Nan Laird is an American statistician known for her significant contributions to the fields of biostatistics and statistical methodology, particularly in the context of clinical trials and epidemiology. She has been influential in developing statistical techniques for analyzing data from complex studies and has authored numerous papers and books in the field. Laird has held academic positions and has been involved with various professional organizations related to statistics and biostatistics.
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Nan Laird develops statistical methods for incomplete observations, repeated measurements and the combination of evidence across studies. Her Harvard biography identifies contributions to the expectation-maximization algorithm, meta-analysis, longitudinal studies and statistical genetics.[ref]
In a longitudinal study, repeated outcomes from one person are usually dependent. A model with random effects can represent persistent differences between people while estimating population relationships. Treating those observations as independent can distort uncertainty. Laird's work on longitudinal analysis addresses this connection between a scientifically plausible dependence structure and practical inference; the same concern arises when results from heterogeneous studies are combined by random-effects meta-analysis.
Homepage: hsph.harvard.edu/profile/nan-m-laird/