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Measuring morbidity: disease counts, binary variables, and statistical power.

K F Ferraro1, J M Wilmoth

  • 1Department of Sociology, Purdue University, West Lafayette, Indiana 47907-1365, USA. ferraro@purdue.edu

The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences
|February 9, 2002
PubMed
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When modeling self-rated health, using binary disease variables offers more explanatory power but may lack adequate statistical power for rare conditions. Consider power estimates and disease severity when choosing your approach.

Area of Science:

  • Health outcomes research
  • Biostatistics
  • Epidemiology

Background:

  • Self-rated health is a key indicator of well-being.
  • Morbidity data can be represented as binary variables or counts.
  • Different data representations may impact statistical analysis.

Purpose of the Study:

  • Compare binary disease variables versus disease counts in self-rated health models.
  • Evaluate the statistical power of the binary variable approach.
  • Identify advantages and disadvantages of each method.

Main Methods:

  • Utilized morbidity measures from two large national adult surveys.
  • Conducted both cross-sectional and longitudinal analyses.
  • Compared statistical power and explanatory power of different variable types.

Related Experiment Videos

Main Results:

  • Binary variable approach showed greater explanatory power and higher R2 values.
  • Statistical power was insufficient for rare conditions or those with modest outcome differences.
  • Differences between approaches were generally modest.

Conclusions:

  • Statistical power estimation is recommended for the binary variable approach, particularly with extensive condition lists.
  • Separate counts for serious and non-serious conditions may be more advantageous than a simple disease count.
  • Careful consideration of the method is needed to avoid inadequate statistical power.