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At least 343 records · Page 19Linked to original sources
PubMed2002

A semi-parametric accelerated failure time cure model.

A cure model is a useful approach for analysing failure time data in which some subjects could eventually experience, and others never experience, the event of interest. A cure model has two components: incidence which indicates whether the event could eventually occur and latency which denotes when the event will occur given the subject is susceptible to the event. In this paper, we propose a semi-parametric cure model in which covariates can affect both the incidence and the latency. A logistic regression model is proposed for the incidence, and the latency is determined by an accelerated failure time regression model with unspecified error distribution. An EM algorithm is developed to fit the model. The procedure is applied to a data set of tonsil cancer patients treated with radiation therapy.

Algorithms↗
PubMed2003

Applications of continuous time hidden Markov models to the study of misclassified disease outcomes.

Disease progression in prospective clinical and epidemiological studies is often conceptualized in terms of transitions between disease states. Analysis of data from such studies can be complicated by a number of factors, including the presence of individuals in various prevalent disease states and with unknown prior disease history, interval censored observations of state transitions and misclassified measurements of disease states. We present an approach where the disease states are modelled as the hidden states of a continuous time hidden Markov model using the imperfect measurements of the disease state as observations. Covariate effects on transitions between disease states are incorporated using a generalized regression framework. Parameter estimation and inference are based on maximum likelihood methods and rely on an EM algorithm. In addition, techniques for model assessment are proposed. Applications to two binary disease outcomes are presented: the oral lesion hairy leukoplakia in a cohort of HIV infected men and cervical human papillomavirus (HPV) infection in a cohort of young women. Estimated transition rates and misclassification probabilities for the hairy leukoplakia data agree well with clinical observations on the persistence and diagnosis of this lesion, lending credibility to the interpretation of hidden states as representing the actual disease states. By contrast, interpretation of the results for the HPV data are more problematic, illustrating that successful application of the hidden Markov model may be highly dependent on the degree to which the assumptions of the model are satisfied.

Adolescent↗
PubMed2002

A hierarchical Poisson mixture regression model to analyse maternity length of hospital stay.

Inpatient length of stay (LOS) is often considered as a proxy of hospital resource consumption. Using statewide obstetrical delivery data, a two-component Poisson mixture model provides a reasonable fit to the heterogeneous LOS distribution. Adopting the generalized linear mixed model (GLMM) approach, random effects are introduced to the two-component Poisson mixture regression model to account for the inherent correlation of patients clustered within hospitals. An EM algorithm is developed for the joint estimation of regression coefficients and variance component parameters. Related diagnostic measures for assessing model adequacy are derived. When applying the method to analyse maternity LOS, appropriate risk factors for the short-stay and long-stay subgroups can be identified from the respective Poisson components. In addition, predicted random hospital effects enable the comparison of relative efficiencies among hospitals after adjustment for patient case-mix and health provision characteristics.

Adult↗
PubMed2003

Joint models for efficient estimation in proportional hazards regression models.

In survival studies, information lost through censoring can be partially recaptured through repeated measures data which are predictive of survival. In addition, such data may be useful in removing bias in survival estimates, due to censoring which depends upon the repeated measures. Here we investigate joint models for survival T and repeated measurements Y, given a vector of covariates Z. Mixture models indexed as f (T/Z) f (Y/T,Z) are well suited for assessing covariate effects on survival time. Our objective is efficiency gains, using non-parametric models for Y in order to avoid introducing bias by misspecification of the distribution for Y. We model (T/Z) as a piecewise exponential distribution with proportional hazards covariate effect. The component (Y/T,Z) has a multinomial model. The joint likelihood for survival and longitudinal data is maximized, using the EM algorithm. The estimate of covariate effect is compared to the estimate based on the standard proportional hazards model and an alternative joint model based estimate. We demonstrate modest gains in efficiency when using the joint piecewise exponential joint model. In a simulation, the estimated efficiency gain over the standard proportional hazards model is 6.4 per cent. In clinical trial data, the estimated efficiency gain over the standard proportional hazards model is 10.2 per cent.

Algorithms↗
PubMed2003

Robustness of sample size re-estimation procedure in clinical trials (arbitrary populations).

In clinical trials, one of the main questions that is being asked is how many additional observations, if any, are needed beyond those originally planned. In a two-treatment double-blind clinical experiment, one is interested in testing the null hypothesis of equality of the means against one-sided alternative when the common variance sigma2 is unknown. We wish to determine the required total sample size when the error probabilities alpha and beta are specified at a predetermined alternative. Shih provided a two-stage procedure which is an extension of Stein's one-sample procedure, assuming normal response. He estimates sigma2 by the method of maximum likelihood via the EM algorithm and carries out a simulation study in order to evaluate the effective level of significance and the power. The author proposed a closed-form estimator for sigma2 and showed analytically that the difference between the effective and nominal levels of significance is negligible and that the power exceeds 1-beta when the initial sample size is large. Here we consider responses from arbitrary distributions in which the mean and the variance are not functionally related and show that when the initial sample size is large, the conclusions drawn previously by the author still hold. The effective coverage probability of a fixed-width interval is also evaluated. Proofs of certain assertions are deferred to the Appendix.

Clinical Trials as Topic↗
PubMed2004

Modelling HIV-RNA viral load in vertically infected children.

Human immunodeficiency virus (HIV) ribo-nucleic acid (RNA) viral load is a measure of actively replicating virus and is used as a marker of disease progression. For a thorough understanding of the dynamics of the evolution of the virus in the early life of HIV-1 vertically infected children, it is important to elucidate the pattern of HIV-RNA viral load over age. An aspect of assay systems used in the quantification of RNA viral load is that they measure values above particular cut-off values for detection, below which the assays used are not sufficiently sensitive. In this way, measurements are potentially left-censored. Recent adult studies suggest that to adequately model RNA pattern over age, it is necessary to account for within-subject correlation, due to repeated measures, and censoring. The aim of this study, therefore, was to establish whether it is necessary to use complex methods to allow for repeated measures within individuals and censoring of the HIV-RNA viral load in children enrolled in a cohort study. The approach involved the identification of an appropriate model for the basic pattern of RNA viral load by age and subsequent assessment of various estimation procedures accounting for repeated measures and censoring in different ways. Methods developed by Hughes involving the expectation-maximization (EM) algorithm and the Gibbs sampler were taken as the benchmark for comparison of simpler alternatives. Other approaches considered involve linear mixed-effects and ordinary least squares in which censoring is dealt with informally by taking the cut-off value as absolute or taking the mid-point between cut-off and zero. Fractional polynomials provided a substantially superior approach for modelling the dynamics of viral load over age compared to conventional polynomials or change-point models. Allowing for repeated measures was necessary to improve the power of the likelihood ratio tests required to establish the final model, but methods beyond taking the mid-point for censored values did not further improve the fit. Although Hughes' methodology is the best approach, its implementation is not necessary for the identification of the optimal model.

Algorithms↗
PubMed2004

Sensitivity analysis of longitudinal normal data with drop-outs.

We propose to perform a sensitivity analysis to evaluate the extent to which results from a longitudinal study can be affected by informative drop-outs. The method is based on a selection model, where the parameter relating the dropout probability to the current observation is not estimated, but fixed to a set of values. This allows to evaluate several hypotheses for the degree of informativeness of the drop-out process. Expectation and variance of missing data, conditional on the drop-out time are computed, and a stochastic EM algorithm is used to obtain maximum likelihood estimates. Simulations show that when the drop-out parameter is correctly specified, unbiased estimates of the other parameters are obtained, and coverage percentages of their confidence intervals are close to their theoretical value. More interestingly, misspecification of the drop-out parameter does not considerably alter these results. This method was applied to a randomized clinical trial, designed to demonstrate non-inferiority of an inhaled corticosteroid in terms of bone density, compared with a reference treatment. Sensitivity analysis showed that the conclusion of non-inferiority was robust against different hypotheses for the drop-out process.

Adolescent↗
PubMed2004

Three validation metrics for automated probabilistic image segmentation of brain tumours.

The validity of brain tumour segmentation is an important issue in image processing because it has a direct impact on surgical planning. We examined the segmentation accuracy based on three two-sample validation metrics against the estimated composite latent gold standard, which was derived from several experts' manual segmentations by an EM algorithm. The distribution functions of the tumour and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic curve, Dice similarity coefficient, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumour cases of three different tumour types, each consisting of a large number of pixels. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds. The performances of these validation metrics were also investigated via Monte Carlo simulation. Extensions of incorporating spatial correlation structures using a Markov random field model were considered.

Algorithms↗
PubMed2004

Simultaneous inference for longitudinal data with detection limits and covariates measured with errors, with application to AIDS studies.

In AIDS studies such as HIV viral dynamics, statistical inference is often complicated because the viral load measurements may be subject to left censoring due to a detection limit and time-varying covariates such as CD4 counts may be measured with substantial errors. Mixed-effects models are often used to model the response and the covariate processes in these studies. We propose a unified approach which addresses the censoring and measurement errors simultaneously. We estimate the model parameters by a Monte-Carlo EM algorithm via the Gibbs sampler. A simulation study is conducted to compare the proposed method with the usual two-step method and a naive method. We find that the proposed method produces approximately unbiased estimates with more reliable standard errors. A real data set from an AIDS study is analysed using the proposed method.

Acquired Immunodeficiency Syndrome↗
PubMed2004

13C-urea breath test for Helicobacter pylori in young children: cut-off point determination by finite mixture model.

The 13C-urea breath test (UBT) is currently regarded as one of the most important noninvasive diagnostic methods for detecting Helicobacter pylori (H. pylori) infection in adults and children. However, for infants and young children, the standard for UBT interpretation has not been validated, and its reliability has not been established for diagnosing H. pylori infection in this group. The primary outcome data from UBT consist of mixture data, which come from subjects whose H. pylori infection classifications are unconfirmed. In this paper, we propose the finite mixture distribution method to identify a reliable UBT cut-off value in a large baseline sample in which gastric biopsy is not available to confirm the H. pylori infection in younger children. Maximum likelihood estimators of the parameters in the mixture model were obtained using an expectation maximization (EM) algorithm. The standard deviation of the cut-off point was estimated by bootstrap methods. We applied the same analytical methods to the UBT results yielded from the follow up, as well as the overall UBT results in the longitudinal cohort data. The cut-off points from those UBT data sets are similar. The advantage of the finite mixture model is that it may be used to calculate sensitivity and specificity in the absence of other diagnostic tests.

Breath Tests↗
PubMed2004

The multiple-record systems estimator when registrations refer to different but overlapping populations.

In multiple-record systems estimation it is usually assumed that all registration relate to the same population. In this paper, we develop a method which can be used when the registrations relate to different populations, in the sense that they cover, for example, different time periods or regions. We show that under certain conditions ignoring that the registrations relate to different populations results in correct estimates of population size. The EM algorithm is presented as a method that can be used for more general problems. The parametric bootstrap is used to construct a confidence interval. The proposed method is then applied to a data set with five registrations of neural tube defects, that cover different time periods.

Algorithms↗
PubMed2004

Modelling the distribution of ischaemic stroke-specific survival time using an EM-based mixture approach with random effects adjustment.

A two-component survival mixture model is proposed to analyse a set of ischaemic stroke-specific mortality data. The survival experience of stroke patients after index stroke may be described by a subpopulation of patients in the acute condition and another subpopulation of patients in the chronic phase. To adjust for the inherent correlation of observations due to random hospital effects, a mixture model of two survival functions with random effects is formulated. Assuming a Weibull hazard in both components, an EM algorithm is developed for the estimation of fixed effect parameters and variance components. A simulation study is conducted to assess the performance of the two-component survival mixture model estimators. Simulation results confirm the applicability of the proposed model in a small sample setting.

Age Factors↗
PubMed2004

A statistical model for high-resolution mapping of quantitative trait loci determining HIV dynamics.

Are there specific genes that control the pathogenesis of HIV infection? This question, which is of fundamental importance in designing personalized strategies of gene therapy to control HIV infection, can be examined by genetic mapping approaches. In this article, we present a new statistical model for unravelling the genetic mechanisms for the dynamic change of HIV that causes AIDS by marker-based linkage disequilibrium (LD) analyses. This new model is the extension of our functional mapping theory to integrate viral load trajectories within a genetic mapping framework. Earlier studies of HIV dynamics have led to various mathematical functions for modelling the kinetic curves of plasma virions and CD4 lymphocytes in HIV patients. Through incorporating these functions into the LD-based mapping procedure, we can identify and map individual quantitative trait loci (or QTL) responsible for viral pathogenesis. We derive a closed-form solution for estimating QTL allele frequency and marker-QTL linkage disequilibrium in the context of EM algorithm and implement the simplex algorithm to estimate the mathematical parameters describing the curve shapes of HIV pathogenesis. We performed different simulation scenarios based on currently used clinical designs in AIDS/HIV research to illustrate the utility and power of our model for genetic mapping of HIV dynamics. The implications of our model for genetic and genomic research into AIDS pathogenesis are discussed.

Acquired Immunodeficiency Syndrome↗
PubMed2006

A statistical model to analyse quantitative trait locus interactions for HIV dynamics from the virus and human genomes.

Viruses can be considered 'parasites' because they cannot survive outside of a host. The progression rate to AIDS caused by human immunodeficiency virus type-1 (HIV-1) is therefore a consequence of HIV-host cell interactions. In this article, we present an innovative statistical model for detecting the effects of genetic interactions on HIV-1 dynamics triggered by different quantitative trait loci (QTL) from the HIV and human genomes. Our model integrates the principles of functional mapping for longitudinal traits and of linkage disequilibrium analysis for high-resolution mapping of QTL within the maximum likelihood context and is implemented with the EM algorithm. We performed Monte Carlo simulation studies to investigate the impacts of different heritability levels and sample sizes on the power to detect interacting QTL. Our model allows for the tests of a number of clinically meaningful hypotheses and provides a powerful tool for unravelling the genetic architecture of HIV-1 dynamics and therefore AIDS progression rate.

Algorithms↗
PubMed2006

Non-linear random effects model for multivariate responses with missing data.

The use of random-effects models for the analysis of longitudinal data with missing responses has been discussed by several authors. In this paper, we extend the non-linear random-effects model for a single response to the case of multiple responses, allowing for arbitrary patterns of observed and missing data. Parameters for this model are estimated via the EM algorithm and by the first-order approximation available in SAS Proc NLMIXED. The set of equations for this estimation procedure is derived and these are appropriately modified to deal with missing data. The methodology is illustrated with an example using data coming from a study involving 161 pregnant women presenting to a private obstetrics clinic in Santiago, Chile.

Algorithms↗
PubMed2006

A multivariate random-effects model with restricted parameters: application to assessing radiation therapy for brain tumours.

In clinical studies, multiple endpoints are often measured for each patient longitudinally. The multivariate random-effects or random coefficient model has been a useful method for analysis. However, medical research problems may impose restrictions on the model parameters of interests. For example, in a paediatric brain tumour study on radiation therapy, there is a natural ordering in the white matter relaxation time of brain tissues among different regions surrounding the primary tumour, i.e. the closer a specific region of brain tissues is to the centre of primary tumour, the shorter is the relaxation time. Such parameter constraints should be accounted for in the analysis. This article proposes a class of multivariate random coefficient models with restricted parameters and derives its maximum likelihood estimates (MLE). We propose a modified EM algorithm for the quadratic optimalization with linear inequality constraints necessary in deriving the MLE. The method is applied to analysing the paediatric brain tumour study.

Adolescent↗
PubMed2007

A shared random effects model for censored medical costs and mortality.

In this paper, we propose a model for medical costs recorded at regular time intervals, e.g. every month, as repeated measures in the presence of a terminating event, such as death. Prior models have related monthly medical costs to time since entry, with extra costs at the final observations at the time of death. Our joint model for monthly medical costs and survival time incorporates two important new features. First, medical cost and survival may be correlated because more 'frail' patients tend to accumulate medical costs faster and die earlier. A joint random effects model is proposed to account for the correlation between medical costs and survival by a shared random effect. Second, monthly medical costs usually increase during the time period prior to death because of the intensive care for dying patients. We present a method for estimating the pattern of cost prior to death, which is applicable if the pattern can be characterized as an additive effect that is limited to a fixed time interval, say b units of time before death. This 'turn back time' method for censored observations censors cost data b units of time before the actual censoring time, while keeping the actual censoring time for the survival data. Time-dependent covariates can be included. Maximum likelihood estimation and inference are carried out through a Monte Carlo EM algorithm with a Metropolis-Hastings sampler in the E-step. An analysis of monthly outpatient EPO medical cost data for dialysis patients is presented to illustrate the proposed methods.

Algorithms↗
PubMed2007

Regression analysis of failure time data with informative interval censoring.

Interval censoring arises when a subject misses prescheduled visits at which the failure is to be assessed. Most existing approaches for analysing interval-censored failure time data assume that the censoring mechanism is independent of the true failure time. However, there are situations where this assumption may not hold. In this paper, we consider such a situation in which the dependence structure between the censoring variables and the failure time can be modelled through some latent variables and a method for regression analysis of failure time data is proposed. The method makes use of the proportional hazards frailty model and an EM algorithm is presented for estimation. Finite sample properties of the proposed estimators of regression parameters are examined through simulation studies and we illustrate the method with data from an AIDS study.

Acquired Immunodeficiency Syndrome↗