Missing data.

424 BMJ | 24 FEBRUARY 2007 | VOLUME 334 Almost all studies have some missing observations. Yet textbooks and software commonly assume that data are complete, and the topic of how to handle missing data is not often discussed outside statistics journals. There are many types of missing data and different reasons for data being missing. Both issues affect the analysis. Some examples are: (1) In a postal questionnaire survey not all the selected individuals respond; (2) In a randomised trial some patients are lost to follow-up before the end of the study; (3) In a multicentre study some centres do not measure a particular variable; (4) In a study in which patients are assessed frequently some data are missing at some time points for unknown reasons; (5) Occasional data values for a variable are missing because some equipment failed; (6) Some laboratory samples are lost in transit or technically unsatisfactory; (7) In a magnetic resonance imaging study some very obese patients are excluded as they are too large for the machine; (8) In a study assessing quality of life some patients die during the follow-up period.