The database for aggregate analysis of ClinicalTrials.gov (AACT) and subsequent regrouping by clinical specialty

BACKGROUND: The ClinicalTrials.gov registry provides information regarding characteristics of past, current, and planned clinical studies to patients, clinicians, and researchers; in addition, registry data are available for bulk download. However, issues related to data structure, nomenclature, and changes in data collection over time present challenges to the aggregate analysis and interpretation of these data in general and to the analysis of trials according to clinical specialty in particular. Improving usability of these data could enhance the utility of ClinicalTrials.gov as a research resource. METHODS/PRINCIPAL RESULTS: The purpose of our project was twofold. First, we sought to extend the usability of ClinicalTrials.gov for research purposes by developing a database for aggregate analysis of ClinicalTrials.gov (AACT) that contains data from the 96,346 clinical trials registered as of September 27, 2010. Second, we developed and validated a methodology for annotating studies by clinical specialty, using a custom taxonomy employing Medical Subject Heading (MeSH) terms applied by an NLM algorithm, as well as MeSH terms and other disease condition terms provided by study sponsors. Clinical specialists reviewed and annotated MeSH and non-MeSH disease condition terms, and an algorithm was created to classify studies into clinical specialties based on both MeSH and non-MeSH annotations. False positives and false negatives were evaluated by comparing algorithmic classification with manual classification for three specialties. CONCLUSIONS/SIGNIFICANCE: The resulting AACT database features study design attributes parsed into discrete fields, integrated metadata, and an integrated MeSH thesaurus, and is available for download as Oracle extracts (.dmp file and text format). This publicly-accessible dataset will facilitate analysis of studies and permit detailed characterization and analysis of the U.S. clinical trials enterprise as a whole. In addition, the methodology we present for creating specialty datasets may facilitate other efforts to analyze studies by specialty groups.     

Penalized estimation of a class of single-index varying-coefficient models for integrative genomic analysis

Recent technological advances have made it possible to collect high-dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single-index varying-coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right-censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study.     

Covariate adjustment in the analysis of microarray data from clinical studies

There is tremendous scientific interest in the analysis of gene expression data in clinical settings, such as oncology. In this paper, we describe the importance of adjusting for confounders and other prognostic factors in order to select for differentially expressed genes for follow-up validation studies. We develop two approaches to the analysis of microarray data in non-randomized clinical settings. The first is an extension of the current significance analysis of microarray procedures, where other covariates are taken into account. The second is a novel covariate-adjusted regression modelling based on the receiver operating characteristic (ROC) curve for the analysis of gene expression data. The ideas are illustrated using data from a prostate cancer molecular profiling study.     

The Missing Medians: Exclusion of Ordinal Data from Meta-Analyses

Background

Meta-analyses are considered the gold standard of evidence-based health care, and are used to guide clinical decisions and health policy. A major limitation of current meta-analysis techniques is their inability to pool ordinal data. Our objectives were to determine the extent of this problem in the context of neurological rating scales and to provide a solution.

Methods

Using an existing database of clinical trials of oral neuroprotective therapies, we identified the 6 most commonly used clinical rating scales and recorded how data from these scales were reported and analysed. We then identified systematic reviews of studies that used these scales (via the Cochrane database) and recorded the meta-analytic techniques used. Finally, we identified a statistical technique for calculating a common language effect size measure for ordinal data.

Results

We identified 103 studies, with 128 instances of the 6 clinical scales being reported. The majority– 80%–reported means alone for central tendency, with only 13% reporting medians. In analysis, 40% of studies used parametric statistics alone, 34% of studies employed non-parametric analysis, and 26% did not include or specify analysis. Of the 60 systematic reviews identified that included meta-analysis, 88% used mean difference and 22% employed difference in proportions; none included rank-based analysis. We propose the use of a rank-based generalised odds ratio (WMW GenOR) as an assumption-free effect size measure that is easy to compute and can be readily combined in meta-analysis.

Conclusion

There is wide scope for improvement in the reporting and analysis of ordinal data in the literature. We hope that adoption of the WMW GenOR will have the dual effect of improving the reporting of data in individual studies while also increasing the inclusivity (and therefore validity) of meta-analyses.     

Predicting breast cancer using an expression values weighted clinical classifier

Background

Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier.LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters.

Results

We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies.

Conclusions

Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.     

Safety and tolerability of sitagliptin in clinical studies: a pooled analysis of data from 10,246 patients with type 2 diabetes

Background

In a previous pooled analysis of 12 double-blind clinical studies that included data on 6,139 patients with type 2 diabetes, treatment with sitagliptin, a dipeptidyl peptidase-4 (DPP-4) inhibitor, was shown to be generally well tolerated compared with treatment with control agents. As clinical development of sitagliptin continues, additional studies have been completed, and more patients have been exposed to sitagliptin. The purpose of the present analysis is to update the safety and tolerability assessment of sitagliptin by pooling data from 19 double-blind clinical studies.

Methods

The present analysis included data from 10,246 patients with type 2 diabetes who received either sitagliptin 100 mg/day (N = 5,429; sitagliptin group) or a comparator agent (placebo or an active comparator) (N = 4,817; non-exposed group). The 19 studies from which this pooled population was drawn represent the double-blind, randomized studies that included patients treated with the usual clinical dose of sitagliptin (100 mg/day) for between 12 weeks and 2 years and for which results were available as of July 2009. These 19 studies assessed sitagliptin taken as monotherapy, initial combination therapy with metformin or pioglitazone, or as add-on combination therapy with other antihyperglycemic agents (metformin, pioglitazone, a sulfonylurea ± metformin, insulin ± metformin, or rosiglitazone + metformin). Patients in the non-exposed group were taking placebo, metformin, pioglitazone, a sulfonylurea ± metformin, insulin ± metformin, or rosiglitazone + metformin. The analysis used patient-level data from each study to evaluate between-group differences in the exposure-adjusted incidence rates of adverse events.

Results

Summary measures of overall adverse events were similar in the sitagliptin and non-exposed groups, except for an increased incidence of drug-related adverse events in the non-exposed group. Incidence rates of specific adverse events were also generally similar between the two groups, except for increased incidence rates of hypoglycemia, related to the greater use of a sulfonylurea, and diarrhea, related to the greater use of metformin, in the non-exposed group and constipation in the sitagliptin group. Treatment with sitagliptin was not associated with an increased risk of major adverse cardiovascular events.

Conclusions

In this updated pooled safety analysis of data from 10,246 patients with type 2 diabetes, sitagliptin 100 mg/day was generally well tolerated in clinical trials of up to 2 years in duration.     

Reproducibility of mass spectrometry based protein profiles for diagnosis of ovarian cancer across clinical studies: A systematic review

The focus of this systematic review is to give an overview of the current status of clinical protein profiling studies using MALDI and SELDI MS platforms in the search for ovarian cancer biomarkers. A total of 34 profiling studies were qualified for inclusion in the review. Comparative analysis of published discriminatory peaks to peaks found in an original MALDI MS protein profiling study was made to address the key question of reproducibility across studies. An overlap was found despite substantial heterogeneity between studies relating to study design, biological material, pre-analytical treatment, and data analysis. About 47% of the peaks reported to be associated to ovarian cancer were also represented in our experimental study, and 34% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility an overlap in peaks between clinical studies was demonstrated, which indicate convergence toward a set of common discriminating, reproducible peaks for ovarian cancer. The potential of the discriminating protein peaks for clinical use as ovarian cancer biomarkers will be discussed and evaluated. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

DBCollHIV: a database system for collaborative HIV analysis in Brazil

We developed a database system for collaborative HIV analysis (DBCollHIV) in Brazil. The main purpose of our DBCollHIV project was to develop an HIV-integrated database system with analytical bioinformatics tools that would support the needs of Brazilian research groups for data storage and sequence analysis. Whenever authorized by the principal investigator, this system also allows the integration of data from different studies and/or the release of the data to the general public. The development of a database that combines sequences associated with clinical/epidemiological data is difficult without the active support of interdisciplinary investigators. A functional database that securely stores data and helps the investigator to manipulate their sequences before publication would be an attractive tool for investigators depositing their data and collaborating with other groups. DBCollHIV allows investigators to manipulate their own datasets, as well as integrating molecular and clinical HIV data, in an innovative fashion.     

Incorporating monotonicity into the evaluation of a biomarker

In the assessment of clinical utility of biomarkers, case-control studies are often undertaken based on existing serum samples. A common assumption made in these studies is that higher levels of the biomarker are associated with increased disease risk. In this article, we consider methods of analysis in which monotonicity is incorporated in associating the biomarker and the clinical outcome. We consider the roles of discrimination versus association and assess methods for both goals. In addition, we propose a semiparametric isotonic regression model for binary data and describe a simple estimation procedure as well as attendant inferential procedures. We apply the various methodologies to data from a prostate cancer study involving a serum biomarker.     

Comparison of Linear,Nonlinear and Semiparametric Mixed‐effects Models for Estimating HIV Dynamic Parameters

The potency of antiretroviral agents in AIDS clinical trials can be assessed on the basis of an early viral response such as viral decay rate or change in viral load (number of copies of HIV RNA) of the plasma. Linear, parametric nonlinear, and semiparametric nonlinear mixed‐effects models have been proposed to estimate viral decay rates in viral dynamic models. However, before applying these models to clinical data, a critical question that remains to be addressed is whether these models produce coherent estimates of viral decay rates, and if not, which model is appropriate and should be used in practice. In this paper, we applied these models to data from an AIDS clinical trial of potent antiviral treatments and found significant incongruity in the estimated rates of reduction in viral load. Simulation studies indicated that reliable estimates of viral decay rate were obtained by using the parametric and semiparametric nonlinear mixed‐effects models. Our analysis also indicated that the decay rates estimated by using linear mixed‐effects models should be interpreted differently from those estimated by using nonlinear mixed‐effects models. The semiparametric nonlinear mixed‐effects model is preferred to other models because arbitrary data truncation is not needed. Based on real data analysis and simulation studies, we provide guidelines for estimating viral decay rates from clinical data. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A semiparametric model for regression analysis of interval-censored failure time data

Left-, right-, and interval-censored response time data arise in a variety of settings, including the analyses of data from laboratory animal carcinogenicity experiments, clinical trials, and longitudinal studies. For such incomplete data, the usual regression techniques such as the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model are inapplicable. In this paper, we present a method for regression analysis which accommodates interval-censored data. We present applications of this methodology to data sets from a study of breast cancer patients who were followed for cosmetic response to therapy, a small animal tumorigenicity study, and a clinical trial.     

Generalized Estimating Equations in Controlled Clinical Trials: Hypotheses Testing

Generalized estimating equations (GEE) for the analysis of clustered data have gained increasing popularity. Recently, the first monograph on this method has been published. GEE have been repeatedly applied in controlled clinical trials. They have, however, been generally used as secondary or supplementary analysis. Instead, the primary analysis was mostly based on a classical method that usually ignored the clustered – mostly longitudinal – nature of the data. In this paper, we discuss the applicability of GEE as primary analysis in controlled clinical trials. From theoretical results in the literature, we derive recommendations how GEE should be used in therapeutic studies for testing statistical hypotheses. We hope that our paper is the starting point for a thorough discussion on the most appropriate analysis of controlled clinical trials with clustered dependent variables. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

临床试验进展：临床数据与实验室质量管理规范

将临床研究数据用于临床日常规范及健康相关决策的制定对于改善全球医疗保健至关重要。汤森路透Cortellis 临床试验情报对临床试验数据的应用价值及各国临床实验室质量管理规范的实施情况进行了介绍,提供描绘临床图景关键元素和当前趋势的专家分析,从而指导临床开发决策。     

生物医学数据分析中的深度学习方法应用

生物医学数据的积累速度史无前例,为生物医学研究带来机遇的同时,也让传统数据分析技术面临巨大挑战.本文综述了深度学习方法应用在生物医学数据分析中的最新研究进展.首先阐述了深度学习方法,列举深度学习方法的主要实现模型,随后总结了目前生物医学数据分析中的深度学习方法应用情况,分析了在数据处理、模型构建和训练方法等方面共有问题的解决方法,最后给出了深度学习方法应用于生物医学数据分析时可能存在的问题及建议.     

Test of association between haplotypes and phenotypes in case-control studies: examination of validity of the application of an algorithm for samples from cohort or clinical trials to case-control samples using simulated and real data

The use of haplotype information in case-control studies is an area of focus for the research on the association between phenotypes and genetic polymorphisms. We examined the validity of the application of the likelihood-based algorithm, which was originally developed to analyze the data from cohort studies or clinical trials, to the data from case-control studies. This algorithm was implemented in a computer program called PENHAPLO. In this program, haplotype frequencies and penetrances are estimated using the expectation-maximization algorithm, and the haplotype-phenotype association is tested using the generalized likelihood ratio. We show that this algorithm was useful not only for cohort studies but also for case-control studies. Simulations under the null hypothesis (no association between haplotypes and phenotypes) have shown that the type I error rates were accurately estimated. The simulations under alternative hypotheses showed that PENHAPLO is a robust method for the analysis of the data from case-control studies even when the haplotypes were not in HWE, although real penetrances cannot be estimated. The power of PENHAPLO was higher than that of other methods using the likelihood-ratio test for the comparison of haplotype frequencies. Results of the analysis of real data indicated that a significant association between haplotypes in the SAA1 gene and AA-amyloidosis phenotype was observed in patients with rheumatoid arthritis, thereby suggesting the validity of the application of PENHAPLO for case-control data.     

A Novel Approach to Delayed-Start Analyses for Demonstrating Disease-Modifying Effects in Alzheimer’s Disease

One method for demonstrating disease modification is a delayed-start design, consisting of a placebo-controlled period followed by a delayed-start period wherein all patients receive active treatment. To address methodological issues in previous delayed-start approaches, we propose a new method that is robust across conditions of drug effect, discontinuation rates, and missing data mechanisms. We propose a modeling approach and test procedure to test the hypothesis of noninferiority, comparing the treatment difference at the end of the delayed-start period with that at the end of the placebo-controlled period. We conducted simulations to identify the optimal noninferiority testing procedure to ensure the method was robust across scenarios and assumptions, and to evaluate the appropriate modeling approach for analyzing the delayed-start period. We then applied this methodology to Phase 3 solanezumab clinical trial data for mild Alzheimer’s disease patients. Simulation results showed a testing procedure using a proportional noninferiority margin was robust for detecting disease-modifying effects; conditions of high and moderate discontinuations; and with various missing data mechanisms. Using all data from all randomized patients in a single model over both the placebo-controlled and delayed-start study periods demonstrated good statistical performance. In analysis of solanezumab data using this methodology, the noninferiority criterion was met, indicating the treatment difference at the end of the placebo-controlled studies was preserved at the end of the delayed-start period within a pre-defined margin. The proposed noninferiority method for delayed-start analysis controls Type I error rate well and addresses many challenges posed by previous approaches. Delayed-start studies employing the proposed analysis approach could be used to provide evidence of a disease-modifying effect. This method has been communicated with FDA and has been successfully applied to actual clinical trial data accrued from the Phase 3 clinical trials of solanezumab.     

Methodologic challenges in designing clinical studies to measure differences in the bioequivalence of n-3 fatty acids

Although epidemiologic studies suggest a role for alpha-linolenic acid (ALA) in the prevention of coronary heart disease and certain types of cancer, the findings of clinical studies suggest that ALA is inferior biologically to the n-3 long-chain fatty acids because its bioconversion to eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) is limited in humans and because the magnitude of its biologic effects is smaller than that of EPA and DHA. This paper reviews several methodologic issues that may confound the findings of clinical studies and complicate our interpretations of them: the ALA and EPA + DHA dietary enrichment levels; the choice of tissue; the choice of lipid species; and the method of reporting fatty acid composition. Although the ALA enrichment levels used in most clinical studies can be achieved by consuming ground flaxseed, flaxseed oil, canola oil and other ALA-rich plants as part of a typical dietary pattern, the EPA + DHA enrichment levels are not practical and can only be obtained from fish oil supplements. The lack of consistency in the choice of lipids species and the reporting of data makes it difficult to compare outcomes across studies. The choice of tissue (blood) for analysis is a limitation that probably cannot be overcome. The use of practical ALA and EPA+ DHA dietary enrichment levels and some standardization of clinical study design would allow for greater comparisons of outcomes across studies and ensure a more realistic analysis of how individual n-3 fatty acids differ in their biologic effects in humans.