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.

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.

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.

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.

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.

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.

Multilevel survival modelling of recurrent urinary tract infections.

A multilevel survival frailty model is presented for analyzing clustered and recurrent urinary tract infections among elderly women residing in aged-care institutions. At the subject level, serial dependence is expected between recurrent events recorded on the same individual. At the cluster level, correlations of observations within the same institution are present due to the inherent residential environment and hierarchical setting. Two random components are therefore incorporated explicitly within the survival frailty model to account for the simultaneous heterogeneity and autoregressive structure. A Splus computer program is developed for the estimation of fixed effect and variance component parameters.

Multi-level zero-inflated poisson regression modelling of correlated count data with excess zeros.

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which render the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

Multiple-vehicle traffic accidents in Hong Kong.

'Multiple-vehicle traffic accident' refers to a crash between two or more moving objects. Unlike single-vehicle accidents, not all drivers involving in a multiple-vehicle accident are responsible for the occurrence of the event. Accordingly, variables such as road type, speed limit and number of vehicles involved in the accident are expected to play a much more important role in association with injury severity in multiple-vehicle accidents. To study the factors influencing injury severity of multiple-vehicle traffic accidents, a population-based study was conducted. The traffic accident data was obtained from the Traffic Accident Data System (TRADS), which was developed by the Transport Department, Police Force and Information Technology Services Department, Hong Kong. Multiple-vehicle traffic accidents (N = 10,630) occurring during the 2-year period 1999/2000 were considered. Potential risk factors such as district, human, vehicle, safety, environmental and site factors were examined. Categorizing injury severity into "fatal/serious" and "slight", a stepwise logistic regression model was applied to the population data set. The district board, time of the accident, driver's gender, vehicle type, road type, speed limit and the number of vehicles involved are significant factors influencing the injury severity. Identification of risk factors for severe traffic accidents provides valuable information to help with new and improved road safety control measures.

Under-nutrition affects time to recurrence of gastroenteritis among Aboriginal and non-Aboriginal children.

This study investigated whether under-nutrition affected time to hospitalization for recurrence of gastroenteritis in Australian children. Linked hospitalization records of all infants, born in 1995 and 1996 in Western Australia, who were admitted for gastroenteritis during their first year of life (n=1001), were retrieved. A survival frailty model was used for determining the factors influencing the recurrent times over the subsequent seven years. Aboriginality and under-nutrition were significantly associated with an increased risk of recurrence (hazard ratios of 2.59 and 1.28). Hospitalizations due to gastroenteritis were common among Aboriginal children who had a higher mean re-admission rate and much shorter intervals between re-admissions than other patients. The proportion of patients with recurrence was also significantly higher for Aboriginals (38.5%) than for other patients (14.2%). Gastroenteritis remains a serious problem in Aboriginal children. This presents a complex challenge to be addressed with public-health principles, political determination and commitment, and adequate resources.

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.

A robustified modeling approach to analyze pediatric length of stay.

PURPOSE: Length of stay (LOS) is an important measure of the cost of pediatric hospitalizations, but the guidelines developed so far are not rigorously evidence-based. This study demonstrates a robust gamma mixed regression approach to analyze the positively skewed LOS variable, which has implications for future studies of pediatric health care management. METHODS: The robustified approach is applied to analyze hospital discharge data on childhood gastroenteritis in Western Australia (n=514). The model accounts for demographic characteristics and co-morbidities of the patients, as well as the dependency of LOS outcomes nested within the 58 hospitals in the State. The method is compared with the standard linear mixed regression with trimming of extreme observations. RESULTS: For the empirical application, the linear mixed regression results are sensitive to the magnitude of trimming. The identified significant factors from the robust regression model, namely infection, failure to thrive, and iron deficiency anemia are resistant to high-LOS outliers. CONCLUSIONS: Robust gamma mixed regression appears to be a suitable alternative to analyze the clustered and positively skewed pediatric LOS, without transforming and trimming the data arbitrarily.

Modelling bivariate count series with excess zeros.

Bivariate time series of counts with excess zeros relative to the Poisson process are common in many bioscience applications. Failure to account for the extra zeros in the analysis may result in biased parameter estimates and misleading inferences. A class of bivariate zero-inflated Poisson autoregression models is presented to accommodate the zero-inflation and the inherent serial dependency between successive observations. An autoregressive correlation structure is assumed in the random component of the compound regression model. Parameter estimation is achieved via an EM algorithm, by maximizing an appropriate log-likelihood function to obtain residual maximum likelihood estimates. The proposed method is applied to analyze a bivariate series from an occupational health study, in which the zero-inflated injury count events are classified as either musculoskeletal or non-musculoskeletal in nature. The approach enables the evaluation of the effectiveness of a participatory ergonomics intervention at the population level, in terms of reducing the overall incidence of lost-time injury and a simultaneous decline in the two mean injury rates.

Hypodensity of >1/3 middle cerebral artery territory versus Alberta Stroke Programme Early CT Score (ASPECTS): comparison of two methods of quantitative evaluation of early CT changes in hyperacute ischemic stroke in the community setting.

BACKGROUND: The one third middle cerebral artery territory ((1/3) MCA) method and the Alberta Stroke Program Early CT Score (ASPECTS) were used to detect significant early ischemic changes (EIC) on CT brain of acute stroke patients. We sought to compare the reliability of the 2 methods in routine clinical practice. METHODS: Eighty consecutive patients admitted to a community hospital in Hong Kong with suspected acute ischemic stroke and a CT brain scan performed within 6 hours of symptom onset were included. Five blinded observers (1 neurologist, 2 general radiologists, and 2 neuroradiologists) independently evaluated the scans, using the ATLANTIS/CT Summit criteria for >(1/3) MCA involvement, and ASPECTS (1/3) MCA involvement, all observers agreed in 57 cases (71%), with moderate interobserver agreement (kappa=0.49). For ASPECTS