Assessing via Simulation the Operating Characteristics of the WHO Scale for COVID-19 Endpoints

O’Kelly, Michael; Li, Siying

doi:10.1080/19466315.2020.1811148

Cited by 4 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In at least half of the trials, both directions of clinical courses were evaluated, regardless of the primary and secondary endpoints. Evaluating both directions in longitudinal changes can result in analyses with moderate to good power [ 24 ]. In clinical trials for infectious diseases, both directions of the clinical course may be considered important in both clinical and statistical aspects.…”

Section: Discussionmentioning

confidence: 99%

Definitions and elements of endpoints in phase III randomized trials for the treatment of COVID-19: a cross-sectional analysis of trials registered in ClinicalTrials.gov

Sakamaki

Uemura

Shimizu

2021

Trials

View full text Add to dashboard Cite

Background There are several challenges in designing clinical trials for the treatment of novel infectious diseases, such as COVID-19. In particular, the definition of endpoints related to the severity, time frame, and clinical course remains unclear. Therefore, we conducted a cross-sectional analysis of phase III randomized trials for COVID-19 registered at ClinicalTrials.gov. Methods We collected the data from ClinicalTrials.gov on March 31, 2021, by specifying the following search conditions under Advanced Search: Condition or disease: (COVID-19) OR (SARS-CoV-2); Study type: Interventional Studies; Study Results: All Studies; Recruitment: Not yet recruiting, Recruiting, Enrolling by invitation, Active, Not recruiting, Suspended, Completed; Sex: All; and Phase: Phase 3. From the downloaded search results, we selected trials that met the following criteria: Primary Purpose: Treatment; Allocation: Randomized. We manually transcribed information not included in the downloaded file, such as Primary Outcome Measures, Secondary Outcome Measures, Time Frame, and Inclusion Criteria. In the analysis, we examined primary and secondary endpoints in trials with severe and non-severe patients, including the types of endpoints, time frame, clinical course, and sample size. Results A total of 406 trials were included in the analysis. The median numbers of endpoints in trials with severe and non-severe patients were 9 and 7, respectively. Approximately 25% of the trials used multiple primary endpoints. Regardless of the type of endpoint, the time frames were longer in the trials with severe patients than in the trials with non-severe patients. In the evaluation of the clinical course, worsening was often considered in binary endpoints, and improvement was considered in time-to-event endpoints. The sample size was the largest in clinical trials using binary endpoints. Conclusions Endpoints can differ with respect to severity, and the clinical course and time frame are important for defining endpoints. This study provides information that can facilitate the achievement of a consensus for the endpoints in evaluating COVID-19 treatments.

show abstract

Section: Discussionmentioning

confidence: 99%

Definitions and elements of endpoints in phase III randomized trials for the treatment of COVID-19: a cross-sectional analysis of trials registered in ClinicalTrials.gov

Sakamaki

Uemura

Shimizu

2021

Trials

View full text Add to dashboard Cite

show abstract

“…Then, the common OR represents the OR for being in a higher category of the ordinal scale on intervention compared to control. 7 For example, consider the hypothetical data in the first three columns of Table 1 ; the common OR is 1.5, which can be interpreted as the intervention’s effect on each level of the ordinal scale, that is, the intervention increases both the odds of being alive versus dead by 50% and also the odds of being alive without mechanical ventilation versus on mechanical ventilation or dead by 50%. The use of the common OR when the proportional odds assumption holds can confer substantial sample size savings; 8 in order to detect a common OR of 1.5, 764 patients are required, compared to the 2526 patients required to detect an OR of 1.5 for the binary outcome of ‘Alive vs dead’.…”

Section: Introductionmentioning

confidence: 99%

Evaluating whether the proportional odds models to analyse ordinal outcomes in COVID-19 clinical trials is providing clinically interpretable treatment effects: A systematic review

Uddin,

Bashir,

Kahan

2023

Clinical Trials

View full text Add to dashboard Cite

Background After an initial recommendation from the World Health Organisation, trials of patients hospitalised with COVID-19 often include an ordinal clinical status outcome, which comprises a series of ordered categorical variables, typically ranging from ‘Alive and discharged from hospital’ to ‘Dead’. These ordinal outcomes are often analysed using a proportional odds model, which provides a common odds ratio as an overall measure of effect, which is generally interpreted as the odds ratio for being in a higher category. The common odds ratio relies on the assumption of proportional odds, which implies an identical odds ratio across all ordinal categories; however, there is generally no statistical or biological basis for which this assumption should hold; and when violated, the common odds ratio may be a biased representation of the odds ratios for particular categories within the ordinal outcome. In this study, we aimed to evaluate to what extent the common odds ratio in published COVID-19 trials differed to simple binary odds ratios for clinically important outcomes. Methods We conducted a systematic review of randomised trials evaluating interventions for patients hospitalised with COVID-19, which used a proportional odds model to analyse an ordinal clinical status outcome, published between January 2020 and May 2021. We assessed agreement between the common odds ratio and the odds ratio from a standard logistic regression model for three clinically important binary outcomes: ‘Alive’, ‘Alive without mechanical ventilation’, and ‘Alive and discharged from hospital’. Results Sixteen randomised clinical trials, comprising 38 individual comparisons, were included in this study; of these, only 6 trials (38%) formally assessed the proportional odds assumption. The common odds ratio differed by more than 25% compared to the binary odds ratios in 55% of comparisons for the outcome ‘Alive’, 37% for ‘Alive without mechanical ventilation’, and 24% for ‘Alive and discharged from hospital’. In addition, the common odds ratio systematically underestimated the odds ratio for the outcome ‘Alive’ by –16.8% (95% confidence interval: –28.7% to –2.9%, p = 0.02), though differences for the other outcomes were smaller and not statistically significant (–8.4% for ‘Alive without mechanical ventilation’ and 3.6% for ‘Alive and discharged from hospital’). The common odds ratio was statistically significant for 18% of comparisons, while the binary odds ratio was significant in 5%, 16%, and 3% of comparisons for the outcomes ‘Alive’, ‘Alive without mechanical ventilation’, and ‘Alive and discharged from hospital’, respectively. Conclusion The common odds ratio from proportional odds models often differs substantially to odds ratios from clinically important binary outcomes, and similar to composite outcomes, a beneficial common OR from a proportional odds model does not necessarily indicate a beneficial effect on the most important categories within the ordinal outcome.

show abstract

“…Wiens and Lipkovich ( 2020 ) provide recommendations on how sponsors can maximize the chance that a non-inferiority trial conducted during the COVID-19 pandemic gives useful information. O’Kelly and Li ( 2020 ) assess the operating characteristics of the World Health Organization (WHO) scale for COVID-19 endpoints via an extensive simulation study. Motivated by four current clinical trials in a variety of disease areas, Kunz et al.…”

mentioning

confidence: 99%

Statistical Challenges in the Conduct and Management of Ongoing Clinical Trials During the COVID-19 Pandemic

Hamasaki

Bretz

Cooner

et al. 2020

Statistics in Biopharmaceutical Research

View full text Add to dashboard Cite

The COVID-19 pandemic has influenced the world in a way that none of us could have imagined. Since February 2020 unplanned changes to daily routines due to the mandatory isolation and social distancing requirements have occurred across the globe. It is the greatest global healthcare crisis of our generation. The COVID-19 pandemic presents enormous challenges in particular to medical research, as these disruptions have wreaked havoc on clinical trials in progress as well as those in the planning stages. For example, the COVID-19 pandemic is reducing the ability and willingness of trial subjects and staff to access clinical sites, disrupting timely data collection or necessitating a move to virtual data collection. These challenges may result in difficulties in complying with protocol-specific procedures, including administration or use of the investigational product or compliance with protocol-required visits, efficacy assessments, and laboratory/diagnostic tests. In many situations, these challenges cause delays or halting of clinical trials altogether.In recognition of these unprecedented challenges, we decided to dedicate a Special Issue of Statistics in Biopharmaceutical Research to the statistical challenges in the conduct and management of ongoing clinical trials during the COVID-19 pandemic. This special issue highlights and discusses critically important topics, including the impact on trial integrity and interpretability, issues around trial management and operations, estimands and missing data handling as well as the application of novel trial designs and analysis approaches. This Special Issue consists of peer-reviewed articles and commentaries, reflecting the vibrancy of ideas and discussions around the COVID-19 pandemic.Meyer et al. ( 2020) put forward a number of strategies and recommendations to assess and address issues related to estimands, missing data, validity and modifications of statistical analysis methods, need for additional analyses, ability to meet objectives as well as overall trial interpretability. This article has been translated into Japanese by the Data Science Expert Committee of the Japan Pharmaceutical Manufacturers Association (JPMA) and available on their website (JPMA 2020). Collins and Levenson (2020) as well as Hemmings (2020) provide insightful regulatory commentaries on the article by Meyer et al. (2020). Akacha et al. (2020) discuss implications of the COVID-19

show abstract

Assessing via Simulation the Operating Characteristics of the WHO Scale for COVID-19 Endpoints

Cited by 4 publications

References 8 publications

Definitions and elements of endpoints in phase III randomized trials for the treatment of COVID-19: a cross-sectional analysis of trials registered in ClinicalTrials.gov

Definitions and elements of endpoints in phase III randomized trials for the treatment of COVID-19: a cross-sectional analysis of trials registered in ClinicalTrials.gov

Evaluating whether the proportional odds models to analyse ordinal outcomes in COVID-19 clinical trials is providing clinically interpretable treatment effects: A systematic review

Statistical Challenges in the Conduct and Management of Ongoing Clinical Trials During the COVID-19 Pandemic

Contact Info

Product

Resources

About