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


CONCLUSIONS
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.
Il'yasova
Dora
D
Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, NC, USA. dilyasov@wfubmc.edu
Hertz-Picciotto
Irva
I
Peters
Ulrike
U
Berlin
Jesse A
JA
Poole
Charles
C
D017677:Age Distribution; D015331:Cohort Studies; D015179:Colorectal Neoplasms / Q000453:epidemiology* / Q000517:prevention & control*; D016001:Confidence Intervals; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D016017:Odds Ratio; D016012:Poisson Distribution; D011322:Primary Prevention / Q000379:methods*; D012044:Regression Analysis; D018570:Risk Assessment; D017678:Sex Distribution; D013662:Tea; D013997:Time Factors; D014481:United States
D013662:Tea