Multilevel model with random effects for clustered survival data with multiple failure outcomes.

We present a multilevel frailty model for handling serial dependence and simultaneous heterogeneity in survival data with a multilevel structure attributed to clustering of subjects and the presence of multiple failure outcomes. One commonly observes such data, for example, in multi-institutional, randomized placebo-controlled trials in which patients suffer repeated episodes (eg, recurrent migraines) of the disease outcome being measured. The model extends the proportional hazards model by incorporating a random covariate and unobservable random institution effect to respectively account for treatment-by-institution interaction and institutional variation in the baseline risk. Moreover, a random effect term with correlation structure driven by a first-order autoregressive process is attached to the model to facilitate estimation of between patient heterogeneity and serial dependence. By means of the generalized linear mixed model methodology, the random effects distribution is assumed normal and the residual maximum likelihood and the maximum likelihood methods are extended for estimation of model parameters. Simulation studies are carried out to evaluate the performance of the residual maximum likelihood and the maximum likelihood estimators and to assess the impact of misspecifying random effects distribution on the proposed inference. We demonstrate the practical feasibility of the modeling methodology by analyzing real data from a double-blind randomized multi-institutional clinical trial, designed to examine the effect of rhDNase on the occurrence of respiratory exacerbations among patients with cystic fibrosis.

Consumption of dietary supplements by Chinese women during pregnancy and postpartum: A prospective cohort study.

The objectives of this study were to investigate usage patterns and factors associated with maternal dietary supplementation by Chinese women. A prospective cohort study of 695 mothers, who gave birth to a singleton baby, was conducted in Jiangyou, Sichuan Province of China. Information on dietary supplement use was collected from participants by personal interview at hospital discharge and followed up by telephone at 1, 3, and 6 months postpartum. Logistic regression analysis and generalised linear-mixed modelling were performed to determine factors affecting dietary supplementation during pregnancy and the first 6 months postpartum, respectively. A total of 81.8 and 32.1% of women consumed dietary supplements during pregnancy and postpartum, respectively. Calcium was the most popular supplement (pregnancy 63.9%; postpartum 28.1%), whereas folic acid was only taken during pregnancy (62.3%) with an average usage duration of 2.5 (standard deviation 1.3) months among users. High school and above education, adjusted odds ratio (OR) = 2.67, 95% confidence interval (CI) [1.63, 4.38], and attendance at prenatal classes (adjusted OR = 1.99, 95% CI [1.05, 3.76]) were associated with dietary supplementation during pregnancy. Mothers with a higher level of education (adjusted OR = 3.10, 95% CI [1.81, 5.30]) were also more likely to use dietary supplements in the postpartum period. Although dietary supplementation appeared to be common among Chinese mothers, maternal intake of folic acid was well below the guidelines. There is a need for further nutrition education on maternal use of micronutrient supplements, especially targeting mothers who are less educated.

Traffic accidents involving fatigue driving and their extent of casualties.

The rapid progress of motorization has increased the number of traffic-related casualties. Although fatigue driving is a major cause of traffic accidents, the public remains not rather aware of its potential harmfulness. Fatigue driving has been termed as a "silent killer." Thus, a thorough study of traffic accidents and the risk factors associated with fatigue-related casualties is of utmost importance. In this study, we analyze traffic accident data for the period 2006-2010 in Guangdong Province, China. The study data were extracted from the traffic accident database of China's Public Security Department. A logistic regression model is used to assess the effect of driver characteristics, type of vehicles, road conditions, and environmental factors on fatigue-related traffic accident occurrence and severity. On the one hand, male drivers, trucks, driving during midnight to dawn, and morning rush hours are identified as risk factors of fatigue-related crashes but do not necessarily result in severe casualties. Driving at night without street-lights contributes to fatigue-related crashes and severe casualties. On the other hand, while factors such as less experienced drivers, unsafe vehicle status, slippery roads, driving at night with street-lights, and weekends do not have significant effect on fatigue-related crashes, yet accidents associated with these factors are likely to have severe casualties. The empirical results of the present study have important policy implications on the reduction of fatigue-related crashes as well as their severity.

Consumption of Chinese herbal medicines during pregnancy and postpartum: A prospective cohort study in China.

OBJECTIVE: to investigate usage patterns and factors associated with maternal consumption of Chinese herbal medicines in China. DESIGN: prospective cohort study. Information on the use of Chinese herbal medicines was collected from mothers by personal interview at hospital discharge and followed up by telephone at one, three and six months postpartum. SETTING: seven hospitals in Jiangyou, Sichuan Province of China. PARTICIPANTS: 695 mothers who gave birth to a singleton infant. MEASUREMENTS: prevalence, type, frequency and duration of herbal medicine usage. Logistic mixed regression analyses were performed to determine factors affecting the use. FINDINGS: a total of 43.5% and 45.0% of mothers consumed Chinese herbal medicines during pregnancy and postpartum, respectively. Angelica sinsensis was the most popular herbal medicine among the participants (pregnancy 28.8%, postpartum 26.8%). Although herbal medicines were taken more regularly by postpartum users, the median usage duration varied from two to three months during pregnancy but 1-1.6 months postpartum. The majority of users (pregnancy 42.9%, postpartum 55.1%) were advised by their mother or mother-in-law to take Chinese herbal medicines. Antenatal alcohol drinking (adjusted odds ratio 2.75, 95% confidence interval 1.01-7.53) was associated with a marginally higher prevalence of herbal consumption during pregnancy, whereas mothers with a lower family income (adjusted odds ratio 1.52, 95% confidence interval 1.12-2.04) were more likely to consume Chinese herbal medicines in the postpartum period. KEY CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: consumption of Chinese herbal medicines appears to be prevalent among Chinese mothers, especially those drinking alcohol whilst pregnant and women from a lower income household. Maternity health professionals need to be aware of the lack of evidence to support the use of Chinese herbal medicines during pregnancy and postpartum, and to provide their clients with scientifically based advice regarding herbal medicine use.

Effective time window in reducing pituitary adenoma size by gamma knife radiosurgery.

OBJECTIVES: Although the effectiveness of gamma knife radiosurgery (GKRS) in controlling the size of pituitary adenomas has been well demonstrated in many studies, the time period in which significant changes in tumor size occurs has been investigated in a limited fashion. It is important to determine the therapeutic window of GKRS in treating pituitary adenomas, i.e., the effective timeframe during which significant size reduction of these tumors occurs, so that alternative treatments such as further GKRS or microsurgery might be prescribed in a timely manner if clinically indicated. METHODS: This was a nested sample of an ongoing local cohort study on GKRS for pituitary adenomas at the University of Virginia. Magnetic resonance imaging (MRI) using dedicated sequences was employed. Only patients with a baseline MRI (TP0) and at least 1 follow-up study performed in the University Hospital after GKRS were included. The follow-up scans were performed at five time-points (TP1-TP5) which were 6, 12, 24, 36 and 48 months after GKRS. The dimensional indices of the tumors were measured in three orthogonal planes, i.e., transverse (TR), antero-posterior (AP) and cranio-caudal (CC). The volumes of the tumors were estimated by using the following formula: [Formula: see text]. Tumor volume decrease by more than 25% from baseline was considered as 'shrinkage', <25% tumor size increase or decrease was considered 'static', and more than 25% increase as 'increment'. Our cohort consisted of 21 patients, with functioning adenomas in 13 subjects i.e. six adrenocorticotrophic hormone (ACTH)-secreting and seven growth hormone (GH)-secreting, and non-functioning (NF) adenomas in eight subjects. RESULTS: In 26 adenomas (8 ACTH, 9 GH and 9 NF), tumor control (tumor shrinkage or static) were achieved in 21 tumors (80.8%); 89, 75, and 78% for GH-secreting, ACTH-secreting and NF adenomas respectively, at the end of the 4-year follow-up period. Analysis of variance showed significant differences of GKRS margin dose among different types of tumors (p = 0.013), but not of baseline tumor volumes (p = 0.240). Logistic regression analysis showed no significant association of margin dose, baseline volume or tumor type with the tumor control outcome. Comparison of tumor change using dimensional indices relative to the base time point (TP0) showed that in the sample there was an average reduction of 1.290 mm at TP1 (6 months) with p values 0.155 (parametric t test) and 0.098 (non-parametric Wilcoxon signed-ranked test) respectively, showing a moderate reduction in tumor dimensional indices. The change in dimensional indices at later time points (TP2-TP5) showed an average reduction ranging from 1.930 to 2.471 mm. Significant reduction in the mean dimensional indices was firstly observed at TP2 (1 year) with p values 0.013 (t test) and 0.018 (Wilcoxon signed-rank test). Such scale of reduction in the dimensional indices appeared to be maintained along the time axis (from TP2 to TP5). CONCLUSIONS: Significant decrease in tumor dimensional indices tended to occur at 1 year post-GKRS. Although to a lesser extent, such decrease in dimensional indices continued up to the end of our follow-up period.

Improvement in institutional protocols leads to decreased mortality in patients with haemodynamically unstable pelvic fractures.

BACKGROUND: The mortality rate in patients with haemodynamically unstable pelvic fractures is as high as 40-60%. In recent years, angioembolisation and pelvic packing have been introduced as part of a multimodality treatment for these patients. Protocol-driven management has been shown to improve outcomes. PATIENTS AND METHODS: This is a Level III retrospective cohort study of patients suffering from unstable pelvic fractures from 1 January 1996 to 30 September 2011. The aim of the study was to review our results, particularly in terms of mortality through the evolution of three phases of treatment protocols: preangiography, angiography and pelvic packing. RESULTS: The overall 30-day mortality rate for all patients was 47.2%, with a rate of 63.5% in the preangiography phase, 42.1% in the angiography phase and 30.6% in the pelvic packing phase. Multivariate logistic regression analysis identified the use of retroperitoneal packing as a significant independent predictive factor for 24 h mortality. CONCLUSIONS: Our results showed an improvement in patient survival with sequential protocols over the study period, during which we incorporated a multidisciplinary approach to managing these complicated pelvic fractures. The results strongly suggest that retroperitoneal packing should be highly recommended for bleeding subsequent to pelvic fracture, in addition to other modalities of treatment.

Analyzing fault and severity in pedestrian-motor vehicle accidents in China.

The number of pedestrian-motor vehicle accidents and pedestrian deaths in China surged in recent years. However, a large scale empirical research on pedestrian traffic crashes in China is lacking. In this study, we identify significant risk factors associated with fault and severity in pedestrian-motor vehicle accidents. Risk factors in several different dimensions, including pedestrian, driver, vehicle, road and environmental factors, are considered. We analyze 6967 pedestrian traffic accident reports for the period 2006-2010 in Guangdong Province, China. These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report. Pedestrian traffic crashes have a unique inevitability and particular high risk, due to pedestrians' fragility, slow movement and lack of lighting equipment. The empirical analysis of the present study has the following policy implications. First, traffic crashes in which pedestrians are at fault are more likely to cause serious injuries or death, suggesting that relevant agencies should pay attention to measures that prevent pedestrians from violating traffic rules. Second, both the attention to elderly pedestrians, male and experienced drivers, the penalty to drunk driving, speeding, driving without a driver's license and other violation behaviors should be strengthened. Third, vehicle safety inspections and safety training sessions for truck drivers should be reinforced. Fourth, improving the road conditions and road lighting at night are important measures in reducing the probability of accident casualties. Fifth, specific road safety campaigns in rural areas, and education programs especially for young children and teens should be developed and promoted. Moreover, we reveal a country-specific factor, hukou, which has significant effect on the severity in pedestrian accidents due to the discrepancy in the level of social insurance/security, suggesting that equal social security level among urban and rural people should be set up. In addition, establishing a comprehensive liability distribution system for non-urban areas and roadways will be conducive to both pedestrians' and drivers' voluntary compliance with traffic rules.

Trauma survival prediction in Asian population: a modification of TRISS to improve accuracy.

UNLABELLED: The probability of survival (PS) in blunt trauma as calculated by Trauma and Injury Severity Score (TRISS) has been an indispensable tool in trauma audit. The aim of this study is to explore the predictive performance of the latest updated TRISS model by investigating the Age variable recategorisations and application of local Injury Severity Score (ISS) and Revised Trauma Score (RTS) coefficients in a logistic model using a level I trauma centre database involving Asian population. METHODS: Prospectively and consecutively collected 5684 trauma patients' data over a 10-year period at a regional level I trauma centre were reviewed. Four modified TRISS (mTRISS) models using Age coefficient from reclassifications of the Age variable according to their correlation with survival by logistic regression on the local dataset were acquired. RTS and ISS coefficients were derived from the local dataset and then applied to the mTRISS models. mTRISS models were compared with the existing Major Trauma Outcome Study (MTOS)-derived TRISS (eTRISS) model. Model 1=Age effect taken as linear; Model 2=Age classified into two groups (0-54, 55+); Model 3=Age classified into four groups (0-15, 16-54, 55-79, 80+) and Model 4=Age classified into two groups (0-69, 70+). Performance measures including sensitivity, specificity, accuracy and area under the Receiver Operating Characteristic (ROC) curve were used to assess the various models. The cross-validation procedure consisted of comparing the P(S) obtained from mTRISS Models 1 and 2 with the P(S) obtained from the MTOS derived from eTRISS. RESULTS: A 5147 blunt trauma patients' dataset was reviewed. Model 1, where Age was taken as a scale variable, demonstrated a substantial improvement in the survival prediction with 91.6% accuracy in blunt injuries as compared with 89.2% in the MTOS-derived TRISS. The 95% CI for ROC derived from mTRISS Model 1 was (0.923, 0.940), when compared with the hypothesised ROC value 0.886 obtained from eTRISS, it clearly indicated a significant improvement in predicting survival at 5% level. Furthermore, ROCs have shown clearly the superiority of Model 1 over Model 2, and of Model 2 over MTOS-derived TRISS. The recategorisation of the Age variable (Models 3 and 4) also demonstrated improved performance, but their strength was not as intense as in Model 1. Overall, the results point to the adoption of Model 1 as the best model for PS. Cross-validation analysis has further assured the validity of these findings. CONCLUSIONS: The present study has demonstrated that (1) having the Age variable being dichotomised (cut-off at 55 years) as in the eTRISS, but with the application of a local dataset-derived coefficients give better TRISS survival prediction in Asian blunt trauma patients; (2) improved performance are found with certain recategorisation of the Age variable and (3) the accuracy can further be enhanced if the Age effect is taken to be linear, with the application of local dataset-derived coefficients.

Traffic violations in Guangdong Province of China: speeding and drunk driving.

The number of speeding- and drunk driving-related injuries in China surged in the years immediately preceding 2004 and then began to decline. However, the percent decrease in the number of speeding and drunk driving incidents (decrease by 22%) is not proportional to the corresponding percent decrease in number of automobile accident-related injuries (decrease by 47%) from the year 2004 to 2010 (Traffic Management Bureau, Ministry of Public Security, Annual Statistical Reports on Road Traffic Accidents). Earlier studies have established traffic violations as one of the major risks threatening road safety. In this study, we examine in greater detail two important types of traffic violation events, speeding and drunk driving, and attempt to identify significant risk factors associated with these types of traffic violations. Risk factors in several different dimensions, including driver, vehicle, road and environmental factors, are considered. We analyze the speeding (N=11,055) and drunk driving (N=10,035) data for the period 2006-2010 in Guangdong Province, China. These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report and are the only comprehensive and official source of traffic accident data in China. Significant risk factors associating with speeding and drunk driving are identified. We find that several factors are associated with a significantly higher probability of both speeding and drunk driving, particularly male drivers, private vehicles, the lack of street lighting at night and poor visibility. The impact of other specific and unique risk factors for either speeding or drunk driving, such as hukou, road type/grades, commercial vehicles, compulsory third party insurance and vehicle safety status, also require particular attention. Legislative or regulatory measures targeting different vehicle types and/or driver groups with respect to the various driver, vehicle, road and environmental risk factors can subsequently be devised to reduce the speeding and drunk driving rates. As the country with the highest number of traffic accident fatalities in the world, applying these findings in workable legislation and enforcement to reduce speeding and drunk driving rates will save tens of thousands of lives.

Risk factors associated with traffic violations and accident severity in China.

With the recent economic boom in China, vehicle volume and the number of traffic accident fatalities have become the highest in the world. Meanwhile, traffic accidents have become the leading cause of death in China. Systematically analyzing road safety data from different perspectives and applying empirical methods/implementing proper measures to reduce the fatality rate will be an urgent and challenging task for China in the coming years. In this study, we analyze the traffic accident data for the period 2006-2010 in Guangdong Province, China. These data, extracted from the Traffic Management Sector-Specific Incident Case Data Report, are the only officially available and reliable source of traffic accident data (with a sample size>7000 per year). In particular, we focus on two outcome measures: traffic violations and accident severity. Human, vehicle, road and environmental risk factors are considered. First, the results establish the role of traffic violations as one of the major risks threatening road safety. An immediate implication is: if the traffic violation rate could be reduced or controlled successfully, then the rate of serious injuries and fatalities would be reduced accordingly. Second, specific risk factors associated with traffic violations and accident severity are determined. Accordingly, to reduce traffic accident incidence and fatality rates, measures such as traffic regulations and legislation-targeting different vehicle types/driver groups with respect to the various human, vehicle and environment risk factors-are needed. Such measures could include road safety programs for targeted driver groups, focused enforcement of traffic regulations and road/transport facility improvements. Data analysis results arising from this study will shed lights on the development of similar (adjusted) measures to reduce traffic violations and/or accident fatalities and injuries, and to promote road safety in other regions.

Robust REML estimation for k-component Poisson mixture with random effects: application to the epilepsy seizure count data and urinary tract infections data.

A robust version of residual maximum likelihood estimation for Poisson log-linear mixed model is developed, and the method is extended to k-component Poisson mixture with random effects. The method not only provides the robust estimators for the fixed effects and variance component parameters but also gives the robust prediction of random effects. Simulation results show that the proposed method is effective in limiting the impact of outliers under different data contamination schemes. The method is adopted to analyze the epilepsy seizure count data and the urinary tract infections data, which are deemed to contain several potential outliers. The results show that the proposed method provides better goodness of fit to the data and demonstrate the effect of the robust tuning mechanism.

Quantitative assessment of cerebral hemodynamic parameters by QUASAR arterial spin labeling in Alzheimer's disease and cognitively normal Elderly adults at 3-tesla.

QUASAR arterial spin labeling (ASL) was used to investigate the role of vascular impairment in Alzheimer's disease (AD). We hypothesized that the hemodynamic parameters monitoring cerebrovascular integrity, i.e., cerebral blood flow (CBF), arterial blood volume (aBV), and arterial transit time (aTT), would be affected. 13 AD patients and 15 healthy control (HC) subjects underwent 3T MRI scanning. Two separate blood flow acquisitions were obtained with 1 slice overlap for whole brain coverage. CBF, aBV, and aTT maps were calculated using in-house software. Preprocessing and statistical analyses were performed on SPM5. Region-of-interest (ROI) studies of ten selected cerebral regions were also conducted. There were significant differences in mini mental status exam (MMSE) (AD: 16.3 ± 4.55, HC: 28.5 ± 2.00) and Alzheimer's disease assessment scale-cognitive subscale (ADAS-cog) scores (AD: 25.25 ± 9.64, HC: 5.51 ± 2.62) between the 2 groups (p < 0.001) but none in age (p = 0.068). CBF decreased significantly (p < 0.01) in AD compared to controls in the right middle cingulate, left cuneus, left inferior and middle frontal, right superior frontal, left inferior parietal, and right supramarginal gyri. ROI studies confirmed significant hemodynamic impairments in AD compared to HC (p < 0.05): CBF in middle and posterior cingulate, aBV in left superior temporal, right inferior parietal, and posterior cingulate, and aTT in left inferior frontal and middle cingulate gyri. CBF correlated positively while aTT correlated negatively to MMSE, and vice versa for ADAS-cog. Using QUASAR ASL, we found patterns of regional hemodynamic impairment typical of moderate AD, suggesting underlying vascular abnormality. As potential biomarkers, these hemodynamic parameters could differentiate patients from volunteers, and possibly indicate the conversion from healthy aging to mild cognitive impairment to AD.

Mixture cure model with random effects for clustered interval-censored survival data.

The mixture cure model is an effective tool for analysis of survival data with a cure fraction. This approach integrates the logistic regression model for the proportion of cured subjects and the survival model (either the Cox proportional hazards or accelerated failure time model) for uncured subjects. Methods based on the mixture cure model have been extensively investigated in the literature for data with exact failure/censoring times. In this paper, we propose a mixture cure modeling procedure for analyzing clustered and interval-censored survival time data by incorporating random effects in both the logistic regression and PH regression components. Under the generalized linear mixed model framework, we develop the REML estimation for the parameters, as well as an iterative algorithm for estimation of the survival function for interval-censored data. The estimation procedure is implemented via an EM algorithm. A simulation study is conducted to evaluate the performance of the proposed method in various practical situations. To demonstrate its usefulness, we apply the proposed method to analyze the interval-censored relapse time data from a smoking cessation study whose subjects were recruited from 51 zip code regions in the southeastern corner of Minnesota.

How to analyze longitudinal multilevel physical activity data with many zeros?

BACKGROUND: Physical activity (PA) is a modifiable lifestyle factor for many chronic diseases with established health benefits. PA outcomes are measured and assessed in many longitudinal studies, but their analyses often pose difficulties due to the presence of many zeros, extreme skewness, and lack of independence, which render standard regression methods inappropriate. METHODS: A two-part multilevel modeling approach is used to analyze the heterogeneous and correlated PA data. In the first part, a logistic mixed regression model is fitted to estimate the prevalence of PA and factors associated with PA participation over time. For subjects engaging in PA, a gamma mixed regression model is adopted in the second part to assess the effects of predictor variables on the repeated PA outcomes nested within clusters. Extra variations are accommodated within the modeling process by random effects assigned to each cluster and each subject in the cohort. RESULTS: The findings in a longitudinal multilevel study of a community-based PA intervention for older adults demonstrate the effectiveness of the intervention program and enable the identification of pertinent factors affecting participation and PA levels over time. CONCLUSIONS: The two-part mixed regression approach provides a practical and statistically valid method to analyze the skewed and correlated PA data with many zeros. The methodology can be extended to handle complex hierarchical or multilevel settings by suitable specification of the covariance structure in the random components, model fitting of which can be performed in STATA using GLLAMM with various user-specified options.

A score test for assessing the cured proportion in the long-term survivor mixture model.

The long-term survivor mixture model is commonly applied to analyse survival data when some individuals may never experience the failure event of interest. A score test is presented to assess whether the cured proportion is significant to justify the long-term survivor mixture model. Sampling distribution and power of the test statistic are evaluated by simulation studies. The results confirm that the proposed test statistic performs well in finite sample situations. The test procedure is illustrated using a breast cancer survival data set and the clustered multivariate failure times from a multi-centre clinical trial of carcinoma.

Multilevel mixture cure models with random effects.

This paper extends the multilevel survival model by allowing the existence of cured fraction in the model. Random effects induced by the multilevel clustering structure are specified in the linear predictors in both hazard function and cured probability parts. Adopting the generalized linear mixed model (GLMM) approach to formulate the problem, parameter estimation is achieved by maximizing a best linear unbiased prediction (BLUP) type log-likelihood at the initial step of estimation, and is then extended to obtain residual maximum likelihood (REML) estimators of the variance component. The proposed multilevel mixture cure model is applied to analyze the (i) child survival study data with multilevel clustering and (ii) chronic granulomatous disease (CGD) data on recurrent infections as illustrations. A simulation study is carried out to evaluate the performance of the REML estimators and assess the accuracy of the standard error estimates.

A computer graphical user interface for survival mixture modelling of recurrent infections.

Recurrent infections data are commonly encountered in medical research, where the recurrent events are characterised by an acute phase followed by a stable phase after the index episode. Two-component survival mixture models, in both proportional hazards and accelerated failure time settings, are presented as a flexible method of analysing such data. To account for the inherent dependency of the recurrent observations, random effects are incorporated within the conditional hazard function, in the manner of generalised linear mixed models. Assuming a Weibull or log-logistic baseline hazard in both mixture components of the survival mixture model, an EM algorithm is developed for the residual maximum quasi-likelihood estimation of fixed effect and variance component parameters. The methodology is implemented as a graphical user interface coded using Microsoft visual C++. Application to model recurrent urinary tract infections for elderly women is illustrated, where significant individual variations are evident at both acute and stable phases. The survival mixture methodology developed enable practitioners to identify pertinent risk factors affecting the recurrent times and to draw valid conclusions inferred from these correlated and heterogeneous survival data.

Long-term survivor model with bivariate random effects: applications to bone marrow transplant and carcinoma study data.

Cured patients (or the so-called long-term survivors) are increasingly being observed in clinical trial studies. As exemplified in two data sets, the bone marrow transplantation study for leukaemia patients and the multi-centre study for patients with carcinoma in the oropharynx, a considerable portion of the patients in these studies are deemed to be cured. With the presence of random hospital/centre effects, a long-term survivor model with bivariate random effects is proposed to analyse clustered survival data with a possible portion of cured patients. This model extends earlier work by allowing random effects in both the cured fraction and the hazard function parts to follow a bivariate normal distribution, which gives a generalized model with an additional correlation parameter governing the relationship between the recovery probability and the instantaneous failure rate due to the hospital/centre effects. By adopting the GLMM formulation, random effects are incorporated in the model via the linear predictor terms. REML estimation of parameters is achieved via the EM algorithm. Application to the two sets of data illustrates the usefulness of the proposed model. A simulation study is conducted to assess the performance of the estimators, under the proposed numerical estimation scheme.

Minimum Hellinger distance estimation for k-component poisson mixture with random effects.

The k-component Poisson regression mixture with random effects is an effective model in describing the heterogeneity for clustered count data arising from several latent subpopulations. However, the residual maximum likelihood estimation (REML) of regression coefficients and variance component parameters tend to be unstable and may result in misleading inferences in the presence of outliers or extreme contamination. In the literature, the minimum Hellinger distance (MHD) estimation has been investigated to obtain robust estimation for finite Poisson mixtures. This article aims to develop a robust MHD estimation approach for k-component Poisson mixtures with normally distributed random effects. By applying the Gaussian quadrature technique to approximate the integrals involved in the marginal distribution, the marginal probability function of the k-component Poisson mixture with random effects can be approximated by the summation of a set of finite Poisson mixtures. Simulation study shows that the MHD estimates perform satisfactorily for data without outlying observation(s), and outperform the REML estimates when data are contaminated. Application to a data set of recurrent urinary tract infections (UTI) with random institution effects demonstrates the practical use of the robust MHD estimation method.

Maternity length of stay modelling by gamma mixture regression with random effects.

Maternity length of stay (LOS) is an important measure of hospital activity, but its empirical distribution is often positively skewed. A two-component gamma mixture regression model has been proposed to analyze the heterogeneous maternity LOS. The problem is that observations collected from the same hospital are often correlated, which can lead to spurious associations and misleading inferences. To account for the inherent correlation, random effects are incorporated within the linear predictors of the two-component gamma mixture regression model. An EM algorithm is developed for the residual maximum quasi-likelihood estimation of the regression coefficients and variance component parameters. The approach enables the correct identification and assessment of risk factors affecting the short-stay and long-stay patient subgroups. In addition, the predicted random effects can provide information on the inter-hospital variations after adjustment for patient characteristics and health provision factors. A simulation study shows that the estimators obtained via the EM algorithm perform well in all the settings considered. Application to a set of maternity LOS data for women having obstetrical delivery with multiple complicating diagnoses is illustrated.