Statistics Colloquium
November 21 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower

Multiple imputation for harmonizing non-commensurate measures in individual participant data meta-analysis

Juned Siddique, Ph.D.
Associate Professor in Preventive Medicine-Biostatistics and
Psychiatry and Behavioral Sciences
Northwestern University

There are many advantages to individual participant data meta-analysis for combining data from multiple studies. These advantages include greater power to detect effects, increased sample heterogeneity, and the ability to perform more sophisticated analyses than meta-analyses that rely on published results. However, a fundamental challenge is that it is unlikely that variables of interest are measured the same way in all of the studies to be combined.

We propose that this situation can be viewed as a missing data problem and use multiple imputation to fill in missing measurements. We apply our method to 5 longitudinal adolescent depression trials where 4 studies used one depression measure and the fifth study used a different depression measure. None of the 5 studies contained both depression measures. We describe a multiple imputation approach for filling in missing depression measures that makes use of external calibration studies in which both depression measures were used. We discuss some practical issues in developing the imputation model including taking into account treatment group and study. We present diagnostics for checking the fit of the imputation model and investigating whether external information is appropriately incorporated into the imputed values.

All statistics students are expected to attend.


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Department of Statistics
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