 1986;43:445-9.
17. International Labour Office. Guidelines for the use of ILO international classification of radiographs of pneumoconioses. Geneva: ILO, 1980. (Occupational Safety and Health, series 22, rev. 80)
18. McCullagh P. On the elimination of nuiaance parameters in the proportional odds model. J R Stat Soc [B] 1984;46:260-6.
19. Cox DR, Hinkley DV. Theoretical statistics. London: Chapman and Hall, 1974:304-97.
20. Marshall RT, Chisholm EM. Hypothesis testing in the polychotomous logistic model with an application to detecting gastrointestinal cancer. Stat Med 1985;4:337-44.
21. Thomas DC, Golberg M, Dewar R, et al. Statistical methods for relating several exposure factors to several diseases in case heterogeneity studies. Stat Med 1986;5:49-60.
22. Begg CB, Gray R. Calculation of polychotomous logistic regression parameters using individualized regressions. Biometrika 1984;71:11-18.
23. Cox C, Chuang C. A comparison of chi-square partitioning and two logit analyses of ordinal pain data from a pharmaceutical study. Stat Med 1984;3:273-85.
24. Anderson JA. Regression and ordered categorical variables (with discussion). J R Stat Soc Bj 1984;46:1-30.
25. McCullagh P. In: Discussion of Anderson JA. Regression and ordered categorical variables. J R Stat Soc [B] 1984;46:22-3.
26. Agresti A. Analysis of ordinal categorical data. New York: John Wiley and Sons, 1984.
27. Chuang C, Agresti A. A new model for ordinal pain data from a pharmaceutical study. Stat Med 1986;5:15-20.
28. Bennett S. Analysis of survival data by the proportional odds model. Stat Med 1983;2:279-85.