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IntroductionTwo randomized controlled trials (RCTs) from Europe recently showed similar weight loss and rates of type 2 diabetes (T2D) remission following laparoscopic gastric bypass (LRYGB) and laparoscopic sleeve gastrectomy (LSG). However, results from observational studies in the United States (US) have discordant results. We compared 1-year weight loss and T2D remission between LRYGB and LSG in a heterogeneous patient cohort from the US, albeit with similar inclusion and exclusion criteria to the European RCTs.MethodsLogistic regression was used to propensity match LSG and LRYGB patients according to age, gender, race, preoperative BMI, and T2D. Inclusion and exclusion criteria were adopted from the two European RCTs. Demographic, anthropometric, weight outcomes, and comorbidities prevalence were compared at baseline and 1-year follow-up.ResultsWe included 278 patients (139 LSG and 139 RYGB; median age 42 years, 89% female, 57% black race, 22% with public health insurance, and 25% with T2D). One year after surgery, mean %EWL was 77.3 ± 19.5% with LRYGB and 63.1 ± 21% with LSG (P < 0.001). Mean %TWL was 34.2 ± 7.3% after LRYGB and 28.1 ± 8.2% after LSG, (P < 0.001). The proportion of patients who achieved T2D remission was comparable between surgeries (LRGYB: 68.6% vs. LSG: 66.7%, P = 0.89). LSG, older age, black race, and higher preoperative BMI were independently associated with lower %EWL. Independent correlates of weight loss were different for LRYGB and LSG.ConclusionsWeight loss, but not the likelihood of T2D remission, was greater with LRYGB than LSG in a diverse patient cohort in the US. Further research efforts connecting population diversity to discordant results across studies is needed to better counsel patients with regards to expected postoperative outcomes.

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.

With statins, the reported rate of adverse events differs widely between randomized clinical trials (RCTs) and observations in clinical practice, the rates being 1-2% in RCTs vs. 10-20% in the so-called real world. One possible explanation is the claim that RCTs mostly use a run-in period with a statin. This would exclude intolerant patients from remaining in the trial and therefore favour a bias towards lower rates of intolerance. We here review data from RCTs with more than 1000 participants with and without a run-in period, which were included in the Cholesterol Treatment Trialists Collaboration. Two major conclusions arise: (i) the majority of RCTs did not have a test dose of a statin in the run-in phase. (ii) A test dose in the run-in phase was not associated with a significantly improved adherence rate within that trial when compared to trials without a test dose. Taken together, the RCTs of statins reviewed here do not suggest a bias towards an artificially higher adherence rate because of a run-in period with a test dose of the statin. Other possible explanations for the apparent disparity between RCTs and real-world observations are also included in this review albeit mostly not supported by scientific data.

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.

Vaccine effectiveness studies are subject to biases due to depletion-of-persons at risk of infection, or at especially high risk of infection, at different rates from different groups (depletion-of-susceptibles bias), a problem that can also lead to biased estimates of waning effectiveness, including spurious inference of waning when none exists. An alternative study design to identify waning is to study only vaccinated persons, and compare for each day the incidence in persons with earlier or later dates of vaccination to assess waning in vaccine protection as a function of vaccination time (namely whether earlier vaccination would result in lower subsequent protection compared to later vaccination). Prior studies suggested under what conditions this alternative would yield correct estimates of waning. Here we define the depletion-ofsusceptibles process formally and show mathematically that for influenza vaccine waning studies, a randomised trial or corresponding observational study that compares incidence at a specific calendar time among individuals vaccinated at different times before the influenza season begins will not be vulnerable to depletion-of-susceptibles bias in its inference of waning as a function of vaccination time under the null hypothesis that none exists, and will – if waning does actually occur – underestimate the extent of waning. Such a design is thus robust in the sense that a finding of waning in that inference framework reflects actual waning of vaccine-induced immunity. We recommend such a design for future studies of waning, whether observational or randomised.

Observational analyses for causal inference often rely on real world data collected for purposes other than research. A frequent goal of these observational analyses is to use the data to emulate a hypothetical randomized experiment, i.e., the target trial, that mimics the design features of a true experiment, including a clear definition of time zero with synchronization of treatment assignment and determination of eligibility. We review a recent observational analysis that explicitly emulated a target trial of screening colonoscopy using insurance claims from U.S. Medicare. We then compare this explicit emulation with alternative, simpler observational analyses that do not synchronize treatment assignment and eligibility determination at time zero and/or do not allow for repeated eligibility. This empirical comparison suggests that lack of an explicit emulation of the target trial leads to biased estimates, and shows that allowing for repeated eligibility increases the statistical efficiency of the estimates.

To compare different methods to handle treatment when developing a prognostic model that aims to produce accurate probabilities of the outcome of individuals if left untreated. Simulations were performed based on two normally distributed predictors, a binary outcome, and a binary treatment, mimicking a randomized trial or an observational study. Comparison was made between simply ignoring treatment (SIT), restricting the analytical data set to untreated individuals (AUT), inverse probability weighting (IPW), and explicit modeling of treatment (MT). Methods were compared in terms of predictive performance of the model and the proportion of incorrect treatment decisions. Omitting a genuine predictor of the outcome from the prognostic model decreased model performance, in both an observational study and a randomized trial. In randomized trials, the proportion of incorrect treatment decisions was smaller when applying AUT or MT, compared to SIT and IPW. In observational studies, MT was superior to all other methods regarding the proportion of incorrect treatment decisions. If a prognostic model aims to produce correct probabilities of the outcome in the absence of treatment, ignoring treatments that affect that outcome can lead to suboptimal model performance and incorrect treatment decisions. Explicitly, modeling treatment is recommended.

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.

Background Immortal time bias (ITB) represents a methodological challenge in evaluating treatment effects in observational studies using routinely collected data (RCD). However, the prevalence of ITB, the strategies used to address ITB and its impact remain inadequate. This study aimed to investigate how ITB was identified and handled in observational studies using RCD, and to assess its impact on treatment effect estimates. Methods A systematic search was performed in PubMed for observational studies published from 2018 to 2020 that used RCD to evaluate drug treatment effects. We examined the synchronization of three time points (eligibility, treatment assignment, and the start of follow-up) to identify ITB and assessed the risk of ITB. For low-risk studies, we summarized the handling approaches. For high-risk studies, we conducted quantitative bias analyses to correct for ITB and calculate ITB-controlled estimates. These ITB-controlled estimates were then compared with original estimates to quantify the impact of ITB. Results Among the 256 studies initially identified, 162 cohort studies with time-to-event outcomes were included. 13 studies (8.0%) lacked sufficient reporting to assess ITB. Of the remaining studies, 35 studies (21.6%) were classified as high risk for ITB, while 114 studies (70.4%) were classified as low risk, with 15 having naturally synchronized time points and 99 using design or analytical approaches to synchronize them. For the 99 low-risk studies, the commonly employed approaches were the active comparator new-user design and the time-varying exposure definition, accounting for 56.6% and 19.2%, respectively. Of the 35 high-risk studies, 16 studies that provided sufficient information for correction were included in the quantitative bias analyses. Among these, 4 studies (25%) showed statistically significant differences between ITB-controlled and original estimates, and 4 studies (25%) yielded conflicting conclusions regarding the statistical significance of these two estimates. Only 5 of the 35 high-risk studies (14.3%) discussed that the results may be affected by ITB. Conclusions ITB is a critical methodological issue in observational studies using RCD, with the potential to significantly distort conclusions. To enhance the validity of treatment effect estimates, researchers should thoroughly examine the presence of ITB and employ appropriate strategies to mitigate its impact.

When reporting observational studies, authors should explicitly discuss the potential for confounding and other biases, but it is unclear to what extent this is carried out within the psychiatric field. We reviewed a random sample of 120 articles in the five psychiatric specialty journals with the highest 5-year impact factor in 2015–2018. We evaluated how confounding and bias was considered in the reporting of the discussion and abstract and assessed the relationship with yearly citations. The term “confounding” was explicitly mentioned in the abstract or discussion in 66 articles (55.0%; 95% confidence interval (CI): 46.1–63.6) and the term “bias” in 68 articles (56.7%; 95% CI: 47.7–65.2). The authors of 25 articles (20.8%; 95% CI: 14.5–28.9) acknowledged unadjusted confounders. With one exception (0.8%, 95% CI: 0.0–4.6), authors never expressed any caution, limitation, or uncertainty in relation to confounding or other bias in their conclusions or in the abstract. Articles acknowledging nonadjusted confounders were not less frequently cited than articles that did not (median 7.9 vs. 5.6 citations per year, P = 0.03). Confounding is overall inadequately addressed in the reporting and bias is often ignored in the interpretation of high-impact observational research in psychiatry.