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The association between multidimensional exposure patterns and outcomes iscommonly investigated by first applying cluster analysis algorithms to derivepatterns and then estimating the associations. However, errors in theunderlying continuous, possibly skewed, exposure variables lead tomisclassified exposure patterns and therefore to biased effect estimates. Thisis often the case for lifestyle exposures in epidemiology, e.g. for dietaryvariables measured on daily basis. We introduce three new algorithms forcorrecting the biased effect estimates, which are based on regressioncalibration (RC), simulation extrapolation (SIMEX) and multiple imputation(MI). In addition, the naive method ignoring the measurement error structure isconsidered for comparison. These methods are combined with the k-means clusteralgorithm and the Gaussian mixture model to derive exposure patterns. Theperformance of the correction methods is compared in a simulation studyregarding absolute, maximum and relative bias. The simulated data mimic atypical situation in nutritional epidemiology when diet is assessed usingrepeated 24-hour dietary recalls. Continuous and binary outcomes areconsidered. Simulation results show, that the correction method based on RC andMI perform better than the naive and the SIMEX-based method. Furthermore, theMI-based approach, which can use outcome information in the error model, issuperior to the RC-based approach in most scenarios. Therefore, we recommendusing the MI-based approach.

Correcting for bias due to categorisation based on cluster analysis using multiple continuous error-prone exposures