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We used combined inferences from multiple imputations to impute for the missing data in the important variables of study interest.
Multiple imputation by chained equations (MICE) approach for categorical variables was used to impute for 'socio-economic status' and 'looking for a paid job', which had 1.3% and 12.4% missing values, respectively.
Hence, procedures to impute for missing data were not needed.
We therefore decided to impute for missing values.
For secondary end points, the last observation carried forward method was used to impute for weeks without SBM data.
In the case of missing data, scores of the non-missing items for each case were added and the mean value was used to impute for the missing values.
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However, because of missing data, we used a multiple imputation procedure to impute values for the missing data for diabetes and gynecomastia.
Finally, we used a generalized additive mixed model (GAMM) with spatial smoothing and a random intercept for each 1 km × 1 km grid cell to impute data for grid cells/days for which AOD measurements were not available (stage 3).
Multiple imputation by chained equations was used to impute values for all missing pre-operative variables (except for organisational factors) for multivariable analysis under the assumption that data were missing at random [ 26, 27].
Stata imputation was used to impute values for missing data on the independent variables, using the remaining variables in the model.
(Sample inverse weighted up to total sample recruited on days when SCID done, n = 503, Stata imputation command used to impute values for missing data on the independent variables).
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