Understanding and checking the assumptions of linear regression: A primer for medical researchers

Clinical and Experimental Ophthalmology

Volumn 42, Issue 6, 2014, Pages 590-596

  Casson, Robert J ^a   Farmer, Lachlan D M ^a  

^a ROYAL ADELAIDE HOSPITAL   (Australia)

Author keywords

Assumption; Normality; Regression; Statistics

Indexed keywords

CLINICAL RESEARCH; CORRELATION COEFFICIENT; LINEAR REGRESSION ANALYSIS; METHODOLOGY; NORMAL DISTRIBUTION; OPHTHALMOLOGY; OUTCOME VARIABLE; PREDICTOR VARIABLE; REVIEW; MEDICAL RESEARCH; STATISTICAL MODEL;

BIOMEDICAL RESEARCH; LINEAR MODELS; MODELS, STATISTICAL;

EID: 84908356991     PISSN: 14426404     EISSN: 14429071     Source Type: Journal    
DOI: 10.1111/ceo.12358     Document Type: Review

References (15)

1. Shaffer JP. The Gauss-Markov theorem and random regressors. Am Stat 1991; 45: 269-273.
2. Babu GJ, Fiegelson ED. Analytical and Monte Carlo comparisons of six different linear least squares fits. Commun Stat Simul Comput 1992; 21: 533-549.
3. Isobe T, Fiegelson ED, Akritas MG, Babu GJ. Linear regression in astronomy I. Astrophys J 1990; 364: 104-113.
4. McArdle BH. The structural relationship: regression in biology. Can J Zool 1988; 66: 2329-2339.
5. Fan Q, Teo YY, Saw SM. Application of advanced statistics in ophthalmology. Invest Ophthalmol Vis Sci 2011; 52: 6059-6065.
6. Hubbard AE, Ahern J, Fleischer NL etal. To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology 2010; 21: 467-474.
7. Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics 1988; 44: 1049-1060.
8. Lumley T, Diehr P, Emerson S, Chen L. The importance of the normality assumption in large public health data sets. Annu Rev Public Health 2002; 23: 151-169.
9. Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med 2004; 66: 411-421.
10. Green SB. How many subjects does it take to do a regression analysis? Multivar Behav Res 1991; 26: 499-510.
11. Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 1995; 48: 1503-1510.
12. Velleman PF, Welsc RE. Efficient computing of regression diagnostics. Am Stat 1981; 35: 234-242.
13. Moons KG, Kengne AP, Grobbee DE etal. Risk prediction models: II. External validation, model updating, and impact assessment. Heart 2012; 98: 691-698.
14. Collins GS, Moons KG. Comparing risk prediction models. BMJ 2012; 344: e3186.
15. Moons KG, Kengne AP, Woodward M etal. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 2012; 98: 683-690.