Inference on Survival Data with Covariate Measurement Error - An Imputation-based Approach

We propose a new method for fitting proportional hazards models with error-prone covariates. Regression coefficients are estimated by solving an estimating equation that is the average of the partial likelihood scores based on imputed true covariates. For the purpose of imputation, a linear spline model is assumed on the baseline hazard. We discuss consistency and asymptotic normality of the resulting estimators, and propose a stochastic approximation scheme to obtain the estimates. The algorithm is easy to implement, and reduces to the ordinary Cox partial likelihood approach when the measurement error has a degenerative distribution. Simulations indicate high efficiency and robustness. We consider the special case where error-prone replicates are available on the unobserved true covariates. As expected, increasing the number of replicate for the unobserved covariates increases efficiency and reduces bias. We illustrate the practical utility of the proposed method with an Eastern Cooperative Oncology Group clinical trial where a genetic marker, c-myc expression level, is subject to measurement error.

Moving tele-monitoring and tele-treatment from promise to practice : a business model approach for a chronic lower back pain application

The availability of new information and communication technologies creates opportunities for new, mobile tele-health services. While many promising tele-health projects deliver working R&D prototypes, they often do not result in actual deployment. We aim to identify critical issues than can increase our understanding and enhance the viability of the mobile tele-health services beyond the R&D phase by developing a business model. The present study describes the systematic development and evaluation of a service-oriented business model for tele-monitoring and -treatment of chronic lower back pain patients based on a mobile technology prototype. We address challenges of multi-sector collaboration and disruptive innovation.

Internet usage, internet intensity and international sales performance SME's : a structural model approach

There has been much written about the Internet’s potential to enhance international market growth opportunities for SME’s. However, the literature is vague as to how Internet usage and the application of Internet marketing also known as Internet marketing intensity has an impact on firm international market growth. This paper examines the level and role of the Internet in the international operations of a sample of 218 Australian SMEs with international customers. This study shows evidence of a statistical relationship between Internet usage and Internet marketing intensity, which in turn leads to international market growth, in terms of increased sales from new customers in new countries, new customers in existing countries and from existing customers.

Evaluation of a skin self examination attitude scale using an item response theory model approach

Introduction The Skin Self-Examination Attitude Scale (SSEAS) is a brief measure that allows for the assessment of attitudes in relation to skin self-examination. This study evaluated the psychometric properties of the SSEAS using Item Response Theory (IRT) methods in a large sample of men ≥ 50 years in Queensland, Australia. Methods A sample of 831 men (420 intervention and 411 control) completed a telephone assessment at the 13-month follow-up of a randomized-controlled trial of a video-based intervention to improve skin self-examination (SSE) behaviour. Descriptive statistics (mean, standard deviation, item–total correlations, and Cronbach’s alpha) were compiled and difficulty parameters were computed with Winsteps using the polytomous Rasch Rating Scale Model (RRSM). An item person (Wright) map of the SSEAS was examined for content coverage and item targeting. Results The SSEAS have good psychometric properties including good internal consistency (Cronbach’s alpha = 0.80), fit with the model and no evidence for differential item functioning (DIF) due to experimental trial grouping was detected. Conclusions The present study confirms the SSEA scale as a brief, useful and reliable tool for assessing attitudes towards skin self-examination in a population of men 50 years or older in Queensland, Australia. The 8-item scale shows unidimensionality, allowing levels of SSE attitude, and the item difficulties, to be ranked on a single continuous scale. In terms of clinical practice, it is very important to assess skin cancer self-examination attitude to identify people who may need a more extensive intervention to allow early detection of skin cancer.

A hidden Markov model approach for determining vessel activity from vessel monitoring system data.

Many fisheries worldwide have adopted vessel monitoring systems (VMS) for compliance purposes. An added benefit of these systems is that they collect a large amount of data on vessel locations at very fine spatial and temporal scales. This data can provide a wealth of information for stock assessment, research, and management. However, since most VMS implementations record vessel location at set time intervals with no regard to vessel activity, some methodology is required to determine which data records correspond to fishing activity. This paper describes a probabilistic approach, based on hidden Markov models (HMMs), to determine vessel activity. A HMM provides a natural framework for the problem and, by definition, models the intrinsic temporal correlation of the data. The paper describes the general approach that was developed and presents an example of this approach applied to the Queensland trawl fishery off the coast of eastern Australia. Finally, a simulation experiment is presented that compares the misallocation rates of the HMM approach with other approaches.

Discovering hidden structures in molecular data using a Bayesian partition model approach

Advancements in the analysis techniques have led to a rapid accumulation of biological data in databases. Such data often are in the form of sequences of observations, examples including DNA sequences and amino acid sequences of proteins. The scale and quality of the data give promises of answering various biologically relevant questions in more detail than what has been possible before. For example, one may wish to identify areas in an amino acid sequence, which are important for the function of the corresponding protein, or investigate how characteristics on the level of DNA sequence affect the adaptation of a bacterial species to its environment. Many of the interesting questions are intimately associated with the understanding of the evolutionary relationships among the items under consideration. The aim of this work is to develop novel statistical models and computational techniques to meet with the challenge of deriving meaning from the increasing amounts of data. Our main concern is on modeling the evolutionary relationships based on the observed molecular data. We operate within a Bayesian statistical framework, which allows a probabilistic quantification of the uncertainties related to a particular solution. As the basis of our modeling approach we utilize a partition model, which is used to describe the structure of data by appropriately dividing the data items into clusters of related items. Generalizations and modifications of the partition model are developed and applied to various problems. Large-scale data sets provide also a computational challenge. The models used to describe the data must be realistic enough to capture the essential features of the current modeling task but, at the same time, simple enough to make it possible to carry out the inference in practice. The partition model fulfills these two requirements. The problem-specific features can be taken into account by modifying the prior probability distributions of the model parameters. The computational efficiency stems from the ability to integrate out the parameters of the partition model analytically, which enables the use of efficient stochastic search algorithms.

Simplified theory of carrier back-scattering in semiconducting carbon nanotubes: A Kane's model approach

We present a simplified yet analytical formulation of the carrier backscattering coefficient for zig-zag semiconducting single walled carbon nanotubes under diffusive regime. The electron-phonon scattering rate for longitudinal acoustic, optical, and zone-boundary phonon emissions for both inter- and intrasubband transition rates have been derived using Kane's nonparabolic energy subband model.The expressions for the mean free path and diffusive resistance have been formulated incorporating the aforementioned phonon scattering. Appropriate overlap function in Fermi's golden rule has been incorporated for a more general approach. The effect of energy subbands on low and high bias zones for the onset of longitudinal acoustic, optical, and zone-boundary phonon emissions and absorption have been analytically addressed. 90% transmission of the carriers from the source to the drain at 400 K for a 5 mu m long nanotube at 105 V m(-1) has been exhibited. The analytical results are in good agreement with the available experimental data. (c) 2010 American Institute of Physics.

Examining the effects of species richness on community stability: an assembly model approach

We build dynamic models of community assembly by starting with one species in our model ecosystem and adding colonists. We find that the number of species present first increases, then fluctuates about some level. We ask: how large are these fluctuations and how can we characterize them statistically? As in Robert May's work, communities with weaker interspecific interactions permit a greater number of species to coexist on average. We find that as this average increases, however, the relative variation in the number of species and return times to mean community levels decreases. In addition, the relative frequency of large extinction events to small extinction events decreases as mean community size increases. While the model reproduces several of May's results, it also provides theoretical support for Charles Elton's idea that diverse communities such as those found in the tropics should be less variable than depauperate communities such as those found in arctic or agricultural settings.

A mixture model approach for formant tracking and the robustness of student's-t distribution

We address the problem of robust formant tracking in continuous speech in the presence of additive noise. We propose a new approach based on mixture modeling of the formant contours. Our approach consists of two main steps: (i) Computation of a pyknogram based on multiband amplitude-modulation/frequency-modulation (AM/FM) decomposition of the input speech; and (ii) Statistical modeling of the pyknogram using mixture models. We experiment with both Gaussian mixture model (GMM) and Student's-t mixture model (tMM) and show that the latter is robust with respect to handling outliers in the pyknogram data, parameter selection, accuracy, and smoothness of the estimated formant contours. Experimental results on simulated data as well as noisy speech data show that the proposed tMM-based approach is also robust to additive noise. We present performance comparisons with a recently developed adaptive filterbank technique proposed in the literature and the classical Burg's spectral estimator technique, which show that the proposed technique is more robust to noise.

Context aware M-commerce services: C-IOB model approach

Context-aware computing is useful in providing individualized services focusing mainly on acquiring surrounding context of user. By comparison, only very little research has been completed in integrating context from different environments, despite of its usefulness in diverse applications such as healthcare, M-commerce and tourist guide applications. In particular, one of the most important criteria in providing personalized service in a highly dynamic environment and constantly changing user environment, is to develop a context model which aggregates context from different domains to infer context of an entity at the more abstract level. Hence, the purpose of this paper is to propose a context model based on cognitive aspects to relate contextual information that better captures the observation of certain worlds of interest for a more sophisticated context-aware service. We developed a C-IOB (Context-Information, Observation, Belief) conceptual model to analyze the context data from physical, system, application, and social domains to infer context at the more abstract level. The beliefs developed about an entity (person, place, things) are primitive in most theories of decision making so that applications can use these beliefs in addition to history of transaction for providing intelligent service. We enhance our proposed context model by further classifying context information into three categories: a well-defined, a qualitative and credible context information to make the system more realistic towards real world implementation. The proposed model is deployed to assist a M-commerce application. The simulation results show that the service selection and service delivery of the system are high compared to traditional system.

Tsunami wave loading on coastal houses: A model approach

The Asian tsunami of 26 December 2004 killed over 220 000 people and devastated coastal structures, including many thousands of traditional brick-built homes. This paper presents the results of model tests that compare the impact of a tsunami wave on a typical coastal house with that on a new tsunami resistant design developed in the USA and now built in Sri Lanka Digital images recorded during the test reveal how the tsunami wave passed through the new house design without damaging it but severely damaged the typical coastal house. Pressure sensor results also provided further insight into tsunami wave loading, indicating that the established Japanese method significantly underestimates maximum impact load.