Mendelian randomization (MR) is a statistical method that uses genetic variants as instrumental variables to estimate the causal effect of exposure on an outcome in the presence of unmeasured confounding. In this review, we argue that it is crucial to acknowledge the instrumental variable assumptions in MR analysis. We describe widely used MR methods, using an example from obesity-related metabolic disorders. We describe situations in which instrumental variable assumptions are violated and explain how to evaluate these violations and employ robust methods for accommodating such violations.

Mendelian randomization (MR) is a statistical method that uses genetic variants as instrumental variables to estimate the causal effect of exposure on an outcome in the presence of unmeasured confounding. In this review, we argue that it is crucial to acknowledge the instrumental variable assumptions in MR analysis. We describe widely used MR methods, using an example from obesity-related metabolic disorders. We describe situations in which instrumental variable assumptions are violated and explain how to evaluate these violations and employ robust methods for accommodating such violations.

Mendelian randomization (MR) is a statistical method that uses genetic variants as instrumental variables to estimate the causal effect of exposure on an outcome in the presence of unmeasured confounding. In this review, we argue that it is crucial to acknowledge the instrumental variable assumptions in MR analysis. We describe widely used MR methods, using an example from obesity-related metabolic disorders. We describe situations in which instrumental variable assumptions are violated and explain how to evaluate these violations and employ robust methods for accommodating such violations.

Directed acyclic graphs (DAGs) are useful tools for visualizing the hypothesized causal structures in an intuitive way and selecting relevant confounders in causal inference. However, in spite of their increasing use in clinical and surgical research, the causal graphs might also be misused by a lack of understanding of the central principles. In this article, we aim to introduce the basic terminology and fundamental rules of DAGs, and DAGitty, a user-friendly program that easily displays DAGs. Specifically, we describe how to determine variables that should or should not be adjusted based on the backdoor criterion with examples. In addition, the occurrence of the various types of biases is discussed with caveats, including the problem caused by the traditional approach using p-values for confounder selection. Moreover, a detailed guide to DAGitty is provided with practical examples regarding minimally invasive surgery. Essentially, the primary benefit of DAGs is to aid researchers in clarifying the research questions and the corresponding designs based on the domain knowledge. With these strengths, we propose that the use of DAGs may contribute to rigorous research designs, and lead to transparency and reproducibility in research on minimally invasive surgery.

Traditional mediation analysis, which relies on linear regression models, has faced criticism due to its limited suitability for cases involving different types of variables and complex covariates, such as interactions. This can result in unclear definitions of direct and indirect effects. As an alternative, causal mediation analysis using the counterfactual framework has been introduced to provide clearer definitions of direct and indirect effects while allowing for more flexible modeling methods. However, the conceptual understanding of this approach based on the counterfactual framework remains challenging for applied researchers. To address this issue, the present article was written to highlight and illustrate the definitions of causal estimands, including controlled direct effect, natural direct effect, and natural indirect effect, based on the key concept of nested counterfactuals. Furthermore, we recommend using 2 R packages, ‘medflex’ and ‘mediation’, to perform causal mediation analysis and provide public health examples. The article also offers caveats and guidelines for accurate interpretation of the results.

Epidemiological studies typically examine the causal effect of exposure on a health outcome. Standardization is one of the most straightforward methods for estimating causal estimands. However, compared to inverse probability weighting, there is a lack of user-centric explanations for implementing standardization to estimate causal estimands. This paper explains the standardization method using basic R functions only and how it is linked to the R package stdReg, which can be used to implement the same procedure. We provide a step-by-step tutorial for estimating causal risk differences, causal risk ratios, and causal odds ratios based on standardization. We also discuss how to carry out subgroup analysis in detail.

Objective: To investigate the feasibility of a public health center-based aerobic and resistance training program for primary prevention of cardiovascular disease in people with visual, auditory, or physical/brain impairments. Methods: The study included 25 adults aged >40 years who lived in Cheorwon-gun in South Korea, had a disability registered for visual, auditory, or physical/brain impairments under the Disability Welfare Act, and had either known cardiovascular disease or two or more risk factors for cardiovascular disease. The program comprised four education sessions and 12 weeks of customized aerobic and strengthening exercises performed twice a week at moderate intensity, with each exercise session lasting for 1 hour. The body mass index (BMI), percent body fat, 6-minute walk distance (6MWD), and 30-second sit-to-stand test results were measured at baseline and on program completion. Results: Seventeen subjects (68%) completed the program. There were significant decreases in BMI and percent body fat (both p<0.05), with a significant increase in 30-second sit-to-stand strength (p<0.05) but no changes in the 6MWD. In subjects with visual or auditory impairments, BMI and percent body fat were significantly decreased after the program; however, there was no significant change in the results of the 30-second sit-to-stand strength test or the 6MWD. Conclusion In people with disabilities, a 3-month community-based exercise program can decrease body mass index and percent body fat and increase sit-to-stand strength. The 30-second sit-to-stand test may be a useful measure of the strength and endurance of the lower extremities in people with disabilities.

Objective: To investigate the feasibility of a public health center-based aerobic and resistance training program for primary prevention of cardiovascular disease in people with visual, auditory, or physical/brain impairments. Methods: The study included 25 adults aged >40 years who lived in Cheorwon-gun in South Korea, had a disability registered for visual, auditory, or physical/brain impairments under the Disability Welfare Act, and had either known cardiovascular disease or two or more risk factors for cardiovascular disease. The program comprised four education sessions and 12 weeks of customized aerobic and strengthening exercises performed twice a week at moderate intensity, with each exercise session lasting for 1 hour. The body mass index (BMI), percent body fat, 6-minute walk distance (6MWD), and 30-second sit-to-stand test results were measured at baseline and on program completion. Results: Seventeen subjects (68%) completed the program. There were significant decreases in BMI and percent body fat (both p<0.05), with a significant increase in 30-second sit-to-stand strength (p<0.05) but no changes in the 6MWD. In subjects with visual or auditory impairments, BMI and percent body fat were significantly decreased after the program; however, there was no significant change in the results of the 30-second sit-to-stand strength test or the 6MWD. Conclusion In people with disabilities, a 3-month community-based exercise program can decrease body mass index and percent body fat and increase sit-to-stand strength. The 30-second sit-to-stand test may be a useful measure of the strength and endurance of the lower extremities in people with disabilities.

Background: Intravenous (IV) dexamethasone prolongs the duration of a peripheral nerve block; however, there is little available information about its optimal effective dose. This study aimed to evaluate the effects of three different doses of IV dexamethasone on the duration of postoperative analgesia to determine the optimal effective dose for a sciatic nerve block. Methods: Patients scheduled for foot and ankle surgery were randomly assigned to receive normal saline or IV dexamethasone (2.5 mg, 5 mg, or 10 mg). An ultrasound-guided popliteal sciatic nerve block was performed using 0.75% ropivacaine (20 ml) before general anesthesia. The duration of postoperative analgesia was the primary outcome, and pain scores, use of rescue analgesia, onset time, adverse effects, and patient satisfaction were assessed as secondary outcomes. Results: Compared with the control group, the postoperative analgesic duration of the sciatic nerve block was prolonged in groups receiving IV dexamethasone 10 mg (P < 0.001), but not in the groups receiving IV dexamethasone 2.5 mg or 5 mg. The use of rescue analgesics was significantly different among the four groups 24 h postoperatively (P = 0.001) and similar thereafter. However, pain scores were not significantly different among the four groups 24 h postoperatively. There were no statistically significant differences in the other secondary outcomes among the four groups. Conclusions This study demonstrated that compared to the controls, only IV dexamethasone 10 mg increased the duration of postoperative analgesia following a sciatic nerve block for foot and ankle surgery without the occurrence of adverse events.