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Sensitivity analysis for randomized trials with missing outcome data

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  • Ian White

    ()
    (MRC Biostatistics Unit, Cambridge University)

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    Abstract

    Any analysis with incomplete data makes untestable assumptions about the missing data, and analysts are therefore urged to conduct sensitivity analyses. Ideally, a model is constructed containing a nonidentifiable parameter d, where d = 0 corresponds to the assumption made in the standard analysis, and the value of d is then varied in a range considered plausible in the substantive context. I have produced Stata software for performing such sensitivity analyses in randomized trials with a single outcome, when the user specifies a value or range of values of d. The analysis model is assumed to be a generalized linear model with adjustment for baseline covariates. I will describe the statistical model used to allow for the missing data, sketch the programming required to obtain a sandwich variance estimator, and describe modifications needed to make the results given when d = 0 correspond exactly to those results available by standard methods. I will illustrate the use of the software for binary and continuous outcomes, when the standard analysis assumes either missing at random or (for a binary outcome) "missing = failure".

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    File URL: http://repec.org/usug2011/UK11_White.pdf
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    Bibliographic Info

    Paper provided by Stata Users Group in its series United Kingdom Stata Users' Group Meetings 2011 with number 03.

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    Date of creation: 26 Sep 2011
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    Handle: RePEc:boc:usug11:03

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    Web page: http://www.stata.com/meeting/uk11
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