Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: Re: Re: st: Situation where multiple imputation may be of no use?

From   Clyde B Schechter <>
To   "" <>
Subject   Re: Re: Re: st: Situation where multiple imputation may be of no use?
Date   Tue, 14 Feb 2012 20:19:50 +0000

On Sunday 12 February, Cameron McIntosh, continuing a thread I initiated some days earlier, wrote:

"Thanks for your reply. So, FIML (MAR) and listwise deletion (MCAR) performed identically in simulations (and with your real data)? However, I'm not sure if that proves conclusively that the missing data did not bias the intervention effect in the actual study (and I don't know how you specified your simulations). "

This thread has become a bit disjointed, because I read the digest, which both delays my responses and breaks the automated threading.

Let me re-summarize the initial problem.  There are no real data from this study yet: we are drawing up a study proposal for a two-arm randomized trial and stumbling over whether we can recruit an adequate sample with the available funding.  Our power calculations tell us that if there were going to be no missing data, the budget would just cover us.  But similar studies in the past have led to about 15-20% of the participants being lost to follow-up.  There aren't enough funds to recruit a larger sample in anticipation of this.  So, a colleague suggested that using multiple imputation or FIML in our final analysis would solve our problem, that our sample with missing data would be sufficient.  My major question is whether this is true: whether the use of MI and FIML would permit us to plan for a sample size that would be adequate with no missing data (but too small with missing data.)

A feature of our study design is that all data is gathered at the time of recruitment except for the outcome, which must be delayed so the intervention has time to work.  We will have missing data only on the outcome--all other variables will be complete.  The other variables, by the way, are mainly for descriptive interest: we do not expect any of them to be of value as ancillary predictors of the outcome.  In fact, really, the only predictor variable in our study is the randomization assignment.

My instincts tell me that in this situation, the use of MI or FIML will not really help because the cases that are missing outcome will not provide any additional information about the coefficient of the study arm indicator.  If they did, the information would seem to come from nowhere at all!  In addition, I have done some simulations looking at statistical power for this design using both MI and FIML, and they appear to be no better in this respect than complete case analysis when I simulate data being MCAR.  In addition, when I simulate the data as being MNAR using a missingness model that I think is plausible for our situation, I also find that the use of MI and FIML do not provide any bias correction compared to complete case analysis.

I'm fairly satisfied at this point that MI and FIML won't help us in this specific situation.  I do appreciate the comments that Cameron McIntosh and Richard Williams have made--they have clarified my thinking about the matter.

Thanks again.

Clyde Schechter
Department of Family & Social Medicine
Albert Einstein College of Medicine
Bronx, NY, USA

*   For searches and help try:

© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index