Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models.

The Cox regression model is the most commonly used regression model for survival data. The Cox regression model has an important assumption that hazard rates are proportional over time. The accelerated failure time model is an alternative method for the analysis of survival data when hazards are not proportional. The accelerated failure time models should lead to more efficient parameter estimates than Cox model under certain circumferences. In this study, The accelerated failure time models are described. The data of stomach cancer patients is used to illustrate these models and the results are discussed

Feature selection based on sensitivity analysis of fuzzy ISODATA

It has been well known that ignoring measurement error may result in substantially biased estimates in many contexts including linear and nonlinear regressions. For survival data with measurement error in covariates there has been extensive discussion in the literature with the focus being on the Cox proportional hazards models. However, the impact of measurement error on accelerated failure time (AFT) models has received little attention, though AFT models are very useful in survival data analysis. In this paper, we discuss AFT models with error-prone covariates and study the bias induced by the naive approach of ignoring measurement error in covariates. To adjust for such a bias, we describe a simulation and extrapolation method. This method is appealing because it is simple to implement and it does not require modelling the true but error-prone covariate process that is often not observable. Asymptotic normality for the resulting estimators is established. Simulation studies are carried out to evaluate the performance of the proposed method as well as the impact of ignoring measurement error in covariates. The proposed method is applied to analyse a data set arising from the Busselton Health study (Australian J. Public Health 1994; 18:129-135).

This study examined the impact of delayed entry in HIV cohorts by analyzing left-truncated and right-censored (LTRC) survival data from 69 HIV-positive male patients (24 deaths) followed at a tertiary infectious diseases center. The primary objective was to empirically compare commonly used survival models in estimating survival and identifying prognostic factors under LTRC conditions. Three LTRC-adapted modeling frameworks were applied with identical covariates: the semiparametric Cox proportional hazards model, accelerated failure time (AFT) models, and parametric proportional hazards models. Median survival time was estimated using Kaplan-Meier methods under both right-censoring-only and LTRC specifications to assess truncation-related differences. Hazard ratios (HRs) and 95% confidence intervals (CIs) were obtained from the LTRC-adjusted Cox model. Model performance was evaluated using information criteria (AIC, BIC, HQIC). Ignoring left truncation substantially inflated median survival estimates (3,885 vs. 2,626 days). In the LTRC-adjusted Cox model, age (HR = 1.049, 95% CI: 1.011-1.089) and log-transformed HIV RNA (HR = 1.214, 95% CI: 1.055-1.400) were significant predictors, whereas CD4 count and comorbidity status were not. Among the evaluated models, the Cox model showed the lowest information criterion values within this dataset. Appropriate risk-set specification under left truncation is essential for reliable survival estimation in delayed-entry HIV cohorts. Within this empirical dataset, the LTRC-adapted Cox model showed favorable performance relative to AFT and parametric PH alternatives.

A general class of semiparametric hazards regression models for survival data is proposed and studied. This general class includes some popular classes of models as subclasses, such as Cox's proportional hazards model, the accelerated failure time model and a recently proposed class of models called the accelerated hazards model. In the general class of models, a covariate's effect is identified as having two separate components, namely a time-scale change on hazard progression and a relative hazards ratio. The new model is flexible in modelling survival data and may yield more accurate prediction of an individual's survival process. By way of the nested structure that includes the proportional hazards model, the accelerated failure time model and the accelerated hazards model, the general class of models may provide a numerical tool for determining which of them is more appropriate for a given dataset.

SAS Macro for Causal Mediation Analysis with Survival Data

Survival models allowing for random effects (e.g., frailty models) have been widely used for analyzing clustered time‐to‐event data. Accelerated failure time (AFT) models with random effects are useful alternatives to frailty models. Because survival times are directly modeled, interpretation of the fixed and random effects is straightforward. Moreover, the fixed effect estimates are robust against various violations of the assumed model. In this paper, we propose a penalized h‐likelihood (HL) procedure for variable selection of fixed effects in the AFT random‐effect models. For the purpose of variable selection, we consider three penalty functions, namely, least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), and HL. We demonstrate via simulation studies that the proposed variable selection procedure is robust against the misspecification of the assumed model. The proposed method is illustrated using data from a bladder cancer clinical trial.

This study was designed to estimate the effects of treatment with linezolid as compared with vancomycin, on the distribution of length of stay (LOS) for hospitalized patients with methicillin-resistant staphylococcal infections. Treatment with intravenous-oral linezolid may allow some patients to be discharged earlier than would treatment with intravenous vancomycin. The analysis is based on the intention-to-treat sample from a randomized multinational phase 3 clinical trial of 460 patients showing that the treatments had equal efficacy. Given the nature of the LOS data, some censoring, and some imbalances between treatment groups, multivariate survival analysis was indicated. Cox proportional hazards assumptions were tested and failed, and accelerated failure time models were tested for best fit. The log-logistic model was selected and used as the basis for estimating the overall treatment effect on LOS. Two methods for multivariate corrections to the survivorship functions allowed more thorough description of the treatment effect on the distribution of LOS, including multivariate-adjusted Kaplan-Meier curves. The average reduction in LOS associated with linezolid treatment, based on the log-logistic model after correction for covariate effects, was 18.1% (p = .041) or 2.53 days at the median. This was consistent with differences at the medians of the adjusted survivorship functions, which were 2 or 3 days depending on the method used. Treatment-based differences exist at each decile of LOS and consistently favor linezolid. Estimated mean reduction in LOS due to linezolid was 1.62 days in both methods. In this study sample, linezolid treatment resulted in statistically significantly shorter hospital LOS as compared with vancomycin treatment. Appropriate use of multivariate survival analysis allows better examination of the nature of the treatment effect on LOS, which may be important for economic analysis.

For primary prevention of spousal violence which is highly prevalent in sub-Saharan Africa (SSA), an important empirical question is “how early in a marital relationship does spousal violence commence? In this study, we employed descriptive statistics, Kaplan-Meier method and accelerated failure time models to estimate prevalence of parental history of spousal violence; estimate the timing of onset of spousal violence for sub-regions of SSA and assess the associated factors. We analyzed a weighted sample of 62,274 women aged 15–49 years from the domestic violence module of Demographic and Health Surveys conducted in 14 SSA countries between 2015 and 2018. Prevalence of spousal violence among ever married women ranged from 20.5% in Nigeria to 45.9%% in Burundi. The median time to first spousal violence after marriage in Western, Middle, Eastern, and Southern Africa was 2 years. Results from accelerated failure time models showed that age at marriage, educational attainment, and parental history of spousal violence were independently associated with early onset of spousal violence in all SSA subregions (West: TR = 0.21, CI 0.19–0.24; Middle: TR = 0.38, CI 0.34–0.43; East: TR = 0.46, CI 0.44–0.49; South: TR = 0.50, CI 0.46–0.54). Adolescents, youth, and older adults should be targeted for preventive and corrective interventions for spousal violence.

Survival data is a type of data that measures the time from a defined starting point until the occurrence of a particular event, such as time to death from small cell lung cancer after diagnosis, Length of time in remission for leukemia patients, Length of stay (i.e., time until discharge) in hospital after surgery. The accelerated failure time (AFT) models are popular linear models for analyzing survival data. It provides a linear relationship between the log of the failure time and covariates that affect the expected failure time by contracting or expanding the time scale. This paper examines the performance of the Rank Set Sampling (RSS) on the AFT models for Progressive Type-II censoring-survival data. The Ranked Set Sampling (RSS) is a sampling scheme that selects a sample based on a baseline auxiliary variable for assessing survival time. Simulation studies show that this approach provides a more robust testing procedure, and a more efficient hazard ratio estimate than simple random sampling (SRS). The lung cancer survival data are used to demonstrate the method.

The study explores issues related to the treatment of brain cancer caused by the heterogeneous nature of different variants of brain tumors. The objective of this study was to identify essential genes present in multiple types of brain cancer by using high dimensional gene expression data available on the Curated Microarray Database CuMiDa. The study’s dataset comprised a total of 130 samples belonging to 4 subtypes of brain cancer and 16384 gene expression variables. Thus, the penalized Elastic net method in conjunction with Multinomial Logistic Regression was used to cope with curse of dimensionality problems. Then, accuracy, Kappa statistic, Area Under the Curve, and F1-score were utilized to evaluate measures of the model efficiency. Elastic Net proved to be quite effective in the sense of the extensiveness of the variables included in the analysis and successfully restricted gene level further analysis as well as highlighted subtype specific expression signatures. The model achieved high precision and AUC values indicating that in general the model had good ability to distinguish all subtypes with some around perfect score of AUC. Robust parameter estimation was supplemented with cross validation and other predictive model validation statistical techniques done in R language programming. Thus, these findings suggest that the best model for evaluating large-scale gene expression data of brain cancers is the use of MLR with an elastic net regularization. There is ample evidence that these selected genes contribute to and serve as targets for therapy, therefore making this study a good starting point for further investigations with respect to understanding their biological role. The corresponding model is also to be applied to test its validity on some other datasets of a quite different nature. This, in turn, may suggest improved diagnostic, prognostic and therapeutic options for the brain tumor.

If the samples, features and information values in a real-valued information system are cells, genes and gene expression values, respectively, then for convenience, this system is said to be a single cell gene space. In the era of big data, people are faced with high dimensional gene expression data with redundancy and noise causing its strong uncertainty. D-S evidence theory excels at tackling the problem of uncertainty, and its conditions to be met are weaker than Bayesian probability theory. Therefore, this paper studies the gene selection in a single cell gene space to remove noise and redundancy with D-S evidence theory. The distance between two cells in each gene is first defined. Then, the tolerance relation is established according to the defined distance. In addition, the belief and plausibility functions to grasp the uncertainty of a single cell gene space are introduced on the basis of the tolerance classes. Statistical analysis shows that they can effectively measure the uncertainty of a single cell gene space. Furthermore, several gene selection algorithms in a single cell gene space are presented using the proposed belief and plausibility. Finally, the performance of the proposed algorithm is compared to other algorithms on some published single-cell data sets. Experimental results and statistical tests show that the classification and clustering performance of the presented algorithm not only exceeds the other three state-of-the-art algorithms, but also its gene reduction rate is very high.

Microarray technology makes it possible to measure expression level of thousands of genes simultaneously in an efficient and inexpensive manner. However, due to various complexities in processing microarrays, expression information of various genes may be missing due to unreliable measurements. The occurrence of missing values in gene expression data can adversely affect downstream analyses such as clustering, dimensionality reduction etc. Different algorithms have been developed to estimate the missing values in different data sets and none of these algorithm works well with all the data sets. In this work, we explore the possible application of Mutual Nearest Neighbor (MNN) algorithm to impute the missing values, which shows comparable results with other well know imputation algorithms. We also have explored five different methods for missing value imputation namely Row Average Imputation, Mean Imputation, Median Imputation, k-Nearest Neighbor Imputation and combination of kNN based feature selection (kNNFS) and kNN-based imputation. The experiments are carried out on very high dimensional gene expression data such as Notterman Carcinoma and Notterman Adenocarcinoma data and the results are illustrated.

Metric trees are designed for improving efficiency of similarity search in high dimensional data. Searching a metric tree always terminates at leaf nodes, restricting the hierarchical search to a certain leaf level, and the linear search in leafs to a particular leaf size. This may result in performing additional unneeded distance comparisons, which increases the search time as the number of dimensions increases. It is proposed that the search adapts itself to the query in question, so as to avoid unneeded distance comparisons. Thus, the hierarchical search should terminate when no more search- space pruning can be useful, and search then continues linearly through the un-pruned data space. Hierarchical search can thus stop any where above or below the original leaf level of a metric tree. Search rules are proposed to adapt the search to the query parameters. A modification to the metric tree is also suggested to adopt the proposed rules. High dimensional gene expression data sets are used to evaluate the new algorithm, showing speed ups of 55% compared to traditional search.

Association rule has shown its usefulness in the gene expression data based disease diagnosis for its good interpretability. The large number of rules generated from the high dimensional gene expression data is one of the main challenges of its applications. In this work, we reveal that the discretization preprocessing is one of the reasons for the association rule number explosion problem. To alleviate this problem, a Regularized Gaussian Mixture Model (RGMM) is proposed to discretize the continuous gene expression data. RGMM explores both the complexity of the discretization model and the information loss of the discretization procedure, under the Minimal Description Length framework. Extensive experiments show the effectiveness of RGMM on real-life gene expression data sets.