Automated Author ProfileJiajun Yan
McMaster University, Hamilton, Ontario, Canada
Jiajun Yan
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.3 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The EQ-5D (EuroQol five-dimensions) questionnaire is a patient-reported outcome that measures individual’s health-related quality of life (HRQoL) from 5 dimensions, including mobility (MO), self-care (SC), usual activities (UA), pain/discomfort (PD), and anxiety/depression (AD). The original version EQ-5D has three response options indicating no, some, and extreme problems in each of the five dimensions (the EQ-5D-3L (EuroQol five-dimension three-level)), which were later expanded into 5 levels: no, slight, moderate, severe, and extreme problems (the EQ-5D-5L (EuroQol five-dimension five-level)). The EQ-5D questionnaire has been widely collected in randomized clinical trials (RCTs) and extensively used in economic evaluation for reimbursement decision across various disease areas. However, using EQ-5D to evaluate treatment efficacy on HRQoL is rather limited, partially due to the lack of methodological guideline. There are guidelines on analyzing and reporting EQ-5D data, but these mostly focus on non-controlled studies where EQ-5D data is only collected once. However, in RCTs, with the aim of testing the effect of the treatment , EQ-5D data are usually collected at multiple timepoints during the trial. Our recent research shows that there are significant variations in the statistical methods that have been used to analyze EQ-5D data in the RCTs. Through access to published RCTs where the EQ-5D was collected, we plan to apply and compare commonly used methods and new methods to analyze the EQ-5D data across a wide range of disease areas and trial designs. We will examine model assumptions, evaluate model performance/goodness of fit, and compare model results in each trial analysis.This project will produce empirical (observed) evidence in comparing statistical models for estimating treatment effect on the EQ-5D. Furthermore, the output from this research program can be used to develop practical guidance on analyzing the EQ-5D in the RCT setting. Such guidance can potentially increase awareness and usage of EQ-5D to assess treatment effect on patients’ HRQoL.
Authors
- Jiajun Yan