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Death: The Simple Clinical Trial Endpoint.

ABSTRACT: Death, as a biological phenomenon, is well understood and a commonly employed endpoint for clinical trials. However, death identification and adjudication may be difficult for pragmatic clinical trials (PCT) that rely upon electronic health record and patient reported data. We propose a novel death identification and verification approach that is being used in the ToRsemide compArisoN with furoSemide FOR Management of Heart Failure (TRANSFORM-HF) PCT. We describe our hybrid approach that includes gathering information from clinical trial sites, a centralized call center, and National Death Index searches. Our methods detail how a possible death is triggered from each of these components and the types of information we require to verify a triggered death. Our different trigger/verification elements collectively define the TRANSFORM-HF PCT's definition of a death event.

SUBMITTER: Eisenstein EL

PROVIDER: S-EPMC8228236 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Death: The Simple Clinical Trial Endpoint.

Eisenstein Eric L EL Prather Kristi K Greene Stephen J SJ Harding Tina T Harrington Amanda A Gabriel Davera D Jones Ingrid I Mentz Robert J RJ Velazquez Eric J EJ Anstrom Kevin J KJ

Studies in health technology and informatics 20190101

Death, as a biological phenomenon, is well understood and a commonly employed endpoint for clinical trials. However, death identification and adjudication may be difficult for pragmatic clinical trials (PCT) that rely upon electronic health record and patient reported data. We propose a novel death identification and verification approach that is being used in the ToRsemide compArisoN with furoSemide FOR Management of Heart Failure (TRANSFORM-HF) PCT. We describe our hybrid approach that include ...[more]

PMID: 30741178

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Project description:IntroductionGlobal guidelines emphasize the ethical obligation of investigators to help participants in HIV-endpoint trials reduce HIV risk by offering an optimal HIV prevention package. Oral pre-exposure prophylaxis (PrEP) has increasingly become part of state-of-the-art HIV prevention. Here we describe the process of integrating oral PrEP delivery into the HIV prevention package of the Evidence for Contraceptive Options and HIV Outcomes (ECHO) Trial.MethodsECHO was an open-label randomized clinical trial that compared HIV incidence among women randomized to one of three effective contraceptives. In total, 7830 women aged 16 to 35 years from 12 sites in four African countries (Eswatini, Kenya, South Africa and Zambia) were enrolled and followed for 12 to 18 months, from 2015 to 2018. Part-way through the course of the trial, oral PrEP was provided to study participants either off-site via referral or on site via trained trial staff. PrEP uptake was compared between different contraceptive users using Chi-squared tests or t-tests. HIV seroincidence rates were compared between participants who never versus ever initiated PrEP using exact Poisson regression.ResultsPrEP access in ECHO began through public availability in Kenya in May 2017 and was available at all sites by June 2018. When PrEP became available, 3626 (46.3%) eligible women were still in follow-up in the study, and of these, 622 (17.2%) initiated PrEP. Women initiating PrEP were slightly older; more likely to be unmarried, not living with their partner, having multiple partners; and less likely to be earning their own income and receiving financial support from partners (all p < 0.05). PrEP initiation did not differ across study randomized groups (p = 0.7). Two-thirds of PrEP users were continuing PrEP at study exit.ConclusionsThere is a need for improved HIV prevention services in clinical trials with HIV endpoints, especially trials among African women. PrEP as a component of a comprehensive HIV prevention package provided to women in a large clinical trial is practical and feasible. Provision of PrEP within clinical trials with HIV outcomes should be standard of prevention.

Project description:BackgroundClinical endpoint prediction remains challenging for health providers. Although predictors such as age, gender, and disease staging are of considerable predictive value, the accuracy often ranges between 60 and 80%. An accurate prognosis assessment is required for making effective clinical decisions.MethodsWe proposed an extended prognostic model based on clinical covariates with adjustment for additional variables that were radio-graphically induced, termed imaging biomarkers. Eight imaging biomarkers were introduced and investigated in a cohort of 68 non-small cell lung cancer subjects with tumor internal characteristic. The subjects comprised of 40 males and 28 females with mean age at 68.7 years. The imaging biomarkers used to quantify the solid component and non-solid component of a tumor. The extended model comprises of additional frameworks that correlate these markers to the survival ends through uni- and multi-variable analysis to determine the most informative predictors, before combining them with existing clinical predictors. Performance was compared between traditional and extended approaches using Receiver Operating Characteristic (ROC) curves, Area under the ROC curves (AUC), Kaplan-Meier (KM) curves, Cox Proportional Hazard, and log-rank tests (p-value).ResultsThe proposed hybrid model exhibited an impressive boosting pattern over the traditional approach of prognostic modelling in the survival prediction (AUC ranging from 77 to 97%). Four developed imaging markers were found to be significant in distinguishing between subjects having more and less dense components: (P = 0.002-0.006). The correlation to survival analysis revealed that patients with denser composition of tumor (solid dominant) lived 1.6-2.2 years longer (mean survival) and 0.5-2.0 years longer (median survival), than those with less dense composition (non-solid dominant).ConclusionThe present study provides crucial evidence that there is an added value for incorporating additional image-based predictors while predicting clinical endpoints. Though the hypotheses were confirmed in a customized case study, we believe the proposed model is easily adapted to various clinical cases, such as predictions of complications, treatment response, and disease evolution.

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Death: The Simple Clinical Trial Endpoint.

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Death: The Simple Clinical Trial Endpoint.

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