Statistical means to enhance the comparability of data within a pooled analysis of individual data in neurobehavioral toxicology.

Meta-analyses of individual participant data (IPD) provide important contributions to toxicological risk assessments. However, comparability of individual data cannot be taken for granted when information from different studies has to be summarized. By means of statistical standardization approaches the comparability of data might be increased. An analysis of individual data on the neurobehavioral impact of manganese (Mn) exemplifies challenges and effects of a multilevel statistical procedure. Confounding from individual-level and study-level covariates was shown by analyses of variance, but could be reduced by linear regressions and z-normalization using data of the respective control groups. Fixed models that were used to estimate the impact of the neurotoxic exposure, provided evidence that the employed procedures, especially the z-normalization, effectively reduced variance that was unrelated to the neurotoxic exposure. Even after this statistical treatment the fixed effect models revealed differences among studies that did not seem to be exhaustively explicable by concentration differences obvious from the Mn biomarker at hand. IPD studies using confounded endpoints as effects markers can be reasonably summarized when appropriate statistical operations are employed. For the data at hand the proposed normalization allowed new insights into exposure-effect relationships, in general it appears appropriate to investigate the effect of the independent variable more closely.