Annals of Clinical Epidemiology
Online ISSN : 2434-4338
最新号
選択された号の論文の4件中1~4を表示しています
SEMINAR
  • Asuka Tsuchiya
    原稿種別: SEMINAR
    2021 年 3 巻 2 号 p. 37-45
    発行日: 2021年
    公開日: 2021/04/01
    ジャーナル オープンアクセス HTML

    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.

ORIGINAL ARTICLE
  • Yuki Kataoka, Shiho Oide, Takashi Ariie, Yasushi Tsujimoto, Toshi A. F ...
    原稿種別: ORIGINAL ARTICLE
    2021 年 3 巻 2 号 p. 46-55
    発行日: 2021年
    公開日: 2021/04/01
    ジャーナル オープンアクセス HTML

    BACKGROUND

    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.

    METHODS

    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.

    RESULTS

    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.

    CONCLUSIONS

    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.

SHORT REPORT
STUDY PROTOCOL
  • Asuka Tsuchiya, Yusuke Tsutsumi, Hideo Yasunaga, Susumu Yasuda, Kenji ...
    原稿種別: STUDY PROTOCOL
    2021 年 3 巻 2 号 p. 59-66
    発行日: 2021年
    公開日: 2021/04/01
    ジャーナル オープンアクセス HTML

    BACKGROUND

    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.

    OBJECTIVE

    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.

    METHODS

    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.

    ANALYSIS

    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.

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