Abstract P5-12-06: Comprehensive assessment of the effect of genetic polymorphisms in drug metabolizing enzymes and transporters on tamoxifen activation to endoxifen

Background: Tamoxifen is the most commonly prescribed hormonal drug for estrogen receptor positive breast cancer treatment. Tamoxifen itself has weak anti-estrogenic activity, but is bioactivated to the more potent inhibitor endoxifen. Recent data suggest inferior efficacy of tamoxifen treatment in patients who have low systemic endoxifen concentration. Genetic variability in drug metabolizing enzymes and transporters, particularly CYP2D6, are known to effect serum endoxifen concentration. The association of CYP2D6 genotype and endoxifen concentration is well established; however, there is a paucity of data regarding the effects of genetic variants in other drug metabolizing enzymes and transporters on endoxifen concentrations. The objective of our study was to comprehensively screen known, functionally consequential polymorphisms and copy number variations in genes of interest to detect additional pharmacogenetic predictors of endoxifen concentration during tamoxifen treatment.

Methods: This analysis includes patients prospectively enrolled on the Lineberger Comprehensive Cancer Center 0801 trial. Patients had received tamoxifen for a minimum of 4 months prior to enrollment and were not concurrently taking strong or moderate CYP2D6 inhibitors. Samples were collected at enrollment for measurement of steady state endoxifen level and collection of germline DNA. Genotyping was performed for CYP2D6 using the Amplichip® CYP450 test (Roche Diagnostics) and for other candidate genes (CYP2C9, CYP3A4, CYP3A5, ABCB1, SLCO1B1, SULT1A1, SULT1A2, and UGT2B7) using the iPLEX® ADME PGx Pro Panel (Agena Bioscience). Activity phenotype for each gene was inferred from genotype data based on known activity of variant alleles or copy numbers. Metabolite concentrations were measured via LC/MS-MS assay at Indiana University and square root transformed prior to analysis to improve normality. Linear regression models were used to evaluate the association of each gene individually with endoxifen concentration, assuming an additive pharmacogenetic effect, after adjustment for CYP2D6 phenotype (EM/UM, IM or PM).

Results: 304 Patients with steady-state endoxifen concentration and successful genotyping were included in the analysis. After transformation and adjustment, endoxifen concentration was significantly associated with carrying low-activity CYP2C9 variant alleles (*2, *3, *5, *6, *8, *11, *12) (p=0.016). Predicted endoxifen concentration based on CYP2C9 and CYP2D6 genotype can be found in.

Predicted endoxifen concentration (ng/mL) based on CYP2C9 and CYP2D6 Phenotype CYP2D6 EM/UMCYP2D6 IMCYP2D6 PMCYP2C9 WT/WT9.716.553.41CYP2C9 WT/Var8.375.482.63CYP2C9 Var/Var7.134.481.96Abbreviations: WT= wild-type, Var=Variant allele for CYP2C9

Phenotype activity of other enzymes and transporters was not associated with endoxifen concentration (all p>0.05).

Conclusions: Polymorphisms in CYP2C9 and CYP2D6, but not other enzymes or transporters, contribute to variation in endoxifen exposure. If endoxifen exposure is validated to predict tamoxifen efficacy, personalized tamoxifen dosing algorithms should include CYP2C9, in addition to CYP2D6 and clinical factors, to improve efficacy and minimize side effects.

Citation Format: Hertz DL, Danko W, Deal A, Walko CM, Flockhart DA, McLeod HL, Ibrahim JG, Irvin Jr WJ. Comprehensive assessment of the effect of genetic polymorphisms in drug metabolizing enzymes and transporters on tamoxifen activation to endoxifen. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-12-06.

A counterfactual p-value approach for benefit-risk assessment in clinical trials

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.

A STATISTICAL MODEL TO ASSESS (ALLELE-SPECIFIC) ASSOCIATIONS BETWEEN GENE EXPRESSION AND EPIGENETIC FEATURES USING SEQUENCING DATA

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.

BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE

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.

Effective intra-S checkpoint responses to UVC in primary human melanocytes and melanoma cell lines

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.

The power prior: theory and applications

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.

In vivo assessment of the metabolic activity of CYP2D6 diplotypes and alleles

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.

The use of Bayesian hierarchical models for adaptive randomization in biomarker-driven phase II studies

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.

Bayesian Inference for Multivariate Meta-regression with a Partially Observed Within-Study Sample Covariance Matrix

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.

Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group

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.

Abstract P1-03-02: CYP2D6 intermediate metabolizers includes patient groups with distinct metabolic activity

Background: Tamoxifen is a selective estrogen receptor modulator that is the most commonly used and cost effective hormonal agent for pre-menopausal hormone-receptor positive breast cancer patients. CYP2D6 activity phenotype, which is classified by genotype, predicts the extent of metabolic activation of tamoxifen to endoxifen. We previously reported that increasing the daily dose to 40 mg/day in intermediate metabolizers (IMs), but not poor metabolizers (PMs), achieves target endoxifen concentrations, defined as that of extensive metabolizers (EMs) on 20 mg/day. There was substantial endoxifen variability in the IM phenotype group, which is composed of several discrete diplophenotypes (EM/IM, EM/PM, IM/IM, IM/PM). We enrolled a second, larger cohort of patients in order to determine whether these diplophenotypes should be combined into a single IM phenotype or segregated.

Methods: 380 patients on tamoxifen ≥ 4 months and not on potent CYP2D6 inhibiting medications enrolled in Lineberger Comprehensive Cancer Center (LCCC) trial 0801. Genotyping was performed using the Amplichip® CYP450 test (Roche Diagnostics) for CYP2D6, followed by systematic assignment of phenotype based on diplophenotype. Tamoxifen was increased from 20 to 40 mg/day in PMs and IMs. Endoxifen concentrations in IM diplophenotypes were compared with EM/EMs and PM/PMs at baseline and at 4 months (after dose increase in patients with IM and PM phenotypes).

Results: After exclusion of UM patients and patients missing endoxifen data at baseline and/or 4 months, 295 patients were included in this analysis. At baseline the EM/IM patients had similar endoxifen level to the EM/EM patients while the IM/IM and IM/PM patients had similar levels to the PM/PMs. After 4 months on 40 mg/day the endoxifen concentrations in EM/IM patients were significantly greater than EM/EMs; EM/PM and IM/IM patients were similar to EM/EMs; but IM/PM patients remained significantly lower than EM/EMs and similar to PM/PMs (See Table 1 for results).

Conclusions: The large group of patients currently defined as CYP2D6 intermediate metabolizers is comprised of four distinct CYP2D6 diplophentoypes. The most metabolically active diplophenotype (EM/IM) are very similar to EM/EMs while the least active diplophenotype (IM/PM) are similar to PM/PMs. A more accurate CYP2D6 activity classification system may be necessary if genetic association testing and genotype-guided therapy are pursued.

Endoxifen Level at Baseline and 4 Months by CYP2D6 Diplophenotype  Baseline Endoxifen  4-Month Endoxifen  DiplophenotypenMedian (SD)P-val vs. EM/EMP-val vs. PM/PMMedian (SD)P-val vs. EM/EMP-val vs. PM/PMEM/EM11038.67 (6.01)NAp=0.00018.23 (5.09)NAp=0.007EM/IM2568.02 (4.75)p=0.09p=0.00213.11 (9.38)p&lt;0.0001p&lt;0.0001EM/PM2745.72 (4.45)p=0.0001p=0.028.91 (5.28)p=0.42p=0.003IM/IM2174.29 (4.10)p=0.001p=0.266.52 (5.53)p=0.27p=0.24IM/PM2323.90 (3.17)p&lt;0.0001p=0.485.82 (3.47)p=0.0009p=0.77PM/PM3133.33 (2.89)p=0.0001NA6.08 (2.57)p=0.007NA1Diplophenotype classified as extensive metabolizer phenotype, continued on 20 mg/day. 2Diplophenotypes classified as intermediate metabolizer phenotype, changed to 40 mg/day. 3Diplophenotype classified as poor metabolizer phenotype, changed to 40 mg/day.

Citation Format: Daniel L Hertz, Anna C Snavely, Howard L McLeod, Christine M Walko, Joseph G Ibrahim, Steven Anderson, Karen E Weck, Peter Rubin, Oludamilola Olajide, Susan Moore, Rachel Raab, Daniel R Carrizosa, Steven Corso, Gary Schwartz, Jeffrey M Peppercorn, James P Evans, Zeruesenay Desta, David A Flockhart, Lisa A Carey, William J Irvin Jr. CYP2D6 intermediate metabolizers includes patient groups with distinct metabolic activity [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P1-03-02.

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression

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 T² 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.

Quantifying the average of the time-varying hazard ratio via a class of transformations

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.

Hypothesis testing for two-stage designs with over or under enrollment

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.

Assessing temporal agreement between central and local progression-free survival times

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.

Functional-mixed effects models for candidate genetic mapping in imaging genetic studies

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.

DNA damage checkpoint responses in the S phase of synchronized diploid human fibroblasts

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.

Multivariate recurrent events in the presence of multivariate informative censoring with applications to bleeding and transfusion events in myelodysplastic syndrome

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.

Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates

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.

Bayesian probability of success for clinical trials using historical data

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.

Bayesian Case-deletion Model Complexity and Information Criterion

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.

A community-based multicenter trial of pharmacokinetically guided 5-fluorouracil dosing for personalized colorectal cancer therapy

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.