Research

Informative cluster size and informative drop-out in complex correlated data

• Mitani AA, Kaye EK, Nelson KP. Accounting for tooth-loss using inverse probability censoring weights in longitudinal clustered data with informative cluster size. Accepted to Annals of Applied Statistics.
• Mitani AA, Kaye EK, Nelson KP. Marginal analysis of multiple outcomes with informative cluster size. To appear in Biometrics. 2021; 77, 271–282. Link to paper & R code
• Mitani AA, Kaye EK, Nelson KP. Marginal analysis of ordinal clustered longitudinal data with informative cluster size. Biometrics. 2019; 73(3), 938– 949. Link to paper & R package

     Software

• CWGEE: Cluster-weighted generalized estimating equations for clustered longitudinal data with informative cluster size
• ipccwGEE: Solves the cluster-weighted generalized estimating equations with inverse probability censoring weights for correlated binary responses in clustered longitudinal data with informative cluster size and informative drop-out.

     Talks

• “Informative cluster size in observational studies”. HIV Working Group, Harvard Chan School, Nov. 2019. Slides

Biased sampling design in survey and observational studies

• Mitani AA, Mercaldo ND, Haneuse S, Schildcrout JS. Survey design and analysis considerations when utilizing misclassified sampling strata. BMC Medical Research Methodology. 2021; 21(145). Link to paper & R code

Modeling agreement in cancer diagnostic tests

• Nelson KP, Mitani AA, Edwards D. Evaluating the effects of rater and subject factors on measures of association. Biomedical Journal. 2018; 60, 639–656. Link to paper
• Mitani AA, Nelson KP. Modeling Agreement between Binary Classifications of Multiple Raters in R and SAS. Journal of Modern Applied Statistical Methods. 2017; 15. PDF
• Mitani AA, Freer PE, Nelson KP. Summary measures of agreement and association between many raters’ ordinal classifications. Annals of Epidemiology. 2017; 27(10). PDF & R package
• Nelson KP, Mitani AA, Edwards D. Assessing the influence of rater and subject characteristics on measures of agreement for ordinal ratings. Statistics in Medicine. 2017; 36(20), 3181–3199. Link to paper

     Software

• modelkappa: Calculates model-based kappa of agreement and association and their standard errors for multiple raters each assessing multiple cases

Multiple imputation of derived variables

• Desai M, Mitani AA, Bryson S, Robinson T. Multiple imputation when rate of change is the outcome of interest. Journal of Modern Applied Statistical Methods. 2016; 15(1), Article 10.
• Mitani AA, Kurian AW, Das AK, Desai M. Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects. Statistical Methodology. 2015; 27. PDF

     Talks

• “Multiple imputation in practice – approaches for handling categorical and interaction variables”. QSU Research Methods Seminar, Stanford University, May 2013. Slides
• “Issues and guidelines of multiple imputation in practice”. Webinar training series, ASA Section for Statistical Programmers and Analysts. Apr. 2013. Slides

Other selected peer-reviewed publications

• Mitani AA, Haneuse S. Small data challenges of studying rare diseases. Invited Commentary. JAMA Network Open. 2020; 3(3). Link to paper

Click here to view the complete list of my publications.