Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-8448b6f56d-qsmjn Total loading time: 0 Render date: 2024-04-18T15:52:41.784Z Has data issue: false hasContentIssue false

8 - Interpreting the results

Published online by Cambridge University Press:  01 April 2011

Mitchell H. Katz
Mitchell H. Katz
Affiliation:
University of California, San Francisco
Get access

Summary

What information will my multivariable analysis produce?

Multivariable techniques produce two major kinds of information: Information about how well the model (all the independent variables together) fit the data and information about the relationship of each of the independent variables to the outcome variable (with adjustment for all other independent variables in the analysis). In this chapter, we will review information that is routinely output from multivariable software programs. In the next chapter we will delve deeper into how well the assumptions of the models are fulfilled and how to improve the fit of the models by looking at supplementary techniques that you may request.

How do I assess how well my model fits the data?

Although there is some overlap, the methods for determining how well a model accounts for the outcome differ by type of multivariable analysis (Table 8.1). The methods for each model are discussed below.

A Multiple linear regression

We start the assessment of a multiple linear regression model by testing whether the independent variables predict the outcome better than assuming that everyone in the study had the mean value for the outcome. If knowing the values of the independent variables improves the fit more than would be expected by chance, then the value of F will be large. A large F value for a given sample size and a given number of variables in the model (which determines the degrees of freedom) will result in a small P value.

Type
Chapter
Information
Multivariable Analysis
A Practical Guide for Clinicians and Public Health Researchers
 , pp. 140 - 161
Publisher: Cambridge University Press
Print publication year: 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Glantz, S.A. and Slinker, B.K.Primer of Applied Regression & Analysis of Variance. 2nd edn. New York, NY: McGraw-Hill, 2001, pp. 294–302Google Scholar
Hosmer, D.W. and Lemeshow, S.Applied Logistic Regression. New York, NY: Wiley, 1989, pp. 8–18Google Scholar
Menard, S.Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage Publications, 1995, pp. 19–21Google Scholar
Harrell, F.E.Regression Modeling Strategies: With applications to linear models, logistic regression, and survival analysis. New York: Springer, 2001, p.231CrossRefGoogle Scholar
Gordon, H.S., Harper, D.L., and Rosenthal, G.E. “Racial variation in predicted and observed in-hospital death.” JAMA 276 (1996): 1639–44CrossRefGoogle ScholarPubMed
Goldman, L., Cook, E.F., Brand, D.A., et al. “A computer protocol to predict myocardial infarction in emergency department patients with chest pain.” N. Engl. J. Med. 318 (1988): 797–803CrossRefGoogle ScholarPubMed
Harrell, F.E., Lee, K.L., Matchar, D.B., et al. “Regression models for prognostic prediction: Advantages, problems, and suggested solutions.” Cancer Treat. Rep. 69 (1985): 1071–7Google ScholarPubMed
Hosmer, D.W. and Lemeshow, S.Applied Logistic Regression. 2nd edn. New York: John Wiley, pp. 305–8CrossRef
Hosmer, D.W. and Lemeshow, S.Applied Logistic Regression. 2nd edn. New York: John Wiley, pp. 280–7CrossRef
Colford, J.M., Tager, I.B., Hirozawa, A.M., et al. “Cryptosporidiosis among patients infected with human immunodeficiency virus: Factors related to symptomatic infection and survival.” Am. J. Epidemiol. 144 (1996): 807–16CrossRefGoogle Scholar
Hauck, W.W. and Donner, A. “Wald's test as applied to hypotheses in logit analysis.” J. Am. Stat. Assoc. 72 (1977): 851–3Google Scholar
,SOS-Kanto Study Group. “Cardiopulmonary resuscitation by bystanders with chest compression only (SOS-Kanto): An observational study.” Lancet 369 (2007): 920–6CrossRefGoogle Scholar
Rothman, K.J. “No adjustments are needed for multiple comparisons.” Epidemiology 1 (1990): 43–6CrossRefGoogle ScholarPubMed
Savitz, D.A. and Olshan, A.F. “Multiple comparisons and related issues in the interpretation of epidemiologic data.” Am. J. Epidemiol. 142 (1995): 904–8CrossRefGoogle ScholarPubMed
Thompson, J. “Re: ‘Multiple comparisons and related issues in the interpretation of epidemiologic data.’” Am. J. Epidemiol. 147 (1997): 801–6CrossRefGoogle Scholar
Goodman, S.N. “Multiple comparisons explained.” Am. J. Epidemiol. 147 (1997): 807–12CrossRefGoogle Scholar
Savitz, D.A. and Olshan, A.F. “Describing data requires no adjustment for multiple comparisons: A reply from Savitz and Olshan.” Am. J. Epidemiol. 147 (1997): 813–14CrossRefGoogle Scholar
Thompson, J.R. “A response to ‘Describing data requires no adjustment for multiple comparisons.’” Am. J. Epidemiol. 147 (1997): 815CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Interpreting the results
  • Mitchell H. Katz, University of California, San Francisco
  • Book: Multivariable Analysis
  • Online publication: 01 April 2011
  • Chapter DOI: https://doi.org/10.1017/CBO9780511974175.009
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Interpreting the results
  • Mitchell H. Katz, University of California, San Francisco
  • Book: Multivariable Analysis
  • Online publication: 01 April 2011
  • Chapter DOI: https://doi.org/10.1017/CBO9780511974175.009
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Interpreting the results
  • Mitchell H. Katz, University of California, San Francisco
  • Book: Multivariable Analysis
  • Online publication: 01 April 2011
  • Chapter DOI: https://doi.org/10.1017/CBO9780511974175.009
Available formats
×