Literature DB >> 34004265

'Methodological evaluation of bias in observational COVID-19 studies on drug effectiveness' - Author's reply.

Martin Wolkewitz1, Maja von Cube2, Oksana Martinuka2.   

Abstract

Entities:  

Mesh:

Substances:

Year:  2021        PMID: 34004265      PMCID: PMC9246504          DOI: 10.1016/j.cmi.2021.05.019

Source DB:  PubMed          Journal:  Clin Microbiol Infect        ISSN: 1198-743X            Impact factor:   13.310


× No keyword cloud information.
To the Editor We thank Guaraldi et al. [1] for the opportunity to clarify specific methodological issues that were identified in our review [2]. We agree that the magnitude of immortal bias may be small if the time span between the start of follow up and the treatment initiation is very short. However, this bias has already created many flawed publications in many epidemiological areas, so it cannot be ignored. We also highlight that immortal time is a time-dependent bias that may refer to other non-fatal outcomes under interest, such as discharge alive or initiation of mechanical ventilation [3]. In addition, we would like to remark that the quantification of the magnitude of the biases was beyond the scope of our review. Competing risk events can occur in both randomized trials and observational studies and competing risk analysis should be performed irrespective of the primary study outcome [4]. In the review we pointed out that there are two main approaches for competing risks and the cause-specific hazard model is considered as an appropriate method for aetiological research [5]. Regarding time-varying confounding, the authors [1] considered glucocorticoids during follow up and therefore potentially later than the initiation of tocilizumab. However, time-varying confounding is evoked by covariates that influence the decision of administrating tocilizumab, so confounders are measured before the potential administration. Finally, regarding the validity assessment of effect estimates obtained from studies with different design, we fully agree that well-designed observational studies with accurate results might reflect findings from randomized trials and should complement the clinicians' knowledge and support clinical decision-making.

Author contributions

MW, MvC and OM contributed to the conceptualization of the letter, writing of the original draft and reviewing of the letter.

Transparency declaration

The authors have no conflicts of interest to declare. No funding was received for this letter.
  5 in total

1.  Biases in Evaluating the Safety and Effectiveness of Drugs for the Treatment of COVID-19: Designing Real-World Evidence Studies.

Authors:  Christel Renoux; Laurent Azoulay; Samy Suissa
Journal:  Am J Epidemiol       Date:  2021-08-01       Impact factor: 4.897

Review 2.  A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions.

Authors:  Aurelien Latouche; Arthur Allignol; Jan Beyersmann; Myriam Labopin; Jason P Fine
Journal:  J Clin Epidemiol       Date:  2013-02-14       Impact factor: 6.437

3.  Tocilizumab in patients with severe COVID-19: a retrospective cohort study.

Authors:  Giovanni Guaraldi; Marianna Meschiari; Alessandro Cozzi-Lepri; Jovana Milic; Roberto Tonelli; Marianna Menozzi; Erica Franceschini; Gianluca Cuomo; Gabriella Orlando; Vanni Borghi; Antonella Santoro; Margherita Di Gaetano; Cinzia Puzzolante; Federica Carli; Andrea Bedini; Luca Corradi; Riccardo Fantini; Ivana Castaniere; Luca Tabbì; Massimo Girardis; Sara Tedeschi; Maddalena Giannella; Michele Bartoletti; Renato Pascale; Giovanni Dolci; Lucio Brugioni; Antonello Pietrangelo; Andrea Cossarizza; Federico Pea; Enrico Clini; Carlo Salvarani; Marco Massari; Pier Luigi Viale; Cristina Mussini
Journal:  Lancet Rheumatol       Date:  2020-06-24

Review 4.  Accounting for competing risks in randomized controlled trials: a review and recommendations for improvement.

Authors:  Peter C Austin; Jason P Fine
Journal:  Stat Med       Date:  2017-01-19       Impact factor: 2.373

Review 5.  Methodological evaluation of bias in observational COVID-19 studies on drug effectiveness.

Authors:  Oksana Martinuka; Maja von Cube; Martin Wolkewitz
Journal:  Clin Microbiol Infect       Date:  2021-04-01       Impact factor: 8.067

  5 in total
  1 in total

1.  Beware of Biases in Observational Studies on Anti-Spike Monoclonal Antibodies.

Authors:  Giuseppe Lapadula; Davide Paolo Bernasconi; Alessandro Soria; Maria Grazia Valsecchi; Paolo Bonfanti
Journal:  Clin Infect Dis       Date:  2022-01-29       Impact factor: 9.079

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.