In clinical epidemiological studies, many exposures and confounders are time dependent. In the presence of time-dependent confounders affected by previous exposures, the usual analytic methods may introduce biases. Marginal structural models are used to deal with time-dependent confounders and exposures. A marginal structural model is a regression model for a pseudo-population using the concept of a potential outcome. The inverse probability of treatment and censoring weighting method is used to create a pseudo-population in which the effects of baseline confounders and time-dependent confounders can be removed when estimating the causal effect of the exposure on the outcome event. If the accuracy of the weights is high, the inverse probability of treatment and censoring weighting method is reliable and the bias of the marginal structural model is small. After the weights are created, a weighted regression model is applied to calculate the treatment effect. This seminar series paper introduces time-dependent confounders, time-dependent treatments, and marginal structural models.
The objective of this study was to investigate the methodological quality of coronavirus disease 2019 (COVID-19) systematic reviews (SRs) indexed in medRxiv and PubMed, compared with Cochrane COVID Reviews.
This is a cross-sectional meta-epidemiological study. We searched medRxiv, PubMed, and Cochrane Database of Systematic Reviews for SRs of COVID-19. We evaluated the methodological quality using A MeaSurement Tool to Assess systematic Reviews (AMSTAR) checklists. The maximum AMSTAR score is 11, and minimum is 0. Higher score means better quality.
We included 9 Cochrane reviews as well as randomly selected 100 non-Cochrane reviews in medRxiv and PubMed. Compared with Cochrane reviews (mean 9.33, standard deviation 1.32), the mean AMSTAR scores of the articles in medRxiv were lower (mean difference (MD): −2.85, 98.3% confidence intervals (CI): −0.96 to −4.74), and those in PubMed were also lower (MD: −3.28, 98.3%CI: −1.40 to −5.15), with no difference between the latter two.
Readers should pay attention to the potentially low methodological quality of SRs related to COVID-19 in both PubMed and medRxiv. Evidence users might be better to search the Cochrane Library rather than medRxiv or PubMed to search SRs related to COVID-19.
Trauma is a leading cause of the loss of social life and a major contributor to the global burden of disease. Although trauma-related short-term mortality has decreased worldwide, knowledge on long-term outcomes, including health-related quality of life, patient trajectory, and reintegration into society, is lacking.
To create a comprehensive long-term trauma outcome database, describe patients’ long-term outcomes in the first 2 years of injury, and explore the association between patients’ background and long-term outcomes.
This study will be a nationwide prospective cohort study. We will prospectively collect data on patients aged ≥16 years with moderate to severe trauma (injury severity score >12) who are admitted to acute care facilities and discharged alive. After obtaining informed consent, we will follow the patients for 2 years and obtain data on their comprehensive long-term outcomes and social backgrounds. Thereafter, we will combine these new data with the existing data from two other databases (the Japan Trauma Data Bank and Diagnosis Procedure Combination database) and subsequently create a larger more comprehensive trauma database.
We will focus on epidemiological and descriptive analyses and analyze the associations between patients’ social backgrounds and long-term outcomes. A generalized linear mixed-effect logistic regression model and a random intercept per hospital will be used to adjust for baseline confounders and institutional differences.
CONTRIBUTION AND SIGNIFICANCE TO THE FIELD
This study will be an essential piece of evidence from a public health policy perspective and will recommend medical care optimized for each patient to help trauma survivors regain control over their lives.