Abstract
It is common practice in clinical trials to analyze efficacy and safety data separately to estimate the benefit and risk aspects of a particular treatment regimen. However, given that these data are generated from the same study subjects and therefore are likely correlated, one may miss the complete picture of the treatment effect by separate analyses of efficacy and safety data. Therefore, it is desirable to jointly analyze these data to obtain a more complete profile of the treatment regimen.
A substantial number of statistical methodologies have been proposed in the last decade to model time-to-event data and longitudinal repeated measures jointly. These methods provide better insight to understand the treatment effect in time-to-event data by incorporating the information contained in the longitudinal repeated measures.
| Original language | American English |
|---|---|
| State | Published - Jul 31 2011 |
| Event | Joint Statistical Meetings (JSM) - Duration: Aug 12 2015 → … |
Conference
| Conference | Joint Statistical Meetings (JSM) |
|---|---|
| Period | 08/12/15 → … |
Keywords
- Clinical trial
- Control
- Data analysis
- Joint modeling
- Safety covariates
- Time-to-event endpoint
- Treatment effect
DC Disciplines
- Biostatistics
- Public Health
