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. Copyright (C) 2004 John Wiley Sons, Ltd.

Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma

A mixture model incorporating long-term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi-centre clinical trial, it is conceived that the environ mental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long-term survivor mixture model with random effects. In this paper, the long-term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi-centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed. Copyright (C) 2001 John Wiley & Sons, Ltd.

Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.

Analysis of Melanoma Onset: Assessing Familial Aggregation by Using Estimating Equations and Fitting Variance Components via Bayesian Random Effects Models

We investigate whether relative contributions of genetic and shared environmental factors are associated with an increased risk in melanoma. Data from the Queensland Familial Melanoma Project comprising 15,907 subjects arising from 1912 families were analyzed to estimate the additive genetic, common and unique environmental contributions to variation in the age at onset of melanoma. Two complementary approaches for analyzing correlated time-to-onset family data were considered: the generalized estimating equations (GEE) method in which one can estimate relationship-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modeled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov Chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the free ware package BUGS. In addition, we also used a Bayesian model to investigate the relative contribution of genetic and environmental effects on the expression of naevi and freckles, which are known risk factors for melanoma.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

The effects of air pollution on hospitalizations for cardiovascular disease in elderly people in Australian and New Zealand cities

OBJECTIVE: The goal of this study was to estimate the associations between outdoor air pollution and cardiovascular hospital admissions for the elderly. DESIGN: Associations were assessed using the case-crossover method for seven cities: Auckland and Christchurch, New Zealand; and Brisbane, Canberra, Melbourne, Perth, and Sydney Australia. Results were combined across cities using a random-effects meta-analysis and stratified for two adult age groups: 15-64 years and >= 65 years of age (elderly). Pollutants considered were nitrogen dioxide, carbon monoxide, daily measures of particulate matter (PM) and ozone. Where multiple pollutant associations were found, a matched case-control analysis was used to identify the most consistent association. RESULTS: In the elderly, all pollutants except 03 were significantly associated with five categories or cardiovascular disease admissions. No associations were found for arrhythmia and stroke. For a 0.9-ppm increase in CO, there were significant increases in elderly hospital admissions for total cardiovascular disease (2.2%), all cardiac disease (2.8%), cardiac failure (6.0%), ischemic heart disease (2.3%), and myocardial infarction (2.9%). There was some heterogeneity between cities, possibly due to differences in humidity and the percentage of elderly people. In matched analyses, CO had the most consistent association. CONCLUSIONS. The results suggest that air pollution arising from common emission sources for CO, NO2, and PM (e.g., motor vehicle exhausts) has significant associations with adult cardiovascular hospital admissions, especially in the elderly, at air pollution concentrations below normal health guidelines. RELEVANCE TO CLINICAL AND PROFESSIONAL PRACTICE: Elderly populations in Australia need to be protected from air pollution arising from outdoor sources to reduce cardiovascular disease.

Predictors of local recurrence after treatment of ductal carcinoma in situ - A meta-analysis

lBACKGROUND. Management of patients with ductal carcinoma in situ (DCIS) is a dilemma, as mastectomy provides nearly a 100% cure rate but at the expense of physical and psychologic morbidity. It would be helpful if we could predict which patients with DCIS are at sufficiently high risk of local recurrence after conservative surgery (CS) alone to warrant postoperative radiotherapy (RT) and which patients are at sufficient risk of local recurrence after CS + RT to warrant mastectomy. The authors reviewed the published studies and identified the factors that may be predictive of local recurrence after management by mastectomy, CS alone, or CS + RT. METHODS. The authors examined patient, tumor, and treatment factors as potential predictors for local recurrence and estimated the risks of recurrence based on a review of published studies. They examined the effects of patient factors (age at diagnosis and family history), tumor factors (sub-type of DCIS, grade, tumor size, necrosis, and margins), and treatment (mastectomy, CS alone, and CS + RT). The 95% confidence intervals (CI) of the recurrence rates for each of the studies were calculated for subtype, grade, and necrosis, using the exact binomial; the summary recurrence rate and 95% CI for each treatment category were calculated by quantitative meta-analysis using the fixed and random effects models applied to proportions. RESULTS, Meta-analysis yielded a summary recurrence rate of 22.5% (95% CI = 16.9-28.2) for studies employing CS alone, 8.9% (95% CI = 6.8-11.0) for CS + RT, and 1.4% (95% CI = 0.7-2.1) for studies involving mastectomy alone. These summary figures indicate a clear and statistically significant separation, and therefore outcome, between the recurrence rates of each treatment category, despite the likelihood that the patients who underwent CS alone were likely to have had smaller, possibly low grade lesions with clear margins. The patients with risk factors of presence of necrosis, high grade cytologic features, or comedo subtype were found to derive the greatest improvement in local control with the addition of RT to CS. Local recurrence among patients treated by CS alone is approximately 20%, and one-half of the recurrences are invasive cancers. For most patients, RT reduces the risk of recurrence after CS alone by at least 50%. The differences in local recurrence between CS alone and CS + RT are most apparent for those patients with high grade tumors or DCIS with necrosis, or of the comedo subtype, or DCIS with close or positive surgical margins. CONCLUSIONS, The authors recommend that radiation be added to CS if patients with DCIS who also have the risk factors for local recurrence choose breast conservation over mastectomy. The patients who may be suitable for CS alone outside of a clinical trial may be those who have low grade lesions with little or no necrosis, and with clear surgical margins. Use of the summary statistics when discussing outcomes with patients may help the patient make treatment decisions. Cancer 1999;85:616-28. (C) 1999 American Cancer Society.

An fMRI study indicating increased cortical recruitment during a cognitive interference task in Huntington's disease patients

We used an event related fMRI design to study the BOLD response in Huntington’s disease (HD) patients during performance of a Simon interference task. We hypothesised that HD patients will demonstrate significantly slower RTs than controls, and that there will be significant differences in the pattern of brain activation between groups. Seventeen HD patients and 15 age and sex matched controls were scanned using 3T GE scanner (FOV = 24 cm2; TE = 40 ms; TR = 3 s; FA = 60°; slice thickness = 6 mm; in-plane resolution = 1.88x1.88 mm2). The task involved two activation conditions, namely congruent (for example, left pointing arrow appearing on the left side of the screen) and incongruent (for example, left pointing arrow appearing on the right side of the screen), and a baseline condition. Each stimulus was presented for 2500 ms followed by a blank screen for 500 ms. Subjects were instructed to press a button using the same hand as indicated by the direction of the arrow head and were given 3000 ms to respond. Data analysis was performed using SPM2 with a random effects analysis model. For each subject parameter estimates for combined task conditions (congruent and incongruent combined) were calculated. Comparisons such as these, based on block designs, have superior statistical power for detecting subtle changes in the BOLD response anywhere in the brain. The activations reported are significant at PFDR_corr

Overweight and obesity increase the risk of coronary heart disease: A pooled analysis of 30 prospective studies

The importance of overweight as a risk factor for coronary heart disease (CHD) remains unsettled. We estimated the relative risk (RR) for CHD associated with underweight (body mass index, BMI < 20 kg/m2), overweight (25 – 30 kg/m2) and obesity (= 30 kg/m2), compared with normal weight (20 – 25 kg/m2) in a random effects meta-analysis of 30 prospective studies, including 389,239 healthy, predominantly Caucasian persons. We also explored sources of heterogeneity between studies and examined effects of systematic adjustment for confounding and intermediary variables. Pooled age-, sex- and smoking-adjusted RRs (95% confidence interval) for overweight, obesity and underweight compared with normal weight were 1.33 (1.24 – 1.43), 1.69 (1.44 – 1.99) and 1.01 (0.85 – 1.20), respectively. Stratified analyses showed that pooled RRs for BMI were higher for studies with longer follow-up (= vs. < 15 years) and younger populations (< vs. = 60 years). Additional adjustment for blood pressure, cholesterol levels and physical activity decreased the RR per 5 BMI units from 1.28 (1.21 – 1.34) to 1.16 (1.11 – 1.21). We conclude that overweight and obesity are associated with a substantially increased CHD risk in Caucasians, whereas underweight is not. Prevention and reduction of overweight and obesity, therefore, remain of importance for preventing CHD.

A methodology for analysis of sugarcane productivity trends - I. Analysis across districts

Historically, few articles have addressed the use of district level mill production data for analysing the effect of varietal change on sugarcane productivity trends. This appears to be due to lack of compiled district data sets and appropriate methods by which to analyse these data. Recently, varietal data on tonnes of sugarcane per hectare (TCH), sugar content (CCS), and their product, tonnes of sugar content per hectare (TSH) on a district basis, have been compiled. This study was conducted to develop a methodology for regular analysis of such data from mill districts to assess productivity trends over time, accounting for variety and variety x environment interaction effects for 3 mill districts (Mulgrave, Babinda, and Tully) from 1958 to 1995. Restricted maximum likelihood methodology was used to analyse the district level data and best linear unbiased predictors for random effects, and best linear unbiased estimates for fixed effects were computed in a mixed model analysis. In the combined analysis over districts, Q124 was the top ranking variety for TCH, and Q120 was top ranking for both CCS and TSH. Overall production for TCH increased over the 38-year period investigated. Some of this increase can be attributed to varietal improvement, although the predictors for TCH have shown little progress since the introduction of Q99 in 1976. Although smaller gains have been made in varietal improvement for CCS, overall production for CCS decreased over the 38 years due to non-varietal factors. Varietal improvement in TSH appears to have peaked in the mid-1980s. Overall production for TSH remained stable over time due to the varietal increase in TCH and the non-varietal decrease in CCS.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

A longitudinal analysis of sex differences in bone mineral accrual in healthy 8-19-year-old boys and girls

Background: Although early in life there is little discernible difference in bone mass between boys and girls, at puberty sex differences are observed. It is uncertain if these differences represent differences in bone mass or just differences in anthropometric dimensions. Aim: The study aimed to identify whether sex independently affects bone mineral content (BMC) accrual in growing boys and girls. Three sites are investigated: total body (TB), femoral neck (FN) and lumbar spine (LS). Subjects and methods: 85 boys and 67 girls were assessed annually for seven consecutive years. BMC was assessed by dual energy X-ray absorptiometry (DXA). Biological age was defined as years from age at peak height velocity (PHV). Data were analysed using a hierarchical (random effects) modelling approach. Results: When biological age, body size and body composition were controlled, boys had statistically significantly higher TB and FN BMC at all maturity levels (p < 0.05). No independent sex differences were found at the LS (p > 0.05). Conclusion: Although a statistical significant sex effect is observed, it is less than the error of the measurement, and thus sex difference are debatable. In general, sex difference are explained by anthropometric difference

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.

Sexual dimorphism of the femoral neck during the adolescent growth spurt: a structural analysis

Before puberty, there are only small sex differences in body shape and composition. During adolescence, sexual dimorphism in bone, lean, and fat mass increases, giving rise to the greater size and strength of the male skeleton. The question remains as to whether there are sex differences in bone strength or simply differences in anthropometric dimensions. To test this, we applied hip structural analysis (HSA) to derive strength and geometric indices of the femoral neck using bone densitometry scans (DXA) from a 6-year longitudinal study in Canadian children. Seventy boys and sixty-eight girls were assessed annually for 6 consecutive years. At the femoral neck, cross-sectional area (CSA, an index of axial strength), subperiosteal width (SPW), and section modulus (Z, an index of bending strength) were determined, and data were analyzed using a hierarchical (random effects) modeling approach. Biological age (BA) was defined as years from age at peak height velocity (PHV). When BA, stature, and total-body lean mass (TB lean) were controlled, boys had significantly higher Z than girls at all maturity levels (P < 0.05). Controlling height and TB lean for CSA demonstrated a significant independent sex by BA interaction effect (P < 0.05). That is, CSA was greater in boys before PHV but higher in girls after PHV The coefficients contributing the greatest proportion to the prediction of CSA, SPW, and Z were height and lean mass. Because the significant sex difference in Z was relatively small and close to the error of measurement, we questioned its biological significance. The sex difference in bending strength was therefore explained by anthropometric differences. In contrast to recent hypotheses, we conclude that the CSA-lean ratio does not imply altered mechanosensitivity in girls because bending dominates loading at the neck, and the Z-lean ratio remained similar between the sexes throughout adolescence. That is, despite the greater CSA in girls, the bone is strategically placed to resist bending; hence, the bones of girls and boys adapt to mechanical challenges in a similar way. (C) 2004 Elsevier Inc. All rights reserved.