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.

Bayesian Modeling and Inference for Nonignorably Missing Longitudinal Binary Response Data with Applications to HIV Prevention Trials

Missing data are frequently encountered in longitudinal clinical trials. To better monitor and understand the progress over time, one must handle the missing data appropriately and examine whether the missing data mechanism is ignorable or nonignorable. In this article, we develop a new probit model for longitudinal binary response data. It resolves a challenging issue for estimating the variance of the random effects, and substantially improves the convergence and mixing of the Gibbs sampling algorithm. We show that when improper uniform priors are specified for the regression coefficients of the joint multinomial model via a sequence of one-dimensional conditional distributions for the missing data indicators under nonignorable missingness, the joint posterior distribution is improper. A variation of Jeffreys prior is thus established as a remedy for the improper posterior distribution. In addition, an efficient Gibbs sampling algorithm is developed using a collapsing technique. Two model assessment criteria, the deviance information criterion (DIC) and the logarithm of the pseudomarginal likelihood (LPML), are used to guide the choices of prior specifications and to compare the models under different missing data mechanisms. We report on extensive simulations conducted to investigate the empirical performance of the proposed methods. The proposed methodology is further illustrated using data from an HIV prevention clinical trial.

Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness

Many psychological concepts are unobserved and usually represented as latent factors apprehended through multiple observed indicators. When multiple-subject multivariate time series data are available, dynamic factor analysis models with random effects offer one way of modeling patterns of within- and between-person variations by combining factor analysis and time series analysis at the factor level. Using the Dirichlet process (DP) as a nonparametric prior for individual-specific time series parameters further allows the distributional forms of these parameters to deviate from commonly imposed (e.g., normal or other symmetric) functional forms, arising as a result of these parameters' restricted ranges. Given the complexity of such models, a thorough sensitivity analysis is critical but computationally prohibitive. We propose a Bayesian local influence method that allows for simultaneous sensitivity analysis of multiple modeling components within a single fitting of the model of choice. Five illustrations and an empirical example are provided to demonstrate the utility of the proposed approach in facilitating the detection of outlying cases and common sources of misspecification in dynamic factor analysis models, as well as identification of modeling components that are sensitive to changes in the DP prior specification.

Comprehensive assessment of cytochromes P450 and transporter genetics with endoxifen concentration during tamoxifen treatment

OBJECTIVES

Tamoxifen bioactivation to endoxifen is mediated primarily by CYP2D6; however, considerable variability remains unexplained. Our aim was to perform a comprehensive assessment of the effect of genetic variation in tamoxifen-relevant enzymes and transporters on steady-state endoxifen concentrations.

PATIENTS AND METHODS

Comprehensive genotyping of CYP enzymes and transporters was performed using the iPLEX ADME PGx Pro Panel in 302 tamoxifen-treated breast cancer patients. Predicted activity phenotype for 19 enzymes and transporters were analyzed for univariate association with endoxifen concentration, and then adjusted for CYP2D6 and clinical covariates.

RESULTS

In univariate analysis, higher activity of CYP2C8 (regression β=0.22, P=0.020) and CYP2C9 (β=0.20, P=0.04), lower body weight (β=-0.014, P<0.0001), and endoxifen measurement during winter (each β<-0.39, P=0.002) were associated with higher endoxifen concentrations. After adjustment for the CYP2D6 diplotype, weight, and season, CYP2C9 remained significantly associated with higher concentrations (P=0.02), but only increased the overall model R by 1.3%.

CONCLUSION

Our results further support a minor contribution of CYP2C9 genetic variability toward steady-state endoxifen concentrations. Integration of clinician and genetic variables into individualized tamoxifen dosing algorithms would marginally improve their accuracy and potentially enhance tamoxifen treatment outcomes.

HFPRM: Hierarchical Functional Principal Regression Model for Diffusion Tensor Image Bundle Statistics

Diffusion-weighted magnetic resonance imaging (MRI) provides a unique approach to understand the geometric structure of brain fiber bundles and to delineate the diffusion properties across subjects and time. It can be used to identify structural connectivity abnormalities and helps to diagnose brain-related disorders. The aim of this paper is to develop a novel, robust, and efficient dimensional reduction and regression framework, called hierarchical functional principal regression model (HFPRM), to effectively correlate high-dimensional fiber bundle statistics with a set of predictors of interest, such as age, diagnosis status, and genetic markers. The three key novelties of HFPRM include the simultaneous analysis of a large number of fiber bundles, the disentanglement of global and individual latent factors that characterizes between-tract correlation patterns, and a bi-level analysis on the predictor effects. Simulations are conducted to evaluate the finite sample performance of HFPRM. We have also applied HFPRM to a genome-wide association study to explore important genetic variants in neonatal white matter development.

FLCRM: Functional linear cox regression model

We consider a functional linear Cox regression model for characterizing the association between time-to-event data and a set of functional and scalar predictors. The functional linear Cox regression model incorporates a functional principal component analysis for modeling the functional predictors and a high-dimensional Cox regression model to characterize the joint effects of both functional and scalar predictors on the time-to-event data. We develop an algorithm to calculate the maximum approximate partial likelihood estimates of unknown finite and infinite dimensional parameters. We also systematically investigate the rate of convergence of the maximum approximate partial likelihood estimates and a score test statistic for testing the nullity of the slope function associated with the functional predictors. We demonstrate our estimation and testing procedures by using simulations and the analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Our real data analyses show that high-dimensional hippocampus surface data may be an important marker for predicting time to conversion to Alzheimer's disease. Data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu).

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. Copyright © 2017 John Wiley & Sons, Ltd.

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. Copyright © 2017 John Wiley & Sons, Ltd.

Single-nucleotide polymorphisms are associated with cognitive decline at Alzheimer's disease conversion within mild cognitive impairment patients

INTRODUCTION

The growing public threat of Alzheimer's disease (AD) has raised the urgency to quantify the degree of cognitive decline during the conversion process of mild cognitive impairment (MCI) to AD and its underlying genetic pathway. The aim of this article was to test genetic common variants associated with accelerated cognitive decline after the conversion of MCI to AD.

METHODS

In 583 subjects with MCI enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI; ADNI-1, ADNI-Go, and ADNI-2), 245 MCI participants converted to AD at follow-up. We tested the interaction effects between individual single-nucleotide polymorphisms and AD diagnosis trajectory on the longitudinal Alzheimer's Disease Assessment Scale-Cognition scores.

RESULTS

Our findings reveal six genes, including BDH1, ST6GAL1, RAB20, PDS5B, ADARB2, and SPSB1, which are directly or indirectly related to MCI conversion to AD.

DISCUSSION

This genome-wide association study sheds light on a genetic mechanism of longitudinal cognitive changes during the transition period from MCI to AD.

Patterns of cell cycle checkpoint deregulation associated with intrinsic molecular subtypes of human breast cancer cells

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.

Statistical design of noninferiority multiple region clinical trials to assess global and consistent treatment effects

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.

Regression Models on Riemannian Symmetric Spaces

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.

Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials

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.

On inference of control-based imputation for analysis of repeated binary outcomes with missing data

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.

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies

To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations show that the L2R2 model outperforms several other competing methods. We apply the L2R2 model to investigate the effect of single nucleotide polymorphisms (SNPs) on the top 10 and top 40 previously reported Alzheimer disease-associated genes. We also identify associations between the interactions of these SNPs with patient age and the tissue volumes of 93 regions of interest from patients' brain images obtained from the Alzheimer's Disease Neuroimaging Initiative.

SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging

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 L₁ penalty function is introduced to ensure the sparse solutions of ODFs, while a scaled L₁ weighted estimator is calculated to correct the bias introduced by the L₁ 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.

Homology cluster differential expression analysis for interspecies mRNA-Seq experiments

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.

Protective estimator for linear regression with nonignorably missing Gaussian outcomes

We propose a method for estimating the regression parameters in a linear regression model for Gaussian data when the outcome variable is missing for some subjects and missingness is thought to be nonignorable. Throughout, we assume that missingness is restricted to the outcome variable and that the covariates are fully observed. Although maximum likelihood estimation of the regression parameters is possible once joint models for the outcome variable and the nonignorable missing data mechanism have been specified, these models are fundamentally nonidentifiable unless unverifiable modeling assumptions are imposed. In this paper, rather than explicitly modeling the nonignorable missingness mechanism, we consider the use of a ‘protective’ estimator of the regression parameters (Brown, 1990). To implement the proposed method, it is necessary to assume that the outcome variable and one of the covariates have an approximate bivariate normal distribution, conditional on the remaining covariates. In addition, it is assumed that the missing data mechanism is conditionally independent of this covariate, given the outcome variable and the remaining covariates; the latter is referred to as the ‘protective’ assumption. A method of moments approach is used to obtain the protective estimator of the regression parameters; the jackknife (Quenouille, 1956) is used to estimate the variance. The method is illustrated using data on the persistence of maternal smoking from the Six Cities Study of the health effects of air pollution (Ware et al., 1984). The results of a simulation study are presented that examine the magnitude of any finite sample bias.

JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data

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.

An Automated High-throughput Array Microscope for Cancer Cell Mechanics

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.

Tamoxifen Dose Escalation in Patients With Diminished CYP2D6 Activity Normalizes Endoxifen Concentrations Without Increasing Toxicity

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.

Diagnostic Measures for the Cox Regression Model with Missing Covariates

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.