Hierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.
BACKGROUND
Occupational studies often involve multiple comparisons and therefore suffer from false positive findings. Semi-Bayes adjustment methods have sometimes been used to address this issue. Hierarchical regression is a more general approach, including Semi-Bayes adjustment as a special case, that aims at improving the validity of standard maximum-likelihood estimates in the presence of multiple comparisons by incorporating similarities between the exposures of interest in a second-stage model.


RESULTS
We re-analysed data from an occupational case-control study of lung cancer, applying hierarchical regression. In the second-stage model, we included the exposure to three known lung carcinogens (asbestos, chromium and silica) for each occupation, under the assumption that occupations entailing similar carcinogenic exposures are associated with similar risks of lung cancer. Hierarchical regression estimates had smaller confidence intervals than maximum-likelihood estimates. The shrinkage toward the null was stronger for extreme, less stable estimates (e.g., "specialised farmers": maximum-likelihood OR: 3.44, 95%CI 0.90-13.17; hierarchical regression OR: 1.53, 95%CI 0.63-3.68). Unlike Semi-Bayes adjustment toward the global mean, hierarchical regression did not shrink all the ORs towards the null (e.g., "Metal smelting, converting and refining furnacemen": maximum-likelihood OR: 1.07, Semi-Bayes OR: 1.06, hierarchical regression OR: 1.26).


CONCLUSIONS
Hierarchical regression could be a valuable tool in occupational studies in which disease risk is estimated for a large amount of occupations when we have information available on the key carcinogenic exposures involved in each occupation. With the constant progress in exposure assessment methods in occupational settings and the availability of Job Exposure Matrices, it should become easier to apply this approach.
Department of Medical Sciences, Cancer Epidemiology Unit, CeRMS and CPO-Piemonte, University of Turin, Turin, Italy. m.corbin@massey.ac.nz
Marine
M
Corbin
Lorenzo
L
Richiardi
Roel
R
Vermeulen
Hans
H
Kromhout
Franco
F
Merletti
Susan
S
Peters
Lorenzo
L
Simonato
Kyle
K
Steenland
Neil
N
Pearce
Milena
M
Maule
D001194:Asbestos / Q000633:toxicity; D001499:Bayes Theorem; D016022:Case-Control Studies; D002857:Chromium / Q000633:toxicity; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D006801:Humans; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000139:chemically induced* / Q000453:epidemiology*; D016273:Occupational Exposure / Q000706:statistics & numerical data*; D016017:Odds Ratio; D012044:Regression Analysis; D012107:Research Design; D012822:Silicon Dioxide / Q000633:toxicity
D002857:Chromium; D001194:Asbestos; D012822:Silicon Dioxide