Target trial emulation with multi-state model analysis to assess treatment effectiveness using clinical COVID-19 data.

Publication date: Sep 02, 2023

Real-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks. For exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (nā€‰=ā€‰501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented. Our study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results. Extending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable.

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Concepts Keywords
Coronavirus Bias
Death COVID-19
Hospital Multi-state models
Methodological Observational data
Target trial emulation

Semantics

Type Source Name
disease VO effectiveness
disease MESH COVID-19
disease VO time
disease VO dose
disease VO protocol
disease MESH death
pathway REACTOME Reproduction
drug DRUGBANK Trestolone
drug DRUGBANK Coenzyme M
drug DRUGBANK Oxygen
disease MESH inflammation
disease MESH Comorbidity
disease IDO healthcare facility
drug DRUGBANK Aspartame
drug DRUGBANK Isoxaflutole
drug DRUGBANK Dimercaprol
disease IDO process
disease IDO object
drug DRUGBANK Methionine
disease MESH clinical importance
disease VO vaccination
disease VO effective
disease MESH influenza
disease MESH cancer
drug DRUGBANK Hydroxychloroquine
disease MESH pneumonia
drug DRUGBANK Tocilizumab
disease MESH critically ill
disease MESH respiratory failure
disease MESH Acute respiratory distress syndrome
disease MESH epilepsy
disease MESH AIDS
drug DRUGBANK Serine
drug DRUGBANK Filgrastim
disease MESH dementia
disease IDO history
drug DRUGBANK Palivizumab
disease VO population
drug DRUGBANK Gold

Original Article

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