Conflicts of Interest in Clinical Research: Advocating for Patient-Subjects

2002 ◽  
Author(s):  
Janet Fleetwood
Keyword(s):  
Clinical Research ◽  
Conflicts Of Interest

The Business of Clinical Research

2018 ◽  
pp. 417-433
Author(s):  
Lívia Caroline Mariano Compte ◽  
Jorge Leite ◽  
Andre Brunoni ◽  
Felipe Fregni
Keyword(s):  
Clinical Trial ◽  
Intellectual Property ◽  
Clinical Research ◽  
Conflict Of Interest ◽  
Clinical Case ◽  
Conflicts Of Interest

This chapter presents a series of important topics that should be considered and evaluated before conducting a clinical trial in which there is a collaboration between industry and academia. It discusses important topics such as the project budget and sources of funding. Additionally, this chapter highlights the advantages of the academia-industry partnership, potential conflicts of interest, and, in the advent of a conflict of interest, strategies to minimize its effects. Intellectual property, indemnifications, publication, and other specific issues are also presented as key elements in a clinical trial agreement. A clinical case is used to exemplify and discuss the practical aspects of this challenging negotiation.

Intrinsic Conflicts of Interest in Clinical Research: A Need for Disclosure

2003 ◽  
Vol 13 (2) ◽  
pp. 83-91 ◽  
Author(s):  
Sharmon Sollitto ◽  
Sharona Hoffman ◽  
Maxwell J. Mehlman ◽  
Robert J. Lederman ◽  
Stuart J. Youngner ◽  
...  
Keyword(s):  
Clinical Research ◽  
Conflicts Of Interest

Managing Conflicts of Interest in Industry-Sponsored Clinical Research

JAMA ◽  
2017 ◽  
Vol 317 (17) ◽  
pp. 1751 ◽  
Author(s):  
Joanne Waldstreicher ◽  
Michael E. Johns
Keyword(s):  
Clinical Research ◽  
Conflicts Of Interest

Comparing The Prognostic Value Of Risk Stratifying Models For Patients With Lower-Risk Myelodysplastic Syndromes (MDS): Is One Model Better? A Report on The Behalf of The MDS Clinical Research Consortium

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1505-1505 ◽  
Author(s):  
Rami S Komrokji ◽  
Xiao-Feng Wang ◽  
Najla H Al Ali ◽  
Guillermo Garcia-Manero ◽  
David P. Steensma ◽  
...  
Keyword(s):  
Clinical Research ◽  
Scoring System ◽  
Lower Risk ◽  
Goodness Of Fit ◽  
Conflicts Of Interest ◽  
Cleveland Clinic ◽  
Information Criteria ◽  
Rank Test ◽  
Multivariable Analyses ◽  
Research Consortium

Abstract Background MDS are a spectrum of diseases commonly divided clinically into lower- and higher-risk subtypes to reflect underlying disease biology and to guide treatment. The International Prognostic Scoring System (IPSS), the most widely used tool for risk stratification is limited in its ability to identify poor prognosis lower-risk patients (pts). A prognostic scoring system specifically for lower-risk MDS pts (LR-PSS) was developed (Garcia-Manero Leukemia 2008) based on unfavorable (non-del(5q), non–diploid) cytogenetic, hemoglobin (hgb) <10g/dl, platelet count (plt) <50 k/uL or 50-200k/uL, bone marrow blast %≥4, and age ≥60 years. The newly revised IPSS (IPSS-R) also addressed some IPSS limitations. We examined the prognostic utility of IPSS, LR-PSS and the IPSS-R in a large cohort of lower-risk MDS pts, including those with secondary (s) MDS and chronic myelomonocytic leukemia (CMML) – both excluded from IPSS and IPSS-R - within the MDS Clinical Research Consortium. Methods MDS pts with IPSS Score <1.5 were identified at Moffitt Cancer Center (MCC) or Cleveland Clinic (CC) from 2002-2012 and included if adequate data for analyses were available. Overall Survival (OS) was calculated from diagnosis. The Kaplan–Meier method was used to estimate median OS. Univariable analyses were performed using the log-rank test and adjusted for multiple comparisons; multivariable analyses used a Cox proportional hazards model. Harrell's c index and the Akaike information criteria (AIC) were used to assess the discriminatory power of the models and relative goodness of fit, respectively. Results The analysis included 1196 MDS patients with IPSS scores <1.5. Comparing MCC (n=668) to CC (n=528), baseline characteristics were similar except plt <50k/uL: 16% vs. 22% (p=0.01); ANC <1.5 k/uL: 40% vs. 28% (p<0.0001); blasts <4%: 78% vs. 72% (p=0.02); sMDS: 11% vs. 6% (p=0.0024); and CMML: 2% vs. 10% (p<0.001 for WHO subgroup). R-IPSS cytogenetic groups were very good/ good/ int/ poor/ and very poor in 2/74/16/6/3 % at MCC and 2/63/19/7/10 % at CCF (p < 0.001). The median OS was 47 months (95% C.I. 44 - 52) and median follow-up of patients still alive was 62 months (range 2-326). LR-PSS and IPSS-R classifications for MCC and CCF Pts and OS are summarized in Table 1 and Figure 1. In univariable analyses, The IPSS, LR-PSS, and IPSS-R were all predictive of OS (p<0.0001 for all). Multivariable analyses confirmed the overall predictive abilities of the 3 prognostic tools adjusted for Hgb, plt, and age (p <0.0001). Compared to the IPSS-R, the LR-PSS had the higher (better) Harrell's c value (.66 vs. .60) and lower (better) AIC (5600 vs. 5605), and both were superior to the IPSS (.46 and 5609, respectively). The LR-PSS upstaged 302 pts (25%) from IPSS low or Int-1 to LR-PSS Category 3, and downstaged 104 pts (8.6%) from Int-1 to Category 1. The IPSS-R upstaged 449 pts (37%) from IPSS low or Int-1 to IPSS-R Categories ≥Intermediate, and downstaged 18 pts (1.5% ) from Int-1 to Very Low. Conclusions The LR-PSS and IPSS-R are superior tools for distinguishing outcome among pts previously thought to have lower-risk disease by the IPSS, including those with sMDS and CMML. Upstaged pts may benefit from earlier interventions with disease-modifying therapies, and should be considered in trials targeting higher-risk MDS pts. The LR-PSS appears to provide slightly better prognostic information. Disclosures: No relevant conflicts of interest to declare.

The Costs of Conducting Clinical Research

2003 ◽  
Vol 21 (22) ◽  
pp. 4145-4150 ◽  
Author(s):  
Ezekiel J. Emanuel ◽  
Lowell E. Schnipper ◽  
Deborah Y. Kamin ◽  
Jenifer Levinson ◽  
Allen S. Lichter
Keyword(s):  
Pharmaceutical Industry ◽  
Clinical Research ◽  
Conflicts Of Interest ◽  
Controlled Trial ◽  
Phase Iii ◽  
Office Visits ◽  
Institutional Review ◽  
Clinical Research Trial ◽  
The Hours ◽  
Time Required

Purpose: Physicians frequently receive payment for enrolling subjects onto clinical trials. Some view these payments as conflicts of interest. Others contend that these payments are necessary reimbursements for conducting clinical research. We evaluated the clinical and nonclinical hours and costs associated with conducting a mock phase III clinical research trial. Methods: We collected data from representatives of 21 clinical sites, on the numbers of hours associated with 13 activities necessary to the conduct of clinical research. The hours were based on enrolling 20 patients in a 12-month randomized placebo-controlled trial of a new chemotherapeutic agent. The outcome measures were disease progression and quality-of-life reports. These costs were evaluated for both government and pharmaceutical industry–sponsored trials. Results: On average, 4,012 hours (range, 1,512 to 13,319 hours) were required for a government-sponsored trial, and 3,998 hours (range: 1735 to 15,699) were required for a pharmaceutical industry–sponsored trial involving 20 subjects with 17 office visits, or approximately 200 hours per subject. Thirty-two percent of the hours were devoted to nonclinical activities, such as institutional review board submission and completion of clinical reporting forms. On average, excluding overhead expenses, it cost slightly more than $6,094 (range, $2,098 to $19,285) per enrolled subject for an industry-sponsored trial, including $1,999 devoted to nonclinical costs. Conclusion: Based on the results of our mock trial, the time required for nontreatment trial activities is considerable, and the associated costs are substantial.

scholarly journals Current clinical research environment: focus on psychiatry

2007 ◽  
Vol 29 (3) ◽  
pp. 283-290 ◽  
Author(s):  
Sonia Mansoldo Dainesi ◽  
Helio Elkis
Keyword(s):  
Clinical Research ◽  
Conflicts Of Interest ◽  
Public Health Care ◽  
Research Network ◽  
Teaching Hospitals ◽  
Research Centers ◽  
Design Strategies ◽  
Clinical Practices ◽  
Care Needs ◽  
Great Opportunity

The introduction of international guidelines on Good Clinical Practices (GCP) in 1996, immediately followed by the publication of Resolution CNS 196/96 in Brazil, created a great opportunity for Brazilian research centers to participate in international trials. Such studies must be strictly monitored in order to assure compliance with the regulations, as well as with the standards of patient safety. Clear agreement among the investigator, the sponsor and the institution carrying out the study must be previously defined in order to avoid any conflicts of interest during or after the study. Operational aspects, such as the time needed to gain regulatory approval of the study design, strategies for patient recruitment/retention and appropriate logistics, are also important. In 2005, the Brazilian National Clinical Research Network was established, bringing together a number of research centers in teaching hospitals. The objective was to subsidize public clinical research with state-of-the-art practices and appropriate technical/scientific training programs. The development of research protocols that prioritize public health care needs in Brazil is other fundamental goal of this network. This article addresses general aspects of clinical research, as well as some specific issues in psychiatry. Improving the health and quality of life of the global population is certainly the major objective of all of the work done in this area.

An Interactive 3D Lymphoma's Stage Calculator Assistant: A New Tool to Help Hematologists in Routine Practice

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 1323-1323
Author(s):  
Mohamed Touati ◽  
Jeremy Assenat ◽  
Jacques Monteil ◽  
Philippe De Souza ◽  
Magdalena Munyamahoro ◽  
...  
Keyword(s):  
Clinical Research ◽  
Lymphatic System ◽  
Conflicts Of Interest ◽  
Disease Stage ◽  
Routine Practice ◽  
Interactive Model ◽  
Expert Radiologist ◽  
Disease Staging ◽  
Ann Arbor ◽  
The University

Abstract Introduction: The lymphomas stages are currently determined by Ann Arbor-Cotswolds classification that have been created during the “Committee On Hodgkin Disease Staging Classification”, in Ann Arbor (Michigan, USA) in 1971, and modified in Cotswolds (England) in 1988. The classification is used in routine hematology, by doctors and Clinical Research Associates (CRA), in order to estimate the lymphomas' spread, stratify patients and select therapies. In spite of its user-friendliness, every clinician knows that certain scenario depends upon operators. Despite lots of progress, there is no computerized tool that could help to establish an easy disease staging. A French hematological network, HEMATOLIM, in association with the University Hospital of Limoges, developed an information technology (IT) tool to calculate lymphoma's Ann Arbor stage thanks to 3D interactive model. The aim of this work is to make available online an easy to use calculator for mapping all the lymphoma localizations on a 3D interactive mannequin and then obtain quickly a standardized Ann Arbor's staging. Methods: That project has been developed by a mechatronic engineer from the “Ecole Nationale Supérieure d'Ingénieur de Limoges, France” since April 2013. This software is a 3D interactive model interfacing with Ann Arbor stage's calculator. We used anatomical 3D library files, which represent human body's tissues and organs. This free sharing and use database has been created by a Japanese team (Database Center for Life Science Research Organization of Information and Systems Faculty of Engineering Bldg.12; The University Of Tokyo; 2-11-16 Yayoi, Bunkyo-ku, Tokyo). That base concerns only males and doesn't include lymphatic system. We modified it and added in this database the simplified lymphatic system and created female body's tissues and organs. An expert radiologist approved the resulted projections. To make easier the selection of pathological areas, different tools were created (sectional view tools, magnifying glass, dissection scissors, list selection modality, display of territory's names…). This tool is PC compatible and uses OpenGL (Open Graphics Library) 3D engine. It should be used with updated Windows software and accepts all OpenGL versions (See figures 1, 2 and 3). With this 1.0 software version, 20 patients with Hodgkin lymphoma (n = 8) and non-Hodgkin lymphomas (n = 12), managed in our institution between May and July 2014, were evaluated. The Ann Arbor stages determined by physicians during multi-disciplinary team sessions (MDT) were compared with the results of software calculated by a junior CRA. Results: The rate of agreement between the two methods is 81%. MDT staging and Ann Arbor calculator results were in disagreement in 1 patient (6%). Finally, 3D dynamics model was unable to determinate stages in 2 patients' files because some parts of body are not yet included in the model. Kappa coefficient was estimated to 0.827 ± 0.163 (CI 95%: 0.508 – 1.000), meaning a very good agreement. Discussion: We have planned a thorough study to confirm these preliminary results with 80 patients for each stage, in total 320 patients, to obtain a sensitivity or specificity of 95% and 5% of precision. This software represents a consistent help for physicians, hematologists or not. Easy to use, intuitive and fast, it could be an Ann Arbor reference calculator. Improvements are planned for better completeness of the affected tissues. This device could help during MDT to have a standardized and rapid staging. Moreover, that software may be used in educational methods for medical students especially with sectional view tools. It should be very useful in clinical research units to assist in collecting lymphomas data. This software can also help to calculates main scores and prognostic indexes (IPI, FLIPI, IPS…). Conclusion: Online and full versions of this 3D lymphoma's stage calculator assistant will be developed by our network to standardize the staging of disease and to optimize treatment decisions. This software developed initially for lymphoma, can be adapted to the TNM classification of cancers. It should be developed with isotopists and radiologists colleagues to create a new tool, in order to deliver the disease stage automatically from CT-scan or PET/CT. Figure 1 Figure 1. Figure 2 Figure 2. Figure 3 Figure 3. Disclosures No relevant conflicts of interest to declare.

Sign in / Sign up

Export Citation Format

Share Document