A multiple imputation approach to the analysis of clustered interval-censored failure time data with the additive hazards model

Abstract

Clustered interval-censored failure time data can occur when the failure time of interest is collected from several clusters and known only within certain time intervals. Regression analysis of clustered interval-censored failure time data is discussed assuming that the data arise from the semiparametric additive hazards model. A multiple imputation approach is proposed for inference. A major advantage of the approach is its simplicity because it avoids estimating the correlation within clusters by implementing a resampling-based method. The presented approach can be easily implemented by using the existing software packages for right-censored failure time data. Extensive simulation studies are conducted, indicating that the proposed imputation approach performs well for practical situations. The proposed approach also performs well compared to the existing methods and can be more conveniently applied to various types of data representation. The proposed methodology is further demonstrated by applying it to a lymphatic filariasis study.

Keywords: Additive hazards model; Clustered interval-censored data; Multiple imputation; Within-cluster resampling.

Fig. 1

Quantile plot of the estimates with covariate-independent censoring intervals (γ₀ = 0, β₀ = −0.25, n = 200).

Fig. 2

Quantile plot of the estimates with covariate-dependent censoring intervals (γ₀ = 0.25, β₀ = 0.25, n = 200).