New method improves RMST analysis for clinical and epidemiological studies
· News-MedicalThe restricted mean survival time (RMST) analysis technique was introduced in health care research about 25 years ago and since then has become widely used in economics, engineering, business and other professions.
In clinical settings, RMST is useful because it is a straightforward way to understand the average survival time-the length of time patients live after diagnosis or treatment and the factors that affect that time-within a specified timeframe.
In addition, unlike Cox regression models and other popular models, estimations and comparisons made using RMST do not rely on the proportional hazard assumption that the likelihood of an event happening will be constant over time.
To determine the optimal threshold, the team calculated a threshold time from significant changepoint(s) in hazard rates and compared what they found with the largest possible threshold time.
They used the new method to measure Type 1 error rates and statistical power in simulations in which the hazard rate was constant for one group and was changed for another group. They compared the groups using the standard logrank test and their new model.
For both scenarios, traditional statistical analysis methods revealed no notable differences between two treatments. When the new model was applied, however, the results for each scenario found that one treatment was clearly superior.
The first scenario compared two treatments over seven months for patients with non-small-cell lung cancer who had lower levels of a key biomarker. The second used a standard assessment to measure the time to decline of people with mild dementia who lived with caregivers compared to those who did not live with caregivers.
"These results are promising, and more research is needed that compares more than two groups and that uses multiple covariates, such as participants' age, ethnicity and socioeconomic status," Han said. "Still, based on these early results, we believe this method could be more powerful than all existing comparisons for two groups in the analysis of time-to-event outcomes."
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