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Over the last few decades, several databases and clinical registries have been used to generate studies for orthopedic surgery research including shoulder arthroplasty. Observational database studies present data that are best used to provide information regarding associations between variables but is unable to prove causation. Yet, there remains a tendency for authors to use causal language and conclusive statements in published shoulder arthroplasty literature using such databases. We systematically identified administrative database and registry studies on shoulder arthroplasty in 5 orthopedic journals from January 2020 to December 2021. Papers were independently graded by 2 reviewers for the presence, absence, or inconsistent use of causal language throughout the publication. χ2 analyses were conducted to determine the relationship between causality language and article characteristics including the journal of publication. There were 65 eligible articles. According to the reviewers, 67.7% of titles and abstracts were either consistently causal or inconsistently causal in language and inference. On reviewing the full text, 69.2% were found to contain some degree of causal language (38.5% consistently causal, 30.8% inconsistently causal). There were no statistically significant associations between the journal and title and abstract final grading (P =.863) or the journal and full-text grading (P =.726). The use of causal language remains highly prevalent in observational database studies related to shoulder arthroplasty. Understanding the limitations of observational database studies is paramount for accurate interpretation of these publications. [ABSTRACT FROM AUTHOR]

Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemio-logic database studies. Such networks may contain data that are not physically pooled but instead are distributed horizontally (separate patients within each data source) or vertically (separate measures within each data source) in order to preserve patient privacy. While multivariable methods for the analysis of horizontally distributed data are frequently employed, few practical approaches have been put forth to deal with vertically distributed healthcare databases. In this paper, we propose 2 propensity score-based approaches to vertically distributed data analysis and test their performance using 5 example studies. We found that these approaches produced point estimates close to what could be achieved without partitioning. We further found a performance benefit (i.e., lower mean squared error) for sequentially passing a propensity score through each data domain (called the "sequential approach") as compared with fitting separate domain-specific propensity scores (called the "parallel approach"). These results were validated in a small simulation study. This proof-of-concept study suggests a new multivariable analysis approach to vertically distributed health-care databases that is practical, preserves patient privacy, and warrants further investigation for use in clinical research applications that rely on health-care databases. [ABSTRACT FROM AUTHOR]

Objective: Falsehood and bias can have tangible effects, whether related to the "hoax" of Corona virus disease/COVID‐19 or the impact of personal protective equipment in city‐wide news. The spread of false information requires the diversion of time and resources into rebolstering the truth. Our objective is thus to elucidate types of bias that may influence our daily work, along with ways to mitigate them. Data Sources: Publications are included which delineate specific aspects of bias or address how to preempt, mitigate, or correct bias, whether conscious or unconscious. Review Methods: We discuss: (1) the background and rationale for proactively considering potential sources of bias, (2) relevant definitions and concepts, (3) potential means to limit effects of inaccurate data sources, and (4) evolving frontiers in the management of bias. In doing so, we review epidemiological concepts and susceptibility to bias within study designs, including database studies, observational studies, randomized controlled trials (RCTs), systematic reviews, and meta‐analyses. We additionally discuss concepts such as the difference between disinformation and misinformation, differential or nondifferential misclassification, bias toward a null result, and unconscious bias, among others. Conclusion: We have the means to mitigate sources of potential bias in database studies, observational studies, RCTs, and systematic reviews, beginning with education and awareness. Implications for Practice: False information may spread faster than true information, so it is beneficial to understand potential sources of falsehood we face, in order to safeguard our daily impressions and decisions. Awareness of potential sources of falsehood and bias forms the foundation for accuracy in our everyday work. [ABSTRACT FROM AUTHOR]

Randomized controlled trials and observational studies have reported conflicting results on the potential beneficial effects of statins on mortality in patients with chronic obstructive pulmonary disease (COPD). We performed a systematic search of the literature to review all observational studies reporting relative risks of death with statin use in COPD, focusing on potential sources of bias. We identified 15 observational studies, out of 2835, of which 12 were affected by time-related and other biases and the remaining 3 by confounding bias. All 15 studies were also subject to confounding bias due to lack of adjustment for important COPD-related factors. The risk of death associated with statin use was reduced across all 15 studies (pooled relative risk (PRR) 0.66; 95% CI: 0.59-0.74). The reduction was observed in 7 studies with immortal time bias (PRR 0.62; 95%: 0.53-0.72), two with collider-stratification bias (PRR 0.60; 95% CI: 0.45-0.80), one with time-window bias (RR 0.61; 95% CI: 0.38-0.98), one with immeasurable time bias (RR 0.50; 95% CI: 0.40-0.62), and one with exposure misclassification (RR 0.86; 95% CI: 0.72-1.03). The three studies that avoided these biases were, however, affected by confounding bias resulting in a PRR of 0.77 (95% CI: 0.61-0.98). In conclusion, the observational studies investigating statin use and mortality in COPD are affected by major biases, many of which can result in spurious protective effects. Well-designed observational studies that carefully emulate randomized trials are needed to resolve this uncertainty regarding the potential beneficial benefits of statins on mortality in patients with COPD. [ABSTRACT FROM AUTHOR]

The case-control design is widely used in retrospective database studies, often leading to spectacular findings. However, results of these studies often cannot be replicated, and the advantage of this design over others is questionable. To demonstrate the shortcomings of applications of this design, we replicate two published case-control studies. The first investigates isotretinoin and ulcerative colitis using a simple case-control design. The second focuses on dipeptidyl peptidase-4 inhibitors and acute pancreatitis, using a nested case-control design. We include large sets of negative control exposures (where the true odds ratio is believed to be 1) in both studies. Both replication studies produce effect size estimates consistent with the original studies, but also generate estimates for the negative control exposures showing substantial residual bias. In contrast, applying a self-controlled design to answer the same questions using the same data reveals far less bias. Although the case-control design in general is not at fault, its application in retrospective database studies, where all exposure and covariate data for the entire cohort are available, is unnecessary, as other alternatives such as cohort and self-controlled designs are available. Moreover, by focusing on cases and controls it opens the door to inappropriate comparisons between exposure groups, leading to confounding for which the design has few options to adjust for. We argue that this design should no longer be used in these types of data. At the very least, negative control exposures should be used to prove that the concerns raised here do not apply. [ABSTRACT FROM AUTHOR]