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Sensitivity Analysis and Nonresponse Bias

For a while now, when I talk about the risk of nonresponse bias, I suggest that researchers look at the problem from as many different angles as possible, employing varied assumptions. I've also pointed to work by Andridge and Little that uses proxy pattern-mixture models and a range of assumptions to do sensitivity analysis. In practice, these approaches have been rare.

A couple of years ago, I saw a presentation at JSM that discussed a method for doing sensitivity analyses for binary outcomes in clinical trials with two treatments. The method they proposed was graphical and seemed like it would be simple to implement. An article on the topic has now come out. I like the idea and think it might have applications in surveys. All we need are binary outcomes where we are comparing two groups. It seems that there are plenty of those situations.

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