Penalised maximum likelihood estimation in multi-state models for interval-censored data

Continuous-time multi-state Markov models can be used to describe transitions over time across health states. Given longitudinal interval-censored data on transitions between states, statistical inference on changing health is possible by specifying models for transition hazards. Parametric time-dependent hazards can be restrictive, and nonparametric hazard specifications using splines are presented as an alternative. The smoothing of the splines is controlled by using penalised maximum likelihood estimation. With multiple time-dependent hazards in a multi-state model, there are multiple penalty parameters and selecting the optimal amount of smoothing is a challenge. A grid search to estimate the penalty parameters is computational intensive especially when combined with methods to deal with interval-censored transition times. A new and efficient method is proposed to estimate multi-state models with splines where the estimation of the penalty parameters is automatic. A simulation study is undertaken to validate the method and to illustrate the effect of interval censoring. The feasibility of the method is illustrated with two applications

Joint models for discrete longitudinal outcomes in ageing research

Given the aging population in the UK, statistical modelling of cognitive function in the older population is of interest. Joint models are formulated for survival and cognitive function in the older population. Because tests of cognitive function often result in discrete outcomes, binomial and beta–binomial mixed effects regression models are applied to analyse longitudinal measurements. Dropout due to death is accounted for by parametric survival models, where the choice of a Gompertz baseline hazard and the specification of the random-effects structure are of specific interest. The measurement model and the survival model are combined in a shared parameter joint model. Estimation is by marginal likelihood. The methods are used to analyse data from the Cambridge City over-75s cohort study and the English Longitudinal Study of Agein

The Reality of Functionally Graded Material Products

Rapid Manufacturing utilizes the application of different materials in parts by stacking a sequence of layers. Based on the requirements of the part, mixtures of materials, so-called Functionally Graded Materials, can be used to compose the product functionality. This process depends completely on the availability of CAD information of the part geometry. Unfortunately, commercially available CAD-systems do not allow the design of graded material structures. TNO developed a computer tool which enables the user to specify Functionally Graded Materials. The system is based on a new approach to define the material composition at any point in the solid.Mechanical Engineerin

Accounting for self-protective responses in randomized response data from a social security survey using the zero-inflated Poisson model

In 2004 the Dutch Department of Social Affairs conducted a survey to assess the extent of noncompliance with social security regulations. The survey was conducted among 870 recipients of social security benefits and included a series of sensitive questions about regulatory noncompliance. Due to the sensitive nature of the questions the randomized response design was used. Although randomized response protects the privacy of the respondent, it is unlikely that all respondents followed the design. In this paper we introduce a model that allows for respondents displaying self-protective response behavior by consistently giving the nonincriminating response, irrespective of the outcome of the randomizing device. The dependent variable denoting the total number of incriminating responses is assumed to be generated by the application of randomized response to a latent Poisson variable denoting the true number of rule violations. Since self-protective responses result in an excess of observed zeros in relation to the Poisson randomized response distribution, these are modeled as observed zero-inflation. The model includes predictors of the Poisson parameters, as well as predictors of the probability of self-protective response behavior.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS135 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org