Dataset Information

Standardized classification and framework for reporting, interpreting, and analysing medication non-adherence in cardiovascular clinical trials: a consensus report from the Non-adherence Academic Research Consortium (NARC).

ABSTRACT: Non-adherence has been well recognized for years to be a common issue that significantly impacts clinical outcomes and health care costs. Medication adherence is remarkably low even in the controlled environment of clinical trials where it has potentially complex major implications. Collection of non-adherence data diverge markedly among cardiovascular randomized trials and, even where collected, is rarely incorporated in the statistical analysis to test the consistency of the primary endpoint(s). The imprecision introduced by the inconsistent assessment of non-adherence in clinical trials might confound the estimate of the calculated efficacy of the study drug. Hence, clinical trials may not accurately answer the scientific question posed by regulators, who seek an accurate estimate of the true efficacy and safety of treatment, or the question posed by payers, who want a reliable estimate of the effectiveness of treatment in the marketplace after approval. The Non-adherence Academic Research Consortium is a collaboration among leading academic research organizations, representatives from the U.S. Food and Drug Administration and physician-scientists from the USA and Europe. One in-person meeting was held in Madrid, Spain, culminating in a document describing consensus recommendations for reporting, collecting, and analysing adherence endpoints across clinical trials. The adoption of these recommendations will afford robustness and consistency in the comparative safety and effectiveness evaluation of investigational drugs from early development to post-marketing approval studies. These principles may be useful for regulatory assessment, as well as for monitoring local and regional outcomes to guide quality improvement initiatives.

SUBMITTER: Valgimigli M

PROVIDER: S-EPMC7963139 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Standardized classification and framework for reporting, interpreting, and analysing medication non-adherence in cardiovascular clinical trials: a consensus report from the Non-adherence Academic Research Consortium (NARC).

Valgimigli Marco M Garcia-Garcia Hector M HM Vrijens Bernard B Vranckx Pascal P McFadden Eugène P EP Costa Francesco F Pieper Karen K Vock David M DM Zhang Min M Van Es Gerrit-Anne GA Tricoci Pierluigi P Baber Usman U Steg Gabriel G Montalescot Gilles G Angiolillo Dominick J DJ Serruys Patrick W PW Farb Andrew A Windecker Stephan S Kastrati Adnan A Colombo Antonio A Feres Fausto F Jüni Peter P Stone Gregg W GW Bhatt Deepak L DL Mehran Roxana R Tijssen Jan G P JGP

European heart journal 20190701 25

Non-adherence has been well recognized for years to be a common issue that significantly impacts clinical outcomes and health care costs. Medication adherence is remarkably low even in the controlled environment of clinical trials where it has potentially complex major implications. Collection of non-adherence data diverge markedly among cardiovascular randomized trials and, even where collected, is rarely incorporated in the statistical analysis to test the consistency of the primary endpoint(s ...[more]

PMID: 29992264

Similar Datasets

Project description:AimsThe Shock Academic Research Consortium (SHARC) recently proposed pragmatic consensus definitions to standardize classification of cardiogenic shock (CS) in registries and clinical trials. We aimed to describe contemporary CS epidemiology using the SHARC definitions in a cardiac intensive care unit (CICU) population.Methods and resultsThe Critical Care Cardiology Trials Network (CCCTN) is a multinational research network of advanced CICUs coordinated by the TIMI Study Group (Boston, MA). Cardiogenic shock was defined as a cardiac disorder resulting in SBP < 90 mmHg for ≥30 min [or the need for vasopressors, inotropes, or mechanical circulatory support (MCS) to maintain SBP ≥ 90 mmHg] with evidence of hypoperfusion. Primary aetiologic categories included acute myocardial infarction-related CS (AMI-CS), heart failure-related CS (HF-CS), and non-myocardial (secondary) CS. Post-cardiotomy CS was not included. Heart failure-related CS was further subcategorized as de novo vs. acute-on-chronic HF-CS. Patients with both cardiogenic and non-cardiogenic components of shock were classified separately as mixed CS. Of 8974 patients meeting shock criteria (2017-23), 65% had isolated CS and 17% had mixed shock. Among patients with CS (n = 5869), 27% had AMI-CS (65% STEMI), 59% HF-CS (72% acute-on-chronic, 28% de novo), and 14% secondary CS. Patients with AMI-CS and de novo HF-CS were most likely to have had concomitant cardiac arrest (P < 0.001). Patients with AMI-CS and mixed CS were most likely to present in more severe shock stages (SCAI D or E; P < 0.001). Temporary MCS use was highest in AMI-CS (59%). In-hospital mortality was highest in mixed CS (48%), followed by AMI-CS (41%), similar in de novo HF-CS (31%) and secondary CS (31%), and lowest in acute-on-chronic HF-CS (25%; P < 0.001).ConclusionSHARC consensus definitions for CS classification can be pragmatically applied in contemporary registries and reveal discrete subpopulations of CS with distinct phenotypes and outcomes that may be relevant to clinical practice and future research.

Project description:We present results on the mortality statistics of the COVID-19 epidemic in a number of countries. Our data analysis suggests classifying countries in five groups, (1) Western countries, (2) East Block, (3) developed Southeast Asian countries, (4) Northern Hemisphere developing countries and (5) Southern Hemisphere countries. Comparing the number of deaths per million inhabitants, a pattern emerges in which the Western countries exhibit the largest mortality rate. Furthermore, comparing the running cumulative death tolls as the same level of outbreak progress in different countries reveals several subgroups within the Western countries and further emphasises the difference between the five groups. Analysing the relationship between deaths per million and life expectancy in different countries, taken as a proxy of the preponderance of elderly people in the population, a main reason behind the relatively more severe COVID-19 epidemic in the Western countries is found to be their larger population of elderly people, with exceptions such as Norway and Japan, for which other factors seem to dominate. Our comparison between countries at the same level of outbreak progress allows us to identify and quantify a measure of efficiency of the level of stringency of confinement measures. We find that increasing the stringency from 20 to 60 decreases the death count by about 50 lives per million in a time window of 20 days. Finally, we perform logistic equation analyses of deaths as a means of tracking the dynamics of outbreaks in the "first wave" and estimating the associated ultimate mortality, using four different models to identify model error and robustness of results. This quantitative analysis allows us to assess the outbreak progress in different countries, differentiating between those that are at a quite advanced stage and close to the end of the epidemic from those that are still in the middle of it. This raises many questions in terms of organisation, preparedness, governance structure and so on.

Project description:BackgroundTreatment non-adherence in randomised trials refers to situations where some participants do not receive their allocated treatment as intended. For cluster randomised trials, where the unit of randomisation is a group of participants, non-adherence may occur at the cluster or individual level. When non-adherence occurs, randomisation no longer guarantees that the relationship between treatment receipt and outcome is unconfounded, and the power to detect the treatment effects in intention-to-treat analysis may be reduced. Thus, recording adherence and estimating the causal treatment effect adequately are of interest for clinical trials.ObjectivesTo assess the extent of reporting of non-adherence issues in published cluster trials and to establish which methods are currently being used for addressing non-adherence, if any, and whether clustering is accounted for in these.MethodsWe systematically reviewed 132 cluster trials published in English in 2011 previously identified through a search in PubMed.ResultsOne-hundred and twenty three cluster trials were included in this systematic review. Non-adherence was reported in 56 cluster trials. Among these, 19 reported a treatment efficacy estimate: per protocol in 15 and as treated in 4. No study discussed the assumptions made by these methods, their plausibility or the sensitivity of the results to deviations from these assumptions.LimitationsThe year of publication of the cluster trials included in this review (2011) could be considered a limitation of this study; however, no new guidelines regarding the reporting and the handling of non-adherence for cluster trials have been published since. In addition, a single reviewer undertook the data extraction. To mitigate this, a second reviewer conducted a validation of the extraction process on 15 randomly selected reports. Agreement was satisfactory (93%).ConclusionDespite the recommendations of the Consolidated Standards of Reporting Trials statement extension to cluster randomised trials, treatment adherence is under-reported. Among the trials providing adherence information, there was substantial variation in how adherence was defined, handled and reported. Researchers should discuss the assumptions required for the results to be interpreted causally and whether these are scientifically plausible in their studies. Sensitivity analyses to study the robustness of the results to departures from these assumptions should be performed.

Project description:Many studies have investigated racial/ethnic disparities in medication non-adherence in patients with type 2 diabetes using common measures such as medication possession ratio (MPR) or gaps between refills. All these measures including MPR are quasi-continuous and bounded and their distribution is usually skewed. Analysis of such measures using traditional regression methods that model mean changes in the dependent variable may fail to provide a full picture about differential patterns in non-adherence between groups.A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM).Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant.Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.

Dataset Information

Standardized classification and framework for reporting, interpreting, and analysing medication non-adherence in cardiovascular clinical trials: a consensus report from the Non-adherence Academic Research Consortium (NARC).

Publications

Standardized classification and framework for reporting, interpreting, and analysing medication non-adherence in cardiovascular clinical trials: a consensus report from the Non-adherence Academic Research Consortium (NARC).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets