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Open Access Publications from the University of California

Scholarly Works (35 results)

  • Article
  • Peer Reviewed
UC San Francisco Previously Published Works (2008)
Recently, several pharmaceutical companies have been shown to have withheld negative clinical trial results from the public. These incidents have resulted in a concerted global effort to register all trials at inception, so that all subsequent results can be tracked regardless of whether they are positive or negative. These trial registration policies have been driven in large part by concern about the pharmaceutical sector. The medical device industry is much smaller, and different from the pharmaceutical industry in some fundamental ways. This paper examines the issues surrounding registration of device trials and argues that these differences with pharmaceutical should not exempt device trials from registration.
    Cover page: Trial Registration for Public Trust: Making the Case for Medical Devices
    • Thesis
    • Peer Reviewed
    UC San Francisco Electronic Theses and Dissertations (2018)

    Psychiatric Phenotyping Using Symptom Profiles: Can Self-Report Symptoms Inform a New Psychiatric Taxonomy?

    by

    Jessica Ross

    The Diagnostic and Statistical Manual (DSM) has served as the gold standard for psychiatric diagnosis for the past several decades in the United States, and it mirrors mental health and substance abuse diagnoses in the ICD-9 and ICD-10, which are used in numerous other countries. However, DSM diagnoses have severe limitations when used as phenotypes for studies of the pathophysiology underlying mental disorders, as well as for clinical treatment and research. This dissertation proposes a novel approach for deconstructing DSM diagnostic criteria using expert knowledge to inform feature selection for unsupervised machine learning. A multimodal dataset comprised of combat veterans, approximately one-third of whom had received a DSM-IV diagnosis of post-traumatic stress disorder (PTSD), is used in these analyses. Unsupervised learning methods are employed to identify robust groups of patients who clustered together with respect to clinical symptoms. Symptom profiles are used to stratify subjects into cohorts who have clinical and biological homogeneity, irrespective of their DSM diagnoses. Clusters identified suggest that prior contrasting biomarker findings in patients with PTSD may be due to heterogeneity that is reduced when using phenotypes derived from self-report psychiatric symptoms. Results of these analyses can be represented in rich clinical phenotypes that relay both clinical and biological markers of interest. These findings suggest that itemized self-report symptom data may be useful to inform a new taxonomy for psychiatry, enhancing the bidirectional translation of knowledge from the bench to the clinic through a common terminology.

      Cover page: Psychiatric Phenotyping Using Symptom Profiles: Can Self-Report Symptoms Inform a New Psychiatric Taxonomy?
      • Article
      • Peer Reviewed
      UC San Francisco Previously Published Works (2021)
      Health applications for mobile and wearable devices continue to experience tremendous growth both in the commercial and research sectors, but their impact on healthcare has yet to be fully realized. This commentary introduces three articles in a special issue that provides guidance on how to successfully address translational barriers to bringing mobile health technologies into clinical research and care. We also discuss how the cross-organizational sharing of data, software, and other digital resources can lower such barriers and accelerate progress across mobile health.
        Cover page: Mobile Health: making the leap to research and clinics
        • Article
        • Peer Reviewed
        UC San Francisco Previously Published Works (2006)

        Objective. Computer-based clinical decision support systems (CDSSs) vary greatly in design and function. Using a taxonomy that we had previously developed, we describe the characteristics of CDSSs reported in the literature. Methods. We searched PubMed and the Cochrane Library for randomized controlled trials (RCTs) published in English between 1998 and 2003 that evaluated CDSSs. We coded each CDSS using our taxonomy. Results. 58 studies met our inclusion criteria. The 74 reported CDSSs varied greatly in context of use, knowledge and data sources, nature of decision support offered, information delivery, and workflow impact. Two distinct subsets of CDSSs were seen: patient-directed systems that provided decision support for preventive care or health-related behaviors via mail or phone (38% of systems), and inpatient systems targeting clinicians with online decision support and direct online execution of the recommendations (18%). 84% of the CDSSs required extra staffing for handling CDSS-related input or output. Conclusion. Reported CDSSs are heterogeneous along many dimensions. Caution should be taken in generalizing the results of CDSS RCTs to different clinical or workflow settings. (c) 2005 Elsevier Inc. All rights reserved.

          Cover page: A taxonomic description of computer-based clinical decision support systems
          • Article
          • Peer Reviewed
          UC San Francisco Previously Published Works (2004)

          Randomized controlled trials (RCTs) are one of the least biased sources of clinical research evidence, and are therefore a critical resource for the practice of evidence-based medicine. With over 10,000 new RCTs indexed in Medline each year, knowledge systems are needed to help clinicians translate evidence into practice. Common ontologies for RCTs and other domains would facilitate the development of these knowledge systems. However, no standard method exists for developing domain ontologies. In this paper, we describe a new systematic approach to specifying and evaluating the conceptual content of ontologies. In this method, called competency decomposition, the target task for an ontology is hierarchically decomposed into subtasks and methods, and the ontology content is specified by identifying the domain information required to complete each of the subtasks. We illustrate the use of this competency decomposition approach for the content specification and evaluation of an RCT ontology for evidence-based practice. (C) 2004 Elsevier Inc. All rights reserved.

            Cover page: An ontology of randomized controlled trials for evidence-based practice: content specification and evaluation using the competency decomposition method
            • Article
            • Peer Reviewed
            UC San Francisco Previously Published Works (2015)

            Objective

            To develop a method for profiling the collective populations targeted for recruitment by multiple clinical studies addressing the same medical condition using one eligibility feature each time.

            Methods

            Using a previously published database COMPACT as the backend, we designed a scalable method for visual aggregate analysis of clinical trial eligibility features. This method consists of four modules for eligibility feature frequency analysis, query builder, distribution analysis, and visualization, respectively. This method is capable of analyzing (1) frequently used qualitative and quantitative features for recruiting subjects for a selected medical condition, (2) distribution of study enrollment on consecutive value points or value intervals of each quantitative feature, and (3) distribution of studies on the boundary values, permissible value ranges, and value range widths of each feature. All analysis results were visualized using Google Charts API. Five recruited potential users assessed the usefulness of this method for identifying common patterns in any selected eligibility feature for clinical trial participant selection.

            Results

            We implemented this method as a Web-based analytical system called VITTA (Visual Analysis Tool of Clinical Study Target Populations). We illustrated the functionality of VITTA using two sample queries involving quantitative features BMI and HbA1c for conditions "hypertension" and "Type 2 diabetes", respectively. The recruited potential users rated the user-perceived usefulness of VITTA with an average score of 86.4/100.

            Conclusions

            We contributed a novel aggregate analysis method to enable the interrogation of common patterns in quantitative eligibility criteria and the collective target populations of multiple related clinical studies. A larger-scale study is warranted to formally assess the usefulness of VITTA among clinical investigators and sponsors in various therapeutic areas.
              Cover page: Visual aggregate analysis of eligibility features of clinical trials
              • Article
              • Peer Reviewed
              UC San Francisco Previously Published Works (2008)

              Background

              The United States (US) Food and Drug Administration (FDA) approves new drugs based on sponsor-submitted clinical trials. The publication status of these trials in the medical literature and factors associated with publication have not been evaluated. We sought to determine the proportion of trials submitted to the FDA in support of newly approved drugs that are published in biomedical journals that a typical clinician, consumer, or policy maker living in the US would reasonably search.

              Methods and findings

              We conducted a cohort study of trials supporting new drugs approved between 1998 and 2000, as described in FDA medical and statistical review documents and the FDA approved drug label. We determined publication status and time from approval to full publication in the medical literature at 2 and 5 y by searching PubMed and other databases through 01 August 2006. We then evaluated trial characteristics associated with publication. We identified 909 trials supporting 90 approved drugs in the FDA reviews, of which 43% (394/909) were published. Among the subset of trials described in the FDA-approved drug label and classified as "pivotal trials" for our analysis, 76% (257/340) were published. In multivariable logistic regression for all trials 5 y postapproval, likelihood of publication correlated with statistically significant results (odds ratio [OR] 3.03, 95% confidence interval [CI] 1.78-5.17); larger sample sizes (OR 1.33 per 2-fold increase in sample size, 95% CI 1.17-1.52); and pivotal status (OR 5.31, 95% CI 3.30-8.55). In multivariable logistic regression for only the pivotal trials 5 y postapproval, likelihood of publication correlated with statistically significant results (OR 2.96, 95% CI 1.24-7.06) and larger sample sizes (OR 1.47 per 2-fold increase in sample size, 95% CI 1.15-1.88). Statistically significant results and larger sample sizes were also predictive of publication at 2 y postapproval and in multivariable Cox proportional models for all trials and the subset of pivotal trials.

              Conclusions

              Over half of all supporting trials for FDA-approved drugs remained unpublished >/= 5 y after approval. Pivotal trials and trials with statistically significant results and larger sample sizes are more likely to be published. Selective reporting of trial results exists for commonly marketed drugs. Our data provide a baseline for evaluating publication bias as the new FDA Amendments Act comes into force mandating basic results reporting of clinical trials.
                Cover page: Publication of clinical trials supporting successful new drug applications: a literature analysis.
                • Article
                • Peer Reviewed
                UC San Francisco Previously Published Works (2010)
                Formal, computer-interpretable representations of eligibility criteria would allow computers to better support key clinical research and care use cases such as eligibility determination. To inform the development of such formal representations for eligibility criteria, we conducted this study to characterize and quantify the complexity present in 1000 eligibility criteria randomly selected from studies in ClinicalTrials.gov. We classified the criteria by their complexity, semantic patterns, clinical content, and data sources. Our analyses revealed significant semantic and clinical content variability. We found that 93% of criteria were comprehensible, with 85% of these criteria having significant semantic complexity, including 40% relying on temporal data. We also identified several domains of clinical content. Using the findings of the study as requirements for computer-interpretable representations of eligibility, we discuss the challenges for creating such representations for use in clinical research and practice.
                  Cover page: Analysis of eligibility criteria complexity in clinical trials.
                  • Article
                  • Peer Reviewed
                  UC Davis Previously Published Works (2022)
                  The term data science usually refers to the process of extracting value from big data obtained from a large group of individuals. An alternative rendition, which we call personalized data science (Per-DS), aims to collect, analyze, and interpret personal data to inform personal decisions. This article describes the main features of Per-DS, and reviews its current state and future outlook. A Per-DS investigation is of, by, and for an individual, the Per-DS investigator, acting simultaneously as her own investigator, study participant, and beneficiary, and making personalized decisions for study design and implementation. The scope of Per-DS studies may include systematic monitoring of physiological or behavioral patterns, case-crossover studies for symptom triggers, pre-post trials for exposure-outcome relationships, and personalized (N-of-1) trials for effectiveness. Per-DS studies produce personal knowledge generalizable to the individuals future self (thus benefiting herself) rather than knowledge generalizable to an external population (thus benefiting others). This endeavor requires a pivot from data mining or extraction to data gardening, analogous to home gardeners producing food for home consumption-the Per-DS investigator needs to cultivate the field by setting goals, specifying study design, identifying necessary data elements, and assembling instruments and tools for data collection. Then, she can implement the study protocol, harvest her personal data, and mine the data to extract personal knowledge. To facilitate Per-DS studies, Per-DS investigators need support from community-based, scientific, philanthropic, business, and government entities, to develop and deploy resources such as peer forums, mobile apps, virtual field guides, and scientific and regulatory guidance.
                    Cover page: Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care.
                    • Article
                    • Peer Reviewed
                    UC San Francisco Previously Published Works (2014)
                    ClinicalTrials.gov presents great opportunities for analyzing commonalities in clinical trial target populations to facilitate knowledge reuse when designing eligibility criteria of future trials or to reveal potential systematic biases in selecting population subgroups for clinical research. Towards this goal, this paper presents a novel data resource for enabling such analyses. Our method includes two parts: (1) parsing and indexing eligibility criteria text; and (2) mining common eligibility features and attributes of common numeric features (e.g., A1c). We designed and built a database called "Commonalities in Target Populations of Clinical Trials" (COMPACT), which stores structured eligibility criteria and trial metadata in a readily computable format. We illustrate its use in an example analytic module called CONECT using COMPACT as the backend. Type 2 diabetes is used as an example to analyze commonalities in the target populations of 4,493 clinical trials on this disease.
                      Cover page: A method for analyzing commonalities in clinical trial target populations.
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