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Clarke et al. recommended trading off recall bias against information loss, which would be caused by incomplete data collection.
This can be achieved by incomplete data collection for shortened periods or complete data collection by extending recall windows.
Conclusively, there still might be a contradiction between stated objectives and actual outcome that is simply caused by incomplete data collection.
Only if the degree of variation introduced by incomplete data collection is smaller than the bias introduced by recall error should a short time period for cost assessment be preferred [ 13].
Because this can be achieved by incomplete data collection or extending recall windows, one should carefully consider and differentiate which time frame and method of cost data collection are appropriate for the respective resource category.
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This can be achieved by data collection for shorter periods, by implication incomplete data collection, or extending recall windows so that data are collected completely.
When conducting economic analysis alongside clinical trials by means of incomplete data collection, sample size calculation has to be modified.
If costs incurred for informal care are determined by the hours of care provided by relatives, neighbours or friends, incomplete data collection can be used in a similar way to the collection of outpatient nursing services or paid household help.
In the case of minor recall bias, complete data collection by extending recall windows was preferred to incomplete data collection.
Incomplete data collection was caused by errors in survey completion, research staffing issues (i.e., staff vacation or sick time, simultaneous patients in excess of what available staff could process), and patient time constraints.
A comparison of our results is restricted by the lack of publications of empirical analyses regarding incomplete data collection.
More suggestions(13)
by different data collection
by massive data collection
by differing data collection
by chaotic data collection
by restrospective data collection
by primary data collection
by automating data collection
by overaggressive data collection
by other data collection
by qualitative data collection
by advanced data collection
by prospective data collection
by strict data collection
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