Constrained nonparametric maximum likelihood estimation of stochastically ordered survivor functions

This paper considers estimators of survivor functions subject to a stochastic ordering constraint based on right censored data. We present the constrained nonparametric maximum likelihood estimator (C‐NPMLE) of the survivor functions in one‐and two‐sample settings where the survivor distributions could be discrete or continuous and discuss the non‐uniqueness of the estimators. We also present a computationally efficient algorithm to obtain the C‐NPMLE. To address the possibility of non‐uniqueness of the C‐NPMLE of $S_1(t)$ when $S_1(t)\le S_2(t)$ , we consider the maximum C‐NPMLE (MC‐NPMLE) of $S_1(t)$ . In the one‐sample case with arbitrary upper bound survivor function $S_2(t)$ , we present a novel and efficient algorithm for finding the MC‐NPMLE of $S_1(t)$ . Dykstra ( 1982 ) also considered constrained nonparametric maximum likelihood estimation for such problems, however, as we show, Dykstra's method has an error and does not always give the C‐NPMLE. We corrected this error and simulation shows improvement in efficiency compared to Dykstra's estimator. Confidence intervals based on bootstrap methods are proposed and consistency of the estimators is proved. Data from a study on larynx cancer are analysed to illustrate the method. The Canadian Journal of Statistics 40: 22–39; 2012 © 2012 Statistical Society of Canada     

Likelihood‐based and marginal inference methods for recurrent event data with covariate measurement error

Recurrent event data arise commonly in medical and public health studies. The analysis of such data has received extensive research attention and various methods have been developed in the literature. Depending on the focus of scientific interest, the methods may be broadly classified as intensity‐based counting process methods, mean function‐based estimating equation methods, and the analysis of times to events or times between events. These methods and models cover a wide variety of practical applications. However, there is a critical assumption underlying those methods–variables need to be correctly measured. Unfortunately, this assumption is frequently violated in practice. It is quite common that some covariates are subject to measurement error. It is well known that covariate measurement error can substantially distort inference results if it is not properly taken into account. In the literature, there has been extensive research concerning measurement error problems in various settings. However, with recurrent events, there is little discussion on this topic. It is the objective of this paper to address this important issue. In this paper, we develop inferential methods which account for measurement error in covariates for models with multiplicative intensity functions or rate functions. Both likelihood‐based inference and robust inference based on estimating equations are discussed. The Canadian Journal of Statistics 40: 530–549; 2012 © 2012 Statistical Society of Canada     

A Bayesian approach to mixture cure models with spatial frailties for population‐based cancer relative survival data

As the treatments of cancer progress, a certain number of cancers are curable if diagnosed early. In population‐based cancer survival studies, cure is said to occur when mortality rate of the cancer patients returns to the same level as that expected for the general cancer‐free population. The estimates of cure fraction are of interest to both cancer patients and health policy makers. Mixture cure models have been widely used because the model is easy to interpret by separating the patients into two distinct groups. Usually parametric models are assumed for the latent distribution for the uncured patients. The estimation of cure fraction from the mixture cure model may be sensitive to misspecification of latent distribution. We propose a Bayesian approach to mixture cure model for population‐based cancer survival data, which can be extended to county‐level cancer survival data. Instead of modeling the latent distribution by a fixed parametric distribution, we use a finite mixture of the union of the lognormal, loglogistic, and Weibull distributions. The parameters are estimated using the Markov chain Monte Carlo method. Simulation study shows that the Bayesian method using a finite mixture latent distribution provides robust inference of parameter estimates. The proposed Bayesian method is applied to relative survival data for colon cancer patients from the Surveillance, Epidemiology, and End Results (SEER) Program to estimate the cure fractions. The Canadian Journal of Statistics 40: 40–54; 2012 © 2012 Statistical Society of Canada     

全球化背景下联合国维和行动的法律机制建设探讨

近年来,联合国维和行动发生了深刻变化,包括联合国维和行动的指导思想,维和行动的职能以及维和行动的任务等方面,都和过去产生了很大的不同。这些变化和不同,原因是多方面的,但都与联合国维和行动与其法律机制建设密切相关。因此,如何科学地定位维和行动,建立科学的法律机制,对联合国维和行动而言显得十分必要。     

氮磷钾沙配比对药用甘薯西蒙1号植株性状及产量的影响

通过田间及盆栽试验,利用西蒙1号药用甘暑蔓头研究了氮、磷、钾、沙综合因素对其产量的影响。结果表明,氮磷钾沙配配合施用,茎、叶、块根中的总糖含量较对照分别高7.35%、19.70%和18.7%左右,而且鲜薯增产10%左右,最高可达50%以上。其中,钾的作用最大,能使地上部与地下部生长趋于协调,促进块根膨大,而氮肥过多时会造成茎叶旺长致使产量降低。     

Analysis of an addictions treatment system

A treatment system consisting of an assessment/referral service, a detox, a mission, a hospital recovery unit, and three recovery homes was examined using a retrospective client-tracking approach over 1 year. There were 4,187 admissions to the system accounted for by 1,522 individuals; system users tended to be males, unmarried, unemployed, and in their 30s and 40s. The best predictor of readmission to the system after treatment was prior system use. Use patterns and referral data indicated that perceived gatekeepers (i.e., detoxication and assessment services) were not consistently coordinating the system and that more socially stable clients appeared to be more likely to be involved with non-addiction specific services (i.e., medical, general counselling).     

Smoothing spline estimation in varying-coefficient models

Summary.  Smoothing spline estimators are considered for inference in varying-coefficient models with one effect modifying covariate. Bayesian 'confidence intervals' are developed for the coefficient curves and efficient computational methods are derived for computing the curve estimators, fitted values, posterior variances and data-adaptive methods for selecting the levels of smoothing. The efficacy and utility of the methodology proposed are demonstrated through a small simulation study and the analysis of a real data set.     

Evaluation in the community mental health centers program: A bold new reproach?

The Federal Community Mental Health Centers Program (CMHC)-from 1963 to 1981-was heralded as a revolution in mental health care. Championed by many, and severely criticized by others, the actual impact of the program on the nation's mental health remains unclear. The authorization to evaluate the CMHC Program came originally from congressional legislation (PL 90-174), and later from the policies and regulations of NIMH under a series of Federal laws, notably PL 94-63. From 1976-1980, two dominant evaluation strategies were prevalent: funds expended by NIMH each year for studies of CMHC services or program-wide evaluations, and a much larger expenditure by CMHCs to conduct their own, independent evaluations following federal guidelines. As the Center's Program was turned over to the states in the form of block grants (PL 97-35), a group of professionals involved with setting and carrying out federal CMHC evaluation policy of both varieties met in public forum to debate the impact of these two evaluation approaches. While some participants cited gains in evaluation technology and impact upon local management of CMHCs, others found the lack of a coordinated and systematic approach to evaluating the CMHC Program to have been an opportunity missed. The impact of CMHC evaluation efforts are also discussed in terms of their major contribution to the field of evaluation research as a whole.