She was always ready to learn new techniques, and she diversified her skill set by working in the animal facility, cell culture room, and also in a mass spectrometry lab. As the years went on, Jesse was spread thin between her own PhD thesis project, mentoring, and collaborations. Jesse tried to reduce her load by asking her advisor to take her off collaborations. Over the course of 7 years Jesse had collected a lot of data, but most of the projects were dead-end or too small for a publication.
Help Application of Multiple imputation in Analysis of missing data in a study of Health-related quality of life Zhu, Chunming Application of Multiple imputation in Analysis of missing data in a study of Health-related quality of life.
Master's Thesis, University of Pittsburgh.
Unpublished PDF Download 1MB Abstract When a new treatment has similar efficacy compared to standard therapy in medical or social studies, the health-related quality of life HRQL becomes the main concern of health care professionals and can be the basis for making a decision in patient management.
However, there was a high proportion of missing values among the HRQL measurements that only Ignoring the missing data issue often leads to inefficient and sometime biased estimates.
The primary objective of this thesis is to evaluate the impact of missing data on the estimated the treatment effect. In this thesis, we analyzed the HRQL data with missing values by multiple imputation.
To the Graduate Council: I am submitting herewith a thesis written by Yan Zeng entitled “A Study of Missing Data Imputation and Predictive Modeling of Strength Properties of Wood Composites.”. Abstract Missing Data Problems in Machine Learning Benjamin M. Marlin Doctor of Philosophy Graduate Department of Computer Science University of Toronto. A commonly occurring problem in all kinds of studies is that of missing data. These missing values can occur for a number of reasons, including equipment malfunctions and, more typically, subjects recruited to a study not participating fully. In particular, in a longitudinal study, one or more of the repeated measurements on a subject might be missing.
Both model-based and nearest neighborhood hot-deck imputation methods were applied. Confidence intervals for the estimated treatment effect were generated based on the pooled imputation analysis. The results based on multiple imputation indicated that missing data did not introduce major bias in the earlier analyses.
However, multiple imputation was worthwhile since the most estimation from the imputation datasets are more efficient than that from incomplete data. These findings have public health importance:Recent Thesis Topics.
Modelling Approach to Assess Treatment Effects in A Major Depressive Disorder Clinical Trial with Non-ignorable Missing Data; Human Disease Network: A Study Based on Taiwan National Health Insurance Research Database Read the MPH thesis guidelines on the Current Student Gateway.
Abstract This thesis addresses the intersection of two important areas in epidemiology and statistics: genetic linkage analysis and missing data methods, respectively. The second challenge, and the focus of this thesis, is that of missing data: nearly every data collection effort has issues with missing data and the impossibility of collecting every measure- ment of interest.
In this thesis, we analyzed the HRQL data with missing values by multiple imputation. Both model-based and nearest neighborhood hot-deck imputation methods were applied.
Confidence intervals for the estimated treatment effect were generated based on the pooled imputation analysis. Handling Data with Three Types of Missing Values Jennifer A. Boyko, Ph.D. University of Connecticut, ABSTRACT Missing values present challenges in . Missing Data Methodology: Sensitivity analysis after multiple imputation Melanie Smuk Thesis submitted in accordance with the requirements for the degree.