Methodological evaluation of bias in observational coronavirus disease 2019 studies on drug effectiveness.
Clin Microbiol Infect
; 27(7): 949-957, 2021 Jul.
Article
in English
| MEDLINE | ID: covidwho-1300714
ABSTRACT
BACKGROUND AND OBJECTIVE:
Observational studies may provide valuable evidence on real-world causal effects of drug effectiveness in patients with coronavirus disease 2019 (COVID-19). As patients are usually observed from hospital admission to discharge and drug initiation starts during hospitalization, advanced statistical methods are needed to account for time-dependent drug exposure, confounding and competing events. Our objective is to evaluate the observational studies on the three common methodological pitfalls in time-to-event analyses immortal time bias, confounding bias and competing risk bias.METHODS:
We performed a systematic literature search on 23 October 2020, in the PubMed database to identify observational cohort studies that evaluated drug effectiveness in hospitalized patients with COVID-19. We included articles published in four journals British Medical Journal, New England Journal of Medicine, Journal of the American Medical Association and The Lancet as well as their sub-journals.RESULTS:
Overall, out of 255 articles screened, 11 observational cohort studies on treatment effectiveness with drug exposure-outcome associations were evaluated. All studies were susceptible to one or more types of bias in the primary study analysis. Eight studies had a time-dependent treatment. However, the hazard ratios were not adjusted for immortal time in the primary analysis. Even though confounders presented at baseline have been addressed in nine studies, time-varying confounding caused by time-varying treatment exposure and clinical variables was less recognized. Only one out of 11 studies addressed competing event bias by extending follow-up beyond patient discharge.CONCLUSIONS:
In the observational cohort studies on drug effectiveness for treatment of COVID-19 published in four high-impact journals, the methodological biases were concerningly common. Appropriate statistical tools are essential to avoid misleading conclusions and to obtain a better understanding of potential treatment effects.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Bias
/
Observational Studies as Topic
/
COVID-19 Drug Treatment
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Reviews
/
Systematic review/Meta Analysis
Limits:
Humans
Language:
English
Journal:
Clin Microbiol Infect
Journal subject:
Communicable Diseases
/
Microbiology
Year:
2021
Document Type:
Article
Affiliation country:
J.cmi.2021.03.003
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