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Methods for comparison of cost data

X H Zhou1, C A Melfi, S L Hui

  • 1Regenstrief Institute for Health Care, Indiana University Medical Center, Indianapolis, USA.

Annals of Internal Medicine
|February 12, 1998
PubMed
Summary
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Common statistical methods for analyzing healthcare costs are unreliable due to skewed data. A Z-score method offers a robust alternative for comparing costs between groups, especially with log-normally distributed data.

Area of Science:

  • Health Economics
  • Biostatistics
  • Medical Informatics

Background:

  • Healthcare cost analysis is increasingly important, with large datasets available.
  • Cost data distributions are often skewed due to a few high-cost patients.
  • The robustness of standard cost analysis methods against skewed data is questionable.

Purpose of the Study:

  • To identify common statistical methods for comparing medical costs.
  • To describe the limitations of these prevalent methods.
  • To propose and evaluate an alternative Z-score method for cost analysis.

Main Methods:

  • A systematic review of statistical methods in medical cost studies (1991-1996).
  • Description of a Z-score method for comparing means of log-normal cost data.

Related Experiment Videos

  • Reanalysis of published study results using the Z-score method.

    • Main Results:

    • Common methods include Student t-tests (untransformed/log-transformed) and Wilcoxon tests.
    • These methods have limitations: ignoring skewness, unequal variances, or testing incorrect hypotheses.
    • Reanalysis using the Z-score method altered conclusions in six of eleven studies, highlighting the impact of skewed data.

    Conclusions:

    • Standard methods for comparing healthcare costs between groups have significant limitations.
    • These limitations can unpredictably alter study conclusions.
    • The Z-score method provides a statistically sound approach for analyzing skewed cost data, particularly for log-normally distributed samples.