Statistical analysis
REGRESSION
To quantify the possible impact of LOT-R optimism/pessimism on post-operative outcomes, several linear regression models were fitted, in which the respective outcomes (KOOS-12 post-operative) were regressed on the study variable LOT-R optimism/pessimism. In addition to the respective study variable, each regression model included the possible confounders: age, ASA score, BMI, pre-operative KOOS-12 at baseline, EQ5D score, HSS expectation score, education background (low vs. high) and sex as covariates.To obtain the regression equation for the association of LOT-R pessimism with the respective KOOS-12 score, the LOT-R- optimism score was replaced with the LOT-R-pessimism score of a given patient.To prevent an attrition bias, all regression models were based on 10 datasets that were obtained by means of multiple imputation (using predictive mean matching as the imputation method). The imputation model included all recorded variables that were significantly correlated with LOT-R optimism/pessimism and the KOOS-variable.All statistical analyses were performed using R [R version 3.5.1, Core Team (2017)] [