This manuscript summarizes a presentation delivered by the first author at the 2024 symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to Dr. Jan Sargeant. Epidemiologic research plays a crucial role in understanding the complex relationships between exposures and health outcomes. However, the accuracy of the conclusions drawn from these investigations relies upon the meticulous selection and measurement of exposure variables. Appropriate exposure variable selection is crucial for understanding disease etiologies, but it is often the case that we are not able to directly measure the exposure variable of interest and use proxy measures to assess exposures instead. Inappropriate use of proxy measures can lead to erroneous conclusions being made about the true exposure of interest. These errors may lead to biased estimates of associations between exposures and outcomes. The consequences of such biases extend beyond research concerns as health decisions can be made based on flawed evidence. Recognizing and mitigating these biases are essential for producing reliable evidence that informs health policies and interventions, ultimately contributing to improved population health outcomes. To address these challenges, researchers must adopt rigorous methodologies for exposure variable selection and validation studies to minimize measurement errors.

BACKGROUND: Although aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) can be informative about relative treatment effects. The individual participant data (IPD) of the study, when available, are preferred to AD for adjusting for important participant characteristics and to better handle heterogeneity and inconsistency in the network.
RESULTS: We developed the R package crossnma to perform cross-format (IPD and AD) and cross-design (RCT and NRS) NMA and network meta-regression (NMR). The models are implemented as Bayesian three-level hierarchical models using Just Another Gibbs Sampler (JAGS) software within the R environment. The R package crossnma includes functions to automatically create the JAGS model, reformat the data (based on user input), assess convergence and summarize the results. We demonstrate the workflow within crossnma by using a network of six trials comparing four treatments.
CONCLUSIONS: The R package crossnma enables the user to perform NMA and NMR with different data types in a Bayesian framework and facilitates the inclusion of all types of evidence recognising differences in risk of bias.

Cereals are the basis of much of the world\'s daily diet. Recently, there has been considerable interest in the beneficial properties of wholegrains due to their content of phytochemicals, particularly polyphenols. Despite this, the existing data on polyphenolic composition of cereal-based foods reported in the most comprehensive databases are still not updated. Many cereal-based foods and phenolic compounds are missing, including pigmented ones. Observational epidemiological studies reporting the intake of polyphenols from cereals are limited and inconsistent, although experimental studies suggest a protective role for dietary polyphenols against cardiovascular disease, diabetes, and cancer. Estimating polyphenol intake is complex because of the large number of compounds present in foods and the many factors that affect their levels, such as plant variety, harvest season, food processing and cooking, making it difficult matching consumption data with data on food composition. Further, it should be taken into account that food composition tables and consumed foods are categorized in different ways. The present work provides an overview of the available data on polyphenols content reported in several existing databases, in terms of presence, missing and no data, and discusses the strengths and weaknesses of methods for assessing cereal polyphenol consumption. Furthermore, this review suggests a greater need for the inclusion of most up-to-date cereal food composition data and for the harmonization of standardized procedures in collecting cereal-based food data and adequate assessment tools for dietary intake.

Psoriasis is a chronic immune-mediated disease affecting the skin, nails, and/or joints. It is associated with systemic inflammation and may also be linked to an increased risk of atherosclerotic cardiovascular disease (ASCVD). The objectives of this study were to determine the overall risk of ASCVD in patients with psoriasis and to evaluate the risk according to ASCVD type and the severity of psoriasis. This was a systematic review and meta-analysis of observational studies reporting the association between psoriasis and one or more of the clinical types of ASCVD. We searched Medical Literature Analysis and Retrieval System Online (MEDLINE) via PubMed, Excerpta Medica Database (EMBASE), Scopus, Bielefeld Academic Search Engine (BASE), and Google Scholar for relevant studies in the English language from the beginning of their records to July 2023. Study selection and data extraction were conducted by four independent reviewers. A total of 21 observational studies (three cross-sectional, one case-control, and 17 cohort) were included in this review, representing a total of 778,049 patients with psoriasis and 16,881,765 control subjects without psoriasis. The included studies had varying degrees of covariate adjustment, and thus, their findings may have been subject to residual confounding. All the meta-analyses used the adjusted effect sizes and were based on the random-effects model. However, the cohort studies were analysed separately from the non-cohort studies (the case-control and cross-sectional studies). There was a significant association between psoriasis and ASCVD (cohort studies: hazard ratio (HR), 1.21; 95% confidence interval (CI), 1.14 to 1.28; I2 = 63%; p < 0.001; non-cohort studies: odds ratio (OR), 1.60; 95% CI, 1.34 to 1.92; I2 = 31%; p = 0.23). Psoriasis was also significantly associated with myocardial infarction (cohort studies: HR, 1.20; 95% CI, 1.10 to 1.31; I2 = 60%; p < 0.001; non-cohort studies: OR, 1.57; 95% CI, 1.15 to 2.15; I2 = 74%; p = 0.05), coronary artery disease (cohort studies: HR, 1.20; 95% CI, 1.13 to 1.28; I2 = 67%; p < 0.001; non-cohort studies: OR, 1.60; 95% CI, 1.34 to 1.92; I2 = 31%; p = 0.23), aortic aneurysm (HR, 1.45; 95% CI, 1.04 to 2.02; I2 = 67%; p = 0.08) but not with ischaemic stroke (HR, 1.14; 95% CI, 0.96 to 1.36; I2 = 44%; p = 0.17). Pooled analysis in terms of the severity of psoriasis showed that both mild (cohort studies: HR, 1.17; 95% CI, 1.08 to 1.26; I2 = 74%; p < 0.001; non-cohort studies: OR, 1.54; 95% CI, 1.25 to 1.90; I2 = 0%; p = 0.50) and severe (cohort studies: HR, 1.43; 95% CI, 1.23 to 1.65; I2 = 65%; p < 0.001; non-cohort studies: OR, 1.65; 95% CI, 1.29 to 2.12; I2 = 25%; p = 0.26) psoriasis were significantly associated with ASCVD. Psoriasis (including mild and severe disease) is associated with an increased risk of ASCVD, including coronary artery disease (CAD) and aortic aneurysm (AA). ASCVD risk assessment and prevention should be prioritised in all adult psoriasis patients. Future observational studies investigating the association between psoriasis and ASCVD should conduct a more comprehensive adjustment of covariates.

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UNASSIGNED: Inhaled corticosteroid (ICS) therapy has been demonstrated to reduce the risk of COPD exacerbations. It should only be prescribed to COPD patients who are not adequately controlled by dual long-acting bronchodilator therapy and who have ≥2 exacerbations per year and a blood eosinophil count ≥300cells/µL. ICS therapy is widely prescribed outside guidelines to COPD patients, making ICS withdrawal an important consideration. This systematic review aims to provide an up-to-date analysis of the effect of ICS withdrawal on exacerbation frequency, change in lung function (FEV1) and to determine the proportion of COPD patients who resume ICS therapy following withdrawal.
UNASSIGNED: Randomised controlled trials (RCTs) and observational studies which compared ICS withdrawal with ICS continuation treatment were included. Cochrane Central, Web of Science, CINHAL, Embase and OVID Medline were searched. Risk of bias was assessed using the Cochrane RoB2 tool and the Newcastle-Ottawa Scale. Quality assessment of RCTs was conducted using GRADE. Meta-analysis of post-hoc analyses of RCTs of ICS withdrawal, stratified by blood eosinophil count (BEC), was undertaken.
UNASSIGNED: Ten RCTs (6642 patients randomised) and 6 observational studies (160,029 patients) were included in the results. When ICS was withdrawn and long-acting bronchodilator therapy was maintained, there was no consistent difference in exacerbation frequency or lung function change between the ICS withdrawal and continuation trial arms. The evidence for these effects was of moderate quality. There was insufficient evidence to draw a firm conclusion on the proportion of patients who resumed ICS therapy following withdrawal (estimated range 12-93% of the participants).
UNASSIGNED: Withdrawal of ICS therapy from patients with COPD is safe and feasible but should be accompanied by maintenance of bronchodilation therapy for optimal outcomes.

Background: Quality-of-life metrics are increasingly important for oncological patients alongside traditional endpoints like mortality and disease progression. Statistical tools such as Win Ratio, Win Odds, and Net Benefit prioritize clinically significant outcomes using composite endpoints. In randomized trials, Win Statistics provide fair comparisons between treatment and control groups. However, their use in observational studies is complicated by confounding variables. Propensity score (PS) matching mitigates confounding variables but may reduce the sample size, affecting the power of win statistics analyses. Alternatively, PS matching can stratify samples, preserving the sample size. This study aims to assess the long-term impact of these methods on decision making, particularly in colorectal cancer patients. Methods: A motivating example involves a cohort of patients from the ReSARCh observational study (2016-2021) with locally advanced adenocarcinoma of the rectum, situated up to 12 cm from the anal verge. These patients underwent either a watch-and-wait approach (WW) or trans-anal local excision (LE). Win statistics compared the effects of WW and LE on a composite outcome (overall survival, recurrence, presence of ostomy, and rectum excision). For matched win statistics, we used robust inference techniques proposed by Matsouaka et al. (2022), and for stratified win statistics, we applied the method proposed by Dong et al. (2018). A simulation study assessed the coverage probability of matched and stratified win statistics in balanced and unbalanced groups, calculating how often the confidence intervals included the true values of WR, NB, and WO across 1000 simulations. Results: The results suggest a better efficacy of the LE approach when considering efficacy outcomes alone (WR: 0.47 (0.01 to 1.14); NB: -0.16 (-0.34 to 0.02); and WO: 0.73 (0.5 to 1.05)). However, when QoL outcomes are included in the analyses, the estimates are closer to 1 (WR: 0.87 (0.06 to 2.06); WO: 0.93 (0.61 to 1.4)) and to 0 (NB: -0.04 (-0.25 to 0.17)), indicating a negative impact of the treatment effect of LE regarding the presence of ostomy and the excision of the rectum. Moreover, based on the simulation study, our findings underscore the superior performance of matched compared to stratified win statistics in terms of coverage probability (matched WR: 97% vs. stratified WR: 33.3% in a high-imbalance setting; matched WR: 98% vs. stratified WR: 34.4% in a medium-imbalance setting; and matched WR: 99.2% vs. stratified WR: 37.4% in a low-imbalance setting). Conclusions: In conclusion, our study sheds light on the interpretation of the results of win statistics in terms of statistical significance, providing insights into the application of pairwise comparison in observational settings, promoting its use to improve outcomes for cancer patients.

UNASSIGNED: This study aimed to investigate the efficacy and safety of sacubitril/valsartan in abnormal renal function (eGFR < 60 ml/min/1.73m2) patients combined with heart failure based on randomized controlled trials (RCTs) and observational studies.
UNASSIGNED: The Embase, PubMed and the Cochrane Library were searched for relevant studies from inception to December 2023. Dichotomous variables were described as event counts with the odds ratio (OR) and 95% confidence interval (CI) values. Continuous variables were expressed as mean standard deviation (SD) with 95% CIs.
UNASSIGNED: A total of 6 RCTs and 8 observational studies were included, involving 17335 eGFR below 60 ml/min/1.73m2 patients combined with heart failure. In terms of efficacy, we analyzed the incidence of cardiovascular events and found that sacubitril/valsartan significantly reduced the risk of cardiovascular death or heart failure hospitalization in chronic kidney disease (CKD) stages 3-5 patients with heart failure (OR: 0.65, 95%CI: 0.54-0.78). Moreover, sacubitril/valsartan prevented the serum creatinine elevation (OR: 0.81, 95%CI: 0.68-0.95), the eGFR decline (OR: 0.83, 95% CI: 0.73-0.95) and the development of end-stage renal disease in this population (OR:0.73, 95%CI:0.60-0.89). As for safety outcomes, we did not find that the rate of hyperkalemia (OR:1.31, 95%CI:0.79-2.17) and hypotension (OR:1.57, 95%CI:0.94-2.62) were increased in sacubitril/valsartan group among CKD stages 3-5 patients with heart failure.
UNASSIGNED: Our meta-analysis proves that sacubitril/valsartan has a favorable effect on cardiac function without obvious risk of adverse events in abnormal renal function patients combined with heart failure, indicating that sacubitril/valsartan has the potential to become perspective treatment for these patients.

This study summarizes a presentation at the symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to the first author. As epidemiologists, we are taught that \"correlation does not imply causation.\" While true, identifying causes is a key objective for much of the research that we conduct. There is empirical evidence that veterinary epidemiologists are conducting observational research with the intent to identify causes; many studies include control for confounding variables, and causal language is often used when interpreting study results. Frameworks for studying causes include the articulation of specific hypotheses to be tested, approaches for the selection of variables, methods for statistical estimation of the relationship between the exposure and the outcome, and interpretation of that relationship as causal. When comparing observational studies in veterinary populations to those conducted in human populations, the application of each of these steps differs substantially. The a priori identification of exposure-outcome pairs of interest are less common in observational studies in the veterinary literature compared to the human literature, and prior knowledge is used to select confounding variables in most observational studies in human populations, whereas data-driven approaches are the norm in veterinary populations. The consequences of not having a defined exposure-outcome hypotheses of interest and using data-driven analytical approaches include an increased probability of biased results and poor replicability of results. A discussion by the community of researchers on current approaches to studying causes in observational studies in veterinary populations is warranted.

Assessing population-level effects of vaccines and other infectious disease prevention measures is important to the field of public health. In infectious disease studies, one person\'s treatment may affect another individual\'s outcome, that is, there may be interference between units. For example, the use of bed nets to prevent malaria by one individual may have an indirect effect on other individuals living in close proximity. In some settings, individuals may form groups or clusters where interference only occurs within groups, that is, there is partial interference. Inverse probability weighted estimators have previously been developed for observational studies with partial interference. Unfortunately, these estimators are not well suited for studies with large clusters. Therefore, in this paper, the parametric g-formula is extended to allow for partial interference. G-formula estimators are proposed for overall effects, effects when treated, and effects when untreated. The proposed estimators can accommodate large clusters and do not suffer from the g-null paradox that may occur in the absence of interference. The large sample properties of the proposed estimators are derived assuming no unmeasured confounders and that the partial interference takes a particular form (referred to as \'weak stratified interference\'). Simulation studies are presented demonstrating the finite-sample performance of the proposed estimators. The Demographic and Health Survey from the Democratic Republic of the Congo is then analyzed using the proposed g-formula estimators to assess the effects of bed net use on malaria.