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We used all non-missing observations available in the relevant analyses as only a small proportion of observations had any missing data.
The primary analysis compared the INT and the CON group using the intention-to-treat principle including all available data, and missing observations handled according to missing-at-random assumption.
The imputation was based on Bayesian regression that imputes missing observations for every patient using all available data (i.e., all repeated measures on weight up to the patient's discontinuation from the study).
Its main advantages are (i) the possibility to handle missing observations and (ii) the possibility to integrate available interaction network information.
In case of missing observations, values are carried backwards or forward from the available observation, leaving a delta-value of 0. True observations reflect those with observations at both baseline and follow-up.
*Level of university education: 10 missing observations in Germany and 6 missing observations in Austria.
Yet, missing observations scattered over the dataset restrict the number of effectively available datapoints and make classical regression techniques inaccurate or inapplicable.
We carried out the analysis only on the available data, and did not impute values for missing observations.
Farm-level predictor variables from the PND were selected for inclusion in the statistical analysis if they were available from the GBPR, and if the proportion of missing observations was not >50% (Table 3).
Third, all relevant variables are screened for missing observations.
The last observation carried forward (LOCF) was used to analyze missing observations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com