Learning from previous methodological pitfalls to propose well-designed trials on vitamin D in COVID-19.
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ABSTRACT:
SUBMITTER: Annweiler C
PROVIDER: S-EPMC8045428 | biostudies-literature | 2021 Jul
REPOSITORIES: biostudies-literature
Publications
Learning from previous methodological pitfalls to propose well-designed trials on vitamin D in COVID-19.
Annweiler Cédric C Mercat Alain A Souberbielle Jean-Claude JC
The Journal of steroid biochemistry and molecular biology 20210414
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Project description:IntroductionThis study aims to discuss and assess the impact of three prevalent methodological biases: competing risks, immortal-time bias, and confounding bias in real-world observational studies evaluating treatment effectiveness. We use a demonstrative observational data example of COVID-19 patients to assess the impact of these biases and propose potential solutions.MethodsWe describe competing risks, immortal-time bias, and time-fixed confounding bias by evaluating treatment effectiveness in hospitalized patients with COVID-19. For our demonstrative analysis, we use observational data from the registry of patients with COVID-19 who were admitted to the Bellvitge University Hospital in Spain from March 2020 to February 2021 and met our predefined inclusion criteria. We compare estimates of a single-dose, time-dependent treatment with the standard of care. We analyze the treatment effectiveness using common statistical approaches, either by ignoring or only partially accounting for the methodological biases. To address these challenges, we emulate a target trial through the clone-censor-weight approach.ResultsOverlooking competing risk bias and employing the naïve Kaplan-Meier estimator led to increased in-hospital death probabilities in patients with COVID-19. Specifically, in the treatment effectiveness analysis, the Kaplan-Meier estimator resulted in an in-hospital mortality of 45.6% for treated patients and 59.0% for untreated patients. In contrast, employing an emulated trial framework with the weighted Aalen-Johansen estimator, we observed that in-hospital death probabilities were reduced to 27.9% in the "X"-treated arm and 40.1% in the non-"X"-treated arm. Immortal-time bias led to an underestimated hazard ratio of treatment.ConclusionOverlooking competing risks, immortal-time bias, and confounding bias leads to shifted estimates of treatment effects. Applying the naïve Kaplan-Meier method resulted in the most biased results and overestimated probabilities for the primary outcome in analyses of hospital data from COVID-19 patients. This overestimation could mislead clinical decision-making. Both immortal-time bias and confounding bias must be addressed in assessments of treatment effectiveness. The trial emulation framework offers a potential solution to address all three methodological biases.
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Project description:BackgroundPneumonitis is one of the most common adverse events induced by the use of immune checkpoint inhibitors (ICI), accounting for a 20% of all ICI-associated deaths. Despite numerous efforts to identify risk factors and develop predictive models, there is no clinically deployed risk prediction model for patient risk stratification or for guiding subsequent monitoring. We believe this is due to systemic suboptimal approaches in study designs and methodologies in the literature. The nature and prevalence of different methodological approaches has not been thoroughly examined in prior systematic reviews.MethodsThe PubMed, medRxiv and bioRxiv databases were used to identify studies that aimed at risk factor discovery and/or risk prediction model development for ICI-induced pneumonitis (ICI pneumonitis). Studies were then analysed to identify common methodological pitfalls and their contribution to the risk of bias, assessed using the QUIPS and PROBAST tools.ResultsThere were 51 manuscripts eligible for the review, with Japan-based studies over-represented, being nearly half (24/51) of all papers considered. Only 2/51 studies had a low risk of bias overall. Common bias-inducing practices included unclear diagnostic method or potential misdiagnosis, lack of multiple testing correction, the use of univariate analysis for selecting features for multivariable analysis, discretization of continuous variables, and inappropriate handling of missing values. Results from the risk model development studies were also likely to have been overoptimistic due to lack of holdout sets.ConclusionsStudies with low risk of bias in their methodology are lacking in the existing literature. High-quality risk factor identification and risk model development studies are urgently required by the community to give the best chance of them progressing into a clinically deployable risk prediction model. Recommendations and alternative approaches for reducing the risk of bias were also discussed to guide future studies.
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Project description:BackgroundThe rapid emergence of clinical trials on COVID-19 stimulated a wave of discussion in scientific community. It is important to understand the characteristics of the ongoing or pending interventional clinical trials on COVID-19.MethodsWe reviewed the characteristics of interventional trials from Chinese Clinical Trial Registration (ChiCTR) and ClinicalTrials.gov. A total of 171 COVID-19-related interventional trials were identified on Feb 22, 2020. These trials are classified into 4 categories based on treatment modalities, including chemical drugs (CDs), biological therapies (BTs), traditional Chinese medicine (TCM) treatments and other therapies.ResultsOur analysis focused on the issues of stage, design, randomization, blinding, primary endpoints (PEs) definition and sample size of these trials. Although most trials use parallel-arm design (88.3%) and randomization (77.2%), blinding is applied in only 25 trials (14.6%). More than half of the trials planned to recruit ≤100 patients, indicating a possibility of insufficient statistical power. About one third of trials will recruit severe and critically ill patients. More trials on traditional Chinese medical treatment use 2 or more PEs than those on CDs or biological treatments (57.6%, 39.4% and 40.5%, respectively).ConclusionsWe found some studies with potential defects including unreasonable design, inappropriate PE and small sample size. Clinical trials on COVID-19 should be designed based on scientific rules, ethics and benefits for patients.
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