Composite likelihood for multiple multistate processes

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Biostatistics following peer review. The version of record Biostat (2014) 15 (4): 690-705 first published online April 9, 2014 doi:10.1093/biostatistics/kxu011 is available online at: http://dx.doi.org/10.1093/biostatistics/kxu011A copula-based model is described which enables joint analysis of multiple progressive multistate processes. Unlike intensity-based or frailty-based approaches to joint modeling, the copula formulation proposed herein ensures that a wide range of marginal multistate processes can be specified and the joint model will retain these marginal features. The copula formulation also facilitates a variety of approaches to estimation and inference including composite likelihood and two-stage estimation procedures. We consider processes with Markov margins in detail, which are often suitable when chronic diseases are progressive in nature. We give special attention to the setting in which individuals are examined intermittently and transition times are consequently interval-censored. Simulation studies give empirical insight into the different methods of analysis and an application involving progression in joint damage in psoriatic arthritis provides further illustration.Natural Sciences and Engineering Research Council || RGPIN/155849 Canadian Institutes for Health Research (FRN 13887

Copula Models for Multi-type Life History Processes

This thesis considers statistical issues in the analysis of data in the studies of chronic diseases which involve modeling dependencies between life history processes using copula functions. Many disease processes feature recurrent events which represent events arising from an underlying chronic condition; these are often modeled as point processes. In addition, however, there often exists a random variable which is realized upon the occurrence of each event, which is called a mark of the point process. When considered together, such processes are called marked point processes. A novel copula model for the marked point process is described here which uses copula functions to govern the association between marks and event times. Specifically, a copula function is used to link each mark with the next event time following the realization of that mark to reflect the pattern in the data wherein larger marks are often followed by longer time to the next event. The extent of organ damage in an individual can often be characterized by ordered states, and interest frequently lies in modeling the rates at which individuals progress through these states. Risk factors can be studied and the effect of therapeutic interventions can be assessed based on relevant multistate models. When chronic diseases affect multiple organ systems, joint modeling of progression in several organ systems is also important. In contrast to common intensity-based or frailty-based approaches to modelling, this thesis considers a copula-based framework for modeling and analysis. Through decomposition of the density and by use of conditional independence assumptions, an appealing joint model is obtained by assuming that the joint survival function of absorption transition times is governed by a multivariate copula function. Different approaches to estimation and inference are discussed and compared including composite likelihood and two-stage estimation methods. Special attention is paid to the case of interval-censored data arising from intermittent assessment. Attention is also directed to use of copula models for more general scenarios with a focus on semiparametric two-stage estimation procedures. In this approach nonparametric or semiparametric estimates of the marginal survivor functions are obtained in the first stage and estimates of the association parameters are obtained in the second stage. Bivariate failure time models are considered for data under right-censoring and current status observation schemes, and right-censored multistate models. A new expression for the asymptotic variance of the second-stage estimator for the association parameter along with a way of estimating this for finite samples are presented under these models and observation schemes

Censoring Unbiased Regression Trees and Ensembles

This paper proposes a novel approach to building regression trees and ensemble learning in survival analysis. By first extending the theory of censoring unbiased transformations, we construct observed data estimators of full data loss functions in cases where responses can be right censored. This theory is used to construct two specific classes of methods for building regression trees and regression ensembles that respectively make use of Buckley-James and doubly robust estimating equations for a given full data risk function. For the particular case of squared error loss, we further show how to implement these algorithms using existing software (e.g., CART, random forests) by making use of a related form of response imputation. Comparisons of these methods to existing ensemble procedures for predicting survival probabilities are provided in both simulated settings and through applications to four datasets. It is shown that these new methods either improve upon, or remain competitive with, existing implementations of random survival forests, conditional inference forests, and recursively imputed survival trees

A copula model for marked point processes

The final publication (Diao, Liqun, Richard J. Cook, and Ker-Ai Lee. (2013) A copula model for marked point processes. Lifetime Data Analysis, 19(4): 463-489) is available at Springer via http://dx.doi.org/10.1007/s10985-013-9259-3Many chronic diseases feature recurring clinically important events. In addition, however, there often exists a random variable which is realized upon the occurrence of each event reflecting the severity of the event, a cost associated with it, or possibly a short term response indicating the effect of a therapeutic intervention. We describe a novel model for a marked point process which incorporates a dependence between continuous marks and the event process through the use of a copula function. The copula formulation ensures that event times can be modeled by any intensity function for point processes, and any multivariate model can be specified for the continuous marks. The relative efficiency of joint versus separate analyses of the event times and the marks is examined through simulation under random censoring. An application to data from a recent trial in transfusion medicine is given for illustration.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626

Doubly robust survival trees

Estimating a patient's mortality risk is important in making treatment decisions. Survival trees are a useful tool and employ recursive partitioning to separate patients into different risk groups. Existing 'loss based' recursive partitioning procedures that would be used in the absence of censoring have previously been extended to the setting of right censored outcomes using inverse probability censoring weighted estimators of loss functions. In this paper, we propose new 'doubly robust' extensions of these loss estimators motivated by semiparametric efficiency theory for missing data that better utilize available data. Simulations and a data analysis demonstrate strong performance of the doubly robust survival trees compared with previously used methods. Copyright © 2016 John Wiley & Sons, Ltd

Censoring Unbiased Regression Trees and Ensembles

<p>This article proposes a novel paradigm for building regression trees and ensemble learning in survival analysis. Generalizations of the classification and regression trees (CART) and random forests (RF) algorithms for general loss functions, and in the latter case more general bootstrap procedures, are both introduced. These results, in combination with an extension of the theory of censoring unbiased transformations (CUTs) applicable to loss functions, underpin the development of two new classes of algorithms for constructing survival trees and survival forests: censoring unbiased regression trees and censoring unbiased regression ensembles. For a certain “doubly robust” CUT of squared error loss, we further show how these new algorithms can be implemented using existing software (e.g., CART, RF). Comparisons of these methods to existing ensemble procedures for predicting survival probabilities are provided in both simulated settings and through applications to four datasets. It is shown that these new methods either improve upon, or remain competitive with, existing implementations of random survival forests, conditional inference forests, and recursively imputed survival trees.</p

Using Decision Trees to Examine Environmental and Behavioural Factors Associated with Youth Anxiety, Depression, and Flourishing

Modifiable environmental and behavioural factors influence youth mental health; however, past studies have primarily used regression models that quantify population average effects. Decision trees are an analytic technique that examine complex relationships between factors and identify high-risk subgroups to whom intervention measures can be targeted. This study used decision trees to examine associations of various risk factors with youth anxiety, depression, and flourishing. Data were collected from 74,501 students across Canadian high schools participating in the 2018&ndash;2019 COMPASS Study. Students completed a questionnaire including validated mental health scales and 23 covariates. Decision trees were grown to identify key factors and subgroups for anxiety, depression, and flourishing outcomes. Females lacking both happy home life and sense of connection to school were at greatest risk for higher anxiety and depression levels. In contrast with previous literature, behavioural factors such as diet, movement and substance use did not emerge as differentiators. This study highlights the influence of home and school environments on youth mental health using a novel decision tree analysis. While having a happy home life is most important in protecting against youth anxiety and depression, a sense of connection to school may mitigate the negative influence of a poor home environment

Feasibility and Safety of Dual-console Telesurgery with the KangDuo Surgical Robot-01 System Using Fifth-generation and Wired Networks: An Animal Experiment and Clinical Study

The coronavirus disease 2019 pandemic has drawn attention to telesurgery. Important advances in fifth-generation (5G) mobile telecommunication technology have facilitated the rapid evolution of telesurgery. Previously, only a single console was used in telesurgery; thus, there was the possibility of open or laparoscopic conversion. Furthermore, the 5G network has not been available for regional hospitals in China. From October 2021 to April 2022, dual-console telesurgeries with the KangDuo Surgical Robot-01 (KD-SR-01) system were performed using 5G and wired networks in an animal experiment and clinical study. A partial nephrectomy in a porcine model was performed successfully using a wired network. The console time, warm ischemia time, and control swap time were 69 min, 27 min, and 3 s, respectively. The mean latency time was 130 (range, 60–200) ms. A 32-yr-old male patient successfully underwent a remote pyeloplasty using a series connection of 5G wireless and wired networks. The console time and control swap time were 98 min and 3 s, respectively. The mean latency time was 271 (range, 206–307) ms. In the two studies, data pocket loss was <1%. The results demonstrated that dual-console telesurgery with the KD-SR-01 system is feasible and safe using 5G and wired networks. Patient summary: Advances in fifth-generation (5G) mobile telecommunication technology helped in the rapid evolution of telesurgery. Dual-console telesurgery performed with the KD-SR-01 system using 5G and wired networks was shown to be feasible and safe in an animal experiment and clinical study

Highly Interfacial Adhesion and Mechanism of Nylon-66/Rubber Composites by Designing Low-Toxic RF-like Dipping Systems

Fiber-reinforced rubber composites (FRRC) are widely used as load-carrying and anti-pressure products. Resorcinol–formaldehyde–latex (RFL) dipping is widely used to improve interfacial adhesion between fiber and rubber in industry. Unfortunately, high volatility and toxicity of RFL do great harm to humans and the environment during the open dipping process. In this work, a phloroglucinol–terephthalaldehyde–latex (PTL) dipping system based on a low-toxic and low-volatile resin was developed to achieve equivalent interfacial adhesion instead of RFL ones. The reaction mechanism, chemical structure, and wettability changes on the Nylon-66 (PA66) fiber surface were characterized. The effect of the phloroglucinol/terephthalaldehyde (P/T) ratio on the dip pick-up, morphology of the fiber surface, and interfacial structure of FRRC was investigated to expound the interfacial adhesion mechanism. At the optimum P/T ratio (1/2.2), a dipping layer with uniform and suitable dip pick-up is achieved on the fiber surface, which ensures the formation of an appropriate graded interfacial layer with enough crosslinking density and modulus, under the synergistic effect of co-vulcanization with rubber, and accordingly strengthens interfacial adhesion. The dipped fiber possesses excellent interfacial adhesion, dynamic fatigue life, and storage stability at the RFL level. This new dipping system is environmentally friendly and easy to scale up without making any change in the process and equipment