A Semi-parametric Approach for Analyzing Longitudinal Measurements with Non-ignorable Missingness Using Regression Spline

In longitudinal studies with missingness, shared parameter models (SPM) provide appropriate framework for the joint modeling of the measurements and missingness process. These models use a set of random effects to account for the interdependence between two processes. Sometimes the longitudinal responses may not be fitted well by using a linear model and some non-parametric methods have to be used. Also, parametric assumptions are typically made for the random effects distribution, and violation of those may affect the parameter estimates and standard errors. To overcome these problems, we propose a semi-parametric model for the joint modelling of longitudinal markers and a missing not at random mechanism. In this model, because of the flexibility in nonparametric regression models, the relationship between the response variables and the covariates has been modeled by semi-parametric mixed effect model. Also, we do not assume any parametric assumption for the random effects distribution and we allow it to be unspecified. The parameter estimations are made using a vertex exchange method. In order to evaluate the performance of the proposed model, we compare SPM using regression spline (Spline-SPM) and semi-parametric SPM (SpSPM) models. We also conduct a simulation study with different parametric assumptions for the random effects distribution. A real example from a recent HIV study is analyzed for illustration of the proposed approach

Outlier Detection and a Method of Adjustment for the Iranian Manufacturing Establishment Survey Data

The role and importance of the industrial sector in the economic development necessitate the need to collect and to analyze accurate and timely data for exact planning. As the occurrence of outliers in establishment surveys are common due to the structure of the economy, the evaluation of survey data by identifying and investigating outliers, prior to the release of data, is necessary. In this paper, different robust multivariate outlier detection methods based on the Mahalanobis distance with blocked adaptive computationally efficient outlier nominators algorithm, minimum volume ellipsoid estimator, minimum covariance determinant estimator and Stahel-Donoho estimator are used in the context of a real dataset. Also some univariate outlier detection methods such as Hadi and Simonoff’s method, and Hidiroglou-Barthelot’s method for periodic manufacturing surveys are applied. The real data set is extracted from the Iranian Manufacturing Establishment Survey. These data are collected each year by the Statistical Center of Iran using sampling weights. In this paper, in addition to comparing different multivariate and univariate robust outlier detection methods, a new empirical method for reducing the effect of outliers based on the value modification method is introduced and applied on some important variables such as input and output. In this paper, a new four-step algorithm is introduced to adjust the input and output values of the manufacturing establishments which are under-reported or over-reported. A simulation study for investigating the performance of our method is also presented

Bayesian modeling of clustered competing risks survival times with spatial random effects

n some studies, survival data are arranged spatially such as geographical regions. Incorporating spatial associationin these data not only can increase the accuracy and efficiency of the parameter estimation, but it also investigatesthe spatial patterns of survivorship. In this paper, we considered a Bayesian hierarchical survival model in thesetting of competing risks for the spatially clustered HIV/AIDS data. In this model, a Weibull Parametric distributionwith the spatial random effects in the form of county-failure type-level was used. A multivariate intrinsic conditionalautoregressive (MCAR) distribution was employed to model the areal spatial random effects. Comparison amongcompeting models was performed by the deviance information criterion and log pseudo-marginal likelihood. Weillustrated the gains of our model through the simulation studies and application to the HIV/AIDS data

Missing Value Imputation for RNA-Sequencing Data Using Statistical Models: A Comparative Study

RNA-seq technology has been widely used as an alternative approach to traditional microarrays in transcript analysis. Sometimes gene expression by sequencing, which generates RNA-seq data set, may have missing read counts. These missing values can adversely affect downstream analyses. Most of the methods for analysing the RNA-seq data sets require a complete matrix of RNA-seq data. In the past few years, researchers have been putting a great deal of effort into presenting evaluations of the different imputation algorithms in microarray gene expression data sets, However, these are limited works for RNA-seq data sets and a comparative study for investigating the performance of the missing value imputation for RNA-seq data is essential. In this paper, we propose the use of some parametric models such as Regression imputation, Bayesian generalized linear model, Poisson mixture model, EM approach , Bayesian Poisson regression, Bayesian quasi-Poisson regression and the Bootstrap version of two latter for single imputation of missing values in RNA-seq count data sets. The approaches are also applied for identifying differentially expressed genes in the presence of missing values. Multiple imputation, proposed by Rubin (1978), is also used for multiple imputation of missing RNA-seq counts. This approach allows appropriate assessment of imputation uncertainty for missing values. The performance of the single and multiple imputations are investigated using some simulation studies. Also, some real data sets are analyzed using the proposed approaches

Effect of training after discharge on re-admission and re-hospitalization of patients with heart failure (randomized single-blind clinical trial)

    Discharge is the process of transferring a patient from hospital which involves a transfer of responsibility from inpatient service providers or hospitalist to the patient and primary care physicians. Inappropriate follow up after discharge will increase the risk of re-admission and re-hospitalization which leads to the poor performance of the health system. The aim of this study was to determine the effect of physician's caring after discharge on re-admission and referral to doctors.This study was conducted as a clinical trial on patients with early intervention for educational instruction. The clinical trial was conducted at a later stage on 120 patients with heart failure who were hospitalized in Taleghani Hospital, Tehran. For a period of five months after discharge, using block randomization, the subjects were divided into two groups, including intervention and control groups. At the time of discharge, the patients in the intervention group received instructions and were trained by physicians, while no intervention was applied for the subjects in the control group. In addition to demographic questions, the patients were asked about two main outcomes, i.e. "re-admission" and "referral to doctors".  To collect the required data, the subjects in both groups were contacted via telephone calls (nine times) every week in the first month after discharge and two times per week in the following two months. Generalized linear mixed effects model method was used for evaluating the effect of physicians caring after discharge on re-admission and re-hospitalization.The results of this study showed that with the passage of time (weekly) after discharge, there was a significant increase in the rate of re-admission in the control group, while there was no significant increase in re-hospitalization. There was no statistical evidence showing a significant difference between the rates of re-admission along with the time in the treatment intervals. In other words, the patients in the control group experienced a significant increase in the odds ratio of re-admission over the time. 

A shared parameter model of longitudinal measurements and survival time with heterogeneous random-effects distribution

© 2016 Informa UK Limited, trading as Taylor & Francis Group. Typical joint modeling of longitudinal measurements and time to event data assumes that two models share a common set of random effects with a normal distribution assumption. But, sometimes the underlying population that the sample is extracted from is a heterogeneous population and detecting homogeneous subsamples of it is an important scientific question. In this paper, a finite mixture of normal distributions for the shared random effects is proposed for considering the heterogeneity in the population. For detecting whether the unobserved heterogeneity exits or not, we use a simple graphical exploratory diagnostic tool proposed by Verbeke and Molenberghs [34] to assess whether the traditional normality assumption for the random effects in the mixed model is adequate. In the joint modeling setting, in the case of evidence against normality (homogeneity), a finite mixture of normals is used for the shared random-effects distribution. A Bayesian MCMC procedure is developed for parameter estimation and inference. The methodology is illustrated using some simulation studies. Also, the proposed approach is used for analyzing a real HIV data set, using the heterogeneous joint model for this data set, the individuals are classified into two groups: a group with high risk and a group with moderate risk.status: publishe

A Bayesian sensitivity analysis of the effect of different random effects distributions on growth curve models

Growth curve data consist of repeated measurements of a continuous growth process of human, animal, plant, microbial or bacterial genetic data over time in a population of individuals. A classical approach for analyzing such data is the use of non-linear mixed effects models under normality assumption for the responses. But, sometimes the underlying population that the sample is extracted from is an abnormal population or includes some homogeneous sub-samples. So, detection of original properties of the population is an important scientific question of interest. (To be continued on page 2388). Key words: Bayesian paradigm; Dirichlet process; growth curve models; mixed effects model; repeated measurements data; sensitivity analysis

Estimating cost of road traffic injuries in Iran using willingness to pay (WTP) method.

We aimed to use the willingness to pay (WTP) method to calculate the cost of traffic injuries in Iran in 2013. We conducted a cross-sectional questionnaire-based study of 846 randomly selected road users. WTP data was collected for four scenarios for vehicle occupants, pedestrians, vehicle drivers, and motorcyclists. Final analysis was carried out using Weibull and maximum likelihood method. Mean WTP was 2,612,050 Iranian rials (IRR). Statistical value of life was estimated according to 20,408 fatalities 402,314,106,073,648 IRR (US$13,410,470,202 based on purchasing power parity at (February 27th, 2014). Injury cost was US$25,637,870,872 (based on 318,802 injured people in 2013, multiple daily traffic volume of 311, and multiple daily payment of 31,030 IRR for 250 working days). The total estimated cost of injury and death cases was 39,048,341,074$. Gross national income of Iran was, US$604,300,000,000 in 2013 and the costs of traffic injuries constituted 6·46% of gross national income. WTP was significantly associated with age, gender, monthly income, daily payment, more payment for time reduction, trip mileage, drivers and occupants from road users. The costs of traffic injuries in Iran in 2013 accounted for 6.64% of gross national income, much higher than the global average. Policymaking and resource allocation to reduce traffic-related death and injury rates have the potential to deliver a huge economic benefit

Estimates of the mixing probabilities for the five patterns of gene expression for the BRCA data set.

<p><i>p</i>₁ : <i>μ</i>₁ = <i>μ</i>₂ = <i>μ</i>₃, <i>p</i>₂ : <i>μ</i>₁ = <i>μ</i>₂ ≠ <i>μ</i>₃, <i>p</i>₃ : <i>μ</i>₂ ≠ <i>μ</i>₁ = <i>μ</i>₃, <i>p</i>₄ : <i>μ</i>₁ ≠ <i>μ</i>₂ = <i>μ</i>₃, <i>p</i>₅ : <i>μ</i>₁ ≠ <i>μ</i>₂ ≠ <i>μ</i>₃.</p

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/