Smoothing in occupational cohort studies: an illustration based on penalised splines.
OBJECTIVE
To illustrate the contribution of smoothing methods to modelling exposure-response data, Cox models with penalised splines were used to reanalyse lung cancer risk in a cohort of workers exposed to silica in California's diatomaceous earth industry. To encourage application of this approach, computer code is provided.


METHODS
Relying on graphic plots of hazard ratios as smooth functions of exposure, the sensitivity of the curve to amount of smoothing, length of the exposure lag, and the influence of the highest exposures was evaluated. Trimming and data transformations were used to down-weight influential observations.


RESULTS
The estimated hazard ratio increased steeply with cumulative silica exposure before flattening and then declining over the sparser regions of exposure. The curve was sensitive to changes in degrees of freedom, but insensitive to the number or location of knots. As the length of lag increased, so did the maximum hazard ratio, but the shape was similar. Deleting the two highest exposed subjects eliminated the top half of the range and allowed the hazard ratio to continue to rise. The shape of the splines suggested a parametric model with log hazard as a linear function of log transformed exposure would fit well.


CONCLUSIONS
This flexible statistical approach reduces the dependence on a priori assumptions, while pointing to a suitable parametric model if one exists. In the absence of an appropriate parametric form, however, splines can provide exposure-response information useful for aetiological research and public health intervention.
Eisen
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EA
Occupational Health Program, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA. eeisen@hsph.harvard.edu
Agalliu
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I
Thurston
S W
SW
Coull
B A
BA
Checkoway
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H
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009784:Occupational Diseases / Q000453:epidemiology*; D016273:Occupational Exposure / Q000009:adverse effects; D012016:Reference Values; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors