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Genomic instability is a hallmark of breast cancer, contributes to tumor heterogeneity, and influences chemotherapy resistance. Although Gap 2 and mitotic checkpoints are thought to prevent genomic instability, the role of these checkpoints in breast cancer is poorly understood. Here, we assess the Gap 2 and mitotic checkpoint functions of 24 breast cancer and immortalized mammary epithelial cell lines representing four of the six intrinsic molecular subtypes of breast cancer. We found that patterns of cell cycle checkpoint deregulation were associated with the intrinsic molecular subtype of breast cancer cell lines. Specifically, the luminal B and basal-like cell lines harbored two molecularly distinct Gap 2/mitosis checkpoint defects (impairment of the decatenation Gap 2 checkpoint and the spindle assembly checkpoint, respectively). All subtypes of breast cancer cell lines examined displayed aberrant DNA synthesis/Gap 2/mitosis progression and the basal-like and claudin-low cell lines exhibited increased percentages of chromatid cohesion defects. Furthermore, a decatenation Gap 2 checkpoint gene expression signature identified in the cell line panel correlated with clinical outcomes in breast cancer patients, suggesting that breast tumors may also harbor defects in decatenation Gap 2 checkpoint function. Taken together, these data imply that pharmacological targeting of signaling pathways driving these phenotypes may lead to the development of novel personalized treatment strategies for the latter two subtypes which currently lack targeted therapeutic options because of their triple negative breast cancer status.

The aim of this paper is to develop a general regression framework for the analysis of manifold-valued response in a Riemannian symmetric space (RSS) and its association with multiple covariates of interest, such as age or gender, in Euclidean space. Such RSS-valued data arises frequently in medical imaging, surface modeling, and computer vision, among many others. We develop an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding specifying any parametric distribution in RSS. We propose various link functions to map from the Euclidean space of multiple covariates to the RSS of responses. We develop a two-stage procedure to calculate the parameter estimates and determine their asymptotic distributions. We construct the Wald and geodesic test statistics to test hypotheses of unknown parameters. We systematically investigate the geometric invariant property of these estimates and test statistics. Simulation studies and a real data analysis are used to evaluate the finite sample properties of our methods.

Noninferiority multiregional clinical trials (MRCTs) have recently received increasing attention in drug development. While a major goal in an MRCT is to estimate the global treatment effect, it is also important to assess the consistency of treatment effects across multiple regions. In this paper, we propose an intuitive definition of consistency of noninferior treatment effects across regions under the random-effects modeling framework. Specifically, we quantify the consistency of treatment effects by the percentage of regions that meet a predefined treatment margin. This new approach enables us to achieve both goals in one modeling framework. We propose to use a signed likelihood ratio test for testing the global treatment effect and the consistency of noninferior treatment effects. In addition, we provide guidelines for the allocation rule to achieve optimal power for testing consistency among multiple regions. Extensive simulation studies are conducted to examine the performance of the proposed methodology. An application to a real data example is provided.

Joint models for longitudinal and survival data are routinely used in clinical trials or other studies to assess a treatment effect while accounting for longitudinal measures such as patient-reported outcomes (PROs). In the Bayesian framework, the deviance information criterion (DIC) and the logarithm of the pseudo marginal likelihood (LPML) are two well-known Bayesian criteria for comparing joint models. However, these criteria do not provide separate assessments of each component of the joint model. In this paper, we develop a novel decomposition of DIC and LPML to assess the fit of the longitudinal and survival components of the joint model, separately. Based on this decomposition, we then propose new Bayesian model assessment criteria, namely, ΔDIC and ΔLPML, to determine the importance and contribution of the longitudinal (survival) data to the model fit of the survival (longitudinal) data. Moreover, we develop an efficient Monte Carlo method for computing the Conditional Predictive Ordinate (CPO) statistics in the joint modeling setting. A simulation study is conducted to examine the empirical performance of the proposed criteria and the proposed methodology is further applied to a case study in mesothelioma.

Missing data are common in longitudinal clinical trials. How to handle missing data is critical for both sponsors and regulatory agencies to assess treatment effect from the trials. Recently, a control-based imputation has been proposed, where the missing data are imputed based on the assumption that patients who discontinued the test drug will have a similar response profile to the patients in the control group. Under control-based imputation, the variance estimation may be biased using Rubin's formula which could produce biased statistical inferences. We evaluate several statistical methods for obtaining appropriate variances under control-based imputation for analysis of repeated binary outcomes with monotone missing data and show that both the analytical method developed by Robins & Wang and the nonparametric bootstrap method provide more appropriate variance estimates under various simulation settings. We use the methods in an application of an antidepressant Phase III clinical trial and give discussion and recommendations on method performance and preference.

Joint models for longitudinal and survival data now have a long history of being used in clinical trials or other studies in which the goal is to assess a treatment effect while accounting for a longitudinal biomarker such as patient-reported outcomes or immune responses. Although software has been developed for fitting the joint model, no software packages are currently available for simultaneously fitting the joint model and assessing the fit of the longitudinal component and the survival component of the model separately as well as the contribution of the longitudinal data to the fit of the survival model. To fulfill this need, we develop a SAS macro, called JMFit. JMFit implements a variety of popular joint models and provides several model assessment measures including the decomposition of AIC and BIC as well as ΔAIC and ΔBIC recently developed in Zhang et al. (2014). Examples with real and simulated data are provided to illustrate the use of JMFit.

Background: Polymorphic CYP2D6 is primarily responsible for metabolic activation of tamoxifen to endoxifen. We previously reported that by increasing the daily tamoxifen dose to 40 mg/day in CYP2D6 intermediate metabolizer (IM), but not poor metabolizer (PM), patients achieve endoxifen concentrations similar to those of extensive metabolizer patients on 20 mg/day. We expanded enrollment to assess the safety of CYP2D6 genotype-guided dose escalation and investigate concentration differences between races., Methods: PM and IM breast cancer patients currently receiving tamoxifen at 20 mg/day were enrolled for genotype-guided escalation to 40 mg/day. Endoxifen was measured at baseline and after 4 months. Quality-of-life data were collected using the Functional Assessment of Cancer Therapy-Breast (FACT-B) and Breast Cancer Prevention Trial Menopausal Symptom Scale at baseline and after 4 months., Results: In 353 newly enrolled patients, genotype-guided dose escalation eliminated baseline concentration differences in IM (p = .08), but not PM (p = .009), patients. Endoxifen concentrations were similar in black and white patients overall (p = .63) and within CYP2D6 phenotype groups (p > .05). In the quality-of-life analysis of 480 patients, dose escalation did not meaningfully diminish quality of life; in fact, improvements were seen in several measures including the FACT Breast Cancer subscale (p = .004) and limitations in range of motion (p < .0001) in IM patients., Conclusion: Differences in endoxifen concentration during treatment can be eliminated by doubling the tamoxifen dose in IM patients, without an appreciable effect on quality of life. Validation of the association between endoxifen concentration and efficacy or prospective demonstration of improved efficacy is necessary to warrant clinical uptake of this personalized treatment strategy., Implications for Practice: This secondary analysis of a prospective CYP2D6 genotype-guided tamoxifen dose escalation study confirms that escalation to 40 mg/day in patients with low-activity CYP2D6 phenotypes (poor or intermediate metabolizers) increases endoxifen concentrations without any obvious increases in treatment-related toxicity. It remains unknown whether endoxifen concentration is a useful predictor of tamoxifen efficacy, and thus, there is no current role in clinical practice for CYP2D6 genotype-guided tamoxifen dose adjustment. If future studies confirm the importance of endoxifen concentrations for tamoxifen efficacy and report a target concentration, this study provides guidance for a dose-adjustment approach that could maximize efficacy while maintaining patient quality of life., (©AlphaMed Press.)

Changes in cellular mechanical properties correlate with the progression of metastatic cancer along the epithelial-to-mesenchymal transition (EMT). Few high-throughput methodologies exist that measure cell compliance, which can be used to understand the impact of genetic alterations or to screen the efficacy of chemotherapeutic agents. We have developed a novel array high-throughput microscope (AHTM) system that combines the convenience of the standard 96-well plate with the ability to image cultured cells and membrane-bound microbeads in twelve independently-focusing channels simultaneously, visiting all wells in eight steps. We use the AHTM and passive bead rheology techniques to determine the relative compliance of human pancreatic ductal epithelial (HPDE) cells, h-TERT transformed HPDE cells (HPNE), and four gain-of-function constructs related to EMT. The AHTM found HPNE, H-ras, Myr-AKT, and Bcl2 transfected cells more compliant relative to controls, consistent with parallel tests using atomic force microscopy and invasion assays, proving the AHTM capable of screening for changes in mechanical phenotype.

High angular resolution diffusion imaging (HARDI) has recently been of great interest in mapping the orientation of intra-voxel crossing fibers, and such orientation information allows one to infer the connectivity patterns prevalent among different brain regions and possible changes in such connectivity over time for various neurodegenerative and neuropsychiatric diseases. The aim of this paper is to propose a penalized multi-scale adaptive regression model (PMARM) framework to spatially and adaptively infer the orientation distribution function (ODF) of water diffusion in regions with complex fiber configurations. In PMARM, we reformulate the HARDI imaging reconstruction as a weighted regularized least-squares regression (WRLSR) problem. Similarity and distance weights are introduced to account for spatial smoothness of HARDI, while preserving the unknown discontinuities (e.g., edges between white matter and grey matter) of HARDI. The L 1 penalty function is introduced to ensure the sparse solutions of ODFs, while a scaled L 1 weighted estimator is calculated to correct the bias introduced by the L 1 penalty at each voxel. In PMARM, we integrate the multiscale adaptive regression models (Li et al., 2011), the propagation-separation method (Polzehl and Spokoiny, 2000), and Lasso (least absolute shrinkage and selection operator) (Tibshirani, 1996) to adaptively estimate ODFs across voxels. Experimental results indicate that PMARM can reduce the angle detection errors on fiber crossing area and provide more accurate reconstruction than standard voxel-wise methods.

Sequencing techniques have been widely used to assess gene expression (i.e., RNA-seq) or the presence of epigenetic features (e.g., DNase-seq to identify open chromatin regions). In contrast to traditional microarray platforms, sequencing data are typically summarized in the form of discrete counts, and they are able to delineate allele-specific signals, which are not available from microarrays. The presence of epigenetic features are often associated with gene expression, both of which have been shown to be affected by DNA polymorphisms. However, joint models with the flexibility to assess interactions between gene expression, epigenetic features and DNA polymorphisms are currently lacking. In this paper, we develop a statistical model to assess the associations between gene expression and epigenetic features using sequencing data, while explicitly modeling the effects of DNA polymorphisms in either an allele-specific or nonallele-specific manner. We show that in doing so we provide the flexibility to detect associations between gene expression and epigenetic features, as well as conditional associations given DNA polymorphisms. We evaluate the performance of our method using simulations and apply our method to study the association between gene expression and the presence of DNase I Hypersensitive sites (DHSs) in HapMap individuals. Our model can be generalized to exploring the relationships between DNA polymorphisms and any two types of sequencing experiments, a useful feature as the variety of sequencing experiments continue to expand.

The objective of this study was to assess potential functional attenuation or inactivation of the intra-S checkpoint during melanoma development. Proliferating cultures of skin melanocytes, fibroblasts, and melanoma cell lines were exposed to increasing fluences of UVC and intra-S checkpoint responses were quantified. Melanocytes displayed stereotypic intra-S checkpoint responses to UVC qualitatively and quantitatively equivalent to those previously demonstrated in skin fibroblasts. In comparison with fibroblasts, primary melanocytes displayed reduced UVC-induced inhibition of DNA strand growth and enhanced degradation of p21Waf1 after UVC, suggestive of enhanced bypass of UVC-induced DNA photoproducts. All nine melanoma cell lines examined, including those with activating mutations in BRAF or NRAS oncogenes, also displayed proficiency in activation of the intra-S checkpoint in response to UVC irradiation. The results indicate that bypass of oncogene-induced senescence during melanoma development was not associated with inactivation of the intra-S checkpoint response to UVC-induced DNA replication stress., (© 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

The power prior has been widely used in many applications covering a large number of disciplines. The power prior is intended to be an informative prior constructed from historical data. It has been used in clinical trials, genetics, health care, psychology, environmental health, engineering, economics, and business. It has also been applied for a wide variety of models and settings, both in the experimental design and analysis contexts. In this review article, we give an A-to-Z exposition of the power prior and its applications to date. We review its theoretical properties, variations in its formulation, statistical contexts for which it has been used, applications, and its advantages over other informative priors. We review models for which it has been used, including generalized linear models, survival models, and random effects models. Statistical areas where the power prior has been used include model selection, experimental design, hierarchical modeling, and conjugate priors. Frequentist properties of power priors in posterior inference are established, and a simulation study is conducted to further examine the empirical performance of the posterior estimates with power priors. Real data analyses are given illustrating the power prior as well as the use of the power prior in the Bayesian design of clinical trials., (Copyright © 2015 John Wiley & Sons, Ltd.)

Any statistical model should be identifiable in order for estimates and tests using it to be meaningful. We consider statistical analysis of physiologically-based pharmacokinetic (PBPK) models in which parameters cannot be estimated precisely from available data, and discuss different types of identifiability that occur in PBPK models and give reasons why they occur. We particularly focus on how the mathematical structure of a PBPK model and lack of appropriate data can lead to statistical models in which it is impossible to estimate at least some parameters precisely. Methods are reviewed which can determine whether a purely linear PBPK model is globally identifiable. We propose a theorem which determines when identifiability at a set of finite and specific values of the mathematical PBPK model (global discete identifiability) implies identifiability of the statistical model. However, we are unable to establish conditions that imply global discrete identifiability, and conclude that the only safe approach to analysis of PBPK models involves Bayesian analysis with truncated priors. Finally, computational issues regarding posterior simulations of PBPK models are discussed. The methodology is very general and can be applied to numerous PBPK models which can be expressed as linear time-invariant systems. A real data set of a PBPK model for exposure to dimethyl arsinic acid (DMA(V)) is presented to illustrate the proposed methodology.

The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer's disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.

There is an increasing demand for exploration of the transcriptomes of multiple species with extraordinary traits such as the naked-mole rat (NMR). The NMR is remarkable because of its longevity and resistance to developing cancer. It is of scientific interest to understand the molecular mechanisms that impart these traits, and RNA-sequencing experiments with comparator species can correlate transcriptome dynamics with these phenotypes. Comparing transcriptome differences requires a homology mapping of each transcript in one species to transcript(s) within the other. Such mappings are necessary, especially if one species does not have well-annotated genome available. Current approaches for this type of analysis typically identify the best match for each transcript, but the best match analysis ignores the inherent risks of mismatch when there are multiple candidate transcripts with similar homology scores. We present a method that treats the set of homologs from a novel species as a cluster corresponding to a single gene in the reference species, and we compare the cluster-based approach to a conventional best-match analysis in both simulated data and a case study with NMR and mouse tissues. We demonstrate that the cluster-based approach has superior power to detect differential expression.

This paper investigates diagnostic measures for assessing the influence of observations and model misspecification in the presence of missing covariate data for the Cox regression model. Our diagnostics include case-deletion measures, conditional martingale residuals, and score residuals. The Q-distance is proposed to examine the effects of deleting individual observations on the estimates of finite-dimensional and infinite-dimensional parameters. Conditional martingale residuals are used to construct goodness of fit statistics for testing possible misspecification of the model assumptions. A resampling method is developed to approximate the p -values of the goodness of fit statistics. Simulation studies are conducted to evaluate our methods, and a real data set is analyzed to illustrate their use.

Aims: A prospectively enrolled patient cohort was used to assess whether the prediction of CYP2D6 phenotype activity from genotype data could be improved by reclassification of diplotypes or alleles., Methods: Three hundred and fifty-five patients receiving tamoxifen 20 mg were genotyped for CYP2D6 and tamoxifen metabolite concentrations were measured. The endoxifen : N-desmethly-tamoxifen metabolic ratio, as a surrogate of CYP2D6 activity, was compared across four diplotypes (EM/IM, EM/PM, IM/IM, IM/PM) that are typically collapsed into an intermediate metabolizer (IM) phenotype. The relative metabolic activity of each allele type (UM, EM, IM, and PM) and each EM and IM allele was estimated for comparison with the activity scores typically assigned, 2, 1, 0.5 and 0, respectively., Results: Each of the four IM diplotypes have distinct CYP2D6 activity from each other and from the EM and PM phenotype groups (each P < 0.05). Setting the activity of an EM allele at 1.0, the relative activities of a UM, IM and PM allele were 0.85, 0.67 and 0.52, respectively. The activity of the EM alleles were statistically different (P < 0.0001), with the CYP2D6*2 allele (scaled activity = 0.63) closer in activity to an IM than an EM allele. The activity of the IM alleles were also statistically different (P = 0.014)., Conclusion: The current systems for translating CYP2D6 genotype into phenotype are not optimally calibrated, particularly in regards to IM diplotypes and the *2 allele. Additional research is needed to improve the prediction of CYP2D6 activity from genetic data for individualized dosing of CYP2D6 dependent drugs., (© 2015 The British Pharmacological Society.)

Simon's two-stage designs are widely used in cancer phase II clinical trials for assessing the efficacy of a new treatment. However in practice, the actual sample size for the second stage is often different from the planned sample size, and the original inference procedure is no longer valid. Previous work on this problem has certain limitations in computation. In this paper, we attempt to maximize the unconditional power while controlling for the type I error for the modified second stage sample size. A normal approximation is used for computing the power, and the numerical results show that the approximation is accurate even under small sample sizes. The corresponding confidence intervals for the response rate are constructed by inverting the hypothesis test, and they have reasonable coverage while preserving the type I error., (Copyright © 2015 John Wiley & Sons, Ltd.)

Explicitly modeling underlying relationships between a survival endpoint and processes that generate longitudinal measured or reported outcomes potentially could improve the efficiency of clinical trials and provide greater insight into the various dimensions of the clinical effect of interventions included in the trials. Various strategies have been proposed for using longitudinal findings to elucidate intervention effects on clinical outcomes such as survival. The application of specifically Bayesian approaches for constructing models that address longitudinal and survival outcomes explicitly has been recently addressed in the literature. We review currently available methods for carrying out joint analyses, including issues of implementation and interpretation, identify software tools that can be used to carry out the necessary calculations, and review applications of the methodology., (Copyright © 2014 John Wiley & Sons, Ltd.)

Multivariate meta-regression models are commonly used in settings where the response variable is naturally multi-dimensional. Such settings are common in cardiovascular and diabetes studies where the goal is to study cholesterol levels once a certain medication is given. In this setting, the natural multivariate endpoint is Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). In this paper, we examine study level (aggregate) multivariate meta-data from 26 Merck sponsored double-blind, randomized, active or placebo-controlled clinical trials on adult patients with primary hypercholesterolemia. Our goal is to develop a methodology for carrying out Bayesian inference for multivariate meta-regression models with study level data when the within-study sample covariance matrix S for the multivariate response data is partially observed. Specifically, the proposed methodology is based on postulating a multivariate random effects regression model with an unknown within-study covariance matrix Σ in which we treat the within-study sample correlations as missing data, the standard deviations of the within-study sample covariance matrix S are assumed observed, and given Σ, S follows a Wishart distribution. Thus, we treat the off-diagonal elements of S as missing data, and these missing elements are sampled from the appropriate full conditional distribution in a Markov chain Monte Carlo (MCMC) sampling scheme via a novel transformation based on partial correlations. We further propose several structures (models) for Σ, which allow for borrowing strength across different treatment arms and trials. The proposed methodology is assessed using simulated as well as real data, and the results are shown to be quite promising.

The hazard ratio derived from the Cox model is a commonly used summary statistic to quantify a treatment effect with a time-to-event outcome. The proportional hazards assumption of the Cox model, however, is frequently violated in practice and many alternative models have been proposed in the statistical literature. Unfortunately, the regression coefficients obtained from different models are often not directly comparable. To overcome this problem, we propose a family of weighted hazard ratio measures that are based on the marginal survival curves or marginal hazard functions, and can be estimated using readily available output from various modeling approaches. The proposed transformation family includes the transformations considered by Schemper et al. (Statist Med 28:2473-2489, 2009) as special cases. In addition, we propose a novel estimate of the weighted hazard ratio based on the maximum departure from the null hypothesis within the transformation family, and develop a Kolmogorov[Formula: see text]Smirnov type of test statistic based on this estimate. Simulation studies show that when the hazard functions of two groups either converge or diverge, this new estimate yields a more powerful test than tests based on the individual transformations recommended in Schemper et al. (Statist Med 28:2473-2489, 2009), with a similar magnitude of power loss when the hazards cross. The proposed estimates and test statistics are applied to a colorectal cancer clinical trial.

In oncology clinical trials, progression-free survival (PFS), generally defined as the time from randomization until disease progression or death, has been a key endpoint to support licensing approval. In the U.S. Food and Drug Administration guidance for industry, May 2007, concerning the PFS as the primary or co-primary clinical trial endpoint, it is recommended to have tumor assessments verified by an independent review committee blinded to study treatments, especially in open-label studies. It is considered reassuring about the lack of reader-evaluation bias if treatment effect estimates from the investigators' and independent review committees' evaluations agree. The agreement between these evaluations may vary for subjects with short or long PFS, while there exist no such statistical quantities that can completely account for this temporal pattern of agreements. Therefore, in this paper, we propose a new method to assess temporal agreement between two time-to-event endpoints, while the two event times are assumed to have a positive probability of being identical. This method measures agreement in terms of the two event times being identical at a given time or both being greater than a given time. Overall scores of agreement over a period of time are also proposed. We propose a maximum likelihood estimation to infer the proposed agreement measures using empirical data, accounting for different censoring mechanisms, including reader's censoring (event from one reader dependently censored by event from the other reader). The proposed method is demonstrated to perform well in small samples via extensive simulation studies and is illustrated through a head and neck cancer trial., (Copyright © 2014 John Wiley & Sons, Ltd.)

Developing sophisticated statistical methods for go/no-go decisions is crucial for clinical trials, as planning phase III or phase IV trials is costly and time consuming. In this paper, we develop a novel Bayesian methodology for determining the probability of success of a treatment regimen on the basis of the current data of a given trial. We introduce a new criterion for calculating the probability of success that allows for inclusion of covariates as well as allowing for historical data based on the treatment regimen, and patient characteristics. A new class of prior distributions and covariate distributions is developed to achieve this goal. The methodology is quite general and can be used with univariate or multivariate continuous or discrete data, and it generalizes Chuang-Stein's work. This methodology will be invaluable for informing the scientist on the likelihood of success of the compound, while including the information of covariates for patient characteristics in the trial population for planning future pre-market or post-market trials., (Copyright © 2014 John Wiley & Sons, Ltd.)

Clinical trials generally allow various efficacy and safety outcomes to be collected for health interventions. Benefit-risk assessment is an important issue when evaluating a new drug. Currently, there is a lack of standardized and validated benefit-risk assessment approaches in drug development due to various challenges. To quantify benefits and risks, we propose a counterfactual p-value (CP) approach. Our approach considers a spectrum of weights for weighting benefit-risk values and computes the extreme probabilities of observing the weighted benefit-risk value in one treatment group as if patients were treated in the other treatment group. The proposed approach is applicable to single benefit and single risk outcome as well as multiple benefit and risk outcomes assessment. In addition, the prior information in the weight schemes relevant to the importance of outcomes can be incorporated in the approach. The proposed CPs plot is intuitive with a visualized weight pattern. The average area under CP and preferred probability over time are used for overall treatment comparison and a bootstrap approach is applied for statistical inference. We assess the proposed approach using simulated data with multiple efficacy and safety endpoints and compare its performance with a stochastic multi-criteria acceptability analysis approach.

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling's T 2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods.

The role of biomarkers has increased in cancer clinical trials such that novel designs are needed to efficiently answer questions of both drug effects and biomarker performance. We advocate Bayesian hierarchical models for response-adaptive randomized phase II studies integrating single or multiple biomarkers. Prior selection allows one to control a gradual and seamless transition from randomized-blocks to marker-enrichment during the trial. Adaptive randomization is an efficient design for evaluating treatment efficacy within biomarker subgroups, with less variable final sample sizes when compared to nested staged designs. Inference based on the Bayesian hierarchical model also has improved performance in identifying the sub-population where therapeutics are effective over independent analyses done within each biomarker subgroup.

We investigated the hypothesis that the strength of the activation of the intra-S DNA damage checkpoint varies within the S phase. Synchronized diploid human fibroblasts were exposed to either 0 or 2.5 J m(-2) UVC in early, mid- and late-S phase. The endpoints measured were the following: (1) radio-resistant DNA synthesis (RDS), (2) induction of Chk1 phosphorylation, (3) initiation of new replicons and (4) length of replication tracks synthesized after irradiation. RDS analysis showed that global DNA synthesis was inhibited by approximately the same extent (30 ± 12%), regardless of when during S phase the fibroblasts were exposed to UVC. Western blot analysis revealed that the UVC-induced phosphorylation of checkpoint kinase 1 (Chk1) on serine 345 was high in early and mid S but 10-fold lower in late S. DNA fiber immunostaining studies indicated that the replication fork displacement rate decreased in irradiated cells at the three time points examined; however, replicon initiation was inhibited strongly in early and mid S, but this response was attenuated in late S. These results suggest that the intra-S checkpoint activated by UVC-induced DNA damage is not as robust toward the end of S phase in its inhibition of the latest firing origins in human fibroblasts., (© 2014 The Authors. Photochemistry and Photobiology published by Wiley Periodicals, Inc. on behalf of American Society for Photobiology.)

The aim of this paper is to develop a functional-mixed effects modeling (FMEM) framework for the joint analysis of high-dimensional imaging data in a large number of locations (called voxels) of a three-dimensional volume with a set of genetic markers and clinical covariates. Our FMEM is extremely useful for efficiently carrying out the candidate gene approaches in imaging genetic studies. FMEM consists of two novel components including a mixed effects model for modeling nonlinear genetic effects on imaging phenotypes by introducing the genetic random effects at each voxel and a jumping surface model for modeling the variance components of the genetic random effects and fixed effects as piecewise smooth functions of the voxels. Moreover, FMEM naturally accommodates the correlation structure of the genetic markers at each voxel, while the jumping surface model explicitly incorporates the intrinsically spatial smoothness of the imaging data. We propose a novel two-stage adaptive smoothing procedure to spatially estimate the piecewise smooth functions, particularly the irregular functional genetic variance components, while preserving their edges among different piecewise-smooth regions. We develop weighted likelihood ratio tests and derive their exact approximations to test the effect of the genetic markers across voxels. Simulation studies show that FMEM significantly outperforms voxel-wise approaches in terms of higher sensitivity and specificity to identify regions of interest for carrying out candidate genetic mapping in imaging genetic studies. Finally, FMEM is used to identify brain regions affected by three candidate genes including CR1, CD2AP, and PICALM, thereby hoping to shed light on the pathological interactions between these candidate genes and brain structure and function., (© 2014 WILEY PERIODICALS, INC.)

In many biomedical studies, patients may experience the same type of recurrent event repeatedly over time, such as bleeding, multiple infections and disease. In this article, we propose a Bayesian design to a pivotal clinical trial in which lower risk myelodysplastic syndromes (MDS) patients are treated with MDS disease modifying therapies. One of the key study objectives is to demonstrate the investigational product (treatment) effect on reduction of platelet transfusion and bleeding events while receiving MDS therapies. In this context, we propose a new Bayesian approach for the design of superiority clinical trials using recurrent events frailty regression models. Historical recurrent events data from an already completed phase 2 trial are incorporated into the Bayesian design via the partial borrowing power prior of Ibrahim et al. (2012, Biometrics 68, 578-586). An efficient Gibbs sampling algorithm, a predictive data generation algorithm, and a simulation-based algorithm are developed for sampling from the fitting posterior distribution, generating the predictive recurrent events data, and computing various design quantities such as the type I error rate and power, respectively. An extensive simulation study is conducted to compare the proposed method to the existing frequentist methods and to investigate various operating characteristics of the proposed design., (© 2014, The International Biometric Society.)

Joint models for longitudinal and survival data now have a long history of being used in clinical trials or other studies in which the goal is to assess a treatment effect while accounting for longitudinal assessments such as patient-reported outcomes or tumor response. Compared to using survival data alone, the joint modeling of survival and longitudinal data allows for estimation of direct and indirect treatment effects, thereby resulting in improved efficacy assessment. Although global fit indices such as AIC or BIC can be used to rank joint models, these measures do not provide separate assessments of each component of the joint model. In this paper, we develop a novel decomposition of AIC and BIC (i.e., AIC = AICLong + AICSurv|Long and BIC = BICLong + BICSurv|Long) that allows us to assess the fit of each component of the joint model and in particular to assess the fit of the longitudinal component of the model and the survival component separately. Based on this decomposition, we then propose ΔAICSurv and ΔBICSurv to determine the importance and contribution of the longitudinal data to the model fit of the survival data. Moreover, this decomposition, along with ΔAICSurv and ΔBICSurv, is also quite useful in comparing, for example, trajectory-based joint models and shared parameter joint models and deciding which type of model best fits the survival data. We examine a detailed case study in mesothelioma to apply our proposed methodology along with an extensive set of simulation studies., (Copyright © 2014 John Wiley & Sons, Ltd.)

We propose a joint model for longitudinal and survival data with time-varying covariates subject to detection limits and intermittent missingness at random. The model is motivated by data from the Multicenter AIDS Cohort Study (MACS), in which HIV+ subjects have viral load and CD4 cell count measured at repeated visits along with survival data. We model the longitudinal component using a normal linear mixed model, modeling the trajectory of CD4 cell count by regressing on viral load, and other covariates. The viral load data are subject to both left censoring because of detection limits (17%) and intermittent missingness (27%). The survival component of the joint model is a Cox model with time-dependent covariates for death because of AIDS. The longitudinal and survival models are linked using the trajectory function of the linear mixed model. A Bayesian analysis is conducted on the MACS data using the proposed joint model. The proposed method is shown to improve the precision of estimates when compared with alternative methods., (Copyright © 2014 John Wiley & Sons, Ltd.)

We establish a connection between Bayesian case influence measures for assessing the influence of individual observations and Bayesian predictive methods for evaluating the predictive performance of a model and comparing different models fitted to the same dataset. Based on such a connection, we formally propose a new set of Bayesian case-deletion model complexity (BCMC) measures for quantifying the effective number of parameters in a given statistical model. Its properties in linear models are explored. Adding some functions of BCMC to a conditional deviance function leads to a Bayesian case-deletion information criterion (BCIC) for comparing models. We systematically investigate some properties of BCIC and its connection with other information criteria, such as the Deviance Information Criterion (DIC). We illustrate the proposed methodology on linear mixed models with simulations and a real data example.

Background: Pharmacokinetically guided (PK-guided) versus body surface area-based 5-fluorouracil (5-FU) dosing results in higher response rates and better tolerability. A paucity of data exists on PK-guided 5-FU dosing in the community setting., Patients and Methods: Seventy colorectal cancer patients, from one academic and five community cancer centers, received the mFOLFOX6 regimen (5-FU 2,400 mg/m(2) over 46 hours every 2 weeks) with or without bevacizumab at cycle 1. The 5-FU continuous-infusion dose was adjusted for cycles 2-4 using a PK-guided algorithm to achieve a literature-based target area under the concentration-time curve (AUC). The primary objective was to demonstrate that PK-guided 5-FU dosing improves the ability to achieve a target AUC within four cycles of therapy. The secondary objective was to demonstrate reduced incidence of 5-FU-related toxicities., Results: At cycles 1 and 4, 27.7% and 46.8% of patients achieved the target AUC (20-25 mg × hour/L), respectively (odds ratio [OR]: 2.20; p = .046). Significantly more patients were within range at cycle 4 compared with a literature rate of 20% (p < .0001). Patients had significantly higher odds of not being underdosed at cycle 4 versus cycle 1 (OR: 2.29; p = .037). The odds of a patient being within range increased by 30% at each subsequent cycle (OR: 1.30; p = .03). Less grade 3/4 mucositis and diarrhea were observed compared with historical data (1.9% vs 16% and 5.6% vs 12%, respectively); however, rates of grade 3/4 neutropenia were similar (33% vs 25%-50%)., Conclusion: PK-guided 5-FU dosing resulted in significantly fewer underdosed patients and less gastrointestinal toxicity and allows for the application of personalized colorectal cancer therapy in the community setting., (©AlphaMed Press.)

A systems biology approach was applied to investigate the mechanisms of chromosomal instability in melanoma cell lines. Chromosomal instability was quantified using array comparative genomic hybridization to identify somatic copy number alterations (deletions and duplications). Primary human melanocytes displayed an average of 8.5 alterations per cell primarily representing known polymorphisms. Melanoma cell lines displayed 25 to 131 alterations per cell, with an average of 68, indicative of chromosomal instability. Copy number alterations included approximately equal numbers of deletions and duplications with greater numbers of hemizygous (-1,+1) alterations than homozygous (-2,+2). Melanoma oncogenes, such as BRAF and MITF, and tumor suppressor genes, such as CDKN2A/B and PTEN, were included in these alterations. Duplications and deletions were functional as there were significant correlations between DNA copy number and mRNA expression for these genes. Spectral karyotype analysis of three lines confirmed extensive chromosomal instability with polyploidy, aneuploidy, deletions, duplications, and chromosome rearrangements. Bioinformatic analysis identified a signature of gene expression that was correlated with chromosomal instability but this signature provided no clues to the mechanisms of instability. The signature failed to generate a significant (P = 0.105) prediction of melanoma progression in a separate dataset. Chromosomal instability was not correlated with elements of DNA damage response (DDR) such as radiosensitivity, nucleotide excision repair, expression of the DDR biomarkers γH2AX and P-CHEK2, nor G1 or G2 checkpoint function. Chromosomal instability in melanoma cell lines appears to influence gene function but it is not simply explained by alterations in the system of DDR., (Copyright © 2014 Wiley Periodicals, Inc.)

Multiple Imputation, Maximum Likelihood and Fully Bayesian methods are the three most commonly used model-based approaches in missing data problems. Although it is easy to show that when the responses are missing at random (MAR), the complete case analysis is unbiased and efficient, the aforementioned methods are still commonly used in practice for this setting. To examine the performance of and relationships between these three methods in this setting, we derive and investigate small sample and asymptotic expressions of the estimates and standard errors, and fully examine how these estimates are related for the three approaches in the linear regression model when the responses are MAR. We show that when the responses are MAR in the linear model, the estimates of the regression coefficients using these three methods are asymptotically equivalent to the complete case estimates under general conditions. One simulation and a real data set from a liver cancer clinical trial are given to compare the properties of these methods when the responses are MAR.

Physical activity (PA) is associated with physiological responses thought to beneficially affect survival after breast cancer diagnosis, yet few studies have considered the entire survivorship experience. Effects of post-diagnosis activity on survival were examined in a cohort of 1,423 women diagnosed with in situ or invasive breast cancer in 1996-1997. Subjects were interviewed soon after diagnosis and again after approximately 5 years to assess breast cancer-related factors, including recreational PA before and after diagnosis. Date and cause of death through 2009 were determined from the National Death Index. Adjusted estimates were obtained using proportional hazards regression and a selection model to account for missing data. Survival was improved among women who were highly active after diagnosis (>9.0 MET h/week) compared to inactive women (0 MET h/week) for all-cause [hazard ratio (HR) (95 % credible interval): 0.33 (0.22, 0.48)] and breast cancer-specific mortality [HR: 0.27 (0.15, 0.46)]. The association of PA with overall mortality appeared stronger in the first 2 years after diagnosis [HR: 0.14 (0.03, 0.44)] compared to 2+ years since diagnosis [HR: 0.37 (0.25, 0.55)]. These findings show that post-diagnosis PA is associated with improved survival among women with breast cancer.

Recently, the Center for Drug Evaluation and Research at the Food and Drug Administration released a guidance that makes recommendations about how to demonstrate that a new antidiabetic therapy to treat type 2 diabetes is not associated with an unacceptable increase in cardiovascular risk. One of the recommendations from the guidance is that phases II and III trials should be appropriately designed and conducted so that a meta-analysis can be performed. In addition, the guidance implies that a sequential meta-analysis strategy could be adopted. That is, the initial meta-analysis could aim at demonstrating the upper bound of a 95% confidence interval (CI) for the estimated hazard ratio to be < 1.8 for the purpose of enabling a new drug application or a biologics license application. Subsequently after the marketing authorization, a final meta-analysis would need to show the upper bound to be < 1.3. In this context, we develop a new Bayesian sequential meta-analysis approach using survival regression models to assess whether the size of a clinical development program is adequate to evaluate a particular safety endpoint. We propose a Bayesian sample size determination methodology for sequential meta-analysis clinical trial design with a focus on controlling the familywise type I error rate and power. We use the partial borrowing power prior to incorporate the historical survival meta-data into the Bayesian design. We examine various properties of the proposed methodology, and simulation-based computational algorithms are developed to generate predictive data at various interim analyses, sample from the posterior distributions, and compute various quantities such as the power and the type I error in the Bayesian sequential meta-analysis trial design. We apply the proposed methodology to the design of a hypothetical antidiabetic drug development program for evaluating cardiovascular risk., (Copyright © 2013 John Wiley & Sons, Ltd.)

Background: Senescent cells, which express p16 (INK4a) , accumulate with aging and contribute to age-related pathology. To understand whether cytotoxic agents promote molecular aging, we measured expression of p16 (INK4a) and other senescence markers in breast cancer patients treated with adjuvant chemotherapy., Methods: Blood and clinical information were prospectively obtained from 33 women with stage I to III breast cancer at four time points: before anthracycline-based chemotherapy, immediately after anthracycline-based chemotherapy, 3 months after anthracycline-based chemotherapy, and 12 months after anthracycline-based chemotherapy. Expression of senescence markers p16 (INK4a) and ARF mRNA was determined using TaqMan quantitative reverse-transcription polymerase chain reaction in CD3(+) T lymphocytes, telomere length was determined by Southern analysis, and senescence-associated cytokines were determined by enzyme-linked immunosorbent assay. Findings were independently assessed in a cross-sectional cohort of 176 breast cancer survivors enrolled a median of 3.4 years after treatment; 39% previously received chemotherapy. All statistical tests were two-sided., Results: In prospectively analyzed patients, expression of p16 (INK4a) and ARF increased immediately after chemotherapy and remained elevated 12 months after treatment. Median increase in log2 p16 (INK4a) was 0.81 (interquartile range = 0.28-1.62; Wilcoxon signed-rank P < .001), or a 75% absolute increase in expression, equivalent to the increase observed over 14.7 years of chronological aging. ARF expression was comparably increased (P < .001). Increased expression of p16 (INK4a) and ARF was associated with dose-dense therapy and hematological toxicity. Expression of two senescence-associated cytokines (VEGFA and MCP1) was durably increased by adjuvant chemotherapy. Telomere length was not affected by chemotherapy. In a cross-sectional cohort, prior chemotherapy exposure was independently associated with a log2-increase in p16 (INK4a) expression of 0.57 (repeated measures model, P < .001), comparable with 10.4 years of chronological aging., Conclusions: Adjuvant chemotherapy for breast cancer is gerontogenic, inducing cellular senescence in vivo, thereby accelerating molecular aging of hematopoietic tissues.

Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures.

The DNA damage response (DDR) coordinates DNA repair with cell cycle checkpoints to ameliorate or mitigate the pathological effects of DNA damage. Automated quantitative analysis (AQUA) and Tissue Studio are commercial technologies that use digitized immunofluorescence microscopy images to quantify antigen expression in defined tissue compartments. Because DDR is commonly activated in cancer and may reflect genetic instability within the lesion, a method to quantify DDR in cancer offers potential diagnostic and/or prognostic value. In this study, both AQUA and Tissue Studio algorithms were used to quantify the DDR in radiation-damaged skin fibroblasts, melanoma cell lines, moles, and primary and metastatic melanomas. Digital image analysis results for three markers of DDR (γH2AX, P-ATM, P-Chk2) correlated with immunoblot data for irradiated fibroblasts, whereas only γH2AX and P-Chk2 correlated with immunoblot data in melanoma cell lines. Melanoma cell lines displayed substantial variation in γH2AX and P-Chk2 expression, and P-Chk2 expression was significantly correlated with radioresistance. Moles, primary melanomas, and melanoma metastases in brain, lung and liver displayed substantial variation in γH2AX expression, similar to that observed in melanoma cell lines. Automated digital analysis of immunofluorescent images stained for DDR biomarkers may be useful for predicting tumor response to radiation and chemotherapy.

In this paper we propose a general class of gamma frailty transformation models for multivariate survival data. The transformation class includes the commonly used proportional hazards and proportional odds models. The proposed class also includes a family of cure rate models. Under an improper prior for the parameters, we establish propriety of the posterior distribution. A novel Gibbs sampling algorithm is developed for sampling from the observed data posterior distribution. A simulation study is conducted to examine the properties of the proposed methodology. An application to a data set from a cord blood transplantation study is also reported.

Background: CYP2D6 is a critical enzyme in the metabolism of tamoxifen and potentially a key determinant in breast cancer outcomes. Our study examined patients' beliefs about how the CYP2D6 genotype would affect their prognoses., Methods: Women enrolled in a pharmacogenomic clinical trial and on tamoxifen for prevention or treatment of breast cancer underwent CYP2D6 genotyping (EM = extensive, IM = intermediate, PM = poor metabolizing alleles). The informed consent said that the purpose of the trial was to examine effects of dose adjustment based on genotype, but that clinical benefits were uncertain. Our embedded sub-study surveyed 320 patients prior to receiving their genotypes. We experimentally manipulated 6 vignettes to describe hypothetical tamoxifen treatment (no or yes) and hypothetical genotype (EM, IM or PM). For each vignette, women gave their perceived recurrence risk (RR; 0-100%)., Results: Women believed that genotype would not affect their RR if they did not take tamoxifen (p = 0.06). However, women believed that if prescribed tamoxifen, genotype would affect their RR (22% if EM, 30% if IM and 40% if PM, p < 0.001)., Conclusion: Women believed that extensive tamoxifen metabolizers had better prognoses, despite study materials stating uncertainty about any benefit. The rapidly changing nature of genomic science calls for caution when communicating clinical utility.

We propose a general novel class of joint models to analyze recurrent events that has a wide variety of applications. The focus in this article is to model the bleeding and transfusion events in myelodysplastic syndrome (MDS) studies, where patients may die or withdraw from the study early due to adverse events or other reasons, such as consent withdrawal or required alternative therapy during the study. The proposed model accommodates multiple recurrent events and multivariate informative censoring through a shared random-effects model. The random-effects model captures both within-subject and within-event dependence simultaneously. We construct the likelihood function for the semiparametric joint model and develop an expectation-maximization (EM) algorithm for inference. The computational burden does not increase with the number of types of recurrent events. We utilize the MDS clinical trial data to illustrate our proposed methodology. We also conduct a number of simulations to examine the performance of the proposed model.

It has been widely recognized that interim analyses of accumulating data in a clinical trial can inflate type I error. Different methods, from group sequential boundaries to flexible alpha spending functions, have been developed to control the overall type I error at prespecified level. These methods mainly apply to testing the same endpoint in multiple interim analyses. In this article, we consider a group sequential design with preplanned endpoint switching after unblinded interim analyses. We extend the alpha spending function method to group sequential stopping boundaries when the parameters can be different between interim, or between interim and final analyses.

This study compared biological responses of normal human fibroblasts (NHF1) to three sources of ultraviolet radiation (UVR), emitting UVC wavelengths, UVB wavelengths, or a combination of UVA and UVB (solar simulator; emission spectrum, 94.3% UVA and 5.7% UVB). The endpoints measured were cytotoxicity, intra-S checkpoint activation, inhibition of DNA replication and mutagenicity. Results show that the magnitude of each response to the indicated radiation sources was best predicted by the density of DNA cyclobutane pyrimidine dimers (CPD). The density of 6-4 pyrimidine-pyrimidone photoproducts was highest in DNA from UVC-irradiated cells (14% of CPD) as compared to those exposed to UVB (11%) or UVA-UVB (7%). The solar simulator source, under the experimental conditions described here, did not induce the formation of 8-oxo-7,8-dihydroguanine in NHF1 above background levels. Taken together, these results suggest that CPD play a dominant role in DNA damage responses and highlight the importance of using endogenous biomarkers to compare and report biological effects induced by different sources of UVR., (© 2013 The American Society of Photobiology.)

In DAE (DNA After Enrichment)-seq experiments, genomic regions related with certain biological processes are enriched/isolated by an assay and are then sequenced on a high-throughput sequencing platform to determine their genomic positions. Statistical analysis of DAE-seq data aims to detect genomic regions with significant aggregations of isolated DNA fragments ("enriched regions") versus all the other regions ("background"). However, many confounding factors may influence DAE-seq signals. In addition, the signals in adjacent genomic regions may exhibit strong correlations, which invalidate the independence assumption employed by many existing methods. To mitigate these issues, we develop a novel Autoregressive Hidden Markov Model (AR-HMM) to account for covariates effects and violations of the independence assumption. We demonstrate that our AR-HMM leads to improved performance in identifying enriched regions in both simulated and real datasets, especially in those in epigenetic datasets with broader regions of DAE-seq signal enrichment. We also introduce a variable selection procedure in the context of the HMM/AR-HMM where the observations are not independent and the mean value of each state-specific emission distribution is modeled by some covariates. We study the theoretical properties of this variable selection procedure and demonstrate its efficacy in simulated and real DAE-seq data. In summary, we develop several practical approaches for DAE-seq data analysis that are also applicable to more general problems in statistics.

The frailty model is increasingly popular for analyzing multivariate time-to-event data. The most common model is the shared frailty model. Although study design consideration is as important as analysis strategies, sample size determination methodology in studies with multivariate time-to-event data is greatly lacking in the literature. In this article, we develop a sample size determination method for the shared frailty model to investigate the treatment effect on multivariate event times. We analyzed the data using both a parametric model and a piecewise model with unknown baseline hazard, and compare the empirical power with the calculated power. Last, we discuss the formula for testing the treatment effect on recurrent events.

We propose a Bayesian generalized low rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing is developed to identify significant covariates on high-dimensional responses. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of GLRR and its comparison with several competing approaches. We apply GLRR to investigate the impact of 1,071 SNPs on top 40 genes reported by AlzGene database on the volumes of 93 regions of interest (ROI) obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI).