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We aimed to examine a) the policies of national and international clinical trial registries regarding observational studies; b) the time trends of observational study registration; and c) the published arguments for and against observational study registration. Scoping review of registry practices and published arguments. We searched the websites and databases of all 19 members of the World Health Organization's Registry Network to identify policies relating to observational studies and the number of observational studies registered annually from the beginning of the registries to 2022. Regarding documents with arguments, we searched Medline, Embase, Google Scholar, and top medical and epidemiological journals from 2009 to 2023. We classified arguments as “main” based on the number (n ≥ 3) of documents they occurred in. Of 19 registries, 15 allowed observational study registration, of which seven (35%) had an explicit policy regarding what to register and two (11%) about when to register. The annual number of observational study registrations increased over time in all registries; for example, ClinicalTrials.gov increased from 313 in 1999 to 9775 in 2022. Fifty documents provided arguments concerning observational study registration: 31 argued for, 18 against, and one was neutral. Since 2012, 19 out of 25 documents argued for. We classified nine arguments as main: five for and four against. The two most prevalent arguments for were the prevention of selective reporting of outcomes (n = 16) and publication bias (n = 12), and against were that it will hinder exploration of new ideas (n = 17) and it will waste resources (n = 6). Few registries have policies regarding observational studies; an increasing number of observational studies were registered; there was a lively debate on the merits of registration of observational studies, which, since 2012, seems to converge toward proregistration. •Only 7 (35%) study registries had an explicit policy for observational studies.•Only 2 (11%) registries specified when to register observational studies.•The annual number of observational study registration increased in all registries.•The debate on observational study registration converges toward pro-registration.

Many analyses of observational data are attempts to emulate a target trial. The emulation of the target trial may fail when researchers deviate from simple principles that guide the design and analysis of randomized experiments. We review a framework to describe and prevent biases, including immortal time bias, that result from a failure to align start of follow-up, specification of eligibility, and treatment assignment. We review some analytic approaches to avoid these problems in comparative effectiveness or safety research.

To our knowledge, no publication providing overarching guidance on the conduct of systematic reviews of observational studies of etiology exists. Conducting Systematic Reviews and Meta-Analyses of Observational Studies of Etiology (COSMOS-E) provides guidance on all steps in systematic reviews of observational studies of etiology, from shaping the research question, defining exposure and outcomes, to assessing the risk of bias and statistical analysis. The writing group included researchers experienced in meta-analyses and observational studies of etiology. Standard peer-review was performed. While the structure of systematic reviews of observational studies on etiology may be similar to that for systematic reviews of randomised controlled trials, there are specific tasks within each component that differ. Examples include assessment for confounding, selection bias, and information bias. In systematic reviews of observational studies of etiology, combining studies in meta-analysis may lead to more precise estimates, but such greater precision does not automatically remedy potential bias. Thorough exploration of sources of heterogeneity is key when assessing the validity of estimates and causality. As many reviews of observational studies on etiology are being performed, this document may provide researchers with guidance on how to conduct and analyse such reviews.

Mendelian randomisation (MR) studies allow a better understanding of the causal effects of modifiable exposures on health outcomes, but the published evidence is often hampered by inadequate reporting. Reporting guidelines help authors effectively communicate all critical information about what was done and what was found. STROBE-MR (strengthening the reporting of observational studies in epidemiology using mendelian randomisation) assists authors in reporting their MR research clearly and transparently. Adopting STROBE-MR should help readers, reviewers, and journal editors evaluate the quality of published MR studies. This article explains the 20 items of the STROBE-MR checklist, along with their meaning and rationale, using terms defined in a glossary. Examples of transparent reporting are used for each item to illustrate best practices.

Knowledge gained from observational cohort studies has dramatically advanced the prevention and treatment of diseases. Many of these cohorts, however, are small, lack diversity, or do not provide comprehensive phenotype data. The All of Us Research Program plans to enroll a diverse group of at least 1 million persons in the United States in order to accelerate biomedical research and improve health. The program aims to make the research results accessible to participants, and it is developing new approaches to generate, access, and make data broadly available to approved researchers. All of Us opened for enrollment in May 2018 and currently enrolls participants 18 years of age or older from a network of more than 340 recruitment sites. Elements of the program protocol include health questionnaires, electronic health records (EHRs), physical measurements, the use of digital health technology, and the collection and analysis of biospecimens. As of July 2019, more than 175,000 participants had contributed biospecimens. More than 80% of these participants are from groups that have been historically underrepresented in biomedical research. EHR data on more than 112,000 participants from 34 sites have been collected. The All of Us data repository should permit researchers to take into account individual differences in lifestyle, socioeconomic factors, environment, and biologic characteristics in order to advance precision diagnosis, prevention, and treatment.

Immortal Time Bias (ITB) is a critical challenge in observational studies estimating treatment effects, often addressed using Mantel–Byar (MB) and Landmark (LM) methods. However, the impact of ITB appears to differ between methodological and empirical studies. This study aims to investigate whether the ITB would be affected by study types and how. We systematically searched PubMed from January 1, 2010, to May 31, 2023, to identify empirical and methodological studies explicitly using LM or MB to address ITB. Eligible studies reported hazard ratio comparing: (i) unadjusted vs MB/LM-adjusted or (ii) MB vs LM-adjusted. We first examined estimate discrepancies across ITB-handling strategies within empirical or methodological studies, and then evaluated concordance across study types. We included 67 studies (46 empirical, 21 methodological). For unadjusted vs adjusted comparisons (58 empirical, 42 methodological), methodological studies exhibited higher rates of conclusion discordance (64.3% vs 32.8%, P = .004), and opposite effect directions (40.5% vs 15.5%, P = .010). For MB vs LM comparisons (20 empirical, 12 methodological), more frequent conclusion discordance was observed in methodological studies (41.7% vs 0%, P = .004), and other discrepancy metrics showed no significant differences between study types. Our findings suggest that ITB tends to have a more pronounced impact in methodological studies, indicating that its influence may vary across different study settings. For methodological studies, it is important to clarify the critical ITB settings and the corresponding handling approaches. For empirical studies suspected of ITB, using rigorous handling strategies can enhance the robustness of treatment effect estimates. [Display omitted] •ITB impact is stronger in methodological than in empirical studies.•First systematic comparison across methodological and empirical studies.•These inconsistencies highlight the need to specify and report ITB setting.•Methodological studies should clarify ITB settings and justify chosen strategies.•Empirical studies should adjust for ITB and align methods to target estimands.

An observational study is a type of epidemiological study design, which can take the form of a cohort, a case-control, or a cross-sectional study. When presenting observational studies in manuscripts, an author needs to ascertain a clear presentation of the work and provide the reader with appropriate information to enable critical appraisal of the research. The Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines were created to aid the author in ensuring high-quality presentation of the conducted observational study. The original articles publishing the STROBE guidelines together with their bibliographies were identified and thoroughly reviewed. These guidelines consist of 22 checklist items that the author needs to fulfil before submitting the manuscript to a journal. The STROBE guidelines were created to aid the authors in presenting their work and not to act as a validation tool for the conducted study or as a framework to conduct an observational study on. The authors complying with these guidelines are more likely to succeed in publishing their observational study work in a journal.

The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest. Various advanced statistical approaches exist that offer certain advantages in terms of addressing these potential biases. However, although these statistical approaches have different underlying statistical assumptions, in practice they cannot always completely remove key sources of bias; therefore, using design-based approaches to improve causal inference is also important. Here it is the design of the study that addresses the problem of potential bias – either by ensuring it is not present (under certain assumptions) or by comparing results across methods with different sources and direction of potential bias. The distinction between statistical and design-based approaches is not an absolute one, but it provides a framework for triangulation – the thoughtful application of multiple approaches (e.g. statistical and design based), each with their own strengths and weaknesses, and in particular sources and directions of bias. It is unlikely that any single method can provide a definite answer to a causal question, but the triangulation of evidence provided by different approaches can provide a stronger basis for causal inference. Triangulation can be considered part of wider efforts to improve the transparency and robustness of scientific research, and the wider scientific infrastructure and system of incentives.

Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.

The effect estimates in systematic reviews of observational studies often exhibit high heterogeneity. A potentially important source of heterogeneity are differences in the control variables across observational studies. However, it remains unclear how often this source of heterogeneity is considered in practice. The objective of this umbrella review is to determine how often control for different sets of variables across primary studies was considered as a source of heterogeneity in published systematic reviews of observational epidemiologic studies. We systematically searched for systematic reviews of observational studies published in a quartile 1 Web of Science or Scopus-indexed epidemiology journal between January 1, 2023, and December 31, 2023. Eligibility screening, data extraction, and quality appraisal were performed by two independent reviewers. Data were summarized using descriptive statistics. Eligibility criteria were met by 297 systematic reviews, of which a random sample of 50 systematic reviews was included in this umbrella review. Differences in confounder sets were mentioned as a potential source of heterogeneity in 5/50 reviews (10.0%), differences in covariate sets in 4/50 reviews (8.0%), control for mediators in 0/50 reviews (0.0%), and control for colliders in 0/50 reviews (0.0%). While differences in control for confounders, mediators, and colliders may explain heterogeneity in systematic reviews of observational studies, these sources of heterogeneity are rarely considered in practice. To avoid invalid pooled effect estimates, it is important that future systematic reviews of observational studies assess these potential sources of heterogeneity. •Risk of bias due to inadequate control variables is often not discussed in reviews.•Most reviews do not study control variables as potential sources of heterogeneity.•Risk of bias due to inadequate control variables needs to be discussed in reviews.•Control variables should be studied as potential sources of heterogeneity.