查看更多>>摘要：? 2022 Elsevier Inc.Background: 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. Methods: 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. Results: 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. 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. Conclusions: 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.
查看更多>>摘要：? 2021 Elsevier Inc.Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.
查看更多>>摘要：? 2022 Elsevier Inc.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.
查看更多>>摘要：? 2022 The Author(s)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.
查看更多>>摘要：? 2021 Elsevier Inc.Background: 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. Methods: 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. Results: 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. Conclusions: 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.
查看更多>>摘要：? 2021 The AuthorsNeurosurgeons today are inundated with rapidly amassing neurosurgical research publications. Systematic reviews and meta-analyses have consequently surged in popularity because, when executed properly, they constitute a high level of evidence and may save busy neurosurgeons many hours of combing and reviewing the literature for relevant articles. Meta-analysis refers to the quantitative (and discretionary) component of systematic reviews. It involves applying statistical techniques to combine effect sizes from multiple studies, which might offer more actionable insights than a systematic review without meta-analysis. Well-executed meta-analyses may prove instructive for clinical practice, but poorly conducted ones sow confusion and have the potential to cause harm. Unfortunately, recent audits have found the conduct and reporting of meta-analyses in neurosurgery (but also other surgical disciplines) to be relatively lackluster in methodologic rigor and compliance to established guidelines. Some of these deficiencies can be easily remedied through better awareness and adherence to prescribed standards—which will be reviewed in this article—but others stem from inherent problems with the source data (e.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.
查看更多>>摘要：? 2021 Elsevier Inc.Background: Survival analyses are heavily used to analyze data in which the time to event is of interest. The purpose of this paper is to introduce some fundamental concepts for survival analyses in medical studies. Methods: We comprehensively review current survival methodologies, such as the nonparametric Kaplan-Meier method used to estimate survival probability, the log-rank test, one of the most popular tests for comparing survival curves, and the Cox proportional hazard model, which is used for building the relationship between survival time and specific risk factors. More advanced methods, such as time-dependent receiver operating characteristic, restricted mean survival time, and time-dependent covariates are also introduced. Results: This tutorial is aimed toward covering the basics of survival analysis. We used a neurosurgical case series of surgically treated brain metastases from non-small cell lung cancer patients as an example. The survival time was defined from the date of craniotomy to the date of patient death. Conclusions: This work is an attempt to encourage more investigators/medical practitioners to use survival analyses appropriately in medical research. We highlight some statistical issues, make recommendations, and provide more advanced survival modeling in this aspect.
查看更多>>摘要：? 2021 The Author(s)Objective: When using observational data to estimate the causal effects of a treatment on clinical outcomes, we need to adjust for confounding. In the presence of time-dependent confounders that are affected by previous treatment, adjustments cannot be made via the conventional regression approach or propensity score–based methods, but requires sophisticated methods called g-methods. We aimed to introduce g-methods to estimate the causal effects of treatment strategies defined by treatment at multiple time points, such as treat 2 days versus treat only day 1 versus never-treat. Methods: Two g-methods were introduced: the g-formula and inverse probability–weighted marginal structural models. Under exchangeability, consistency, and positivity assumptions, they provide a consistent estimate of the causal effects of the treatment strategy. Results: Using a numeric example that mimics the observational study data, we presented how the g-formula and inverse probability–weighted marginal structural models can estimate the effect of the treatment strategy. Conclusions: Both g-formula and inverse probability–weighted marginal structural models can correctly estimate the effect of the treatment strategy under 3 identifiability assumptions, which conventional regression analysis cannot. G-methods may assist in estimating the effect of treatment strategy defined by treatment at multiple time points.
查看更多>>摘要：? 2021 Elsevier Inc.Background: It is well accepted that randomized controlled trials provide the greatest quality of evidence about effectiveness and safety of new interventions. In neurosurgery, randomized controlled trials face challenges, with their use remaining relatively low compared with other clinical areas. Adaptive designs have emerged as a method for improving the efficiency and patient benefit of trials. They allow modifications to the trial design to be made as patient outcome data are collected. The benefit they provide is highly variable, predominantly governed by the time taken to observe the primary endpoint compared with the planned recruitment rate. They also face challenges in design, conduct, and reporting. Methods: We provide an overview of the benefits and challenges of adaptive designs, with a focus on neurosurgery applications. To investigate how often an adaptive design may be advantageous in neurosurgery, we extracted data on recruitment rates and endpoint lengths for ongoing neurosurgery trials registered in ClinicalTrials.gov. Results: We found that a majority of neurosurgery trials had a relatively short endpoint length compared with the planned recruitment period and therefore may benefit from an adaptive trial. However, we did not identify any ongoing ClinicalTrials.gov registered neurosurgery trials that mentioned using an adaptive design. Conclusions: Adaptive designs may provide benefits to neurosurgery trials and should be considered for use more widely. Use of some types of adaptive design, such as multiarm multistage, may further increase the number of interventions that can be tested with limited patient and financial resources.
查看更多>>摘要：? 2021 Elsevier Inc.Background: Stepped wedge cluster randomized trials enable rigorous evaluations of health intervention programs in pragmatic settings. In the present study, we aimed to update neurosurgeon scientists on the design of stepped wedge randomized trials. Methods: We have presented an overview of recent methodological developments for stepped wedge designs and included an update on the newer associated methodological tools to aid with future study designs. Results: We defined the stepped wedge trial design and reviewed the indications for the design in depth. In addition, key considerations, including mainstream methods of analysis and sample size determination, were discussed. Conclusions: Stepped wedge designs can be attractive for study intervention programs aiming to improve the delivery of patient care, especially when examining a small number of heterogeneous clusters.
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