Deductive Semiparametric Estimation in Double-Sampling Designs with Application to PEPFAR
- PDF / 815,590 Bytes
- 29 Pages / 439.37 x 666.142 pts Page_size
- 98 Downloads / 157 Views
Deductive Semiparametric Estimation in Double-Sampling Designs with Application to PEPFAR Tianchen Qian1
· Constantine Frangakis2
· Constantin Yiannoutsos3
Received: 23 October 2018 / Revised: 26 June 2019 / Accepted: 9 October 2019 © International Chinese Statistical Association 2019
Abstract Non-ignorable dropout is common in studies with long follow-up time, and it can bias study results unless handled carefully in the study design and the statistical analysis. A double-sampling design allocates additional resources to pursue a subsample of the dropouts and find out their outcomes, which can address potential biases due to non-ignorable dropout. It is desirable to construct semiparametric estimators for the double-sampling design because of their robustness properties. However, obtaining such semiparametric estimators remains a challenge due to the requirement of the analytic form of the efficient influence function (EIF), the derivation of which can be ad hoc and difficult for the double-sampling design. Recent work has shown how the derivation of EIF can be made deductive and computerizable using the functional derivative representation of the EIF in nonparametric models. This approach, however, requires deriving the mixture of a continuous distribution and a point mass, which can itself be challenging for complicated problems such as the double-sampling design. We propose semiparametric estimators for the survival probability in double-sampling designs by generalizing the deductive and computerizable estimation approach. In particular, we propose to build the semiparametric estimators based on a discretized support structure, which approximates the possibly continuous observed data distribution and circumvents the derivation of the mixture distribution. Our approach is deductive in the sense that it is expected to produce semiparametric locally efficient estimators within finite steps without knowledge of the EIF. We apply the proposed estimators to estimating the mortality rate in a double-sampling design component of the President’s Emergency Plan for AIDS Relief (PEPFAR) program. We evaluate the impact of double-sampling selection criteria on the mortality rate estimates. Simulation studies are conducted to evaluate the robustness of the proposed estimators.
B
Tianchen Qian [email protected]
1
Department of Statistics, Harvard University, Cambridge, USA
2
Department of Biostatistics, Johns Hopkins University, Baltimore, USA
3
Department of Biostatistics, Indiana University, Indianapolis, USA
123
Statistics in Biosciences
Keywords Deductive estimator · Double-sampling design · Missing data · Semiparametric estimator · Survival analysis · Turing-computerization
1 Introduction Studies with long follow-up often suffer from a high dropout rate. Dropouts can depend on the outcome of interest, even after adjusting for observed covariates. This makes the dropouts “non-ignorable” and biases the analysis based solely on the non-dropouts [23]. As a way to handle non-ignorable dropouts, dou
Data Loading...
