18 citations · 23 across the 4 of their papers we have counts for
5 papers
Linear mixed model vs two-stage methods: Developing prognostic models of diabetic kidney disease progression
Brian Kwan, Lin Liu, David Strong +2
Identifying prognostic factors for disease progression is a cornerstone of medical research. Repeated assessments of a marker outcome are often used to evaluate disease progression…
Inference and Prediction Using Functional Principal Components Analysis: Application to Diabetic Kidney Disease Progression in the Chronic Renal Insufficiency Cohort (CRIC) Study
Brian Kwan, Wei Yang, Daniel Montemayor +9
Repeated longitudinal measurements are commonly used to model long-term disease progression, and timing and number of assessments per patient may vary, leading to irregularly space…
BayesTime: Bayesian Functional Principal Components for Sparse Longitudinal Data
Lingjing Jiang, Yuan Zhong, Chris Elrod +3
Modeling non-linear temporal trajectories is of fundamental interest in many application areas, such as in longitudinal microbiome analysis. Many existing methods focus on estimati…
Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data
Lingjing Jiang, Niina Haiminen, Anna-Paola Carrieri +7
Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high-dimensional, underdetermined, sparse and com…
Statistical tests for the intersection of independent lists of genes: Sensitivity, FDR, and type I error control
Loki Natarajan, Minya Pu, Karen Messer
Public data repositories have enabled researchers to compare results across multiple genomic studies in order to replicate findings. A common approach is to first rank genes accord…