Publications

Letters20 August 2019Lost Evidence From Registered Large Long-Unpublished Randomized Controlled Trials: A SurveyAthina Tatsioni, MD, Fotini B. Karassa, MD, Steven N. Goodman, MD, PhD, Deborah A. Zarin, MD, Daniele Fanelli, PhD, and John P.A. Ioannidis, MD, DScAthina Tatsioni, MDUniversity of Ioannina School of Health Sciences, Ioannina, Greece (A.T., F.B.K.)Search for more papers by this author, Fotini B. Karassa, MDUniversity of Ioannina School of Health Sciences, Ioannina, Greece (A.T., F.B.K.)Search for more papers by this author, Steven N. Goodman, MD, PhDStanford University, Stanford, California (S.N.G., J.P.I.)Search for more papers by this author, Deborah A. Zarin, MDBrigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts (D.A.Z.)Search for more papers by this author, Daniele Fanelli, PhDLondon School of Economics and Political Science, London, United Kingdom (D.F.)Search for more papers by this author, and John P.A. Ioannidis, MD, DScStanford University, Stanford, California (S.N.G., J.P.I.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M19-0440 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Background: Empirical studies have demonstrated that many randomized controlled trials (RCTs) remain unpublished (1–3). Besides publication in journals, trial results also may be disseminated by posting on ClinicalTrials.gov, a public, searchable registry. Loss of information is most worrisome when the largest and thus potentially most informative trials both remain unpublished and lack posted results long after completion.Objective: To assess the largest preregistered RCTs that were still both unpublished and lacked reported results at ClinicalTrials.gov more than 46 months after completion. We evaluated whether these trials subsequently were published or posted any results. These studies are an important part of ...References1. Lee K, Bacchetti P, Sim I. Publication of clinical trials supporting successful new drug applications: a literature analysis. PLoS Med. 2008;5:e191. [PMID: 18816163] doi:10.1371/journal.pmed.00501911 CrossrefMedlineGoogle Scholar2. Rising K, Bacchetti P, Bero L. Reporting bias in drug trials submitted to the Food and Drug Administration: review of publication and presentation. PLoS Med. 2008;5:e217; discussion e217. [PMID: 19067477] doi:10.1371/journal.pmed.0050217 CrossrefMedlineGoogle Scholar3. Lexchin J, Bero LA, Djulbegovic B, Clark O. Pharmaceutical industry sponsorship and research outcome and quality: systematic review. BMJ. 2003;326:1167-70. [PMID: 12775614] CrossrefMedlineGoogle Scholar4. Moher D, Naudet F, Cristea IA, Miedema F, Ioannidis JPA, Goodman SN. Assessing scientists for hiring, promotion, and tenure. PLoS Biol. 2018;16:e2004089. [PMID: 29596415] doi:10.1371/journal.pbio.2004089 CrossrefMedlineGoogle Scholar5. Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet. 2018;391:1357-1366. [PMID: 29477251] doi:10.1016/S0140-6736(17)32802-7 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: University of Ioannina School of Health Sciences, Ioannina, Greece (A.T., F.B.K.)Stanford University, Stanford, California (S.N.G., J.P.I.)Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts (D.A.Z.)London School of Economics and Political Science, London, United Kingdom (D.F.)See Also: Editorial comment (page 293).Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M19-0440.Reproducible Research Statement: Study protocol: Not available (exploratory analysis). Statistical code: Not pertinent. Data set: Available from Dr. Ioannidis (e-mail, [email protected]edu).This article was published at Annals.org on 7 May 2019. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoNot Reporting Results of a Clinical Trial Is Academic Misconduct Joshua D. Wallach and Harlan M. Krumholz Lost Evidence From Registered Large Long-Unpublished Randomized Controlled Trials Fernando Rico-Villademoros Metrics Cited byNon-inferiority in cancer clinical trials was associated with more lenient margins and higher hypothesized outcome event ratesWhy an automated tracker finds poor sharing of clinical trial results for an academic sponsor: a bibliometric analysisPublication Rates of Heart Failure Clinical Trials Remain LowProportion of published primary health care protocols with published results in subsequent papers was decreasingRoom for improvement in clinical trials for rare diseasesLost Evidence From Registered Large Long-Unpublished Randomized Controlled TrialsFernando Rico-Villademoros, MD, PhDInformationEXCEL trial—medicine turned on its head!Clinical trials in the pandemic age: What is fit for purpose?Harms From Uninformative Clinical TrialsNot Reporting Results of a Clinical Trial Is Academic MisconductJoshua D. Wallach, MS, PhD and Harlan M. Krumholz, MD, SM 20 August 2019Volume 171, Issue 4Page: 300-301KeywordsAllergic diseasesCardiovascular diseasesClinical trialsDisclosureInformation retrievalInsomniaInterquartile rangeMotivationObstetrics and gynecology ePublished: 7 May 2019 Issue Published: 20 August 2019 Copyright & PermissionsCopyright © 2019 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...

Cyanobacteria are important agents in global carbon and nitrogen cycling and hold great promise for biotechnological applications. Model organisms such as Synechocystis sp. and Synechococcus sp. have advanced our understanding of photosynthetic capacity and circadian behavior, mostly using population-level measurements in which the behavior of individuals cannot be monitored. Synechocystis sp. cells are small and divide slowly, requiring long-term experiments to track single cells. Thus, the cumulative effects of drift over long periods can cause difficulties in monitoring and quantifying cell growth and division dynamics. To overcome this challenge, we enhanced a microfluidic cell-culture device and developed an image analysis pipeline for robust lineage reconstruction. This allowed simultaneous tracking of many cells over multiple generations, and revealed that cells expand exponentially throughout their cell cycle. Generation times were highly correlated for sister cells, but not between mother and daughter cells. Relationships between birth size, division size, and generation time indicated that cell-size control was inconsistent with the "sizer" rule, where division timing is based on cell size, or the "timer" rule, where division occurs after a fixed time interval. Instead, single cell growth statistics were most consistent with the "adder" rule, in which division occurs after a constant increment in cell volume. Cells exposed to light-dark cycles exhibited growth and division only during the light period; dark phases pause but do not disrupt cell-cycle control. Our analyses revealed that the "adder" model can explain both the growth-related statistics of single Synechocystis cells and the correlation between sister cell generation times. We also observed rapid phenotypic response to light-dark transitions at the single cell level, highlighting the critical role of light in cyanobacterial cell-cycle control. Our findings suggest that by monitoring the growth kinetics of individual cells we can build testable models of circadian control of the cell cycle in cyanobacteria.

Achieving high code reuse in physical design flows is challenging but increasingly necessary to build complex systems. Unfortunately, existing approaches based on parameterized Tcl generators support very limited reuse and struggle to preserve reusable code as designers customize flows for specific designs and technologies. We present a vision and framework based on modular flow generators that encapsulates coarse-grain and fine-grain reusable code in modular nodes and assembles them into complete flows. The key feature is a flow consistency and instrumentation layer embedded in Python, which supports mechanisms for rapid and early feedback on inconsistent composition. The approach gradually types the Tcl language and allows both automatic and user-annotated static assertion checks. We evaluate the design flows of successive generations of silicon prototypes designed in TSMC16, TSMC28, TSMC40, SKY130, and IBM180 technologies, showing how our approach can enable significant code reuse in future flows.

Objective: Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data. Methods: We searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation. Results: We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71). Conclusions: EHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies.