DeepAI
Log In Sign Up

The use of restricted mean time lost under competing risks data

10/05/2020
by   Jingjing Lyu, et al.
0

Background: Under competing risks, the commonly used sub-distribution hazard ratio (SHR) is not easy to interpret clinically and is valid only under the proportional sub-distribution hazard (SDH) assumption. This paper introduces an alternative statistical measure: the restricted mean time lost (RMTL). Methods: First, the definition and estimation methods of the measures are introduced. Second, based on the differences in RMTLs, a basic difference test (Diff) and a supremum difference test (sDiff) are constructed. Then, the corresponding sample size estimation method is proposed. The statistical properties of the methods and the estimated sample size are evaluated using Monte Carlo simulations, and these methods are also applied to two real examples. Results: The simulation results show that sDiff performs well and has relatively high test efficiency in most situations. Regarding sample size calculation, sDiff exhibits good performance in various situations. The methods are illustrated using two examples. Conclusions: RMTL can meaningfully summarize treatment effects for clinical decision making, which can then be reported with the SDH ratio for competing risks data. The proposed sDiff test and the two calculated sample size formulas have wide applicability and can be considered in real data analysis and trial design.

READ FULL TEXT

page 1

page 2

page 3

page 4

06/20/2021

Combined tests based on restricted mean time lost for competing risks data

Competing risks data are common in medical studies, and the sub-distribu...
06/25/2021

Implementation of an alternative method for assessing competing risks: restricted mean time lost

In clinical and epidemiological studies, hazard ratios are often applied...
02/28/2018

Sample size for a non-inferiority clinical trial with time-to-event data in the presence of competing risks

The analysis and planning methods for competing risks model have been de...
08/09/2018

Sample size estimation for power and accuracy in the experimental comparison of algorithms

Experimental comparisons of performance represent an important aspect of...
11/07/2019

Scalable Algorithms for Large Competing Risks Data

This paper develops two orthogonal contributions to scalable sparse regr...