5 research outputs found
Estimating optimal treatment regimes in survival contexts using an instrumental variable
In survival contexts, substantial literature exists on estimating optimal
treatment regimes, where treatments are assigned based on personal
characteristics for the purpose of maximizing the survival probability. These
methods assume that a set of covariates is sufficient to deconfound the
treatment-outcome relationship. Nevertheless, the assumption can be limiting in
observational studies or randomized trials in which noncompliance occurs. Thus,
we advance a novel approach for estimating the optimal treatment regime when
certain confounders are not observable and a binary instrumental variable is
available. Specifically, via a binary instrumental variable, we propose two
semiparametric estimators for the optimal treatment regime, one of which
possesses the desirable property of double robustness, by maximizing
Kaplan-Meier-like estimators within a pre-defined class of regimes. Because the
Kaplan-Meier-like estimators are jagged, we incorporate kernel smoothing
methods to enhance their performance. Under appropriate regularity conditions,
the asymptotic properties are rigorously established. Furthermore, the finite
sample performance is assessed through simulation studies. We exemplify our
method using data from the National Cancer Institute's (NCI) prostate, lung,
colorectal, and ovarian cancer screening trial
Partial Replacement Imputation Estimation Method for Complex Missing Covariates in Additive Partially Linear Models
Missing data is a common problem in clinical data collection, which causes
difficulty in the statistical analysis of such data. In this article, we
consider the problem under a framework of a semiparametric partially linear
model when observations are subject to missingness with complex patterns. If
the correct model structure of the additive partially linear model is
available, we propose to use a new imputation method called Partial Replacement
IMputation Estimation (PRIME), which can overcome problems caused by incomplete
data in the partially linear model. Also, we use PRIME in conjunction with
model averaging (PRIME-MA) to tackle the problem of unknown model structure in
the partially linear model. In simulation studies, we use various error
distributions, sample sizes, missing data rates, covariate correlations, and
noise levels, and PRIME outperforms other methods in almost all cases. With an
unknown correct model structure, PRIME-MA has satisfactory performance in terms
of prediction, while slightly worse than PRIME. Moreover, we conduct a study of
influential factors in Pima Indians Diabetes data, which shows that our method
performs better than the other models.Comment: 28 pages, 8 figures and 6 table
Engineering the Phases and Heterostructures of Ultrathin Hybrid Perovskite Nanosheets
Low-dimensional perovskites have gained increasing attention recently, and engineering their material phases, structural patterning and interfacial properties is crucial for future perovskite-based applications. Here a phase and heterostructure engineering on ultrathin perovskites, through the reversible cation exchange of hybrid perovskites and efficient surface functionalization of low-dimensional materials, is demonstrated. Using PbI2 as precursor and template, perovskite nanosheets of varying thickness and hexagonal shape on diverse substrates is obtained. Multiple phases, such as PbI2, MAPbI3 and FAPbI3, can be flexibly designed and transformed as a single nanosheet. A perovskite nanosheet can be patterned using masks made of 2D materials, fabricating lateral heterostructures of perovskite and PbI2. Perovskite-based vertical heterostructures show strong interfacial coupling with 2D materials. As a demonstration, monolayer MoS2/MAPbI3 stacks give a type-II heterojunction. The ability to combine the optically efficient perovskites with versatile 2D materials creates possibilities for new designs and functionalities
Engineering the Phases and Heterostructures of Ultrathin Hybrid Perovskite Nanosheets
Low-dimensional perovskites have gained increasing attention recently, and engineering their material phases, structural patterning and interfacial properties is crucial for future perovskite-based applications. Here a phase and heterostructure engineering on ultrathin perovskites, through the reversible cation exchange of hybrid perovskites and efficient surface functionalization of low-dimensional materials, is demonstrated. Using PbI2 as precursor and template, perovskite nanosheets of varying thickness and hexagonal shape on diverse substrates is obtained. Multiple phases, such as PbI2, MAPbI3 and FAPbI3, can be flexibly designed and transformed as a single nanosheet. A perovskite nanosheet can be patterned using masks made of 2D materials, fabricating lateral heterostructures of perovskite and PbI2. Perovskite-based vertical heterostructures show strong interfacial coupling with 2D materials. As a demonstration, monolayer MoS2/MAPbI3 stacks give a type-II heterojunction. The ability to combine the optically efficient perovskites with versatile 2D materials creates possibilities for new designs and functionalities