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Longitudinal data from labour force surveys permit the investigation of income dynamics at the individual level. However, the data often originate from surveys with a complex multistage sampling scheme. In addition, the hierarchical structure of the data that is imposed by the different stages of the sampling scheme often represents the natural grouping in the population. Motivated by how income dynamics...
The assessment of the lifetime prevalence of mental disorders under comorbidity conditions is an important area in mental health research. Because information on lifetime disorders is usually gathered retrospectively within cross‐sectional studies, the information is necessarily right censored and this should be taken into account when setting up models for the estimation of lifetime prevalences....
Many statistical models are available for spatial data but the vast majority of these assume that spatial separation can be measured by Euclidean distance. Data which are collected over river networks constitute a notable and commonly occurring exception, where distance must be measured along complex paths and, in addition, account must be taken of the relative flows of water into and out of confluences...
We consider non‐parametric estimation of disease onset distribution functions in multiple populations by using censored data with unknown population identifiers. The problem is motivated from studies aiming at estimating the age‐specific disease risk distribution in deleterious mutation carriers for genetic counselling and design of therapeutic intervention trials to modify disease progression (i...
Wind direction plays an important role in the spread of air pollutants over a geographical region. We discuss how to include wind directional information in the covariance function of spatial models. Our models are based on a constructive convolution approach, wherein a spatial process is described as a convolution between a spatially varying smoothing kernel and a white noise process. We describe...
Using 31 unique crime series from two US cities, the spatial pattern that individual criminal decision makers follow is investigated. Locations within a city vary in the likelihood for crimes of a given type to occur, and this is accounted for by using kernel density estimation based on all crimes of each type. Kernel density estimation is highly influenced by the bandwidth, so an objective approach...
We extend a mixture cure fraction model with random effects to allow estimation of relative survival of cancer patients by region in a country with a parsimonious number of parameters. The heterogeneity in the expected survival was taken into account such that the expected mortality rate was considered as a random quantity varying across regions. Two sets of random effects were used to describe regional...
Bayesian semiparametric logit models are fitted to grouped data related to in‐hospital survival outcome of patients hospitalized with an ST‐segment elevation myocardial infarction diagnosis. Dependent Dirichlet process priors are considered for modelling the random‐effects distribution of the grouping factor (hospital of admission), to provide a cluster analysis of the hospitals. The clustering structure...
A frailty modelling framework is presented for representing and making inference on individual heterogeneities that are relevant to the transmission of infectious diseases, including heterogeneities that evolve over time. Central to this framework is the use of multivariate data on several infections. We explore new simple but flexible families of time‐dependent frailty models, in which the frailty...
Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which efficacy and toxicity monotonically increase with dose, biological agents may exhibit non‐monotonic patterns in their dose–response relationships. Using a trial with two biological agents as an example, we propose a dose finding design to identify the biologically optimal dose...
Linear mixed modelling is a useful approach for double mixed factorial designs with covariates. It is explained how these designs are appropriate for the study of human behaviour as a function of characteristics of individuals and situations and stimuli in the situations. The behaviour of subjects nested in types of individual responding to stimuli nested in types of stimuli defines a mixed factorial...
The paper addresses means of generalizing from an experiment based on a non‐probability sample to a population of interest and to subpopulations of interest, where information is available about relevant covariates in the whole population. Using stratification based on propensity score matching with an external populationwide data set, an estimator of the population average treatment effect is constructed...
Responses to a set of indicators, or items, or variables are often used in social sciences for measuring unobserved constructs as attitudes. Latent variable models, which are also known as factor analysis models, are used for linking the observed responses to the latent constructs. Often, some respondents provide random responses to the items. We distinguish between two response strategies: a primary...
The item count method is a way of asking sensitive survey questions which protects the anonymity of the respondents by randomization before the interview. It can be used to estimate the probability of sensitive behaviour and to model how it depends on explanatory variables. We analyse item count survey data on the illegal behaviour of buying stolen goods. The analysis of an item count question is...
An accuracy indicator is an observed variable which is related to the size of measurement error. Basic and extended models are introduced to represent the properties of a binary accuracy indicator. Under specified assumptions, it is shown that an accuracy indicator can identify a measurement error model. An approach to estimating a distribution function is presented together with methodology for variance...
We consider the effect of non‐ignorable dropout in the analysis of residential mobility in household panel surveys. To investigate the effect of such dropout, we consider two types of selection model: the first allows dropout to depend directly on the individual's potentially missing moving status, and the second is a Heckman‐type selection model with correlated errors. We discuss the identification...
Motivated by an application to a longitudinal data set coming from the Health and Retirement Study about self‐reported health status, we propose a model for longitudinal data which is based on a latent process to account for the unobserved heterogeneity between sample units in a dynamic fashion. The latent process is modelled by a mixture of auto‐regressive AR(1) processes with different means and...
Distinguishing between longitudinal dependence due to the effects of previous responses on subsequent responses and dependence due to unobserved heterogeneity is important in many disciplines. For example, wheezing is an inflammatory reaction that may ‘remodel’ a child's airway structure and thereby affect the probability of future wheezing (state dependence). Alternatively, children could vary in...
As women approach menopause, the patterns of their menstrual cycle lengths change. To study these changes, we need to model jointly both the mean and the variability of cycle length. Our proposed model incorporates separate mean and variance change points for each woman and a hierarchical model to link them together, along with regression components to include predictors of menopausal onset such as...
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