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

Martinuka et al. BMC Medical Research Methodology (2023) 23:197 https://doi.org/10.1186/s12874-023-02001-8 BMC Medical Research Methodology Open Access RESEARCH Target trial emulation with multi‑state model analysis to assess treatment effectiveness using clinical COVID‑19 data Oksana Martinuka1* , Derek Hazard1 , Hamid Reza Marateb2 , Camille Maringe3 , Marjan Mansourian2,4 , Manuel Rubio‑Rivas5 and Martin Wolkewitz1    Abstract Background 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 treat‑ ment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks. Methods For exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 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 cen‑ soring 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 treat‑ ment 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. Results 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 analy‑ sis allows us to summarize results using stacked probability plots that make it easier to interpret results. Conclusions 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. Keywords Bias, COVID-19, Multi-state models, Observational data, Target trial emulation *Correspondence: Oksana Martinuka Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Martinuka et al. BMC Medical Research Methodology (2023) 23:197 Introduction During the coronavirus disease 2019 (COVID-19) pandemic, observational patient data have increasingly been used to evaluate treatment effectiveness, in addition to randomized controlled trials (RCTs) [1]. However, evaluating treatment effectiveness using real-world data can be challenging due to observational data limitations [2, 3]. Immortal time bias occurs when there is misalignment of start of follow-up and exposure, leading to a spurious increase in survival time for exposed patients [4]. Confounding bias relates to unequal distributions of patient’s characteristics between exposure groups, leading to an over- or underestimation of effects [5]. Furthermore, a competing risk bias occurs when competing events are treated as censoring events, and naïve Kaplan–Meier analysis is applied, leading to an overestimation of the cumulative incidence of the primary event [6, 7]. A methodological review that evaluated observational COVID-19 studies published in four high-ranking medical journals demonstrated that immortal time and confounding biases remain prevalent in pharmaco-epidemiological studies assessing treatment effectiveness when they rely on retrospective observational data [1, 2]. Ignoring the pitfalls of observational study design and the application of standard methods for survival analysis can lead to biased results and flawed conclusions [7]. The target trial emulation framework is attracting more attention, and has become a preferred method for evaluating treatment effectiveness using real-world observational data [8, 9]. During the COVID-19 pandemic, this valuable framework demonstrated its utility by providing early evidence on repurposed therapies for hospitalized patients [10, 11]. Target trial emulation can be essential to complement clinical trial findings or when RCT data are unavailable [11–14]. Importantly, this approach enables to emulate a hypothetical trial and address common observational design limitations [9]. Applying the emulated trial framework encourages researchers to carefully consider their data and setting, highlighting their strengths and limitations. Previously, we described the extension of the target trial emulation framework to competing risk analysis, which enabled us to estimate the treatment effects on in-hospital death probabilities for COVID-19 patients, taking hospital discharge into account as a competing risk event [15]. In this article, we aim to extend the target trial emulation framework to a setting of multistate model analysis, and demonstrate the benefits of this development on exemplary data from hospitalized patients with COVID-19. Multi-state modelling methodology enables detailed description of disease pathways in complex settings, and makes the assessm (...truncated)