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g., poor reporting of original research) as well as unique constraints faced by surgery as a field (e.g., lack of equipoise for randomized trials, or existence of learning curves for novel surgical procedures, which can lead to temporal heterogeneity), which may require unconventional tools (e.g., cumulative meta-analysis) to address. Therefore, it is also our goal to take stock of the unique issues encountered by surgeons who do meta-analysis and to highlight various techniques-some of which less well-known-to address such challenges.
Missing data is a typical problem in clinical studies, where the value of variables of interest is not measured or collected for some patients. This article aimed to review imputation approaches for missing values and their application in neurosurgery.
We reviewed current practices on detecting missingness patterns and applications of multiple imputation approaches under different scenarios. Statistical considerations and importance of sensitivity analysis were explained. Various imputation methods were applied to a retrospective cohort.
For illustration purposes, a retrospective cohort of 609 patients harboring both ruptured and unruptured intracranial aneurysms and undergoing microsurgical clip reconstruction at Erasmus MC University Medical Center, Rotterdam, The Netherlands, between 2000 and 2019 was used. modified Rankin Scale score at 6 months was the clinical outcome, and potential predictors were age, sex, size of aneurysm, hypertension, smoking, World Federation of Neurosurgical Societies grade, and aneurysm location. Associations were investigated using different imputation approaches, and the results were compared and discussed.
Missing values should be treated carefully. Advantages and disadvantages of multiple imputation methods along with imputation in small and big data should be considered depending on the research question and specifics of the study.
Missing values should be treated carefully. Advantages and disadvantages of multiple imputation methods along with imputation in small and big data should be considered depending on the research question and specifics of the study.The application and interpretation of P values have caused debate for several decades, and this debate has become particularly relevant in the past few years. The P value represents the probability of seeing results as extreme or more extreme than those observed in a data analysis, were the null hypothesis and other underlying assumptions to be true. While P values are useful in pointing out where an effect may be present, they have often been misused in an attempt to oversell "statistically significant" findings. As P values rely on the spread and number of measurements, a smaller P value does not necessarily imply a larger effect size, which is better assessed via an effect estimate and confidence interval interpreted in the context of the study. The clinical relevance of a computed P value is context dependent. We investigated the current use of P values in a small sample of recent neurosurgical literature. Only a minority of manuscripts that reported statistical significance described confounder adjustment, or effect sizes. A common, incorrect assumption often observed was that statistical significance equals clinical relevance. To enable correct interpretation of clinical significance, it is crucial that authors describe the clinical implications of their findings.The hallmark of case-control study design involves dividing groups based on outcome and looking back at exposures to determine associations. Case-control studies are ideal for scenarios when outcomes are rare, making them well suited to the infrequent events often found among neurosurgical diseases. It is also a favorable design for scenarios when it would be infeasible or unethical to assign treatment groups as is necessary for a randomized controlled trial. Case-control studies are powerful but often misapplied and mislabeled. This article provides an overview of case-control study design along with discussion of a real-world example of an effectively executed case-control study.
Although randomized interventional studies are the gold standard of clinical study designs, they are not always feasible or necessary. In such cases, observational studies can bring insights into critical questions while minimizing harm and cost. There are numerous observational study designs, each with strengths and demerits. Unfortunately, it is not uncommon for observational study designs to be poorly designed or reported. In this article, the authors discuss similarities and differences between observational study designs, their application, and tenets of good use and proper reporting focusing on neurosurgery.
The authors illustrated neurosurgical case scenarios to describe case reports, case series, and cohort, cross-sectional, and case-control studies. The study design definitions and applications are taken from seminal research methodology readings and updated observational study reporting guidelines.
The authors have given a succinct account of the structure, functioning, and uses of common observational study designs in Neurosurgery. Specifically, they discussed the concepts of study direction, temporal sequence, advantages, and disadvantages. Also, they highlighted the differences between case reports and case series; case series and descriptive cohort studies; and cohort and case-control studies. Also, they discussed their impacts on internal validity, external validity, and relevance.
This paper disambiguates widely held misconceptions on the different observational study designs. In addition, it uses case-based scenarios to facilitate comprehension and relevance to the academic neurosurgery audience.
This paper disambiguates widely held misconceptions on the different observational study designs. In addition, it uses case-based scenarios to facilitate comprehension and relevance to the academic neurosurgery audience.
With the advent of personalized and stratified medicine, there has been much discussion about predictive modeling and the role of classical regression in modern medical research. We describe and distinguish the goals in these 2 frameworks for analysis.
The assumptions underlying and utility of classical regression are reviewed for continuous and binary outcomes. The tenets of predictive modeling are then discussed and contrasted. Principles are illustrated by simulation and through application of methods to a neurosurgical study.
Classical regression can be used for insights into causal mechanisms if careful thought is given to the role of variables of interest and potential confounders. find more In predictive modeling, interest lies more in accuracy of predictions and so alternative metrics are used to judge adequacy of models and methods; methods which average predictions over several contending models can improve predictive performance but these do not admit a single risk score.
Both classical regression and predictive modeling have important roles in modern medical research. Understanding the distinction between the 2 frameworks for analysis is important to place them in their appropriate context and interpreting findings from published studies appropriately.
Both classical regression and predictive modeling have important roles in modern medical research. Understanding the distinction between the 2 frameworks for analysis is important to place them in their appropriate context and interpreting findings from published studies appropriately.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.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.
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.
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 January2019.
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.
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.
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.
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.
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.
