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Eleven patients returned sequences containing one or more missing observations, thereby necessitating interpolation.
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Another, progressively substituted, set was made by substituting e.g. the missing observations on Day 5 with observations from Day 6, thereby placing emphasis on the observations towards the end of the observation period.
*Level of university education: 10 missing observations in Germany and 6 missing observations in Austria.
Due to missing observations, observations were carried backward and forward at baseline and follow-up, respectively, thereby avoiding bias of non-random drop-out [ 26, 27].
Third, all relevant variables are screened for missing observations.
The last observation carried forward (LOCF) was used to analyze missing observations.
We propose a new and computationally efficient forward backward algorithm for HSMM with missing observations and multiple observation sequences.
The effects of missing observations on complete and partial diallel cross designs are examined.
Subset designs are studied for their robustness to missing observations in different experimental regions.
*Three missing observations.
Some variables have missing observations.
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