Abstract:

In clinical trials, there may be differences between individuals, and treatment effects are often heterogeneous, so how to identify the population sensitive to specific treatments has become one of the issues of great concern in the field of precision medicine. In addition, due to the limitation of upper and lower thresholds of measurement methods or instruments, the actual observed data values are usually limited to an interval, resulting in doubly censored data. In this paper, we construct a threshold longitudinal Tobit composite quantile regression model to study the problem of identifying treatment-sensitive subgroups, in order to enhance the identification effect of treatment-sensitive subgroups. For the parameters of the model, we borrow the idea of the Alternating Direction Method of Multipliers algorithm to establish a method for calculating the parameter estimators, and use the random weighting method to calculate the variance of the parameter estimators. Under some regular conditions, we prove the consistency of the parameter estimators. Numerical simulations show that the proposed method is more effective than the single quantile regression method, and verify the feasibility of the random weighting method in estimating the variance of the parameter estimators. Finally, the method proposed in this paper is applied to analyse the data of the CO.17 Trial, identifying the treatment-sensitive subgroups according to age.

Key words: doubly censored data, Longitudinal data, random weighting, Tobit model, threshold regression, composite quantile regression, subgroup analysis