&lt;p&gt;Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them&lt;/p&gt;

Wolkewitz, Martin; Lambert, Jérôme; Cube, Maja von; Bugiera, Lars; Grodd, Marlon; Hazard, Derek; White, Nicole; Barnett, Adrian; Kaier, Klaus

doi:10.2147/clep.s256735

Cited by 40 publications

(47 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since the number of studies on COVID-19 treatment effectiveness and the speed of publishing new data in journals has drastically increased [32], unbiased results from observational studies are extremely important as a complement to randomized controlled trials. The methodological challenges in studying observational COVID-19 data and performing statistical analysis on drug effectiveness has been already described in detail [33,34]. However, our review has demonstrated that methodological issues such as immortal time bias, confounding bias and competing risk bias are commonly found in articles published in high-impact medical journals.…”

Section: Discussionmentioning

confidence: 85%

See 1 more Smart Citation

Methodological evaluation of bias in observational coronavirus disease 2019 studies on drug effectiveness

Martinuka

Cube

Wolkewitz

2021

Clinical Microbiology and Infection

Self Cite

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Section: Discussionmentioning

confidence: 85%

“…As the primary step, a complete competing risk analysis should be presented that includes a cause-specific Cox regression analysis for the event of interest and for all competing events [7,33,36,37]. An initial model should include baseline covariates, further, time-fixed confounders need to be adjusted.…”

Section: Discussionmentioning

confidence: 99%

Methodological evaluation of bias in observational coronavirus disease 2019 studies on drug effectiveness

Martinuka

Cube

Wolkewitz

2021

Clinical Microbiology and Infection

Self Cite

View full text Add to dashboard Cite

show abstract

“…For this analysis, cumulative incidence graphs and estimates were obtained using death as event of interest and discharge alive as competing risk. 5 Patients who did not die by the end of the study period and were not discharged alive were censored at the time of last clinical follow-up.…”

Section: Methodsmentioning

confidence: 99%

Extubation Failure in Critically Ill COVID-19 Patients: Risk Factors and Impact on In-Hospital Mortality

Ionescu

Zimmer

Petrescu

et al. 2021

J Intensive Care Med

View full text Add to dashboard Cite

Purpose: We sought to identify clinical factors that predict extubation failure (reintubation) and its prognostic implications in critically ill COVID-19 patients. Materials and Methods: Retrospective, multi-center cohort study of hospitalized COVID-19 patients. Multivariate competing risk models were employed to explore the rate of reintubation and its determining factors. Results: Two hundred eighty-one extubated patients were included (mean age, 61.0 years [±13.9]; 54.8% male). Reintubation occurred in 93 (33.1%). In multivariate analysis accounting for death, reintubation risk increased with age (hazard ratio [HR] 1.04 per 1-year increase, 95% confidence interval [CI] 1.02 -1.06), vasopressors (HR 1.84, 95% CI 1.04-3.60), renal replacement (HR 2.01, 95% CI 1.22-3.29), maximum PEEP (HR 1.07 per 1-unit increase, 95% CI 1.02 -1.12), paralytics (HR 1.48, 95% CI 1.08-2.25) and requiring more than nasal cannula immediately post-extubation (HR 2.19, 95% CI 1.37-3.50). Reintubation was associated with higher mortality (36.6% vs 2.1%; P < 0.0001) and risk of inpatient death after adjusting for multiple factors (HR 23.2, 95% CI 6.45-83.33). Prone ventilation, corticosteroids, anticoagulation, remdesivir and tocilizumab did not impact the risk of reintubation or death. Conclusions: Up to 1 in 3 critically ill COVID-19 patients required reintubation. Older age, paralytics, high PEEP, need for greater respiratory support following extubation and non-pulmonary organ failure predicted reintubation. Extubation failure strongly predicted adverse outcomes.

show abstract

“…Analyses that stratify patients by in-hospital statin use are subject to both immortal time bias and time-varying confounding, as a patient’s changing health condition affected when and whether they initiated statins ( 28 ). To address these challenges, we used marginal structural Cox proportional hazards models to evaluate the effect of statin initiation on the primary and secondary outcomes ( 29 ).…”

Section: Methodsmentioning

confidence: 99%

Statins Are Associated with Improved 28-day Mortality in Patients Hospitalized with SARS-CoV-2 Infection

Memel

Lee

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Statins may be protective in viral infection and have been proposed as treatment in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Objective: We evaluated the effect of statins on mortality in four groups hospitalized with (SARS-CoV-2) infection (continued statin, newly initiated statin, discontinued statin, never on statin). Design: In a single center cohort study of 1179 patients hospitalized with SARS-CoV-2 infection, the outcome of death, Intensive Care Unit (ICU) admission or hospital discharge was evaluated. Patients statin use, laboratory data, and co-morbidities were determined via chart review and electronic health records. Using marginal structural models to account for timing of statin initiation and competing risks, we compared the likelihood of severe outcomes in the four statin exposure groups. Setting: Academic medical center in the United States Participants: Patients hospitalized with SARS-CoV-2 infection Measurements: 28-day mortality, ICU admission, or discharge Results: Among 1179 patients, 360 were never on a statin, 311 were newly initiated on a statin, 466 were continued on a statin, and 42 had a statin discontinued. In this cohort, 154 (13.1%) patients died by 28-days. With marginal structural model analysis, statin use reduced the hazard of 28-day mortality (HR 0.566 [CI 0.372, 0.862], p = 0.008). Both new initiation of statins (HR 0.493 [CI 0.253, 0.963], p=0.038) and continuing statin therapy reduced the hazard of 28-day mortality (HR 0.270 [CI 0.114, 0.637], p=0.003). Sensitivity analysis found that statin use was associated with improved mortality for patients > 65 years, but not for patients 65 years or younger. Limitation: Observational design Conclusion: Statin therapy during hospitalization for SARS-CoV-2 infection, including new initiation and continuation of therapy, was associated with reduced short-term mortality.

show abstract

<p>Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them</p>

Cited by 40 publications

References 30 publications

Methodological evaluation of bias in observational coronavirus disease 2019 studies on drug effectiveness

Methodological evaluation of bias in observational coronavirus disease 2019 studies on drug effectiveness

Extubation Failure in Critically Ill COVID-19 Patients: Risk Factors and Impact on In-Hospital Mortality

Statins Are Associated with Improved 28-day Mortality in Patients Hospitalized with SARS-CoV-2 Infection

Contact Info

Product

Resources

About