Design and modeling for drug combination experiments with order effects

Combinations of drugs are now ubiquitous in treating complex diseases such as cancer and HIV due to their potential for enhanced efficacy and reduced side effects. The traditional combination experiments of drugs focus primarily on the dose effects of the constituent drugs. However, with the doses of drugs remaining unchanged, different sequences of drug administration may also affect the efficacy endpoint. Such drug effects shall be called as order effects. The common order-effect linear models are usually inadequate for analyzing combination experiments due to the nonlinear relationships and complex interactions among drugs. In this article, we propose a random field model for order-effect modeling. This model is flexible, allowing nonlinearities, and interaction effects to be incorporated with a small number of model parameters. Moreover, we propose a subtle experimental design that will collect good quality data for modeling the order effects of drugs with a reasonable run size. A real-data analysis and simulation studies are given to demonstrate that the proposed design and model are effective in predicting the optimal drug sequences in administration.

Interpreting the CPASS Trial: Do Not Shift Motor Therapy to the Subacute Phase

The Critical Periods After Stroke Study (CPASS, n = 72) showed that, compared to controls, an additional 20 hours of intensive upper limb therapy led to variable gains on the Action Research Arm Test depending on when therapy was started post-stroke: the subacute group (2-3 months) improved beyond the minimal clinically important difference and the acute group (0-1 month) showed smaller but statistically significant improvement, but the chronic group (6-9 months) did not demonstrate improvement that reached significance. Some have misinterpreted CPASS results to indicate that all inpatient motor therapy should be shifted to outpatient therapy delivered 2 to 3 months post-stroke. Instead, however, CPASS argues for a large dose of motor therapy delivered continuously and cumulatively during the acute and subacute phases. When interpreting trials like CPASS, one must consider the substantial dose of early usual customary care (UCC) motor therapy that all participants received. CPASS participants averaged 27.9 hours of UCC occupational therapy (OT) during the first 2 months and 9.8 hours of UCC OT during the third and fourth months post-stroke. Any recovery experienced would therefore result not just from CPASS intensive motor therapy but the combined effects of experimental therapy plus UCC. Statistical limitations also did not allow direct comparisons of the acute and subacute group outcomes in CPASS. Instead of shifting inpatient therapy hours to the subacute phase, CPASS argues for preserving inpatient UCC. We also recommend conducting multi-site dosing trials to determine whether additional intensive motor therapy delivered in the first 2 to 3 months following inpatient rehabilitation can further improve outcomes.

Highly robust causal semiparametric U-statistic with applications in biomedical studies

With our increased ability to capture large data, causal inference has received renewed attention and is playing an ever-important role in biomedicine and economics. However, one major methodological hurdle is that existing methods rely on many unverifiable model assumptions. Thus robust modeling is a critically important approach complementary to sensitivity analysis, where it compares results under various model assumptions. The more robust a method is with respect to model assumptions, the more worthy it is. The doubly robust estimator (DRE) is a significant advance in this direction. However, in practice, many outcome measures are functionals of multiple distributions, and so are the associated estimands, which can only be estimated via U-statistics. Thus most existing DREs do not apply. This article proposes a broad class of highly robust U-statistic estimators (HREs), which use semiparametric specifications for both the propensity score and outcome models in constructing the U-statistic. Thus, the HRE is more robust than the existing DREs. We derive comprehensive asymptotic properties of the proposed estimators and perform extensive simulation studies to evaluate their finite sample performance and compare them with the corresponding parametric U-statistics and the naive estimators, which show significant advantages. Then we apply the method to analyze a clinical trial from the AIDS Clinical Trials Group.

Robust estimates of regional treatment effects in multiregional randomized clinical trials with ordinal responses

Global clinical trials involving multiple regions are common in current drug development processes. Determining the regional treatment effects of a new therapy over an existing therapy is important to both the sponsors and the regulatory agencies in the regions. Existing methods are mainly for continuous primary endpoints and use subjectively specified models, which may deviate from the true model. Here, we consider trials that have ordinal responses as the primary endpoint. This article extends the recently developed robust semiparametric ordinal regression model to estimate regional treatment effects, in which the regression coefficients and regional effects are modeled parametrically for ease of interpretation, and the regression link function is specified nonparametrically for robustness. The model parameters are estimated by semiparametric maximum likelihood estimation, and the null hypothesis of no regional effect is tested by the Wald test. Simulation studies are conducted to evaluate the performance of the proposed method and compare it with the commonly used parametric model. The results of the former show an improved overall performance over the latter. In particular, the model yields much higher precision in estimation and prediction than the fixed-link model. This result is especially appealing since our interest is to estimate the treatment effect more efficiently and the estimand is of particular interest in multiregional clinical trials. We then apply the method by analyzing real multiregional clinical trials with ordinal responses as their primary endpoint.

Identification of subgroups via partial linear regression modeling approach

In clinical trials, treatment effects often vary from subject to subject. Some subjects may benefit more than others from a specific treatment. One of the aims of subgroup analysis is to identify if there are subgroups of subjects with differential treatment effects. As in standard analysis, we first test if subgroups with differential treatment effects exist; if they do, we classify the subjects into different subgroups based on their covariate profiles; otherwise, we conclude no subgroups have differential treatment effects in this population. Existing methods utilize regression models, particularly linear models, for such analysis. However, in practice, not all effects of covariates on responses are linear. To address this issue, the article proposes a more flexible model, the partial linear model with a nonlinear monotone function to describe some specific effects of covariates and with a linear component to describe the effects of other covariates, develops model-fitting algorithm and derives model asymptotics. We then utilize the Wald statistic to test the existence of subgroups and the Neyman-Pearson rule to classify subjects into the subgroups. Simulation studies are conducted to evaluate the finite sample performance of the proposed method by comparing it with the commonly used linear models. Finally, we apply the methods to analyzing a real clinical trial.

Set-regression with applications to subgroup analysis

Regression is a commonly used statistical model. It is the conditional mean of the response given covariates μ ( x ) = E ( Y | X = x ) . However, in some practical problems, the interest is the conditional mean of the response given the covariates belonging to some set A. Notably, in precision medicine and subgroup analysis in clinical trials, the aim is to identify subjects who benefit the most from the treatment, or identify an optimal set in the covariate space which manifests treatment favoritism if a subject's covariates fall in this set and the subject is classified to the favorable treatment subgroup. Existing methods for subgroup analysis achieve this indirectly by using classical regression. This motivates us to develop a new type of regression: set-regression, defined as μ ( A ) = E ( Y | X ∈ A ) which directly addresses the subgroup analysis problem. This extends not only the classical regression model but also improves recursive partitioning and support vector machine approaches, and is particularly suitable for objectives involving optimization of the regression over sets, such as subgroup analysis. We show that the new versatile set-regression identifies the subgroup with increased accuracy. It is easy to use. Simulation studies also show superior performance of the proposed method in finite samples.

486 Spontaneous haemoperitoneum due to acute hemorrhagic pancreatitis in a child: A Case report

Introduction

Hemoperitoneum resulting from acute pancreatitis (AP) is rare. It is more commonly associated with chronic pancreatitis. Seldom, it has been associated with paediatric population. Here we report a case of spontaneous hemoperitoneum due to acute hemorrhagic pancreatitis in a child presented with the clinical features suggestive of acute appendicitis.

Presentation of the case

A- 9-year-old girl presented with abdominal pain and clinical features consistent with perforated appendicitis, underwent diagnostic laparoscopy. This revealed hemoperitoneum due to acute hemorrhagic pancreatitis with fat saponification in the omentum around the splenic flexure. She was stable postoperatively and was transferred to a tertiary paediatric unit for further management.

Discussion

The incidence of paediatric AP is increasing at 1/10,000 children per year. The definition of paediatric AP is based on the Atlanta criteria in adults. Biliary condition, systemic illness, and medications remain the main causes of AP in children. Intravenous fluid therapy with crystalloids remains the mainstay of treatment.

Conclusions

A high index of suspicion is required to reach the diagnosis as symptoms are commonly comprised of abdominal pain, irritability, nausea, vomiting, and epigastric pain. USS is the investigation of choice. Majority of the patient recovers completely with a recurrence reported only on 15-35% of the cases.

Key Statement

Inclusion of amylase or lipase to be considered in the routine workup if there is a suspicion of an alternate diagnosis. Diagnostic laparoscopy remains a viable option for patients presented with features of peritonism to establish a diagnosis.

Robust estimates of regional treatment effects in multiregional randomized clinical trials with semiparametric logistic model

In multiregional randomized clinical trials (MRCTs), determining the regional treatment effect of a new treatment over an existing one is important to both the sponsor and related regulatory agencies. Also of particular interest is to test the null hypothesis that the treatment benefit is the same among all the regions. Existing methods are mainly for continuous endpoint and use parametric models, which are not robust. MRCTs are known for facing increased variation and heterogeneity and a robust model for its design and analysis would be desirable. We consider clinical trials with a binary primary endpoint and propose a robust semiparametric logistic model which has a known parametric and an unknown nonparametric component. The parametric component represents our prior knowledge about the model, and the nonparametric part reflects uncertainty. Compared to the classic logistic model for this problem, the proposed model has the following advantages: robust to model assumption, more flexible and accurate to model the relationship between the response and covariates, and possibly more accurate parameter estimates. The model parameters are estimated by profile maximum likelihood approach, and the null hypothesis of regional treatment difference being the same is tested by the profile likelihood ratio statistic. Asymptotic properties of the estimates are derived. Simulation studies are conducted to evaluate the performance of the proposed model, which demonstrated clear advantages over the classic logistic model. The method is then applied to analyzing a real MRCT.

Antibacterial activity of Hibiscus rosa-sinensis L. red flower against antibiotic-resistant strains of Helicobacter pylori and identification of the flower constituents

Utilization of plant resources for treatment of Helicobacter pylori infections is one of the appealing approaches as rapid emergence of antibiotic-resistant strains is occurring throughout the world. Ethanol extract and its fractions from Hibiscus rosa-sinensis red flower were assessed for antibacterial and urease inhibitory activities towards forty-three clinical strains and two reference strains of H. pylori. The ethyl acetate fraction exhibited the most potent bacteriostatic activity with minimum inhibitory concentrations (MICs) of 0.2-0.25 mg/mL and minimum bactericidal concentrations (MBCs) of 1.25-1.5 mg/mL against all test strains, including forty-three strains resistant to one to four antibiotics, azithromycin (MICs, 8-256 µg/mL), erythromycin (MICs, 8-128 µg/mL), levofloxacin (MICs, 8-256 µg/mL), and/or metronidazole (MICs, 8-256 µg/mL). The fraction had similar antibacterial activities toward these test strains suggesting the preparation and the antibiotics do not have a common mechanism of anti-H. pylori activity. The fraction also had stronger effects on biofilm formation, morphological conversion, and urease activity of H. pylori than the other fractions and the ethanol extract. These flower preparations were non-toxic to three human cell lines, and nine compounds were also isolated and identified from the ethyl acetate fraction. In vivo research needs to be conducted to confirm the potential usefulness of H. rosa-sinensis flower and its constituents for effective prevention and treatment of H. pylori disease.

Targeted design for adaptive clinical trials via semiparametric model

Precision medicine approach that assigns treatment according to an individual's personal (including molecular) profile is revolutionizing health care. Existing statistical methods for clinical trial design typically assume a known model to estimate characteristics of treatment outcomes, which may yield biased results if the true model deviates far from the assumed one. This article aims to achieve model robustness in a phase II multi-stage adaptive clinical trial design. We propose and study a semiparametric regression mixture model in which the mixing proportions are specified according to the subjects' profiles, and each sub-group distribution is only assumed to be unimodal for robustness. The regression parameters and the error density functions are estimated by semiparametric maximum likelihood and isotonic regression estimators. The asymptotic properties of the estimates are studied. Simulation studies are conducted to evaluate the performance of the method after a real data analysis.

Integrative analysis with a system of semiparametric projection non-linear regression models

In integrative analysis parametric or nonparametric methods are often used. The former is easier for interpretation but not robust, while the latter is robust but not easy to interpret the relationships among the different types of variables. To combine the advantages of both methods and for flexibility, here a system of semiparametric projection non-linear regression models is proposed for the integrative analysis, to model the innate coordinate structure of these different types of data, and a diagnostic tool is constructed to classify new subjects to the case or control group. Simulation studies are conducted to evaluate the performance of the proposed method, and shows promising results. Then the method is applied to analyze a real omics data from The Cancer Genome Atlas study, compared the results with those from the similarity network fusion, another integrative analysis method, and results from our method are more reasonable.

Effect of Laser Moxibustion for Knee Osteoarthritis: A Multisite, Double-blind Randomized Controlled Trial

OBJECTIVE

To examine the effects of laser moxibustion on pain and function in patients with knee osteoarthritis (OA).

METHODS

A double-blind randomized clinical trial (4-week treatment, 20-week follow-up) was conducted. A total of 392 symptomatic knee OA patients with moderate to severe clinically significant knee pain were randomly assigned to laser treatment or sham laser control group (1:1). Twelve sessions of laser moxibustion or sham laser treatments on the acupuncture points at the affected knee(s) were performed 3 times a week for 4 weeks. The primary outcome measurement was change in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score from baseline to Week 4.

RESULTS

Among the 392 randomized participants, 364 (92.86%) completed the trial. The median WOMAC pain score decreased significantly at Week 4 in the active group than in the sham group (2.1, 95% CI 1.6-2.6, P < 0.01). At Week 24, compared to the sham laser, active laser treatment resulted in significant pain reduction and function improvement (3.0, 95% CI 2.5-3.6, P < 0.01, and 14.8, 95% CI 11.9-17.6, P < 0.01, respectively). The physical component of the quality of life significantly improved in the active group vs the sham controls at Week 4 (3.2, 95% CI 1.3-5.0, P = 0.001) up to Week 24 (5.1, 95% CI 3.3-7.0, P < 0.001). No serious adverse effects were reported.

CONCLUSION

Laser moxibustion resulted in statistically and clinically significant pain reduction and function improvement following a 4-week treatment in patients with knee OA.

Prospective evaluation of the cardiac safety of HER2-targeted therapies in patients with HER2-positive breast cancer and compromised heart function: the SAFE-HEaRt study

Purpose

HER2-targeted therapies have substantially improved the outcome of patients with breast cancer, however, they can be associated with cardiac toxicity. Guidelines recommend holding HER2-targeted therapies until resolution of cardiac dysfunction. SAFE-HEaRt is the first trial that prospectively tests whether these therapies can be safely administered without interruptions in patients with cardiac dysfunction.

Methods

Patients with stage I–IV HER2-positive breast cancer candidates for trastuzumab, pertuzumab or ado-trastuzumab emtansine (TDM-1), with left ventricular ejection fraction (LVEF) 40–49% and no symptoms of heart failure (HF) were enrolled. All patients underwent cardiology visits, serial echocardiograms and received beta blockers and ACE inhibitors unless contraindicated. The primary endpoint was completion of the planned HER2-targeted therapies without developing either a cardiac event (CE) defined as HF, myocardial infarction, arrhythmia or cardiac death or significant asymptomatic worsening of LVEF. The study was considered successful if planned oncology therapy completion rate was at least 30%.

Results

Of 31 enrolled patients, 30 were evaluable. Fifteen patients were treated with trastuzumab, 14 with trastuzumab and pertuzumab, and 2 with TDM-1. Mean LVEF was 45% at baseline and 46% at the end of treatment. Twenty-seven patients (90%) completed the planned HER2-targeted therapies. Two patients experienced a CE and 1 had an asymptomatic worsening of LVEF to ≤ 35%.

Conclusion

This study provides safety data of HER2-targeted therapies in patients with breast cancer and reduced LVEF while receiving cardioprotective medications and close cardiac monitoring. Our results demonstrate the importance of collaboration between cardiology and oncology providers to allow for delivery of optimal oncologic care to this unique population.

Electronic supplementary material

The online version of this article (10.1007/s10549-019-05191-2) contains supplementary material, which is available to authorized users.

Subgroup analysis with semiparametric models toward precision medicine

In analyzing clinical trials, one important objective is to classify the patients into treatment-favorable and nonfavorable subgroups. Existing parametric methods are not robust, and the commonly used classification rules ignore the fact that the implications of treatment-favorable and nonfavorable subgroups can be different. To address these issues, we propose a semiparametric model, incorporating both our knowledge and uncertainty about the true model. The Wald statistics is used to test the existence of subgroups, while the Neyman-Pearson rule to classify each subject. Asymptotic properties are derived, simulation studies are conducted to evaluate the performance of the method, and then method is used to analyze a real-world trial data.

Pembrolizumab in patients with thymic carcinoma: a single-arm, single-centre, phase 2 study

BACKGROUND

Treatment options are limited for patients with thymic carcinoma. These aggressive tumours are not typically associated with paraneoplastic autoimmune disorders, and strong PD-L1 expression has been reported in thymic epithelial tumours. We aimed to assess the activity of pembrolizumab, a monoclonal antibody that targets PD-1, in patients with advanced thymic carcinoma.

METHODS

We completed a single-arm phase 2 study of pembrolizumab in patients with recurrent thymic carcinoma who had progressed after at least one line of chemotherapy. This was a single-centre study performed at Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. Key inclusion criteria were an Eastern Cooperative Oncology Group performance status of 0-2, no history of autoimmune disease or other malignancy requiring treatment or laboratory abnormality, and adequate organ function. Patients received 200 mg of pembrolizumab every 3 weeks for up to 2 years. The primary objective of the study was the proportion of patients who had achieved a response assessed with Response Evaluation Criteria in Solid Tumors version 1.1. Analysis was per protocol, in all eligible patients. The study is registered with ClinicalTrials.gov, number NCT02364076, and is closed to accrual; we report the final analysis.

FINDINGS

41 patients were enrolled from March 12, 2015, to Dec 16, 2016, of whom 40 were eligible and evaluable and one was excluded because of elevated liver enzymes at screening. The median follow-up was 20 months (IQR 14-26). The proportion of patients who achieved a response was 22·5% (95% CI 10·8-38·5); one (3%) patient achieved a complete response, eight (20%) patients achieved partial responses, and 21 (53%) patients achieved stable disease. The most common grade 3 or 4 adverse events were increased aspartate aminotransferase and alanine aminotransferase (five [13%] patients each). Six (15%) patients developed severe autoimmune toxicity, including two (5%) patients with myocarditis. There were 17 deaths at the time of analysis, but no deaths due to toxicity.

INTERPRETATION

Pembrolizumab is a promising treatment option in patients with thymic carcinoma. Because severe autoimmune disorders are more frequent in thymic carcinoma than in other tumour types, careful monitoring is essential.

FUNDING

Merck & Co.

Experimental design for multi-drug combination studies using signaling networks

Combinations of multiple drugs are an important approach to maximize the chance for therapeutic success by inhibiting multiple pathways/targets. Analytic methods for studying drug combinations have received increasing attention because major advances in biomedical research have made available large number of potential agents for testing. The preclinical experiment on multi-drug combinations plays a key role in (especially cancer) drug development because of the complex nature of the disease, the need to reduce development time and costs. Despite recent progresses in statistical methods for assessing drug interaction, there is an acute lack of methods for designing experiments on multi-drug combinations. The number of combinations grows exponentially with the number of drugs and dose-levels and it quickly precludes laboratory testing. Utilizing experimental dose-response data of single drugs and a few combinations along with pathway/network information to obtain an estimate of the functional structure of the dose-response relationship in silico, we propose an optimal design that allows exploration of the dose-effect surface with the smallest possible sample size in this article. The simulation studies show our proposed methods perform well.

SAFE-HEaRt: Rationale and Design of a Pilot Study Investigating Cardiac Safety of HER2 Targeted Therapy in Patients with HER2-Positive Breast Cancer and Reduced Left Ventricular Function

BACKGROUND

Human epidermal growth receptor 2 (HER2) targeted therapies have survival benefit in adjuvant and metastatic HER2 positive breast cancer but are associated with cardiac dysfunction. Current U.S. Food and Drug Administration recommendations limit the use of HER2 targeted agents to patients with normal left ventricular (LV) systolic function.

METHODS

The objective of the SAFE-HEaRt study is to evaluate the cardiac safety of HER2 targeted therapy in patients with HER2 positive breast cancer and mildly reduced left ventricular ejection fraction (LVEF) with optimized cardiac therapy. Thirty patients with histologically confirmed HER2 positive breast cancer (stage I-IV) and reduced LVEF (40% to 49%) who plan to receive HER2 targeted therapy for ≥3 months will be enrolled. Prior to initiation on study, optimization of heart function with beta-blockers and angiotensin converting enzyme inhibitors will be initiated. Patients will be followed by serial echocardiograms and cardiac visits during and 6 months after completion of HER2 targeted therapy. Myocardial strain and blood biomarkers, including cardiac troponin I and high-sensitivity cardiac troponin T, will be examined at baseline and during the study.

DISCUSSION

LV dysfunction in patients with breast cancer poses cardiac and oncological challenges and limits the use of HER2 targeted therapies and its oncological benefits. Strategies to prevent cardiac dysfunction associated with HER2 targeted therapy have been limited to patients with normal LVEF, thus excluding patients who may receive the highest benefit from those strategies. SAFE-HEaRt is the first prospective pilot study of HER2 targeted therapies in patients with reduced LV function while on optimized cardiac treatment that can provide the basis for clinical practice changes. The Oncologist 2017;22:518-525 IMPLICATIONS FOR PRACTICE: Human epidermal growth receptor 2 (HER2) targeted therapies have survival benefit in adjuvant and metastatic HER2 positive breast cancer but are associated with cardiac dysfunction. To our knowledge, SAFE-HEaRt is the first clinical trial that prospectively tests the hypothesis that HER2 targeted therapies may be safely administered in patients with mildly reduced cardiac function in the setting of ongoing cardiac treatment and monitoring. The results of this study will provide cardiac safety data and inform consideration of clinical practice changes in patients with HER2 positive breast cancer and reduced cardiac function, as well as provide information regarding cardiovascular monitoring and treatment in this population.

A New Preprocedure Risk Score for Predicting Contrast-Induced Acute Kidney Injury

BACKGROUND

Most of the risk models for predicting contrast-induced acute kidney injury (CI-AKI) are available for only postcontrast exposure prediction; however, prediction before the procedure is more valuable in practice. This study aimed to develop a risk scoring system based on preprocedural characteristics for early prediction of CI-AKI in patients after coronary angiography or percutaneous coronary intervention (PCI).

METHODS

We prospectively recruited 1777 consecutive patients who were randomized in an approximate 3:2 ratio to create a development data set (n = 1076) and a validation data set (n = 701). A risk score model based on preprocedural risk factors was developed using stepwise logistic regression. Validation was performed by bootstrap and split-sample methods.

RESULTS

The occurrence of CI-AKI was 5.97% (106 of 1777), 5.95% (64 of 1076), and 5.99% (42 of 701) in the overall, developmental, and validation data sets, respectively. The risk score was developed with 5 prognostic factors (age, serum creatinine levels, N-terminal pro b-type natriuretic peptide levels, high-sensitivity C-reactive protein, and primary PCI), ranged from 0-36, and was well calibrated (Hosmer-Lemeshow χ² = 4.162; P = 0.842). Good discrimination was obtained both in the developmental and validation data sets (C-statistic, 0.809 and 0.798, respectively). The risk score was highly and positively associated with CI-AKI (P for trend < 0.001) in-hospital and long-term outcomes.

CONCLUSIONS

The novel risk score model we developed is a simple and accurate tool for early/preprocedural prediction of CI-AKI in patients undergoing coronary angiography or PCI. This tool allows assessment of the risk of CI-AKI before contrast exposure, allowing for timely initiation of appropriate preventive measures.

A novel rank-based non-parametric method for longitudinal ordinal data

Longitudinal ordinal data are common in biomedical research. Although various methods for the analysis of such data have been proposed in the past few decades, they are limited in several ways. For instance, the constraints on parameters in the proportional odds model may result in convergence problems; the rank-based aligned rank transform method imposes constraints on other parameters and the distributional assumptions with parametric model. We propose a novel rank-based non-parametric method that models the profile rather than the distribution of the data to make an effective statistical inference without the constraint conditions. We construct the test statistic of the interaction first, and then construct the test statistics of the main effects separately with or without the interaction, while "adjusted coefficient" for the case of ties is derived. A simulation study is conducted for comparison between rank-based non-parametric and rank-transformed analysis of variance. The results show that type I errors of the two methods are both maintained closer to the priori level, but the statistical power of rank-based non-parametric is greater than that of rank-transformed analysis of variance, suggesting higher efficiency of the former. We then apply rank-based non-parametric to two real studies on acne and osteoporosis, and the results also illustrate the effectiveness of rank-based non-parametric, particularly when the distribution is skewed.

A novel nonparametric confidence interval for differences of proportions for correlated binary data

Various confidence interval estimators have been developed for differences in proportions resulted from correlated binary data. However, the width of the mostly recommended Tango’s score confidence interval tends to be wide, and the computing burden of exact methods recommended for small-sample data is intensive. The recently proposed rank-based nonparametric method by treating proportion as special areas under receiver operating characteristic provided a new way to construct the confidence interval for proportion difference on paired data, while the complex computation limits its application in practice. In this article, we develop a new nonparametric method utilizing the U-statistics approach for comparing two or more correlated areas under receiver operating characteristics. The new confidence interval has a simple analytic form with a new estimate of the degrees of freedom of n − 1. It demonstrates good coverage properties and has shorter confidence interval widths than that of Tango. This new confidence interval with the new estimate of degrees of freedom also leads to coverage probabilities that are an improvement on the rank-based nonparametric confidence interval. Comparing with the approximate exact unconditional method, the nonparametric confidence interval demonstrates good coverage properties even in small samples, and yet they are very easy to implement computationally. This nonparametric procedure is evaluated using simulation studies and illustrated with three real examples. The simplified nonparametric confidence interval is an appealing choice in practice for its ease of use and good performance.

Development and Validation of a Risk Score for Prediction of Acute Kidney Injury in Patients With Acute Decompensated Heart Failure: A Prospective Cohort Study in China

Background

Although several risk factors for acute kidney injury (AKI) have been identified, early detection of AKI in acute decompensated heart failure patients remains a challenge. The aim of this study was to develop and validate a risk score for early prediction of AKI in acute decompensated heart failure patients.

Methods and Results

A total of 676 consecutive acute decompensated heart failure participants were prospectively enrolled from 6 regional central hospitals. Data from 507 participants were analyzed. Participants from 4 of the 6 hospitals (n=321) were used to develop a risk score and conduct internal validation. External validation of the developed risk score was conducted in participants from the other 2 hospitals (n=186). Sequential logistic regression was used to develop and validate the risk score. The c statistic and calibration plot were used to assess the discrimination and calibration of the proposed risk score. The overall occurrence of AKI was 33.1% (168/507). The risk score, ranging from 0 to 55, demonstrated good discriminative power with an optimism‐corrected c statistic of 0.859. Similar results were obtained from external validation with c statistic of 0.847 (95% CI 0.819‐0.927). The risk score had good calibration with no apparent over‐ or under‐prediction observed from calibration plots.

Conclusions

The novel risk score is a simple and accurate tool that can help clinicians assess the risk of AKI in acute decompensated heart failure patients, which in turn helps them plan and initiate the most appropriate disease management for patients in time.

Adaptive Design for Staggered-Start Clinical Trial

In phase II and/or III clinical trial study, there are several competing treatments, the goal is to assess the performances of the treatments at the end of the study, the trial design aims to minimize risks to the patients in the trial, according to some given allocation optimality criterion. Recently, a new type of clinical trial, the staggered-start trial has been proposed in some studies, in which different treatments enter the same trial at different times. Some basic questions for this trial are whether optimality can still be kept? under what conditions? and if so how to allocate the the coming patients to treatments to achieve such optimality? Here we propose and study a class of adaptive designs of staggered-start clinical trials, in which for given optimality criterion object, we show that as long as the initial sizes at the beginning of the successive trials are not too large relative to the total sample size, the proposed design can still achieve optimality criterion asymptotically for the allocation proportions as the ordinary trials; if these initial sample sizes have about the same magnitude as the total sample size, full optimality cannot be achieved. The proposed method is simple to use and is illustrated with several examples and a simulation study.

[Submandibular gland resection via subclavian into the road under the endoscope]

Objective:To evaluate resection of submandibular gland through a minimal skin incision under the endoscope. Method:A retrospective analysis of the clinical data from 28 cases of submandibular gland resection by endoscope surgery via subclavian approach, 14 cases of preoperative diagnosis of pleomorphic adenoma, submandibular gland of chronic inflammation in 11 cases, 3 cases of the submandibular gland stone,one case of lymphatic cyst,all cases were evaluated by preoperative imaging or 3 d sonography. Result:All patients' submandibular gland and tumors were resected totally under the endoscope, no open surgery, no surgical complications, and postoperative aesthetic outcome was good, patients were satisfied, pleomorphic adenoma patients were postoperative followed up of 4 to 24 months, and no recurrence. Conclusion:Under the cavity mirror via subclavian path submandibular gland resection is safe and feasible, and has a good cosmetic effect.

Plasma 24-metabolite Panel Predicts Preclinical Transition to Clinical Stages of Alzheimer's Disease

We recently documented plasma lipid dysregulation in preclinical late-onset Alzheimer's disease (LOAD). A 10 plasma lipid panel, predicted phenoconversion and provided 90% sensitivity and 85% specificity in differentiating an at-risk group from those that would remain cognitively intact. Despite these encouraging results, low positive predictive values limit the clinical usefulness of this panel as a screening tool in subjects aged 70-80 years or younger. In this report, we re-examine our metabolomic data, analyzing baseline plasma specimens from our group of phenoconverters (n = 28) and a matched set of cognitively normal subjects (n = 73), and discover and internally validate a panel of 24 plasma metabolites. The new panel provides a classifier with receiver operating characteristic area under the curve for the discovery and internal validation cohort of 1.0 and 0.995 (95% confidence intervals of 1.0-1.0, and 0.981-1.0), respectively. Twenty-two of the 24 metabolites were significantly dysregulated lipids. While positive and negative predictive values were improved compared to our 10-lipid panel, low positive predictive values provide a reality check on the utility of such biomarkers in this age group (or younger). Through inclusion of additional significantly dysregulated analyte species, our new biomarker panel provides greater accuracy in our cohort but remains limited by predictive power. Unfortunately, the novel metabolite panel alone may not provide improvement in counseling and management of at-risk individuals but may further improve selection of subjects for LOAD secondary prevention trials. We expect that external validation will remain challenging due to our stringent study design, especially compared with more diverse subject cohorts. We do anticipate, however, external validation of reduced plasma lipid species as a predictor of phenoconversion to either prodromal or manifest LOAD.

ReCAP: Impact of Multidisciplinary Care on Processes of Cancer Care: A Multi-Institutional Study

PURPOSE

The role of multidisciplinary care (MDC) on cancer care processes is not fully understood. We investigated the impact of MDC on the processes of care at cancer centers within the National Cancer Institute Community Cancer Centers Program (NCCCP).

METHODS

The study used data from patients diagnosed with stage IIB to III rectal cancer, stage III colon cancer, and stage III non–small-cell lung cancer at 14 NCCCP cancer centers from 2007 to 2012. We used an MDC development assessment tool—with levels ranging from evolving MDC (low) to achieving excellence (high)—to measure the level of MDC implementation in seven MDC areas, such as case planning and physician engagement. Descriptive statistics and cluster-adjusted regression models quantified the association between MDC implementation and processes of care, including time from diagnosis to treatment receipt.

RESULTS

A total of 1,079 patients were examined. Compared with patients with colon cancer treated at cancer centers reporting low MDC scores, time to treatment receipt was shorter for patients with colon cancer treated at cancer centers reporting high or moderate MDC scores for physician engagement (hazard ratio [HR] for high physician engagement, 2.66; 95% CI, 1.70 to 4.17; HR for moderate physician engagement, 1.50; 95% CI, 1.19 to 1.89) and longer for patients with colon cancer treated at cancer centers reporting high 2MDC scores for case planning (HR, 0.65; 95% CI, 0.49 to 0.85). Results for patients with rectal cancer were qualitatively similar, and there was no statistically significant difference among patients with lung cancer.

CONCLUSION

MDC implementation level was associated with processes of care, and direction of association varied across MDC assessment areas.

Critical periods after stroke study: translating animal stroke recovery experiments into a clinical trial

Introduction: Seven hundred ninety-five thousand Americans will have a stroke this year, and half will have a chronic hemiparesis. Substantial animal literature suggests that the mammalian brain has much potential to recover from acute injury using mechanisms of neuroplasticity, and that these mechanisms can be accessed using training paradigms and neurotransmitter manipulation. However, most of these findings have not been tested or confirmed in the rehabilitation setting, in large part because of the challenges in translating a conceptually straightforward laboratory experiment into a meaningful and rigorous clinical trial in humans. Through presentation of methods for a Phase II trial, we discuss these issues and describe our approach.

Methods: In rodents there is compelling evidence for timing effects in rehabilitation; motor training delivered at certain times after stroke may be more effective than the same training delivered earlier or later, suggesting that there is a critical or sensitive period for strongest rehabilitation training effects. If analogous critical/sensitive periods can be identified after human stroke, then existing clinical resources can be better utilized to promote recovery. The Critical Periods after Stroke Study (CPASS) is a phase II randomized, controlled trial designed to explore whether such a sensitive period exists. We will randomize 64 persons to receive an additional 20 h of upper extremity therapy either immediately upon rehab admission, 2–3 months after stroke onset, 6 months after onset, or to an observation-only control group. The primary outcome measure will be the Action Research Arm Test (ARAT) at 1 year. Blood will be drawn at up to 3 time points for later biomarker studies.

Conclusion: CPASS is an example of the translation of rodent motor recovery experiments into the clinical setting; data obtained from this single site randomized controlled trial will be used to finalize the design of a Phase III trial.

Randomized, double-blind, placebo-controlled, multicenter phase II study of the efficacy and safety of apricoxib in combination with either docetaxel or pemetrexed in patients with biomarker-selected non-small-cell lung cancer

PURPOSE

Overexpression of COX-2 correlates with advanced stage and worse outcomes in non-small-cell lung cancer (NSCLC), possibly as a result of elevated levels of COX-2-dependent prostaglandin E2 (PGE2). Exploratory analyses of studies that used COX-2 inhibitors have demonstrated potentially superior outcome in patients in whom the urinary metabolite of PGE2 (PGE-M) is suppressed. We hypothesized that patients with disease defined by PGE-M suppression would benefit from the addition of apricoxib to second-line docetaxel or pemetrexed.

PATIENTS AND METHODS

Patients with NSCLC who had disease progression after one line of platinum-based therapy, performance status of 0 to 2, and normal organ function were potentially eligible. Only patients with a ≥ 50% decrease in urinary PGE-M after 5 days of treatment with apricoxib could enroll. Docetaxel 75 mg/m(2) or pemetrexed 500 mg/m(2) once every 21 days per the investigator was administered with apricoxib or placebo 400 mg once per day. The primary end point was progression-free survival (PFS). Exploratory analysis was performed regarding baseline urinary PGE-M and outcomes.

RESULTS

In all, 101 patients completed screening, and 72 of the 80 who demonstrated ≥ 50% suppression were randomly assigned to apricoxib or placebo. Toxicity was similar between the arms. No improvement in PFS was seen with apricoxib versus placebo. The median PFS for the control arm was 97 days (95% CI, 52 to 193 days) versus 85 days (95% CI, 67 to 142 days) for the experimental arm (P = .91).

CONCLUSION

Apricoxib did not improve PFS, despite biomarker-driven patient selection.

Plasma phospholipids identify antecedent memory impairment in older adults

Alzheimer's disease causes a progressive dementia that currently affects over 35 million individuals worldwide and is expected to affect 115 million by 2050 (ref. 1). There are no cures or disease-modifying therapies, and this may be due to our inability to detect the disease before it has progressed to produce evident memory loss and functional decline. Biomarkers of preclinical disease will be critical to the development of disease-modifying or even preventative therapies. Unfortunately, current biomarkers for early disease, including cerebrospinal fluid tau and amyloid-β levels, structural and functional magnetic resonance imaging and the recent use of brain amyloid imaging or inflammaging, are limited because they are either invasive, time-consuming or expensive. Blood-based biomarkers may be a more attractive option, but none can currently detect preclinical Alzheimer's disease with the required sensitivity and specificity. Herein, we describe our lipidomic approach to detecting preclinical Alzheimer's disease in a group of cognitively normal older adults. We discovered and validated a set of ten lipids from peripheral blood that predicted phenoconversion to either amnestic mild cognitive impairment or Alzheimer's disease within a 2-3 year timeframe with over 90% accuracy. This biomarker panel, reflecting cell membrane integrity, may be sensitive to early neurodegeneration of preclinical Alzheimer's disease.

Abstract OT1-1-12: SAFE-HEaRt: A pilot study assessing the cardiac SAFEty of HER2 targeted therapy in patients with HER2 positive breast cancer and reduced left ventricular function

Background: Substantial benefit of trastuzumab is associated with cardiac side effects, mostly asymptomatic. According to current FDA approved package insert, patients should not receive trastuzumab or newer HER2 targeted therapies if there is evidence of cardiomyopathy.

Trial design: SAFE-HEaRt is a pilot study evaluating the cardiac safety of trastuzumab, pertuzumab and ado-trastuzumab emtansine in 30 patients with HER2 positive breast cancer (BC) and mild LV dysfunction (LVEF≥40% and &lt;50%) while on optimized cardiac therapy with B-Blockers and ACE inhibitors. Left ventricular ejection fraction (LVEF) and myocardial strain will be assessed at baseline, week 6,12,18 and then every 12 weeks. Troponin I (cTnI) and high-sensitivity cardiac troponin T (hs-cTnT) will be collected at the same time points.

Main Eligibility criteria: Patients diagnosed with stage I-IV HER2 positive BC; LVEF≥40% and &lt;50% prior to or while receiving non-lapatinib HER2 targeted therapy.

Specific aims: The primary endpoint is the proportion of patients who complete planned oncologic therapy without the development of a cardiac event (presence of symptoms attributable to heart failure (HF) confirmed by a cardiologist, cardiac arrhythmia requiring pharmacological or electrical treatment, myocardial infarction, sudden cardiac death or death due to myocardial infarct, arrhythmia or HF) or asymptomatic worsening of cardiac function (i.e. asymptomatic decline in LVEF &gt;10% points from baseline and/or LVEF&lt;35% corroborated by a confirmatory echocardiogram in 2-4 weeks).

Additional endpoints include: Median time to development of an event; absolute changes in LVEF during HER2 targeted therapy; HER2 therapy holds attributed to cardiotoxicity; correlation of myocardial strain and serum biomarkers with cardiac events and asymptomatic worsening of cardiac function.

Statistical methods: A two-stage design is used to test if the completion rate of planned oncologic therapy will be at least 30% versus less than 10% with 80% power at a significance level of 5%. At the first stage, 15 patients will be entered. If one or more patients complete therapy in the absence of a cardiac event, then additional 15 patients will be enrolled in the second stage. Early stopping rules are incorporated for safety based on cardiac death and symptomatic HF. Safety monitoring plan consists of an internal cardiac review panel and a Data Safety Monitoring Board (DSMB) that includes an external cardiologist expert.

Present accrual and target accrual: The Institutional Review Board has approved the study. Enrollment of the first patient is expected in July 2013 with a total of 30 patients planned to be recruited. The initial recruitment sites are MedStar Washington Hospital Center and MedStar Georgetown University Hospital. This trial is partially supported by Genentech and funded by a Conquer Cancer Foundation of ASCO Young Investigator Award, supported by The Breast Cancer Research Foundation. Any opinions, findings, and conclusions expressed in this material are those of the author(s) and do not necessarily reflect those of the American Society of Clinical Oncology, the Conquer Cancer Foundation, or The Breast Cancer Research Foundation.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr OT1-1-12.

Comparative antigen-induced gene expression profiles unveil novel aspects of susceptibility/resistance to adjuvant arthritis in rats

Lewis (LEW) and Wistar Kyoto (WKY) rats of the same major histocompatibility complex (MHC) haplotype (RT.1(l)) display differential susceptibility to adjuvant-induced arthritis (AIA). LEW are susceptible while WKY are resistant to AIA. To gain insights into the mechanistic basis of these disparate outcomes, we compared the gene expression profiles of the draining lymph node cells (LNC) of these two rat strains early (day 7) following a potentially arthritogenic challenge. LNC were tested both ex vivo and after restimulation with the disease-related antigen, mycobacterial heat-shock protein 65. Biotin-labeled fragment cRNA was generated from RNA of LNC and then hybridized with an oligonucleotide-based DNA microarray chip. The differentially expressed genes (DEG) were compared by limiting the false discovery rate to <5% and fold change ≥2.0, and their association with quantitative trait loci (QTL) was analyzed. This analysis revealed overall a more active immune response in WKY than LEW rats. Important differences were observed in the association of DEG with QTL in LEW vs. WKY rats. Both the number of upregulated DEG associated with rat arthritis-QTL and their level of expression were relatively higher in LEW when compared to WKY rat; however, the number of downregulated DEG-associated with rat arthritis-QTL as well as AIA-QTL were found to be higher in WKY than in LEW rats. In conclusion, distinct gene expression profiles define arthritis-susceptible versus resistant phenotype of MHC-compatible inbred rats. These results would advance our understanding of the pathogenesis of autoimmune arthritis and might also offer potential novel targets for therapeutic purposes.

[Adequate application of quantitative and qualitative statistic analytic methods in acupuncture clinical trials]

Recently, proper use of the statistical methods in traditional Chinese medicine (TCM) randomized controlled trials (RCTs) has received increased attention. Statistical inference based on hypothesis testing is the foundation of clinical trials and evidence-based medicine. In this article, the authors described the methodological differences between literature published in Chinese and Western journals in the design and analysis of acupuncture RCTs and the application of basic statistical principles. In China, qualitative analysis method has been widely used in acupuncture and TCM clinical trials, while the between-group quantitative analysis methods on clinical symptom scores are commonly used in the West. The evidence for and against these analytical differences were discussed based on the data of RCTs assessing acupuncture for pain relief. The authors concluded that although both methods have their unique advantages, quantitative analysis should be used as the primary analysis while qualitative analysis can be a secondary criterion for analysis. The purpose of this paper is to inspire further discussion of such special issues in clinical research design and thus contribute to the increased scientific rigor of TCM research.

Evaluating traditional Chinese medicine using modern clinical trial design and statistical methodology: application to a randomized controlled acupuncture trial

Traditional Chinese medicine (TCM), used in China and other Asian counties for thousands of years, is increasingly utilized in Western countries. However, due to inherent differences in how Western medicine and this ancient modality are practiced, employing the so-called Western medicine-based gold standard research methods to evaluate TCM is challenging. This paper is a discussion of the obstacles inherent in the design and statistical analysis of clinical trials of TCM. It is based on our experience in designing and conducting a randomized controlled clinical trial of acupuncture for post-operative dental pain control in which acupuncture was shown to be statistically and significantly better than placebo in lengthening the median survival time to rescue drug. We demonstrate here that PH assumptions in the common Cox model did not hold in that trial and that TCM trials warrant more thoughtful modeling and more sophisticated models of statistical analysis. TCM study design entails all the challenges encountered in trials of drugs, devices, and surgical procedures in the Western medicine. We present possible solutions to some but leave many issues unresolved.

Cyclin D1 gene ablation confers neuroprotection in traumatic brain injury

Cell cycle activation (CCA) is one of the principal secondary injury mechanisms following brain trauma, and it leads to neuronal cell death, microglial activation, and neurological dysfunction. Cyclin D1 (CD1) is a key modulator of CCA and is upregulated in neurons and microglia following traumatic brain injury (TBI). In this study we subjected CD1-wild-type (CD1(+/+)) and knockout (CD1(-/-)) mice to controlled cortical impact (CCI) injury to evaluate the role of CD1 in post-traumatic neurodegeneration and neuroinflammation. As early as 24 h post-injury, CD1(+/+) mice showed markers of CCA in the injured hemisphere, including increased CD1, E2F1, and proliferating cell nuclear antigen (PCNA), as well as increased Fluoro-Jade B staining, indicating neuronal degeneration. Progressive neuronal loss in the hippocampus was observed through 21 days post-injury in these mice, which correlated with a decline in cognitive function. Microglial activation in the injured hemisphere peaked at 7 days post-injury, with sustained increases at 21 days. In contrast, CD1(-/-) mice showed reduced CCA and neurodegeneration at 24 h, as well as improved cognitive function, attenuated hippocampal neuronal cell loss, decreased lesion volume, and cortical microglial activation at 21 days post-injury. These findings indicate that CD1-dependent CCA plays a significant role in the neuroinflammation, progressive neurodegeneration, and related neurological dysfunction resulting from TBI. Our results further substantiate the proposed role of CCA in post-traumatic secondary injury, and suggest that inhibition of CD1 may be a key therapeutic target for TBI.

The gene expression profile of preclinical autoimmune arthritis and its modulation by a tolerogenic disease-protective antigenic challenge

Introduction

Autoimmune inflammation is a characteristic feature of rheumatoid arthritis (RA) and other autoimmune diseases. In the natural course of human autoimmune diseases, it is rather difficult to pinpoint the precise timing of the initial event that triggers the cascade of pathogenic events that later culminate into clinically overt disease. Therefore, it is a challenge to examine the early preclinical events in these disorders. Animal models are an invaluable resource in this regard. Furthermore, considering the complex nature of the pathogenic immune events in arthritis, microarray analysis offers a versatile tool to define the dynamic patterns of gene expression during the disease course.

Methods

In this study, we defined the profiles of gene expression at different phases of adjuvant arthritis (AA) in Lewis rats and compared them with those of antigen mycobacterial heat shock protein 65 (Bhsp65)-tolerized syngeneic rats. Purified total RNA (100 ng) extracted from the draining lymph node cells was used to generate biotin-labeled fragment cRNA, which was then hybridized with an oligonucleotide-based DNA microarray chip. Significance analysis of microarrays was used to compare gene expression levels between the two different groups by limiting the false discovery rate to < 5%. Some of the data were further analyzed using a fold change ≥2.0 as the cutoff. The gene expression of select genes was validated by quantitative real-time PCR.

Results

Intriguingly, the most dramatic changes in gene expression in the draining lymphoid tissue ex vivo were observed at the preclinical (incubation) phase of the disease. The affected genes represented many of the known proteins that participate in the cellular immune response. Interestingly, the preclinical gene expression profile was significantly altered by a disease-modulating, antigen-based tolerogenic regimen. The changes mostly included upregulation of several genes, suggesting that immune tolerance suppressed disease by activating disease-regulating pathways. We identified a molecular signature comprising at least 12 arthritis-related genes altered by Bhsp65-induced tolerance.

Conclusions

This is the first report of microarray analysis in the rat AA model. The results of this study not only advance our understanding of the early phase events in autoimmune arthritis but also help in identifying potential targets for the immunomodulation of RA.

A flexible multi-stage design for phase II oncology trials

Phase II trials evaluate whether a new drug or a new therapy is worth further pursuing or certain treatments are feasible or not. A typical phase II is a single arm (open label) trial with a binary clinical endpoint (response to therapy). Although many oncology Phase II clinical trials are designed with a two-stage procedure, multi-stage design for phase II cancer clinical trials are now feasible due to increased capability of data capture. Such design adjusts for multiple analyses and variations in analysis time, and provides greater flexibility such as minimizing the number of patients treated on an ineffective therapy and identifying the minimum number of patients needed to evaluate whether the trial would warrant further development. In most of the NIH sponsored studies, the early stopping rule is determined so that the number of patients treated on an ineffective therapy is minimized. In pharmaceutical trials, it is also of importance to know as early as possible if the trial is highly promising and what is the likelihood the early conclusion can sustain. Although various methods are available to address these issues, practitioners often use disparate methods for addressing different issues and do not realize a single unified method exists. This article shows how to utilize a unified approach via a fully sequential procedure, the sequential conditional probability ratio test, to address the multiple needs of a phase II trial. We show the fully sequential program can be used to derive an optimized efficient multi-stage design for either a low activity or a high activity, to identify the minimum number of patients required to assess whether a new drug warrants further study and to adjust for unplanned interim analyses. In addition, we calculate a probability of discordance that the statistical test will conclude otherwise should the trial continue to the planned end that is usually at the sample size of a fixed sample design. This probability can be used to aid in decision making in a drug development program. All computations are based on exact binomial distribution.

Detection and sequence analysis of equine gammaherpesviruses from horses with respiratory tract disease in Turkey

The equid herpesvirus 2 (EHV-2) and 5 (EHV-5), identified agents of respiratory infections and keratoconjunctivitis cases in some equids, comprise a high degree of antigenic heterogeneity. Prevalence and genetic characterization of EHV-2 and EHV-5 strains from Turkey were investigated in this study. A total of 73 nasal swabs and 54 blood specimens were sampled from horses with respiratory tract diseases characterized by mucopurulent nasal discharge and occasional coughing. Overall, EHV-2- and EHV-5-specific DNA amplicons were obtained from 19.2% (14/73) and 21.9% (16/73) of horses tested by multiplex nested PCR. Sequences of EHV-2 and EHV-5 glycoprotein B (gB) gene were used in a phylogenetic analysis that included six EHV-2 and three EHV-5 isolates, which showed that the Turkish EHV-2 and EHV-5 strains have marked sequence divergence from European strains and from each other. Turkish EHV-2 isolates were divided into two distinct subdivisions, and a few isolates were located on a separate branch. This study provides the first epidemiological and phylogenetical report about EHV-2 and EHV-5 infections in Turkey.

Abstract 5413: Maximal Power Design and Analysis of Drug Combination Studies: Applications to Vorinostat with Cytosine Arabinoside and Etoposide and Other Combination Studies

Drug combinations are the hallmark of cancer therapy. Preclinical experiments on multi-drug combinations are important steps to bring the therapy to clinic. A statistical approach for evaluating the joint effect of the combination is necessary because even in vitro experiments often demonstrate significant variation in dose-effect. Such variation needs to be accounted for in the experimental design and analysis. Our research has developed a maximal power (MP) design and interaction index surface (IIS) analysis methods for in vitro and in vivo (e.g., xenograft) combination studies so that the joint effect of a combination can be estimated efficiently and the most synergistic combination can be identified. We demonstrate that these statistical methods and software have resulted in the identification of highly synergistic dose combinations that could have been missed with classic methods.

The first study is the combination of vorinostat (suberoylanilide hydroxamic acid) combined with ara-C and with etoposide in leukemia cell lines. The doses in the experiment were generated by the MP design and the data analyzed using the IIS approach so that synergistic, additive and antagonistic interaction dose regions are identified. Cytotoxic antagonism resulted when vorinostat was combined concomitantly with ara-C; however, when vorinostat was given first followed by a drug-free interval before ara-C treatment, this sequential combination was mostly synergistic. Etoposide combined with vorinostat was additive to synergistic, and the synergism became more pronounced when etoposide was given post a drug-free interval after vorinostat. These findings are used in designing the CTEP trial (NCI 6829: Phase I trial of vorinostat in combination with cytarabine and etoposide in patients with advanced acute leukemia and high-risk myelodysplastic syndromes, PI: Gojo/Ross). The interim results on toxicity and response have been consistent with the model.

Another study utilized a novel thiazolidine compound plus Sorafnib where initial experiments using classic methods failed to identify synergistic combinations. Subsequent experiments using the MP methodologies demonstrated significant synergistic drug combinations. The SynStat R program for the design and analysis of drug synergy is available at http://www.umgcc.org/research/biostat_software.htm).

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5413.

Sparse support vector machines with Lp penalty for biomarker identification

The development of high-throughput technology has generated a massive amount of high-dimensional data, and many of them are of discrete type. Robust and efficient learning algorithms such as LASSO [1] are required for feature selection and overfitting control. However, most feature selection algorithms are only applicable to the continuous data type. In this paper, we propose a novel method for sparse support vector machines (SVMs) with L_(p) (p < 1) regularization. Efficient algorithms (LpSVM) are developed for learning the classifier that is applicable to high-dimensional data sets with both discrete and continuous data types. The regularization parameters are estimated through maximizing the area under the ROC curve (AUC) of the cross-validation data. Experimental results on protein sequence and SNP data attest to the accuracy, sparsity, and efficiency of the proposed algorithm. Biomarkers identified with our methods are compared with those from other methods in the literature. The software package in Matlab is available upon request.