PDF-jomo: an R package for Multilevel Multiple Imputation

Matteo QuartagnoMRC Clinical Trials Unit at UCL3rd April 2019 NASH MRC CTU at UCL Missing Data. I.Wasito. . Faculty of Computer Science. University of Indonesia. . F. aculty of Computer Science (Fasilkom), University of indonesia. . at a glance. Initiated . as the . C. enter . of Computer Science (. 6 December 2012. Introduction. I. mputation describes the process of predicting genotypes that have not been directly typed in a sample of individuals:. m. issing genotypes at typed variants;. genotypes at un-typed variants that are present in an external high-density “reference panel” of phased . longitudinal health . records. Irene Petersen and Cathy Welch. Primary Care & Population Health. Today. Issues with missing data and multiple imputation of longitudinal records. Twofold algorithm . l. 1. . penalized least-squares. Eran Treister. . and Irad Yavneh. Computer Science, Technion. (with thanks . to Michael Elad). Part I:. . Background. Sparse Representation of Signals. Applications. Cattram Nguyen, Katherine Lee, John . Carlin. Biometrics by the Harbour, 30 Nov, 2015. Motivating example: Longitudinal Study of Australian Children (LSAC). 5107 infants (0-1 year) recruited in 2004. Daniel Lee. Presentation for MMM conference . May 24, 2016. University of Connecticut. 1. 2. Introduction: Finite Mixture Models. Class of statistical models that treat group membership as a latent categorical variable. Matt Spangler. University of Nebraska-Lincoln. Imputation. Imputation creates data that were not actually collected . I. mputation allows us to retain observations that would otherwise be left out of an analysis. Sarah Medland. Boulder 2015. What is imputation? . (. Marchini. & . Howie. 2010). . 3 main reasons for imputation. Meta-analysis. Fine Mapping. Combining data from different . chips. Other less common uses. Imry. Rosenbaum. Jeremy . Staum. Outline. What is simulation . metamodeling. ?. Metamodeling. approaches. Why use function approximation?. Multilevel Monte Carlo. MLMC in . metamodeling. Simulation . Boulder 2015. What is imputation? . (. Marchini. & . Howie. 2010). . 3 main reasons for imputation. Meta-analysis. Fine Mapping. Combining data from different . chips. Other less common uses. sporadic missing data imputation . longitudinal health . records. Irene Petersen and Cathy Welch. Primary Care & Population Health. Today. Issues with missing data and multiple imputation of longitudinal records. Twofold algorithm . Used for a variety of purposes, including prediction, data reduction, and causal inference.. From experiments and observational studies.. Slide . 2. Hierarchical Data. Data structures are often hierarchical or “nested”. Deserved Run Average and other Applications. By: . Jonathan Judge. GLASC 2017. Baseball Prospectus. Longtime baseball sabermetrics website.. Alumni well-represented across MLB front offices. Continue to develop new statistics today:. 2010 NAACCR Conference. Quebec City, June 22, 2010. Bin . Huang. Kentucky Cancer . Registry. University of Kentucky. The Pre-invasive Cervical Cancer Study. HPV vaccine . Quadrivalent. vaccine licensed for females in June 2006.