Missing data in longitudinal data and fitted by linear mixed model

[Jeffery Xu]

Hi all,

I have a question about handling the following missing data scenario using the linear mixed effect model.

Suppose I have a closed longitudinal cohort followed by six years. There are 1500 individuals at the initial wave.
Available observations by each wave are as the following:

Wave 1: 1500
Wave 2: 1400
Wave 3: 1000
Wave 4: 800
Wave 5: 500
Wave 6: 67

There are two reasons for the missing observations. First, people dropped out. Second, the data collection process is ongoing, and not all individuals have been interviewed yet (this is more likely in the later wave).

I know the linear mixed effect model can address the missing problem using the maximum likelihood if MAR or MCAR. My question is: if I assume all missing happens at random, should I drop observations from wave 7 to avoid biased estimates? Or in other words, if I assume the missingness in my data set is happened at random, should I drop a specific wave with substantial amount of missingness to avoid a biased estimate?

Many thanks

[Fei Wang]

Jeffery, if the missing is truly random, your analysis based on the non-missing observations won't bias estimates. The only problem of keeping waves with too many missings is the loss of efficiency -- but dropping the waves also result in loss of information and efficiency. Need to trade off.