ArticleClinical epidemiology2020
Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them.
Article in Clinical epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Methodological assessment of systematic reviews and meta-analyses on COVID-19: A meta-epidemiological study.Journal of evaluation in clinical practice · 2021Pooled it
- Methodological evaluation of bias in observational coronavirus disease 2019 studies on drug effectiveness.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2021Pooled it
- Article
- Association between immunosuppressive medications and COVID-19 hospitalisation and death: a retrospective cohort study.BMJ open · 2024Article
- Prioritising deteriorating patients using time-to-event analysis: prediction model development and internal-external validation.Critical care (London, England) · 2024Article
- Lessons learned: avoiding bias via multi-state analysis of patients' trajectories in real-time.Frontiers in medicine · 2024Article
- Methodological biases in observational hospital studies of COVID-19 treatment effectiveness: pitfalls and potential.Frontiers in medicine · 2024Article
- Comparison of clinical outcomes over time of inpatients with healthcare-associated or community-acquired coronavirus disease 2019 (COVID-19): A multicenter, prospective cohort study.Infection control and hospital epidemiology · 2024Observational
- Target trial emulation with multi-state model analysis to assess treatment effectiveness using clinical COVID-19 data.BMC medical research methodology · 2023Observational
- Target Trial Emulation Using Hospital-Based Observational Data: Demonstration and Application in COVID-19.Life (Basel, Switzerland) · 2023Article
- Informative Censoring-A Cause of Bias in Estimating COVID-19 Mortality Using Hospital Data.Life (Basel, Switzerland) · 2023Article
- Impact of the Severe acute respiratory syndrome coronavirus 2 pandemic on mortality associated with healthcare-associated infections.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023Article
- Association between Immunosuppressive Drugs and Coronavirus Disease 2019 Outcomes in Patients with Noninfectious Uveitis in a Large US Claims Database.Ophthalmology · 2022Article
- Handling death as an intercurrent event in time to recovery analysis in COVID-19 treatment clinical trials.Contemporary clinical trials · 2022Article
- Differences and Similarities Among COVID-19 Patients Treated in Seven ICUs in Three Countries Within One Region: An Observational Cohort Study.Critical care medicine · 2022Observational
- Association of Statins and 28-Day Mortality Rates in Patients Hospitalized With Severe Acute Respiratory Syndrome Coronavirus 2 Infection.The Journal of infectious diseases · 2022Article
- Statistical Analysis of the COVID-19 Mortality Rates with Probability Distributions: The Case of Pakistan and Afghanistan.Computational and mathematical methods in medicine · 2022Article
- Predicting COVID-19 progression in hospitalized patients in Belgium from a multi-state model.Frontiers in medicine · 2022Article
- Assessment of 28-Day In-Hospital Mortality in Mechanically Ventilated Patients With Coronavirus Disease 2019: An International Cohort Study.Critical care explorations · 2021Article
- Open Data Resources on COVID-19 in Six European Countries: Issues and Opportunities.International journal of environmental research and public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
By definition, in-hospital patient data are restricted to the time between hospital admission and discharge (alive or dead). For hospitalised cases of COVID-19, a number of events during hospitalization are of interest regarding the influence of risk factors on the likelihood of experiencing these events. The same is true for predicting times from hospital admission of COVID-19 patients to intensive care or from start of ventilation (invasive or non-invasive) to extubation. This logical restriction of the data to the period of hospitalisation is associated with a substantial risk that inappropriate methods are used for analysis. Here, we briefly discuss the most common types of bias which can occur when analysing in-hospital COVID-19 data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.