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Cancer Causes & Control

, Volume 16, Issue 4, pp 383–388 | Cite as

Choice of exposure scores for categorical regression in meta-analysis: a case study of a common problem

  • Dora Il’yasovaEmail author
  • Irva Hertz-Picciotto
  • Ulrike Peters
  • Jesse A. Berlin
  • Charles Poole
Article

Abstract

Objective: Reporting categorical relative risk estimates for a series of exposure levels versus a common reference category is a widespread practice. In meta-analysis, categorical regression estimates a dose–response trend from such results. This method requires the assignment of a single score to each exposure category. We examined how closely meta-analytical categorical regression approximates the results of analysis based on the individual-level continuous exposure.

Methods: The analysis included five studies on tea intake and outcomes related to colorectal cancer. In addition, we derived categorical mean and median values from published distributions of tea consumption in similar populations to assign scores to the categories of tea intake when possible. We examined whether these derived mean and median values well approximate the individual-level results.

Results: In meta-analytical categorical regression, using the midrange scores approximated the individual-level continuous analyses reasonably well, if the value assigned to the uppermost, open-ended category was at least as high as the lower bound plus the width of the second-highest category. Categorical mean values derived from the published distributions of regular tea (in the US) and green tea (in Japan) well approximated the slope obtained from individual-level analysis.

Conclusion: Publication of both the categorical and the continuous estimates of effect in primary studies, with their standard errors, can enhance the quality of meta-analysis, as well as providing intrinsically valuable information on dose–response.

Keywords

colorectal cancer dose–response relationships epidemiology epidemiologic method meta-analyses tea. 

Abbreviations:

CLR

confidence limit ratios

RR

relative risk

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Copyright information

© Springer 2005

Authors and Affiliations

  • Dora Il’yasova
    • 1
    • 4
    Email author
  • Irva Hertz-Picciotto
    • 1
    • 5
  • Ulrike Peters
    • 2
  • Jesse A. Berlin
    • 3
  • Charles Poole
    • 1
  1. 1.Department Epidemiology, School of Public HealthUniversity of North CarolinaChapel HillUSA
  2. 2.Division of Cancer Epidemiology and GeneticsNational Cancer Institute, NIH, DHHSBethesdaUSA
  3. 3.Center for Clinical Epidemiology and Biostatistics and Department of Biostatistics and EpidemiologyUniversity of Pennsylvania School of MedicineUSA
  4. 4.Medical Center BoulevardWake Forest University School of MedicineWinston-Salem
  5. 5.Department of Epidemiology and Preventive MedicineUniversity of CaliforniaDavisUSA

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