Regression modelling and other methods to control confounding

Occupational and Environmental Medicine

Volumn 62, Issue 7, 2005, Pages 500-506

  McNamee, R ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FORCED EXPIRATORY VOLUME; METHODOLOGY; MODEL; MORTALITY; OUTCOMES RESEARCH; PRIORITY JOURNAL; REGRESSION ANALYSIS; REVIEW; SCORING SYSTEM; STATISTICAL ANALYSIS;

CONFOUNDING FACTORS (EPIDEMIOLOGY); HUMANS; MODELS, STATISTICAL; REGRESSION ANALYSIS;

EID: 21344459222     PISSN: 13510711     EISSN: None     Source Type: Journal    
DOI: 10.1136/oem.2002.001115     Document Type: Review

References (18)

1. McNamee R. Confounding and confounders. Occup Environ Med 2003;60:227-34. Contrasts competing definitions of a confounder, including those based on data and those based on notions of comparability.
2. Weinberg CR. Towards a clearer definition of confounding. Am J Epidemiol 1993;137:1-8. Highlights the problem of over-adjustment.
3. Greenland S, Morgenstern H. Confounding in health research. Annu Rev Public Health 2001;22:189-212. A review of current thinking.
4. Greenland S, Pearl J, Robins JM. Causal diagrams far epidemiological research. Epidemiology 1999;10:37-47. The problem of identifying confounders of an exposure-disease relationship is addressed through causal diagrams.
5. Armitage P, Berry G, Matthews JNS. Statistical methods in medical research, 4th edn. Blackwell Publishing, 2002. A very good intermediate level textbook, even though not especially orientated towards epidemiology.
6. Last J, ed. Dictionary of epidemiology, 4th edn. Oxford University Press, 2000.
7. Maldonado G, Greenland S. Simulation study of cofounder-selection strategies. Am J Epidemiol 1993;138:923-36. Compares a number of data based strategies for selecting variables to include in regression models when the aim is to control confounding.
8. Rothman KJ, Greenland S. Modern epidemiology, 2nd edn. Philadelphia: Lippincott-Raven Publishers, 1998. Insightful on almost all methodological questions in epidemiology and includes two chapters on regression modelling.
9. Robins JM, Greenland S. The role of model selection in causal inference from non-experimental data. Am J Epidemiol 1986;123:392-402.
10. Armstrong B. The effect of measurement error on relative risk regressions. Am J Epidemiol 1990;132:1176-84. Easy to read introduction to this topic.
11. Checkoway H, Pearce NE, Crawford-Brown DJ. Research methods in occupational epidemiology. New York: Oxford University Press, 2004. Useful section on modelling of dose and exposure data as well as being a good all-round textbook.
12. Jewell NP. Statistics for epidemiology. Chapman and Hall, 2004. Intermediate text with good coverage of epidemiological statistics.
13. Rosenbaum PR, Rubin DB. The central role of fhe propensity score in observational studies for causal effects. Biometrika 1983;70:41-55.
14. Wiles N, Lunt M, Barrett E, et al. Reduced disability at five years with early treatment of inflammatory polyarthritis: results from a large observational cohort, using propensity models to adjust for disease severity. Arthritis Rheum 2001;44:1033-42. This practical application of the method of propensity score adjustment may be easier to read than reference 13.
15. Ruppert D, Wand M, Corroll R. Semi-parametric regression. Cambridge University Press, 2003.
16. Eisen EA, Agalliu I, Thurston SW, et al. Smoothing in occupational cohort studies: an illustration based on penalised splines. Occup Environ Med 2004;61:854-60.
17. Kleinbaum D, Kupper L, Muller K, et al. Applied regression analysis and multivartate methods. Duxbury Press, 1998. A thorough exposition of all aspects of parametric regression, including a chapter on confounding and interaction in regression.
18. Kleinbaum D, Kupper L, Morgenstern H. Epidemiological research. Principles and quantitative methods. Wiley, 1982. A sophisticated text which examines the problem of control of confounding-whether by stratification or by regression-in detail.