The Singularity of the Information Matrix of the Mixed Proportional Hazard Model

This paper presents new identification conditions for the mixed proportional hazard model. In particular, the baseline hazard is assumed to be bounded away from 0 and ∞ near t = 0. These conditions ensure that the information matrix is nonsingular. The paper also presents an estimator for the mixed proportional hazard model that converges at rate N^−1/2.     

Effectiveness of the Coral Tree Family Service family inpatient unit: a longitudinal study exploring change in family functioning

Child mental health disorders represent a major burden to public health in Australia due to high prevalence rates, the widespread impact across domains, and the potential for difficulties to persist into adolescence and adulthood. Extensive evidence exists for the use of parent management training and cognitive behavioural therapy to treat difficulties experienced by children; however, a proportion of children do not benefit as expected from these treatments. The use of complementary therapeutic approaches and variations to the mode and intensity of existing intervention is warranted; family inpatient units represent a unique example of this. The aim of this study is to evaluate the effectiveness of a well-established Australian inpatient unit that admits the whole family, for children aged 12 years and younger referred with mental health, behavioural, or emotional difficulties. This study's longitudinal within-subject study design utilised routinely collected outcome data from the systemic clinical outcome and routine evaluation, 15 item (SCORE-15)—a valid and reliable measure of family functioning—from a sample of 980 participants attending the Family Residential Program. This study reported significant improvement in family functioning across all outcome variables between baseline and post-intervention, with no variables returning to pre-intervention levels at follow-up. Clinically significant changes in overall family functioning showed that 37% of participant scores moved from the clinical range at baseline to the nonclinical range post-intervention. This study represents the first empirical evaluation of the Family Residential Program, and the reported results provide compelling evidence for the program to effect improvement in family functioning for families with long-standing and severe difficulties. Poor rates of questionnaire completion following intervention frequent the literature base of family inpatient units, with low post-intervention (56%) and follow-up (12%) rates evident in this study. This study uniquely contributes to the growing evidence base of family inpatient units using rigorous evaluation methods.     

A comparison study on modeling of clustered and overdispersed count data for multiple comparisons

Data collected in various scientific fields are count data. One way to analyze such data is to compare the individual levels of the factor treatment using multiple comparisons. However, the measured individuals are often clustered – e.g. according to litter or rearing. This must be considered when estimating the parameters by a repeated measurement model. In addition, ignoring the overdispersion to which count data is prone leads to an increase of the type one error rate. We carry out simulation studies using several different data settings and compare different multiple contrast tests with parameter estimates from generalized estimation equations and generalized linear mixed models in order to observe coverage and rejection probabilities. We generate overdispersed, clustered count data in small samples as can be observed in many biological settings. We have found that the generalized estimation equations outperform generalized linear mixed models if the variance-sandwich estimator is correctly specified. Furthermore, generalized linear mixed models show problems with the convergence rate under certain data settings, but there are model implementations with lower implications exists. Finally, we use an example of genetic data to demonstrate the application of the multiple contrast test and the problems of ignoring strong overdispersion.     

Life and labor on the internal colonial edge: Political economy of kolberi in Rojhelat

Through the investigation of kolberi (cross-border labor), this paper sheds light on the state’s policy of de-development (or internal colonialization) of the Kurdish region (known as Rojhelat) in Iran. While the most dangerous form of labor, kolberi has become a dominant employment opportunity for Rojhelat Kurds in the last decade. There are no Iranian state laws criminalizing kolberi, and yet those laborers die on a regular basis—being shot or thrown off mountain cliffs by the state forces, stepping into minefields, and so forth. Nevertheless, there is not a single scholarly paper on this subject. Using the mixed methods research approach, our study analyzes the existing data along with in-depth interviews with 20 people who are currently engaged in kolberi to contextualize this understudied phenomenon. Our finding demonstrates that kolberi is a direct outcome of a uni-ethno-religious policies of development and part and parcel of the state's Perso-Shi‘ification strategy in Kurdistan. Therefore, kolberi is more of a political phenomenon than an economic one.     

Investigating the differential mobility experiences of Chinese cross-border students

Recent migration studies have adopted the lens of mobility to examine the stratifying effects of border policies, but few investigate the differential mobility of migrant families and children. This paper aims to contribute to the migration literature by considering the interplay between border policies, family configurations, and differential mobility. We apply the lens of differential mobility to the experiences of Chinese cross-border pupils – young child migrants with Hong Kong permanent residency who reside in Shenzhen, China, and cross the border to attend school. We begin by describing shifts in Hong Kong’s border and immigration policies since 1997, which have created a typology of families differentiated by mixed status, citizenship rights, and mobility. We then turn to four case studies of students with unequal border-crossing experiences to elucidate how border control constrains or promotes family mobility and perpetuates inequalities.     

Multiphase experiments in practice: A look back

Multiphase experiments are introduced and an overview of their design and analysis as it is currently practised is given via an account of their development since 1955 and a literature survey. Methods that are available for designing and analysing them are outlined, with an emphasis on making explicit the role of the model in their design. The availability of software and its use is described in detail. Overall, while multiphase designs have been applied in areas such as plant breeding, plant pathology, greenhouse experimentation, product storage, gene expression studies, and sensory evaluation, their deployment has been limited.     

Enabling robust assessment of QTc prolongation in early phase clinical trials

Since the implementation of the International Conference on Harmonization (ICH) E14 guideline in 2005, regulators have required a “thorough QTc” (TQT) study for evaluating the effects of investigational drugs on delayed cardiac repolarization as manifested by a prolonged QTc interval. However, TQT studies have increasingly been viewed unfavorably because of their low cost effectiveness. Several researchers have noted that a robust drug concentration‐QTc (conc‐QTc) modeling assessment in early phase development should, in most cases, obviate the need for a subsequent TQT study. In December 2015, ICH released an “E14 Q&As (R3)” document supporting the use of conc‐QTc modeling for regulatory decisions. In this article, we propose a simple improvement of two popular conc‐QTc assessment methods for typical first‐in‐human crossover‐like single ascending dose clinical pharmacology trials. The improvement is achieved, in part, by leveraging routinely encountered (and expected) intrasubject correlation patterns encountered in such trials. A real example involving a single ascending dose and corresponding TQT trial, along with results from a simulation study, illustrate the strong performance of the proposed method. The improved conc‐QTc assessment will further enable highly reliable go/no‐go decisions in early phase clinical development and deliver results that support subsequent TQT study waivers by regulators.     

Testing for bioequivalence of highly variable drugs from TR‐RT crossover designs with heterogeneous residual variances

Traditional bioavailability studies assess average bioequivalence (ABE) between the test (T) and reference (R) products under the crossover design with TR and RT sequences. With highly variable (HV) drugs whose intrasubject coefficient of variation in pharmacokinetic measures is 30% or greater, assertion of ABE becomes difficult due to the large sample sizes needed to achieve adequate power. In 2011, the FDA adopted a more relaxed, yet complex, ABE criterion and supplied a procedure to assess this criterion exclusively under TRR‐RTR‐RRT and TRTR‐RTRT designs. However, designs with more than 2 periods are not always feasible. This present work investigates how to evaluate HV drugs under TR‐RT designs. A mixed model with heterogeneous residual variances is used to fit data from TR‐RT designs. Under the assumption of zero subject‐by‐formulation interaction, this basic model is comparable to the FDA‐recommended model for TRR‐RTR‐RRT and TRTR‐RTRT designs, suggesting the conceptual plausibility of our approach. To overcome the distributional dependency among summary statistics of model parameters, we develop statistical tests via the generalized pivotal quantity (GPQ). A real‐world data example is given to illustrate the utility of the resulting procedures. Our simulation study identifies a GPQ‐based testing procedure that evaluates HV drugs under practical TR‐RT designs with desirable type I error rate and reasonable power. In comparison to the FDA's approach, this GPQ‐based procedure gives similar performance when the product's intersubject standard deviation is low (≤0.4) and is most useful when practical considerations restrict the crossover design to 2 periods.     

The Frisch–Waugh–Lovell theorem for the lasso and the ridge regression

The Frisch–Waugh–Lovell (FWL) (partitioned regression) theorem is essential in regression analysis. This is partly because it is quite useful to derive theoretical results. The lasso regression and the ridge regression, both of which are penalized least-squares regressions, have become popular statistical techniques. This article describes that the FWL theorem remains valid for these penalized least-squares regressions. More precisely, we demonstrate that the covariates corresponding to unpenalized regression parameters in these penalized least-squares regression can be projected out. Some other results related to the FWL theorem in such penalized least-squares regressions are also presented.     

Bayesian quantile regression for skew-normal linear mixed models

Linear mixed models have been widely used to analyze repeated measures data which arise in many studies. In most applications, it is assumed that both the random effects and the within-subjects errors are normally distributed. This can be extremely restrictive, obscuring important features of within-and among-subject variations. Here, quantile regression in the Bayesian framework for the linear mixed models is described to carry out the robust inferences. We also relax the normality assumption for the random effects by using a multivariate skew-normal distribution, which includes the normal ones as a special case and provides robust estimation in the linear mixed models. For posterior inference, we propose a Gibbs sampling algorithm based on a mixture representation of the asymmetric Laplace distribution and multivariate skew-normal distribution. The procedures are demonstrated by both simulated and real data examples.