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The results of the independent t-tests and nonparametric equivalents are thus mainly illustrative, and we base our conclusions on the results of the linear mixed models. We first determined whether the trends of hematological parameters over time were best described by a linear, quadratic, or cubic function. To test whether the trends of hematological parameters differed between outcome groups, we fitted two models for each hematological parameter. The first model allowed the outcome groups to differ both on average and in trend over time, and thus included fixed effects for the appropriate polynomial time trends: an indicator for ��outcome group�� (normal versus impaired fracture healing), and the interaction between ��outcome group�� and time trends. The second model assumes that the outcome groups have the same average and trend over time, and thus only had fixed effects for time trends. We corrected for possible KU-55933 molecular weight confounding by adding clinical parameters to both of these models that significantly differed between outcome groups. The percentage of patients that were treated nonoperatively and the percentage of patients that had open fractures (Gustilo grade I and higher)18 significantly differed between outcome groups, and thus these parameters were added to both models. The given P-values therefore represent differences between outcome groups that cannot solely be explained by differences in type of management or presence of open fractures. The two models were compared Wee1 inhibitor using a likelihood-ratio test: when the first model significantly fitted the observed data better than the second model (which assumes that both outcome groups have the same average and trend over time), it was concluded that the curve of that hematological parameter significantly differed between outcome groups after correcting for possible confounders. In order to minimize CYTH4 multicollinearity of the polynomial terms for time, orthogonal polynomials were used.23 For each outcome, random effects per patient for the intercept and time trends were used in the models to account for the correlation of repeated measurements within patients. P