Give your child and adolescent mental health service a health economics makeover

Specialist child and adolescent mental health services (CAMHS) must make decisions about what treatments to provide to whom, when, where and how, within limited budgets. This raises questions about how services make such decisions, to best meet the mental health needs of their catchment. The methods and practices of Health Economics, a field with considerable expertise in measuring performance in health systems, can help CAMHS make better informed decisions regarding service provision. This paper identifies a process through a set of focused questions to help CAMHS examine and improve their performance. The aspects covered are service profile, costs, conceptualisation of outcomes and identification of value for money. The recommended approach should help CAMHS redirect resources to maximise benefits for their catchment population.     

An EM type estimation procedure for the destructive exponentially weighted Poisson regression cure model under generalized gamma lifetime

In this paper, we assume the number of competing causes to follow an exponentially weighted Poisson distribution. By assuming the initial number of competing causes can undergo destruction and that the population of interest has a cure fraction, we develop the EM algorithm for the determination of the MLEs of the model parameters of such a general cure model. This model is more flexible than the promotion time cure model and also provides an interesting and realistic interpretation of the biological mechanism of the occurrence of an event of interest. Instead of assuming a particular parametric distribution for the lifetime, we assume the lifetime to belong to the wider class of generalized gamma distribution. This allows us to carry out a model discrimination to select a parsimonious lifetime distribution that provides the best fit to the data. Within the EM framework, a two-way profile likelihood approach is proposed to estimate the shape parameters. An extensive Monte Carlo simulation study is carried out to demonstrate the performance of the proposed estimation method. Model discrimination is carried out by means of the likelihood ratio test and information-based methods. Finally, a data on melanoma is analyzed for illustrative purpose.     

Multiple-index varying-coefficient models for longitudinal data

In haemodialysis patients, vascular access type is of paramount importance. Although recent studies have found that central venous catheter is often associated with poor outcomes and switching to arteriovenous fistula is beneficial, studies have not fully elucidated how the effect of switching of access on outcomes changes over time for patients on dialysis and whether the effect depends on switching time. In this paper, we characterise the switching access type effect on outcomes for haemodialysis patients. This is achieved by using a new class of multiple-index varying-coefficient (MIVC) models. We develop a new estimation procedure for MIVC models based on local linear, profile least-square method and Cholesky decomposition. Monte Carlo simulation studies show excellent finite sample performance. Finally, we analyse the dialysis data using our method.     

A profile Godambe information of power transformations for ARCH time series

Due to Godambe (1985 Godambe, V.P. (1985). The foundation of finite sample estimation in stochastic processes. Biometrika 72:419–428.[Crossref], [Web of Science ®] , [Google Scholar]), one can obtain the Godambe optimum estimating functions (EFs) each of which is optimum (in the sense of maximizing the Godambe information) within a linear class of EFs. Quasi-likelihood scores can be viewed as special cases of the Godambe optimum EFs (see, for instance, Hwang and Basawa, 2011 Hwang, S.Y., Basawa, I.V. (2011). Godambe estimating functions and asymptotic optimal inference. Stat. Probab. Lett. 81:1121–1127.[Crossref], [Web of Science ®] , [Google Scholar]). The paper concerns conditionally heteroscedastic time series with unknown likelihood. Power transformations are introduced in innovations to construct a class of Godambe optimum EFs. A “best” power transformation for Godambe innovation is then obtained via maximizing the “profile” Godambe information. To illustrate, the KOrea Stock Prices Index is analyzed for which absolute value transformation and square transformation are recommended according to the ARCH(1) and GARCH(1,1) models, respectively.     

功率器件有 JTE 结构的电场的数值分析与模拟

本文用 damped-Newton(DN)法求解由一般非线性方程获得的Poisson 方程。研究了阻尼因子λ对其收敛性和求解效率的影响。利用此方法计算了横向表面掺杂为 Gauss 分布的器件的归一化电场与掺杂浓度和掺杂层宽度的关系。     

Probabilistic Expert Systems for Forensic Inference from Genetic Markers

We present a number of real and fictitious examples in illustration of a new approach to analysing complex cases of forensic identification inference. This is effected by careful restructuring of the relevant pedigrees as a Probabilistic Expert System. Existing software can then be used to perform the required inferential calculations. Specific complications which are readily handled by this approach include missing data on one or more relevant individuals, and genetic mutation. The method is particularly valuable for disputed paternity cases, but applies also to certain criminal cases.     

Extending conditional likelihood in models for stratified binary data

The conditional likelihood is widely used in logistic regression models with stratified binary data. In particular, it leads to accurate inference for the parameters of interest, which are common to all strata, eliminating stratum-specific nuisance parameters. The modified profile likelihood is an accurate approximation to the conditional likelihood, but has the advantage of being available for general parametric models. Here, we propose the modified profile likelihood as an ideal extension of the conditional likelihood in generalized linear models for binary data, with generic link function. An important feature is that for the implementation we only need standard outputs of routines for generalized linear models. The accuracy of the method is supported by theoretical properties and is confirmed by simulation results.This research was supported by MIUR COFIN 2001-2003.     

Estimation and testing in generalized partial linear models—A comparative study

A particular semiparametric model of interest is the generalized partial linear model (GPLM) which extends the generalized linear model (GLM) by a nonparametric component.The paper reviews different estimation procedures based on kernel methods as well as test procedures on the correct specification of this model (vs. a parametric generalized linear model). Simulations and an application to a data set on East–West German migration illustrate similarities and dissimilarities of the estimators and test statistics.     

The Cox Proportional Hazards Model with a Partly Linear Relative Risk Function

The conventional Cox proportional hazards regression model contains a loglinear relative risk function, linking the covariate information to the hazard ratio with a finite number of parameters. A generalization, termed the partly linear Cox model, allows for both finite dimensional parameters and an infinite dimensional parameter in the relative risk function, providing a more robust specification of the relative risk function. In this work, a likelihood based inference procedure is developed for the finite dimensional parameters of the partly linear Cox model. To alleviate the problems associated with a likelihood approach in the presence of an infinite dimensional parameter, the relative risk is reparameterized such that the finite dimensional parameters of interest are orthogonal to the infinite dimensional parameter. Inference on the finite dimensional parameters is accomplished through maximization of the profile partial likelihood, profiling out the infinite dimensional nuisance parameter using a kernel function. The asymptotic distribution theory for the maximum profile partial likelihood estimate is established. It is determined that this estimate is asymptotically efficient; the orthogonal reparameterization enables employment of profile likelihood inference procedures without adjustment for estimation of the nuisance parameter. An example from a retrospective analysis in cancer demonstrates the methodology.     

A simultaneous variable selection methodology for linear mixed models

Selecting an appropriate structure for a linear mixed model serves as an appealing problem in a number of applications such as in the modelling of longitudinal or clustered data. In this paper, we propose a variable selection procedure for simultaneously selecting and estimating the fixed and random effects. More specifically, a profile log-likelihood function, along with an adaptive penalty, is utilized for sparse selection. The Newton-Raphson optimization algorithm is performed to complete the parameter estimation. By jointly selecting the fixed and random effects, the proposed approach increases selection accuracy compared with two-stage procedures, and the usage of the profile log-likelihood can improve computational efficiency in one-stage procedures. We prove that the proposed procedure enjoys the model selection consistency. A simulation study and a real data application are conducted for demonstrating the effectiveness of the proposed method.