fragility index
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2021 ◽  
Vol 118 (49) ◽  
pp. e2105254118
Author(s):  
Benjamin R. Baer ◽  
Mario Gaudino ◽  
Mary Charlson ◽  
Stephen E. Fremes ◽  
Martin T. Wells

The fragility index is a clinically meaningful metric based on modifying patient outcomes that is increasingly used to interpret the robustness of clinical trial results. The fragility index relies on a concept that explores alternative realizations of the same clinical trial by modifying patient measurements. In this article, we propose to generalize the fragility index to a family of fragility indices called the incidence fragility indices that permit only outcome modifications that are sufficiently likely and provide an exact algorithm to calculate the incidence fragility indices. Additionally, we introduce a far-reaching generalization of the fragility index to any data type and explain how to permit only sufficiently likely modifications for nondichotomous outcomes. All of the proposed methodologies follow the fragility index concept.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Benjamin R. Baer ◽  
Stephen E. Fremes ◽  
Mario Gaudino ◽  
Mary Charlson ◽  
Martin T. Wells

Abstract Background Clinical trials routinely have patients lost to follow up. We propose a methodology to understand their possible effect on the results of statistical tests by altering the concept of the fragility index to treat the outcomes of observed patients as fixed but incorporate the potential outcomes of patients lost to follow up as random and subject to modification. Methods We reanalyse the statistical results of three clinical trials on coronary artery bypass grafting (CABG) to study the possible effect of patients lost to follow up on the treatment effect statistical significance. To do so, we introduce the LTFU-aware fragility indices as a measure of the robustness of a clinical trial’s statistical results with respect to patients lost to follow up. Results The analyses illustrate that clinical trials can either be completely robust to the outcomes of patients lost to follow up, extremely sensitive to the outcomes of patients lost to follow up, or in an intermediate state. When a clinical trial is in an intermediate state, the LTFU-aware fragility indices provide an interpretable measure to quantify the degree of fragility or robustness. Conclusions The LTFU-aware fragility indices allow researchers to rigorously explore the outcomes of patients who are lost to follow up, when their data is the appropriate kind. The LTFU-aware fragility indices are sensitivity measures in a way that the original fragility index is not.


2021 ◽  
Vol 10 (22) ◽  
pp. 5287
Author(s):  
Maria Vargas ◽  
Annachiara Marra ◽  
Pasquale Buonanno ◽  
Antonio Coviello ◽  
Carmine Iacovazzo ◽  
...  

Background: The effectiveness of corticosteroids in acute respiratory distress syndrome (ARDS) and COVID-19 still remains uncertain. Since ARDS is due to a hyperinflammatory response to a direct injury, we decided to perform a meta-analysis and an evaluation of robustness of randomised clinical trials (RCTs) investigating the impact of corticosteroids on mortality in ARDS in both COVID-19 and non-COVID-19 patients. We conducted a systematic search of the literature from inception up to 30 October 2020, using the MEDLINE database and the PubMed interface. We evaluated the fragility index (FI) of the included RCTs using a two-by-two contingency table and the p-value produced by the Fisher exact test; the fragility quotient (FQ) was calculated by dividing the FI score by the total sample size of the trial. Results: Thirteen RCTs were included in the analysis; five of them were conducted in COVID-19 ARDS, including 7692 patients, while 8 RCTS were performed in non-COVID ARDS with 1091 patients evaluated. Three out of eight RCTs in ARDS had a FI > 0 while 2 RCTs out of five in COVID-19 had FI > 0. The median of FI for ARDS was 0.625 (0.47) while the median of FQ was 0.03 (0.014). The median of FI for COVID-19 was 6 (2) while the median of FQ was 0.059 (0.055). In this systematic review, we found that FI and FQ of RCTs evaluating the use of corticosteroids in ARDS and COVID-19 were low.


Author(s):  
Cristina Bagacean ◽  
Jean-Christophe Ianotto ◽  
Nanthara Sritharan ◽  
Florence Cymbalista ◽  
Christian Berthou ◽  
...  

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Julien Riou ◽  
Carole Dupont ◽  
Silvia Bertagnolio ◽  
Ravindra K. Gupta ◽  
Roger D. Kouyos ◽  
...  

Abstract Introduction The rise of HIV-1 drug resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTI) threatens antiretroviral therapy's long-term success (ART). NNRTIs will remain an essential drug for the management of HIV-1 due to safety concerns associated with integrase inhibitors. We fitted a dynamic transmission model to historical data from 2000 to 2018 in nine countries of southern Africa to understand the mechanisms that have shaped the HIV-1 epidemic and the rise of pretreatment NNRTI resistance. Methods We included data on HIV-1 prevalence, ART coverage, HIV-related mortality, and survey data on pretreatment NNRTI resistance from nine southern Africa countries from a systematic review, UNAIDS and World Bank. Using a Bayesian hierarchical framework, we developed a dynamic transmission model linking data on the HIV-1 epidemic to survey data on NNRTI drug resistance in each country. We estimated the proportion of resistance attributable to unregulated, off-programme use of ART. We examined each national ART programme's vulnerability to NNRTI resistance by defining a fragility index: the ratio of the rate of NNRTI resistance emergence during first-line ART over the rate of switching to second-line ART. We explored associations between fragility and characteristics of the health system of each country. Results The model reliably described the dynamics of the HIV-1 epidemic and NNRTI resistance in each country. Predicted levels of resistance in 2018 ranged between 3.3% (95% credible interval 1.9–7.1) in Mozambique and 25.3% (17.9–33.8) in Eswatini. The proportion of pretreatment NNRTI resistance attributable to unregulated antiretroviral use ranged from 6% (2–14) in Eswatini to 64% (26–85) in Mozambique. The fragility index was low in Botswana (0.01; 0.0–0.11) but high in Namibia (0.48; 0.16–10.17), Eswatini (0.64; 0.23–11.8) and South Africa (1.21; 0.83–9.84). The combination of high fragility of ART programmes and high ART coverage levels was associated with a sharp increase in pretreatment NNRTI resistance. Conclusions This comparison of nine countries shows that pretreatment NNRTI resistance can be controlled despite high ART coverage levels. This was the case in Botswana, Mozambique, and Zambia, most likely because of better HIV care delivery, including rapid switching to second-line ART of patients failing first-line ART.


2021 ◽  
Vol 11 ◽  
pp. 239-251
Author(s):  
Carl L. Herndon ◽  
Kyle L. McCormick ◽  
Anastasia Gazgalis ◽  
Elise C. Bixby ◽  
Matthew M. Levitsky ◽  
...  

Author(s):  
Benjamin R. Baer ◽  
Mario Gaudino ◽  
Stephen E. Fremes ◽  
Mary Charlson ◽  
Martin T. Wells

2021 ◽  
Vol 17 ◽  
Author(s):  
Debdipta Bose ◽  
Mahanjit Konwar

Background: It is essential for Randomized controlled trials [RCTs] to report its results in a comprehensive manner. Hence, it is necessary to assess the robustness of the trials with statistically significant and as well as non-significant results. Robustness can be evaluated using fragility index (FI) and reverse fragility index [RFI] is for trials with statistically significant and as well as non-significant results. The primary aim of this study was to calculate FI and RFI for cardiovascular outcome trials [CVOT]. Materials & Methods: PubMed/MEDLINE was searched to identify all RCTs of antidiabetic drugs where the primary objective was to evaluate the cardiovascular outcomes. We recorded the trial characteristics of each CVOT trial. The FI, RFI, Fragility quotient [FQ] and reverse fragility quotient [FQ] was calculated to evaluate the robustness of the trials. Spearman rank correlation test was used for correlation. Findings: A total of 889 studies were identified and 24 RCTs was included. Among the 24 trials, 12 [50%] trials achieved statistical significance. The median FI and RFI were 29 [4-12] and 22.5 [1-37] for trials with statistically significant and non-significant results. The median FQ and RFQ were 0.0075 [0.002-0.013] and 0.0003 [0.0001-0.004] for trials with statistically significant and non-significant results. The hazard ratio, p value and NNT-B had strong negative relation with FI. Interpretation: Our study showed that half of the trials showing superiority of cardioprotective benefits have favourable FI. The trials failed to show superiority also have a reasonable RFI indicating the robustness of these trials. But the results pf the trials where patients lost to follow-up exceed the FI of that trial demands caution during interpretation.


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