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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.

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.

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 Analyses of multicenter studies often need to account for center clustering to ensure valid inference. For binary outcomes, it is particularly challenging to properly adjust for center when the number of centers or total sample size is small, or when there are few events per center. Our objective was to evaluate the performance of generalized estimating equation (GEE) log-binomial and Poisson models, generalized linear mixed models (GLMMs) assuming binomial and Poisson distributions, and a Bayesian binomial GLMM to account for center effect in these scenarios. Methods We conducted a simulation study with few centers (≤30) and 50 or fewer subjects per center, using both a randomized controlled trial and an observational study design to estimate relative risk. We compared the GEE and GLMM models with a log-binomial model without adjustment for clustering in terms of bias, root mean square error (RMSE), and coverage. For the Bayesian GLMM, we used informative neutral priors that are skeptical of large treatment effects that are almost never observed in studies of medical interventions. Results All frequentist methods exhibited little bias, and the RMSE was very similar across the models. The binomial GLMM had poor convergence rates, ranging from 27% to 85%, but performed well otherwise. The results show that both GEE models need to use small sample corrections for robust SEs to achieve proper coverage of 95% CIs. The Bayesian GLMM had similar convergence rates but resulted in slightly more biased estimates for the smallest sample sizes. However, it had the smallest RMSE and good coverage across all scenarios. These results were very similar for both study designs. Conclusions For the analyses of multicenter studies with a binary outcome and few centers, we recommend adjustment for center with either a GEE log-binomial or Poisson model with appropriate small sample corrections or a Bayesian binomial GLMM with informative priors.

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.

Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records’ analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits and whether a sensitivity analysis regarding the missingness mechanism is required; 2) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data and transparently reporting the potential effect on the study results, therefore increasing the confidence in and reproducibility of research findings. •Missing data are ubiquitous in medical research.•Guidance is available, but missing data are still often not handled appropriately.•We present a framework for handling and reporting analyses of incomplete data.•This framework encourages researchers to think systematically about missing data.•Adoption of this framework will increase the reproducibility of research findings.•This article provides a much needed framework for handling and reporting the analysis of incomplete data in observational studies.•The framework puts a strong emphasis on preplanning the statistical analysis and encourages transparency when reporting the results of a study.•Adoption of this framework will increase the confidence in and reproducibility of research findings.

The FDA is developing guidance on the use of “real-world evidence” — health care information from atypical sources, including electronic health records, billing databases, and product and disease registries — to assess the safety and effectiveness of drugs and devices. The term “real-world evidence” is widely used by those who develop medical products or who study, deliver, or pay for health care, but its specific meaning is elusive. We believe it refers to information on health care that is derived from multiple sources outside typical clinical research settings, including electronic health records (EHRs), claims and billing data, product and disease registries, and data gathered through personal devices and health applications. 1 , 2 Key to understanding the usefulness of real-world evidence is an appreciation of its potential for complementing the knowledge gained from traditional clinical trials, whose well-known limitations make it difficult . . .

Background Idiopathic pulmonary fibrosis (IPF) is a progressive debilitating lung disease with considerable morbidity. Heterogeneity in epidemiologic studies means the full impact of the disease is unclear. Methods A targeted literature search for population-based, observational studies reporting incidence and/or prevalence of IPF from January 2009 to April 2020 was conducted. Identified studies were aggregated by country. For countries with multiple publications, a weighted average was determined. Incidence and prevalence data were adjusted for between-study differences where possible. The final model included adjusted estimates of incidence and prevalence per 10,000 of the population with 95% confidence intervals. As prevalence estimates vary depending on the definitions used, estimates were based on a specific case definition of IPF. Results Overall, 22 studies covering 12 countries met the inclusion criteria, with 15 reporting incidence and 18 reporting prevalence estimates. The adjusted incidence estimates (per 10,000 of the population) ranged from 0.35 to 1.30 in Asia–Pacific countries, 0.09 to 0.49 in Europe, and 0.75 to 0.93 in North America. Unadjusted and adjusted incidence estimates were consistent. The adjusted prevalence estimates ranged from 0.57 to 4.51 in Asia–Pacific countries, 0.33 to 2.51 in Europe, and 2.40 to 2.98 in North America. South Korea had the highest incidence and prevalence estimates. When prevalence estimates were compared to country-specific rare disease thresholds, IPF met the definition of a rare disease in all countries except South Korea. There were notable geographic gaps for IPF epidemiologic data. Conclusions Due to differences in study methodologies, there is worldwide variability in the reported incidence and prevalence of IPF. Based on the countries included in our analysis, we estimated the adjusted incidence and prevalence of IPF to be in the range of 0.09–1.30 and 0.33–4.51 per 10,000 persons, respectively. According to these prevalence estimates, IPF remains a rare disease. For consistency, future epidemiologic studies of IPF should take age, sex, smoking status, and the specificity of case definitions into consideration.

For researchers using observational data, a useful way to answer a causal question is to design the target trial that would answer it and then emulate its protocol. The example of the HIV-treatment-as-prevention strategy illustrates the benefits of this approach.