Dataset Information

Corticosteroids in COVID-19: Optimizing Observational Research through Target Trial Emulations.

ABSTRACT:

Background

Observational research provides a unique opportunity to learn causal effects when randomized trials are unavailable, but obtaining the correct estimates hinges on a multitude of design and analysis choices. We illustrate the advantages of modern causal inference methods and compare to standard research practice to estimate the effect of corticosteroids on mortality in hospitalized COVID-19 patients in an observational dataset. We use several large RCTs to benchmark our results.

Methods

Our retrospective data consists of 3,298 COVID-19 patients hospitalized at New York-Presbyterian March 1-May 15, 2020. We design our study using the target trial framework. We estimate the effect of an intervention consisting of six days of corticosteroids administered at the time of severe hypoxia and contrast with an intervention consisting of no corticosteroids. The dataset includes dozens of time-varying confounders. We estimate the causal effects using a doubly robust estimator where the probabilities of treatment, outcome, and censoring are estimated using flexible regressions via super learning. We compare these analyses to standard practice in clinical research, consisting of two approaches: (i)Cox models for an exposure of corticosteroids receipt within various time windows of hypoxia, and (ii)Cox time-varying model where the exposure is daily administration of corticosteroids beginning at hospitalization.

Results

Our target trial emulation estimates corticosteroids to reduce 28-day mortality from 32% (95% confidence interval: 31-34) to 23% (21-24). This is qualitatively identical to the WHO's RCT meta-analysis result. Hazard ratios from the Cox models range in size and direction from 0.50 (0.41-0.62) to 1.08 (0.80-1.47) and all study designs suffer from various forms of bias.

Conclusion

We demonstrate that clinical research based on observational data can unveil true causal relations. However, the correctness of these effect estimates requires designing and analyzing the data based on principles which are different from the current standard in clinical research. Widespread communication and adoption of these design and analytical techniques is of high importance for the improvement of clinical research.

SUBMITTER: Hoffman KL

PROVIDER: S-EPMC9196111 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

Comparison of a Target Trial Emulation Framework to Cox Regression to Estimate the Effect of Corticosteroids on COVID-19 Mortality.

Hoffman Katherine L KL Schenck Edward J EJ Satlin Michael J MJ Whalen William W Pan Di D Williams Nicholas N Díaz Iván I

medRxiv : the preprint server for health sciences 20220830

<h4>Importance</h4>Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data.<h4>Objective</h4>To compare (1) a modern method for causal inference including a target trial emulation framework and doubly robust estimation to (2) approaches common in the clinical literature such as Cox proportional hazards models. To do this, we estimate the effect of corticosteroids on mortality for moderate-t ...[more]

PMID: 35702149

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Project description:ImportanceCommunication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data.ObjectiveTo compare a modern method for statistical inference, including a target trial emulation framework and doubly robust estimation, with approaches common in the clinical literature, such as Cox proportional hazards models.Design, setting, and participantsThis retrospective cohort study used longitudinal electronic health record data for outcomes at 28-days from time of hospitalization within a multicenter New York, New York, hospital system. Participants included adult patients hospitalized between March 1 and May 15, 2020, with COVID-19 and not receiving corticosteroids for chronic use. Data were analyzed from October 2021 to March 2022.ExposuresCorticosteroid exposure was defined as more than 0.5 mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, exposures were corticosteroids for 6 days if and when a patient met criteria for severe hypoxia vs no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models varied by study design (no time frame, 1 day, and 5 days from time of severe hypoxia).Main outcomes and measuresThe main outcome was 28-day mortality from time of hospitalization. The association of corticosteroids with mortality for patients with moderate to severe COVID-19 was assessed using the World Health Organization (WHO) meta-analysis of corticosteroid randomized clinical trials as a benchmark.ResultsA total of 3298 patients (median [IQR] age, 65 [53-77] years; 1970 [60%] men) were assessed, including 423 patients who received corticosteroids at any point during hospitalization and 699 patients who died within 28 days of hospitalization. Target trial emulation analysis found corticosteroids were associated with a reduced 28-day mortality rate, from 32.2%; (95% CI, 30.9%-33.5%) to 25.7% (95% CI, 24.5%-26.9%). This estimate is qualitatively identical to the WHO meta-analysis odds ratio of 0.66 (95% CI, 0.53-0.82). Hazard ratios using methods comparable with current corticosteroid research range in size and direction, from 0.50 (95% CI, 0.41-0.62) to 1.08 (95% CI, 0.80-1.47).Conclusions and relevanceThese findings suggest that clinical research based on observational data can be used to estimate findings similar to those from randomized clinical trials; however, the correctness of these estimates requires designing the study and analyzing the data based on principles that are different from the current standard in clinical research.

Project description:IntroductionThe COVID-19 pandemic caused by SARS-CoV-2 places immense worldwide demand on healthcare services. Earlier identification of patients at risk of severe disease may allow intervention with experimental targeted treatments, mitigating the course of their disease and reducing critical care service demand.Methods and analysisThis prospective observational study of patients tested or treated for SARS-CoV-2, who are under the care of the tertiary University Hospital Southampton NHS Foundation Trust (UHSFT), captured data from admission to discharge; data collection commenced on 7 March 2020. Core demographic and clinical information, as well as results of disease-defining characteristics, was captured and recorded electronically from hospital clinical record systems at the point of testing. Manual data were collected and recorded by the clinical research team for assessments which are not part of the structured electronic healthcare record, for example, symptom onset date. Thereafter, participant records were continuously updated during hospital stay and their follow-up period. Participants aged >16 years were given the opportunity to provide consent for excess clinical sample storage with optional further biological sampling. These anonymised samples were linked to the clinical data in the Real-time Analytics for Clinical Trials platform and were stored within a biorepository at UHSFT.Ethics and disseminationEthical approval was obtained from the HRA Specific Review Board (REC 20/HRA/2986) for waiver of informed consent for the database-only cohort; the procedures conform with the Declaration of Helsinki. The study design, protocol and patient-facing documentation for the biobanking arm of the study have been approved by North West Research Ethics Committee (REC 17/NW/0632) as an amendment to the National Institute for Health Research Southampton Clinical Research Facility-managed Southampton Research Biorepository. This study will be published as peer-reviewed articles and presented at conferences, presentations and workshops.