Calculating Stage Duration Statistics in Multistage Diseases
Many human diseases are characterized by multiple stages of progression. While the typical sequence of disease progression can be identified, there may be large individual variations among patients. Identifying mean stage durations and their variations is critical for statistical hypothesis testing...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2011
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3233570/ https://www.ncbi.nlm.nih.gov/pubmed/22163291 http://dx.doi.org/10.1371/journal.pone.0028298 |
_version_ | 1782218434395766784 |
---|---|
author | Komarova, Natalia L. Thalhauser, Craig J. |
author_facet | Komarova, Natalia L. Thalhauser, Craig J. |
author_sort | Komarova, Natalia L. |
collection | PubMed |
description | Many human diseases are characterized by multiple stages of progression. While the typical sequence of disease progression can be identified, there may be large individual variations among patients. Identifying mean stage durations and their variations is critical for statistical hypothesis testing needed to determine if treatment is having a significant effect on the progression, or if a new therapy is showing a delay of progression through a multistage disease. In this paper we focus on two methods for extracting stage duration statistics from longitudinal datasets: an extension of the linear regression technique, and a counting algorithm. Both are non-iterative, non-parametric and computationally cheap methods, which makes them invaluable tools for studying the epidemiology of diseases, with a goal of identifying different patterns of progression by using bioinformatics methodologies. Here we show that the regression method performs well for calculating the mean stage durations under a wide variety of assumptions, however, its generalization to variance calculations fails under realistic assumptions about the data collection procedure. On the other hand, the counting method yields reliable estimations for both means and variances of stage durations. Applications to Alzheimer disease progression are discussed. |
format | Online Article Text |
id | pubmed-3233570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-32335702011-12-12 Calculating Stage Duration Statistics in Multistage Diseases Komarova, Natalia L. Thalhauser, Craig J. PLoS One Research Article Many human diseases are characterized by multiple stages of progression. While the typical sequence of disease progression can be identified, there may be large individual variations among patients. Identifying mean stage durations and their variations is critical for statistical hypothesis testing needed to determine if treatment is having a significant effect on the progression, or if a new therapy is showing a delay of progression through a multistage disease. In this paper we focus on two methods for extracting stage duration statistics from longitudinal datasets: an extension of the linear regression technique, and a counting algorithm. Both are non-iterative, non-parametric and computationally cheap methods, which makes them invaluable tools for studying the epidemiology of diseases, with a goal of identifying different patterns of progression by using bioinformatics methodologies. Here we show that the regression method performs well for calculating the mean stage durations under a wide variety of assumptions, however, its generalization to variance calculations fails under realistic assumptions about the data collection procedure. On the other hand, the counting method yields reliable estimations for both means and variances of stage durations. Applications to Alzheimer disease progression are discussed. Public Library of Science 2011-12-07 /pmc/articles/PMC3233570/ /pubmed/22163291 http://dx.doi.org/10.1371/journal.pone.0028298 Text en Komarova, Thalhauser. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Komarova, Natalia L. Thalhauser, Craig J. Calculating Stage Duration Statistics in Multistage Diseases |
title | Calculating Stage Duration Statistics in Multistage Diseases |
title_full | Calculating Stage Duration Statistics in Multistage Diseases |
title_fullStr | Calculating Stage Duration Statistics in Multistage Diseases |
title_full_unstemmed | Calculating Stage Duration Statistics in Multistage Diseases |
title_short | Calculating Stage Duration Statistics in Multistage Diseases |
title_sort | calculating stage duration statistics in multistage diseases |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3233570/ https://www.ncbi.nlm.nih.gov/pubmed/22163291 http://dx.doi.org/10.1371/journal.pone.0028298 |
work_keys_str_mv | AT komarovanatalial calculatingstagedurationstatisticsinmultistagediseases AT thalhausercraigj calculatingstagedurationstatisticsinmultistagediseases |