A Note on the Validity of Statistical Bootstrapping for Estimating the Uncertainty of Tensor Parameters in Diffusion Tensor Images

Diffusion tensors are estimated from magnetic resonance images (MRIs) that are diffusion-weighted, and those images inherently contain noise. Therefore, noise in the diffusion-weighted images produces uncertainty in estimation of the tensors and their derived parameters, which include eigenvalues, eigenvectors, and the trajectories of fiber pathways that are reconstructed from those eigenvalues and eigenvectors. Although repetition and wild bootstrap methods have been widely used to quantify the uncertainty of diffusion tensors and their derived parameters, we currently lack theoretical derivations that would validate the use of these two bootstrap methods for the estimation of statistical parameters of tensors in the presence of noise. The aim of this paper is to examine theoretically and numerically the repetition and wild bootstrap methods for approximating uncertainty in estimation of diffusion tensor parameters under two different schemes for acquiring diffusion weighted images. Whether these bootstrap methods can be used to quantify uncertainty in some diffusion tensor parameters, such as fractional anisotropy (FA), depends critically on the morphology of the diffusion tensor that is being estimated. The wild and repetition bootstrap methods in particular cannot quantify uncertainty in the principal direction (PD) of isotropic (or oblate) tensor. We also examine the use of bootstrap methods in estimating tensors in a voxel containing multiple tensors, demonstrating their limitations when quantifying the uncertainty of tensor parameters in those locations. Simulation studies are also used to understand more thoroughly our theoretical results. Our findings raise serious concerns about the use of bootstrap methods to quantify the uncertainty of fiber pathways when those pathways pass through voxels that contain either isotropic tensors, oblate tensors, or multiple tensors

An Assessment of Road Traffic Accident in Zaria Urban Area, Kaduna State, Nigeria

The study assessed road traffic accident along Pz/Samaru route Zaria Kaduna state. The study was conducted using the accident statistics from the Federal Road Safety Commission 2010 to 2013 and field observations. The data were analyzed using descriptive statistics and presented in table and percentages. The study revealed that 2013 recorded the highest number of road accidents of which 15% of the accidents across the sampled year occurred at aviation junction and 15.8% occurred in the month of October. Among the causes of the road traffic accidents in the study area are over speeding representing 23.3% been the highest, followed by loss of control 16.7%. the study indicated that the month of march recorded the highest accident fatality with 52.2% and that in most of the accidents there are no traffic management measures except few with traffic warders which despite their presence, accident has being on the increase in the study area. The study concluded by calling the attention of the stakeholders on the need to ensure safe urban travel with some recommendations which among them are channelization of roads junctions, construction of speed bumps, strict enforcement of road traffic laws and public awareness on road safety measures. KEYWORDS: Road Traffic Accident, Fatality, Road Control Measures and Urban Zari

Modeling event count data in the presence of informative dropout with application to bleeding and transfusion events in myelodysplastic syndrome

In many biomedical studies, it is often of interest to model event count data over the study period. For some patients, we may not follow up them for the entire study period owing to informative dropout. The dropout time can potentially provide valuable insight on the rate of the events. We propose a joint semiparametric model for event count data and informative dropout time that allows for correlation through a Gamma frailty. We develop efficient likelihood-based estimation and inference procedures. The proposed nonparametric maximum likelihood estimators are shown to be consistent and asymptotically normal. Furthermore, the asymptotic covariances of the finite-dimensional parameter estimates attain the semiparametric efficiency bound. Extensive simulation studies demonstrate that the proposed methods perform well in practice. We illustrate the proposed methods through an application to a clinical trial for bleeding and transfusion events in myelodysplastic syndrome

A Statistical Analysis of Brain Morphology Using Wild Bootstrapping

Methods for the analysis of brain morphology, including voxel-based morphology and surface-based morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphometric measures usually involves two statistical procedures: 1) invoking a statistical model at each voxel (or point) on the surface of the brain or brain subregion, followed by mapping test statistics (e.g., t test) or their associated p values at each of those voxels; 2) correction for the multiple statistical tests conducted across all voxels on the surface of the brain region under investigation. We propose the use of new statistical methods for each of these procedures. We first use a heteroscedastic linear model to test the associations between the morphological measures at each voxel on the surface of the specified subregion (e.g., cortical or subcortical surfaces) and the covariates of interest. Moreover, we develop a robust test procedure that is based on a resampling method, called wild bootstrapping. This procedure assesses the statistical significance of the associations between a measure of given brain structure and the covariates of interest. The value of this robust test procedure lies in its computationally simplicity and in its applicability to a wide range of imaging data, including data from both anatomical and functional magnetic resonance imaging (fMRI). Simulation studies demonstrate that this robust test procedure can accurately control the family-wise error rate. We demonstrate the application of this robust test procedure to the detection of statistically significant differences in the morphology of the hippocampus over time across gender groups in a large sample of healthy subjects

Pattern mixture models for clinical validation of biomarkers in the presence of missing data

Targeted therapies for cancers are sometimes only effective in a subset of patients with a particular biomarker status. In clinical development, the biomarker status is typically determined by an investigational-use-only/laboratory-developed test. A market ready test (MRT) is developed later to meet regulatory requirements and for future commercial use. In the USA, the clinical validation of MRT showing efficacy and safety profile of the targeted therapy in the biomarker subgroups determined by MRT is needed for pre-market approval. One of the major challenges in carrying out clinical validation is that the biomarker status per MRT is often missing for many subjects. In this paper, we treat biomarker status as a missing covariate and develop a novel pattern mixture model in the setting of a proportional hazards model for the time-to-event outcome variable. We specify a multinomial regression model for the missing biomarker statuses, and develop an expectation–maximization algorithm by the Method of Weights (Ibrahim, Journal of the American Statistical Association, 1990) to estimate the parameters in the regression model. We use Louis' formula (Louis, Journal of the Royal Statistical Society. Series B, 1982) to obtain standard errors estimates. We examine the performance of our method in extensive simulation studies and apply our method to a clinical trial in metastatic colorectal cancer

Reply to Comments

The comments from Englert and Kieser considered an adaptive design, where the over enrollment of the second stage can potentially depend on the observed responses in the first stage. However, such an adaptive design could be challenging because it involves the change of the original two-stage design and hypothesis setting, while the trial is already ongoing. The study protocol may need to be amended, and hence, the integrity of the trial becomes questionable. Furthermore, our method is applicable to the case for which the study team may want to add more subjects after claiming success of the trial based on the Simon two-stage design

Estimating Treatment Effects for Recurrent Events in the Presence of Rescue Medications: An Application to the Immune Thrombocytopenia Study

In many clinical studies, patients may experience the same type of event of interest repeatedly over time. However, the assessment of treatment effects is often complicated by the rescue medication uses due to ethical reasons. For example, in the motivating trial in studying the Immune Thrombocytopenia (ITP), when the interest lies in evaluating the treatment benefit of investigational product (IP) on reducing patient’s repeated bleeding, rescue medication such as platelet transfusions may be allowed to raise platelet counts. Both the intention-to-treat analysis and treating the intermediate rescue medication as covariate tend to attenuate the treatment benefit, and the estimates can be biased if interpreted as causal. In this paper, we propose a general causal framework when intermediate rescue medications are informative. We adopt the inverse weighted estimation approach to estimate the treatment effect, where weights are constructed to reflect time-dependent medication use probabilities. The proposed estimators are shown to be asymptotically normal and are demonstrated to perform well in small-sample simulation studies. The application to the ITP studies reveals a stronger benefit of using IP in reducing bleeding. © 2016, International Chinese Statistical Association

The TBX21 transcription factor T-1993C polymorphism is associated with decreased IFN-γ and IL-4 production by primary human lymphocytes

T-bet is a transcription factor that drives the Th1 immune response primarily through promoting expression of the IFN-γ gene. Polymorphisms in the T-bet gene, TBX21, have been associated with immune-mediated diseases such as asthma and systemic sclerosis. We found that the TBX21 promoter polymorphism T-1993C is associated with a significant decrease in IL-4 and IFN-γ production by stimulated primary human lymphocytes from healthy participants

Semiparametric regression analysis for composite endpoints subject to componentwise censoring

Composite endpoints with censored data are commonly used as study outcomes in clinical trials. For example, progression-free survival is a widely used composite endpoint, with disease progression and death as the two components. Progression-free survival time is often defined as the time from randomization to the earlier occurrence of disease progression or death from any cause. The censoring times of the two components could be different for patients not experiencing the endpoint event. Conventional approaches, such as taking the minimum of the censoring times of the two components as the censoring time for progression-free survival time, may suffer from efficiency loss and could produce biased estimates of the treatment effect. We propose a new likelihood-based approach that decomposes the endpoints and models both the progression-free survival time and the time from disease progression to death. The censoring times for different components are distinguished. The approach makes full use of available information and provides a direct and improved estimate of the treatment effect on progression-free survival time. Simulations demonstrate that the proposed method outperforms several other approaches and is robust against various model misspecifications. An application to a prostate cancer clinical trial is provided

Biomarker threshold adaptive designs for survival endpoints

Due to the importance of precision medicine, it is essential to identify the right patients for the right treatment. Biomarkers, which have been commonly used in clinical research as well as in clinical practice, can facilitate selection of patients with a good response to the treatment. In this paper, we describe a biomarker threshold adaptive design with survival endpoints. In the first stage, we determine subgroups for one or more biomarkers such that patients in these subgroups benefit the most from the new treatment. The analysis in this stage can be based on historical or pilot studies. In the second stage, we sample subjects from the subgroups determined in the first stage and randomly allocate them to the treatment or control group. Extensive simulation studies are conducted to examine the performance of the proposed design. Application to a real data example is provided for implementation of the first-stage algorithms