1,721,506 research outputs found
Enhancing robustness in acute cardiovascular observational studies: evaluating covariate adjustment
No full textIn clinical trials, the primary objective often involves studying the associations between several variables. In randomized clinical trials (RCTs), the focus typically lies on the association between clinical outcomes and two or several treatment options. Conversely, in observational studies, interest extends beyond treatments and may include associations with various patient characteristics (e.g. demographic and disease-specific factors), frequently for predictive or prognostic purposes. These associations, however, may be subject to influence by external factors, commonly known as confounding factors, which may introduce biases in the conclusions. To mitigate the impact of these confounding factors, appropriate measures should be taken to avoid imbalances. One strategy to eliminate potential bias is to balance subjects across these factors through randomization and stratification. Another approach is to adjust the statistical analyses for these covariates. Although covariate adjustment is not strictly necessary in RCTs, it may enhance efficiency. 1 Covariate adjustment is well studied in RCTs, with available regulatory guidance, 2,3 although the quality of implementation varies and may be improved. 1 There is also a need to study covariate adjustment in observational studies and to develop guidance documents, 4 as the requirement to adjust is higher in non-randomized studies. Moreover, the variability in implementation of covariate adjustments is equally, if not more, present in observational studies. The latter is demonstrated by a review of publications in the European Heart Journal: Acute Cardiovascular Care over the past 5 years, which included 55 non-randomized studies, representing 62 covariate-adjusted analyses. In the observational studies, the most commonly used covariate adjustments were multiple or multivariable regression models (n = 49; 79%), similar to those in RCTs. These included Cox proportional hazards (n = 23; 47%), logistic (n = 22; 45%), and linear (n = 4; 8%) regression models. Due to the non-randomized nature of observational studies, often more covariates are imbalanced compared with rando-mized trials and need to be included in these models. Adding numerous covariates in regression models, however, can be cumbersome and may lead to over-fitting, i.e. tailoring the model too much to the available data, thereby reducing generalizability. It is important to note that mul-tiple/multivariable regression models should not be confused with multivariate models, which aim at modelling multiple clinical outcomes in a multilevel or joint approach. An alternative method for covariate adjustment, aimed at correcting potential bias rather than examining covariate-outcome associations, is covariate adjustment through propensity scores 5 (n = 13; 21%). These scores, calculated through logistic models, represent the probability of patients belonging to a subgroup given a set of covariates, summarizing all patient characteristics into a single value. Propensity scores reduce the potential for over-fitting and can be used for covariate adjustment through matching, stratification, inverse probability weighting, or as a covariate in a regression model. 5 Although there is no clear superior method, 5 matched propensity scores were most commonly used (n = 8; 62%), followed by modelling as a covariate (n = 3; 23%) and inverse treatment weighting (n = 2; 15%). Propensity score matching has the potential disadvantage of excluding unmatched observations. When interpreting treatment effects, it is important to realize that inverse probability weighting and matching estimate marginal treatment effects, whereas multivariable regression and stratification estimate conditional effects. 5 The methods for selecting covariates for adjustment are another source of variability. Most studies pre-selected covariates based on prior knowledge (n = 33; 61%), while some used automated selection procedures (n = 11; 20%) or included all covariates with a P-value below a pre-specified threshold in a univariate analysis (n = 8; 15%). A few studies selected covariates based on observed imbalances between groups (n = 2, 4%). Pre-selecting covariates risk missing important con-founders, whereas automated selection procedures may lead to over-fitting. Regardless of the method, the selection procedure should be clearly presented, as in some cases involving automated procedures, it was insufficiently detailed whether a forward or backward selection was used. Additionally, variation existed in evaluating the validity and robust-ness of conclusions. Robust conclusions require assessing the validity of the models' implicit assumptions, such as proportional hazards and the form of the association (n = 4; 7%). The reliability of conclusions also depends on the absence of the influence of extreme observations (n = 2; 4%), which may cause bias, and the absence of multi-collinearity European Heart Journal: Acute Cardiovascular Care (2024) 00, 1-2 https://doi
Towards patient-centred benefit-risk assessment with generalised pairwise comparisons
No full textThe authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors
Evaluation of inferential methods for the net benefit and win ratio statistics
No full textGeneral Pairwise Comparison (GPC) statistics, such as the net benefit and the win ratio, have been applied in clinical trial data analysis and design. In the literature, inferential methods based on re-sampling, asymptotic or exact methods have been proposed for these GPC statistics, but they have not been compared to each other. In this paper, the small sample bias of the variance estimation, Type I error control and 95% confidence interval coverage of the GPC inferential methods are evaluated using simulations. The exact permutation and bootstrap tests perform best in all evaluated aspects for the net benefit, while the exact bootstrap test performs best for the win ratio.This work was supported by the European Cardiovascular Research Institute (ECRI).Verbeeck, J (reprint author), Agoralaan Bldg D, B-3590 Diepenbeek, Belgium.
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Rethinking survival analysis: advancing beyond the hazard ratio?
No full textRandomized clinical trials are the foundation of evidence-based medicine , offering vital insights into the efficacy of interventions. Although these trials guide clinical decision-making through hypothesis testing, relying solely on a significant P-value is insufficient; it is essential to convey in addition the magnitude of the treatment effect. In cardiovascular trials, the primary endpoint frequently comprises (a composite of) time-to-event outcomes, for which the magnitude of the treatment effect is typically quantified using the hazard ratio (HR). Despite the omni-presence of HRs, their clinical interpretation is not straightforward and, unfortunately, often incorrect. 1,2 The HR is a ratio of the hazard rates in each group, the latter being the instantaneous risk of the occurrence of events under each treatment. Consequently, it is a rate ratio and should be interpreted as a relative rate reduction, rather than a relative risk reduction. Although the HR shares the same direction of the treatment effect with the risk ratio, and they converge if the event rate is rare, in general, the HR tends to overestimate the relative risk reduction. 1 For example, a HR of 0.7 indicates that the risk decreases, but not necessarily by 30%. Instead, the instantaneous event rate decreases by 30%, and the relative risk reduction may well vary over time. Unfortunately, this is not the only source of confusion, as the HR has also been interpreted as a reduction in absolute risk, or as events occurring later. 2 The distinction between these measures is presented in the table. The appeal of the HR to express the treatment effect is that if the hazard rates are assumed proportional over time, the HR is independent of time, allowing it to be expressed as a single value. However, violations of the proportional hazards assumption complicate its interpretation, rendering the HR time-dependent and in many circumstances unsuitable to represent the magnitude of the treatment effect. 1,3 This violation occurs when the population includes patients with varied treatment effects or when the treatment effect changes over time, something visually evident in survival curves displaying time-dependent effects like late separation, convergence, or crossing. The HR may thus not be the most optimal measure for effectively communicating treatment effects in (composite) survival endpoints, prompting the consideration of alternative measures (see Table 1). While relative measures are well-established in time-to-event outcomes, absolute measures are generally more appropriate for expressing treatment effects over time and are recommended for a correct interpretation of relative effects. 3 Although straightforward in interpretation and use, the absolute difference in median survival time is often impractical in cardiovascular trials, as the survival proportions commonly do not drop below 50%. In contrast, the difference in mean survival time is generally applicable, does not require proportional hazards, and quantifies the mean gain in event-free time. Two main methods can be distinguished to estimate this difference. The restricted mean survival time assesses the difference in the area under the survival curve over a limited period of time, 4,5 while parametric survival models predict the mean survival in each group through modelling of entire survival curves. 6 The latter are often more powerful in detecting a treatment difference, but depend on correct distributional assumptions, which can be readily assessed. 6 Generalized pairwise comparisons (GPC), an extension of the Wilcoxon-Mann-Whitney test, does not depend on the proportional hazards assumption for survival outcomes and offers both relative and absolute measures of treatment effects. 7 The GPC method works by forming all possible pairs of patients, one from each arm of the trial, and comparing the two patients in a pair regarding the outcome(s) of interest. By doing this, the GPC method quantifies proportions of pairs of patients, one from each treatment arm, that are favourable or unfavourable to the treatment of interest. The net treatment benefit (NTB), an absolute measure, is the difference in proportions of favourable and unfavourable pairs and quantifies the difference in the probability that a patient on the treatment of interest will fare better. Unlike other methods, GPC can accommodate the analysis of outcome types beyond survival. Interestingly, in GPC, a threshold of clinical relevance can be assumed in the comparison of two patients, such that a difference between two patients is not confined to a single day. Importantly, the method allows the analysis of 'time to worst outcome' rather than 'time to first outcome', offering a solution to the criticism of conventional analyses of composite endpoints, which ignore serious events if they didn't occur first in a patient. Moreover, a NTB restricted in time can be formulated. 5 To enhance understanding of clinical trial results and to facilitate decision-making and effective communication to patients, it is important to acknowledge that no single metric can capture the entire profile of differences between treatments. 3,4 Hence, it is crucial, in addition to the appropriate interpretation of the HR, to incorporate various measures of treatment effect, preferably absolute measures, for a more comprehensive assessment of trial results. Encouraging statisticians European Heart Journal: Acute Cardiovascular Care (2024) 13, 313-315 https://doi
Covariate-adjusted generalized pairwise comparisons in small samples
No full textSemiparametric Probabilistic Index Models allow for the comparison of two groups of observations, whilst adjusting for covariates, thereby fitting nicely within the framework of Generalized Pairwise Comparisons. As with most regression approaches in this setting, the limited amount of data results in invalid inference as the asymptotic normality assumption is not met. In addition, separation issues might arise when considering small samples. In this paper, we show that the parameters of the Probabilistic Index Model can be estimated using Generalized Estimating Equations, for which adjustments exist that lead to estimators of the
sandwich variance-covariance matrix with improved finite sample properties and that can deal with bias due to separation. In this way, appropriate inference can be performed as is shown through extensive simulation studies. The known relationships between the probabilistic index and other GPC statistics allow to also provide valid inference for e.g. the net treatment benefit or the success odds.Funding information
Fonds Wetenschappelijk Onderzoek, Grant/Award Number: G0D1221N; European Union’s Horizon 2020 Research and Innovation Programme, Grant/Award Number: 825575
ACKNOWLEDGEMENTS
The authors gratefully acknowledge Research Foundation - Flanders (FWO) for providing the funding for this research (Grant no. G0D1221N). JV acknowledges the iSTORE project, which is part of the European Joint Programme on Rare Diseases, under the European Union’s Horizon 2020 Research and Innovation Programme Grant Agreement Number 825575. Finally, the authors also wish to thank prof. dr. Frank Konietschke for some valuable discussions and for providing a suitable dataset to analyse
Acoramidis in Transthyretin Amyloid Cardiomyopathy
No full textTo the Editor: In a phase 3 trial of acoramidis for transthyretin amyloid cardiomyopathy, Gillmore et al. (Jan. 11 issue)(1) used the Finkelstein-Schoenfeld test to assess the hierarchical composite primary outcome. They expressed the treatment effect as a win ratio, thereby following the initial presentation of the analysis of a prioritized outcome.(2) However, the generalized pairwise comparison method, to which the Finkelstein-Schoenfeld test and the win ratio belong, has evolved substantially since this first application.(3) To aid the interpretation of the results, it is now recommended that the proportions of wins and losses for each outcome are reported to understand the . .
From non-inferiority to superiority: the shift towards patient-centric outcomes
No full textThe recently published REC-CAGEFREEI trial 1 provides an interesting example of non-inferiority vs. superiority design of clinical trials. The trial failed to show non-inferiority (P = 0.65) of drug-coated balloon angioplasty (with the option of rescue stenting) to the intended deployment of drug-eluting stents on the primary composite endpoint of car-diovascular death, target vessel myocardial infarction, and target lesion revascularization. However, not the result, but the motivation and reporting on the trial draw our attention (Figure 1). Non-inferiority designs aim to show that a novel therapy is not worse than a standard of care by more than a pre-specified non-inferiority margin on an efficacy outcome. This margin represents an acceptable loss on efficacy, which is justified by a putative advantage of the novel therapy on patient outcomes other than efficacy, such as improved safety, better quality of life, more convenient administration, or lower cost. A non-inferiority design should thus be motivated by a clear advantage. In stent trials, for example, a short-term reduction i
Exact Permutation and Bootstrap Distribution of Generalized Pairwise Comparisons Statistics
No full textTo analyze multivariate outcomes in clinical trials, several authors have suggested generalizations of the univariate Mann–Whitney test. As the Mann–Whitney statistic compares the subjects’ outcome pairwise, the multivariate generalizations are known as generalized pairwise comparisons (GPC) statistics. For GPC statistics such as the net treatment benefit, the win ratio, and the win odds, asymptotic based or re-sampling tests have been suggested in the literature. However, asymptotic methods require a sufficiently high sample size to be accurate, and re-sampling methods come with a high computational burden. We use graph theory notation to obtain closed-form formulas for the expectation and the variance of the permutation and bootstrap sampling distribution of the GPC statistics, which can be utilized to develop fast and accurate inferential tests for each of the GPC statistics. A simple example and a simulation study demonstrate the accuracy of the exact permutation and bootstrap methods, even in very small samples. As the time complexity is O(N2), where N is the total number of patients, the exact methods are fast. In situations where asymptotic methods have been used to obtain these variance matrices, the new methods will be more accurate and equally fast. In situations where bootstrap has been used, the new methods will be both more accurate and much faster
The impact of allocation bias on test decisions in clinical trials with multiple endpoints using multiple testing strategies
No full textBackground Considering multiple endpoints in clinical trials provide a more comprehensive understanding of treatment effects and may lead to increased power or reduced sample size, which may be beneficial in rare diseases. Besides the small sample sizes, allocation bias is an issue that affects the validity of these trials. We investigate the impact of allocation bias on testing decisions in clinical trials with multiple endpoints and offer a tool for selecting an appropriate randomization procedure (RP). Methods We derive a model for quantifying the effect of allocation bias depending on the RP in the case of two-arm parallel group trials with continuous multiple endpoints. We focus on two approaches to analyze multiple endpoints, either the Sidak procedure to show efficacy in at least one endpoint and the all-or-none procedure to show efficacy in all endpoints. Results To evaluate the impact of allocation bias on the test decision we propose a biasing policy for multiple endpoints. The impact of allocation on the test decision is measured by the family-wise error rate of the Sidak procedure and the type I error rate of the all-or-none procedure. Using the biasing policy we derive formulas to calculate these error rates. In simulations we show that, for the Sidak procedure as well as for the all-or-none procedure, allocation bias leads to inflation of the mean family-wise error and mean type I error, respectively. The strength of this inflation is affected by the choice of the RP. Conclusion Allocation bias should be considered during the design phase of a trial to increase validity. The developed methodology is useful for selecting an appropriate RP for a clinical trial with multiple endpoints to minimize allocation bias effects.Funding
Open Access funding enabled and organized by Projekt DEAL. The present research is part of the iSTORE and EPISTOP-IDeAl Projects funded by the Euro‑ pean Union through the European Joint Programme on Rare Diseases under the European Union’s Horizon 2020 Research and Innovation Programme Grant Agreement Number 825575. Ralf-Dieter Hilgers received funding from the European Union’s Horizon 2020 research and innovation programme under the EJP RD COFUND-EJP no. 825575, and ERICA under Grant Agreement. no. 964908. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Acknowledgements
Simulations were performed with computing resources granted by the High Performance Computer Cluster of the RWTH Aachen Universit
Unraveling the impact of the COVID-19 pandemic on the mortality trends in Belgium between 2020-2022
No full textBackgroundOver the past four years, the COVID-19 pandemic has exerted a profound impact on public health, including on mortality trends. This study investigates mortality patterns in Belgium by examining all-cause mortality, excess mortality, and cause-specific mortality. MethodsWe retrieved all-cause mortality data from January 1, 2009, to December 31, 2022, stratified by age group and sex. A linear mixed model, informed by all-cause mortality from 2009 to 2019, was used to predict non-pandemic all-cause mortality rates in 2020-2022 and estimate excess mortality. Further, we also analyzed trends in cause-specific and premature mortality. ResultsDifferent all-cause mortality patterns could be observed between the younger (<45 years) and older age groups. The impact of the COVID-19 pandemic was particularly evident among older age groups. The highest excess mortality occurred in 2020, while a reversal in this trend was evident in 2022. We observed a notable effect of COVID-19 on cause-specific and premature mortality patterns over the three-year period. ConclusionsDespite a consistent decline in COVID-19 reported mortality over this three-year period, it remains imperative to meticulously monitor mortality trends in the years ahead.The authors declare that no funds, grants, or other support were received during the preparation of this manuscript
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