查看更多>>摘要：? 2022 Elsevier Inc.Medical research is increasing in complexity with every year. The breadth of possibilities is expanding with every new methodologic and statistical innovation. Because of the pace at which this process evolves, it can feel impossible to keep up with developments. Neurosurgery has had a slow start in terms of clinical research and innovations, relying on small, single-center studies and underpowered randomized controlled trials. Lately, owing to serious improvements made to study design quality, the neurosurgical evidence base is growing. The special section on neurosurgical study design highlights the most important methodologic concepts to date and illustrates the most important methodologic advancements, which will shape the future of neurosurgical study design.
查看更多>>摘要：? 2022 The AuthorsBackground: Neurosurgical randomized controlled trials (RCTs) are difficult to carry out due to the low incidence of certain diseases, heterogeneous disease phenotypes, and ethical issues. This results in a weak evidence base in terms of both the number of trials and their robustness. The fragility index (FI) measures the robustness of an RCT and is the minimum number of patients in a trial whose status would have to change from a nonevent to an event to change a statistically significant result to a nonsignificant result. The smaller the FI, the more fragile the trial's outcome. Methods: RCTs that have influenced neurosurgical practice were included in this analysis. Simulations were run to calculate the FI. To determine associations with a high or low FI, multivariable logistic regression was used to calculate adjusted odds ratios and 95% confidence intervals adjusting for baseline confounders. Results: Of 2975 papers screened, 74 were included. The median FI was 4.5 (interquartile range: 1.5–10). RCTs included a median of 165 patients (interquartile range: 75–330), with a maximum of 10,008. A total of 38 trials had lost to follow-up patients that might have impacted the robustness of the results (51%). Conclusion: Results of neurosurgical RCTs on which we base our clinical decision-making and treatment guidelines are often fragile. Improved methodologies, international collaboration, and cooperation between specialties might improve the evidence base in the future.
查看更多>>摘要：? 2022 The AuthorsBackground: Regression analysis quantifies the relationships between one or more independent variables and a dependent variable and is one of the most frequently used types of analysis in medical research. The aim of this article is to provide a brief theoretical and practical tutorial for neurosurgeons wishing to conduct or interpret regression analyses. Methods and Results: Data preparation, univariable and multivariable analysis, choice of model, model requirements and assumptions are discussed, as essential prerequisites to any regression analysis. Four main types of regression techniques are presented: linear, logistic, multinomial logistic, and proportional odds logistic. To illustrate the applications of regression to real-world data and exemplify the concepts introduced, we used a previously reported data set of patients with intracranial aneurysms treated by microsurgical clip reconstruction at the Department of Neurosurgery of Erasmus MC University Medical Center Rotterdam, between January 2000 and January 2019. Conclusions: Regression analysis is a powerful and versatile instrument in data analysis. This material is intended as a starter for those wishing to critically interpret or perform regression analysis and we recommend multidisciplinary collaborations with trained methodologists, statisticians, or epidemiologists.
查看更多>>摘要：? 2021 The Author(s)Biomedical research can generally be categorized into 1 of 3 aims: describing the occurrence of disease; identifying persons with or at increased risk of disease including diagnostic and prognostic studies; and explaining the occurrence of disease including etiologic and efficacy studies.
查看更多>>摘要：? 2021 Elsevier Inc.It is essential for any epidemiologic and clinical investigation to determine the appropriate covariates for which to ascertain measures and subsequently model. A number of recent articles have sought to elucidate covariate selection in the context of data analysis. Unfortunately, few articles characterize covariate selection in the context of data collection and discuss their principles under the assumption that data are measured and available for analyses. Additionally, many articles delineating the appropriate principles use jargon that may be inaccessible to the audiences that need to understand them most. Considering these gaps, this paper first seeks to put forth a simple foundational guide to primary data collection by explaining four sets of covariates for which to ascertain measures: 1) all covariates that cause both the exposure and outcome; 2) selected covariates that cause the exposure; 3) selected covariates that cause the outcome; and 4) relevant sociodemographic and baseline covariates. To the extent possible, this paper attempts to communicate these principles clearly and in the absence of advanced causal inference terminology. Finally, this paper provides a conceptual framework for covariate inclusion and exclusion with respect to data analysis and regression modeling. Specifically, this framework suggests that regression models 1) include all known common cause covariates; 2) include all sociodemographic covariates; 3) exclude any covariate that is known to be both a consequence of the exposure and cause of the outcome; and 4) generally, for every term included in the statistical model, there should be at least 10 observations in the data set.
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