Inverse probability weighted generalised estimating equations for longitudinal data
(2021) In Master's Thesis in Mathematical Sciences MASM02 20211Mathematical Statistics
- Abstract
- Longitudinal study designs, in which variables of interest are observed at multiple time points in a study population, are frequently used in clinical research. Missing data are common in these types of studies. Moreover, in studies investigating a population where the mortality rate is high, data can be truncated by death. To handle missing or truncated data, it is important to investigate the underlying causes to be able to mitigate potentially biased results. By simulating data with different underlying missingness mechanisms, several estimands were investigated in an elderly study population with distinct handling of missing data and truncation by death. Unweighted and weighted generalized estimating equations (IPWGEE) were used to... (More)
- Longitudinal study designs, in which variables of interest are observed at multiple time points in a study population, are frequently used in clinical research. Missing data are common in these types of studies. Moreover, in studies investigating a population where the mortality rate is high, data can be truncated by death. To handle missing or truncated data, it is important to investigate the underlying causes to be able to mitigate potentially biased results. By simulating data with different underlying missingness mechanisms, several estimands were investigated in an elderly study population with distinct handling of missing data and truncation by death. Unweighted and weighted generalized estimating equations (IPWGEE) were used to estimate the lung function decline by age. The results suggest that the IPWGEE method is robust when applied to different estimands based on the simulated data set. (Less)
- Popular Abstract (Swedish)
- The investigation of lung function in a population plays an important role in diagnosis and assessment of many airway diseases. In addition, an overview of the lung function status can help provide better health-care options and inform medical decisions.
When studying the lung function, the collection of measurements (data) of the lung function from voluntary individuals is a crucial part. Often, several measurements are collected from the same participants over a follow-up period, which allows to explore how the lung function changes over time at both individual and group level. However, sometimes planned measurements cannot be collected due to various reasons. This is referred to as missing data. There are several reasons for missing... (More) - The investigation of lung function in a population plays an important role in diagnosis and assessment of many airway diseases. In addition, an overview of the lung function status can help provide better health-care options and inform medical decisions.
When studying the lung function, the collection of measurements (data) of the lung function from voluntary individuals is a crucial part. Often, several measurements are collected from the same participants over a follow-up period, which allows to explore how the lung function changes over time at both individual and group level. However, sometimes planned measurements cannot be collected due to various reasons. This is referred to as missing data. There are several reasons for missing data, for example if an individual does not show up for a planned follow-up visit, if he or she struggles to perform the measurement or if he or she dies. Missing data have implications for the analysis of the data since information that would help the investigation is lost. Furthermore, the reasons for the data being missing can affect the interpretation of the results.
Gott Åldrande i Skåne (GÅS) is an ongoing study investigating the lung function in elderly participants living in Southern Sweden. The aim of this thesis is to explore the impact of different reasons for missing data on the lung function in elderly individuals, inspired by GÅS.
Among the various approached in how missing data were handled in this thesis, the computations show similar results, indicating that the method is robust under different assumptions. In addition, the results highlight the importance of investigating the reasons why some data were not collected to be able to draw reliable conclusions and make relevant health-care related decisions. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9063218
- author
- Mattsson, Andrea LU
- supervisor
-
- Aldana Rosso LU
- organization
- course
- MASM02 20211
- year
- 2021
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- weighted generalised estimating equations, inverse probability weighting, longitudinal study, missing data, truncation by death, lung function, FEV1
- publication/series
- Master's Thesis in Mathematical Sciences
- report number
- LUNFMS-3103-2021
- ISSN
- 1404-6342
- other publication id
- 2021:E58
- language
- English
- id
- 9063218
- date added to LUP
- 2021-11-18 10:10:33
- date last changed
- 2022-02-02 16:21:20
@misc{9063218,
abstract = {{Longitudinal study designs, in which variables of interest are observed at multiple time points in a study population, are frequently used in clinical research. Missing data are common in these types of studies. Moreover, in studies investigating a population where the mortality rate is high, data can be truncated by death. To handle missing or truncated data, it is important to investigate the underlying causes to be able to mitigate potentially biased results. By simulating data with different underlying missingness mechanisms, several estimands were investigated in an elderly study population with distinct handling of missing data and truncation by death. Unweighted and weighted generalized estimating equations (IPWGEE) were used to estimate the lung function decline by age. The results suggest that the IPWGEE method is robust when applied to different estimands based on the simulated data set.}},
author = {{Mattsson, Andrea}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master's Thesis in Mathematical Sciences}},
title = {{Inverse probability weighted generalised estimating equations for longitudinal data}},
year = {{2021}},
}