Prospective individual patient data meta‐analysis: Evaluating convalescent plasma for COVID‐19

Abstract

As the world faced the devastation of the COVID‐19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID‐19 encountered at participating sites. It has become clear that it might take several more COVID‐19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient‐level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta‐analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID‐19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.

Association of Convalescent Plasma Treatment With Clinical Status in Patients Hospitalized With COVID-19

“… 35 , 36 , 37 The statistical analysis plan was supported by extensive simulations to understand the impact of prior distributions and other modeling choices and to approximate the conventional frequentist operating characteristics that could be expected with application of our stopping rules. 38 There was no predetermined sample size; trials that were still ongoing during the project continued to accrue participants. Analyses were performed using R statistical software version 4.1.1 (R Project for Statistical Computing) 39 and Stan statistical software version 2.28 (Stan Development Team).…”

Development and Validation of a Treatment Benefit Index to Identify Hospitalized Patients With COVID-19 Who May Benefit From Convalescent Plasma

“…While the TBIs were derived on the ordinal WHO score at day 14, efficacy was assessed with respect to all 6 outcomes. ORs were obtained from models used in the main COMPILE analysis 13 , 14 : Bayesian POMs and logistic regressions, adjusted for the same covariates as in the COMPILE analysis with the same prior distributions. Bayesian posterior distributions of the respective ORs were obtained for each benefit level.…”

“… 13 The precision medicine investigations were prespecified in the COMPILE study’s statistical analysis plan. 14 …”

Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis

“…39,41,42 We are aware of only 4 additional published IPD meta-analyses for COVID-19 therapeutics (3 planned; 1 smaller one completed). [43][44][45][46] Two metaanalyses planned to use IPD data to supplement aggregate data but could not obtain them. 41,47 At least 50 aggregate data meta-analyses evaluating HCQ/CQ in hospitalized COVID-19 patients have been published, with the overwhelming majority finding lack of evidence of convincing clinical benefit, and many finding worse clinical outcomes and increased AE rates.…”

“…To our knowledge, this is the first published meta-analysis of HCQ/CQ trials in hospitalized COVID-19 patients to use IPD rather than aggregate data. We are aware of only 4 additional published IPD meta-analyses for COVID-19 therapeutics (3 planned; 1 smaller one completed) [41][42][43][44]. Two meta-analyses planned to use IPD data to supplement aggregate data but could not obtain them [45,46].…”