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Statistical Issues and Lessons Learned From COVID-19 Clinical Trials With Lopinavir-Ritonavir and Remdesivir.
JMIR Public Health Surveill. 2020 07 10; 6(3):e19538.JP

Abstract

BACKGROUND

Recently, three randomized clinical trials on coronavirus disease (COVID-19) treatments were completed: one for lopinavir-ritonavir and two for remdesivir. One trial reported that remdesivir was superior to placebo in shortening the time to recovery, while the other two showed no benefit of the treatment under investigation.

OBJECTIVE

The aim of this paper is to, from a statistical perspective, identify several key issues in the design and analysis of three COVID-19 trials and reanalyze the data from the cumulative incidence curves in the three trials using more appropriate statistical methods.

METHODS

The lopinavir-ritonavir trial enrolled 39 additional patients due to insignificant results after the sample size reached the planned number, which led to inflation of the type I error rate. The remdesivir trial of Wang et al failed to reach the planned sample size due to a lack of eligible patients, and the bootstrap method was used to predict the quantity of clinical interest conditionally and unconditionally if the trial had continued to reach the originally planned sample size. Moreover, we used a terminal (or cure) rate model and a model-free metric known as the restricted mean survival time or the restricted mean time to improvement (RMTI) to analyze the reconstructed data. The remdesivir trial of Beigel et al reported the median recovery time of the remdesivir and placebo groups, and the rate ratio for recovery, while both quantities depend on a particular time point representing local information. We use the restricted mean time to recovery (RMTR) as a global and robust measure for efficacy.

RESULTS

For the lopinavir-ritonavir trial, with the increase of sample size from 160 to 199, the type I error rate was inflated from 0.05 to 0.071. The difference of RMTIs between the two groups evaluated at day 28 was -1.67 days (95% CI -3.62 to 0.28; P=.09) in favor of lopinavir-ritonavir but not statistically significant. For the remdesivir trial of Wang et al, the difference of RMTIs at day 28 was -0.89 days (95% CI -2.84 to 1.06; P=.37). The planned sample size was 453, yet only 236 patients were enrolled. The conditional prediction shows that the hazard ratio estimates would reach statistical significance if the target sample size had been maintained. For the remdesivir trial of Beigel et al, the difference of RMTRs between the remdesivir and placebo groups at day 30 was -2.7 days (95% CI -4.0 to -1.2; P<.001), confirming the superiority of remdesivir. The difference in the recovery time at the 25th percentile (95% CI -3 to 0; P=.65) was insignificant, while the differences became more statistically significant at larger percentiles.

CONCLUSIONS

Based on the statistical issues and lessons learned from the recent three clinical trials on COVID-19 treatments, we suggest more appropriate approaches for the design and analysis of ongoing and future COVID-19 trials.

Authors+Show Affiliations

Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China (Hong Kong).Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China (Hong Kong).Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China (Hong Kong).

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32589146

Citation

Yin, Guosheng, et al. "Statistical Issues and Lessons Learned From COVID-19 Clinical Trials With Lopinavir-Ritonavir and Remdesivir." JMIR Public Health and Surveillance, vol. 6, no. 3, 2020, pp. e19538.
Yin G, Zhang C, Jin H. Statistical Issues and Lessons Learned From COVID-19 Clinical Trials With Lopinavir-Ritonavir and Remdesivir. JMIR Public Health Surveill. 2020;6(3):e19538.
Yin, G., Zhang, C., & Jin, H. (2020). Statistical Issues and Lessons Learned From COVID-19 Clinical Trials With Lopinavir-Ritonavir and Remdesivir. JMIR Public Health and Surveillance, 6(3), e19538. https://doi.org/10.2196/19538
Yin G, Zhang C, Jin H. Statistical Issues and Lessons Learned From COVID-19 Clinical Trials With Lopinavir-Ritonavir and Remdesivir. JMIR Public Health Surveill. 2020 07 10;6(3):e19538. PubMed PMID: 32589146.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Statistical Issues and Lessons Learned From COVID-19 Clinical Trials With Lopinavir-Ritonavir and Remdesivir. AU - Yin,Guosheng, AU - Zhang,Chenyang, AU - Jin,Huaqing, Y1 - 2020/07/10/ PY - 2020/04/22/received PY - 2020/06/25/accepted PY - 2020/06/18/revised PY - 2020/6/27/pubmed PY - 2020/7/23/medline PY - 2020/6/27/entrez KW - COVID-19 KW - coronavirus KW - cure rate model KW - restricted mean survival time KW - sample size adjustment KW - terminal event KW - type I error rate SP - e19538 EP - e19538 JF - JMIR public health and surveillance JO - JMIR Public Health Surveill VL - 6 IS - 3 N2 - BACKGROUND: Recently, three randomized clinical trials on coronavirus disease (COVID-19) treatments were completed: one for lopinavir-ritonavir and two for remdesivir. One trial reported that remdesivir was superior to placebo in shortening the time to recovery, while the other two showed no benefit of the treatment under investigation. OBJECTIVE: The aim of this paper is to, from a statistical perspective, identify several key issues in the design and analysis of three COVID-19 trials and reanalyze the data from the cumulative incidence curves in the three trials using more appropriate statistical methods. METHODS: The lopinavir-ritonavir trial enrolled 39 additional patients due to insignificant results after the sample size reached the planned number, which led to inflation of the type I error rate. The remdesivir trial of Wang et al failed to reach the planned sample size due to a lack of eligible patients, and the bootstrap method was used to predict the quantity of clinical interest conditionally and unconditionally if the trial had continued to reach the originally planned sample size. Moreover, we used a terminal (or cure) rate model and a model-free metric known as the restricted mean survival time or the restricted mean time to improvement (RMTI) to analyze the reconstructed data. The remdesivir trial of Beigel et al reported the median recovery time of the remdesivir and placebo groups, and the rate ratio for recovery, while both quantities depend on a particular time point representing local information. We use the restricted mean time to recovery (RMTR) as a global and robust measure for efficacy. RESULTS: For the lopinavir-ritonavir trial, with the increase of sample size from 160 to 199, the type I error rate was inflated from 0.05 to 0.071. The difference of RMTIs between the two groups evaluated at day 28 was -1.67 days (95% CI -3.62 to 0.28; P=.09) in favor of lopinavir-ritonavir but not statistically significant. For the remdesivir trial of Wang et al, the difference of RMTIs at day 28 was -0.89 days (95% CI -2.84 to 1.06; P=.37). The planned sample size was 453, yet only 236 patients were enrolled. The conditional prediction shows that the hazard ratio estimates would reach statistical significance if the target sample size had been maintained. For the remdesivir trial of Beigel et al, the difference of RMTRs between the remdesivir and placebo groups at day 30 was -2.7 days (95% CI -4.0 to -1.2; P<.001), confirming the superiority of remdesivir. The difference in the recovery time at the 25th percentile (95% CI -3 to 0; P=.65) was insignificant, while the differences became more statistically significant at larger percentiles. CONCLUSIONS: Based on the statistical issues and lessons learned from the recent three clinical trials on COVID-19 treatments, we suggest more appropriate approaches for the design and analysis of ongoing and future COVID-19 trials. SN - 2369-2960 UR - https://www.unboundmedicine.com/medline/citation/32589146/Statistical_Issues_and_Lessons_Learned_From_COVID_19_Clinical_Trials_With_Lopinavir_Ritonavir_and_Remdesivir_ DB - PRIME DP - Unbound Medicine ER -