Bibliographic Details
| Title: |
Methodology to standardize heterogeneous statistical data presentations for combining time-to-event oncologic outcomes |
| Authors: |
April E. Hebert, Usha S. Kreaden, Ana Yankovsky, Dongjing Guo, Yang Li, Shih-Hao Lee, Yuki Liu, Angela B. Soito, Samira Massachi, April E. Slee |
| Source: |
PLoS ONE, Vol 17, Iss 2 (2022) |
| Publisher Information: |
Public Library of Science (PLoS), 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Medicine
LCC:Science |
| Subject Terms: |
Medicine, Science |
| More Details: |
Survival analysis following oncological treatments require specific analysis techniques to account for data considerations, such as failure to observe the time of event, patient withdrawal, loss to follow-up, and differential follow up. These techniques can include Kaplan-Meier and Cox proportional hazard analyses. However, studies do not always report overall survival (OS), disease-free survival (DFS), or cancer recurrence using hazard ratios, making the synthesis of such oncologic outcomes difficult. We propose a hierarchical utilization of methods to extract or estimate the hazard ratio to standardize time-to-event outcomes so that study inclusion into meta-analyses can be maximized. We also provide proof-of concept results from a statistical analysis that compares OS, DFS, and cancer recurrence for robotic surgery to open and non-robotic minimally invasive surgery. In our example, use of the proposed methodology would allow for the increase in data inclusion from 108 hazard ratios reported to 240 hazard ratios reported or estimated, resulting in an increase of 122%. While there are publications summarizing the motivation for these analyses, and comprehensive papers describing strategies to obtain estimates from published time-dependent analyses, we are not aware of a manuscript that describes a prospective framework for an analysis of this scale focusing on the inclusion of a maximum number of publications reporting on long-term oncologic outcomes incorporating various presentations of statistical data. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1932-6203 |
| Relation: |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870464/?tool=EBI; https://doaj.org/toc/1932-6203 |
| Access URL: |
https://doaj.org/article/349edfe6c1534e14adea94cc8bc165fb |
| Accession Number: |
edsdoj.349edfe6c1534e14adea94cc8bc165fb |
| Database: |
Directory of Open Access Journals |
