1,721,167 research outputs found
A Systematic Review of the Literature Demonstrates Some Errors in the Use of Decision Curve Analysis but Generally Correct Interpretation of Findings
No full textBackground. Decision curve analysis (DCA) is a widely used methodology in clinical research studies. Purpose. We performed a literature review to identify common errors in the application of DCA and provide practical suggestions for appropriate use of DCA. Data Sources. We first conducted an informal literature review and identified 6 errors found in some DCAs. We then used Google Scholar to conduct a systematic review of studies applying DCA to evaluate a predictive model, marker, or test. Data Extraction. We used a standard data collection form to collect data for each reviewed article. Data Synthesis. Each article was assessed according to the 6 predefined criteria for a proper analysis, reporting, and interpretation of DCA. Overall, 50 articles were included in the review: 54% did not select an appropriate range of probability thresholds for the x-axis of the DCA, with a similar proportion (50%) failing to present smoothed curves. Among studies with internal validation of a predictive model and correction for overfit, 61% did not clearly report whether the DCA had also been corrected. However, almost all studies correctly interpreted the DCA, used a correct outcome (92% for both), and clearly reported the clinical decision at issue (81%). Limitations. A comprehensive assessment of all DCAs was not performed. However, such a strategy would not influence the main findings. Conclusions. Despite some common errors in the application of DCA, our finding that almost all studies correctly interpreted the DCA results demonstrates that it is a clear and intuitive method to assess clinical utility
Can We Improve the Preoperative Prediction of Prostate Cancer Recurrence With Multiparametric MRI?
No full textINTRODUCTION: The use of multiparametric magnetic resonance imaging (mpMRI) to assess prostate cancer (PCa) has increased over the past decade. We aimed to assess if preoperative mpMRI lesion score, a variable routinely available for men undergoing pre-biopsy MRI, improves the performance of commonly used preoperative predictive models for PCa recurrence. PATIENTS AND METHODS: We analyzed data from 372 patients with PCa treated with radical prostatectomy in 2012 to 2017 and assessed with pre-biopsy mpMRI within 6 months prior to surgery. Suspicious areas for cancer were scored on a standardized 5-point scale. Cox regression was used to assess the association between mpMRI score and the risk of postoperative biochemical recurrence. Two different models were tested accounting for factors included in the Kattan nomogram and in the D'Amico risk-classification. RESULTS: Overall, 53% and 30% of patients were found with a lesion scored 4 or 5 at pre-biopsy mpMRI, respectively. Risk varied widely by mpMRI (29% 2-year risk of biochemical recurrence for a score of 5 vs. 5% for a score of 1-2), and mpMRI score was associated with large hazard ratios after adjusting for stage, grade, and prostate-specific antigen: 1.66, 1.96, and 2.71 for scores 3, 4, and 5, respectively. However, 95% confidence intervals were very wide (0.19-14.20, 0.26-14.65, and 0.36-20.55, respectively) and included 1. CONCLUSIONS: Our data did not show that preoperative models, commonly used to assess PCa risk, were improved after including the pre-biopsy mpMRI score. However, the value of pre-biopsy mpMRI to improve preoperative risk models should be investigated in larger data sets
Are We Improving Erectile Function Recovery After Radical Prostatectomy? Analysis of Patients Treated over the Last Decade
No full textBACKGROUND: The last decade has seen several advances in radical prostatectomy (RP) technique and post-RP care that are relevant to erectile function (EF) recovery. OBJECTIVE: We examined whether these practice changes have led to observed improvements in EF rates over time. DESIGN, SETTING, AND PARTICIPANTS: We identified 2364 patients treated with either open or minimally-invasive RP at a single academic center in 2008-2015. To mitigate confounding by the surgical learning curve, only patients treated by surgeons who performed at least 100 procedures were considered. INTERVENTION: EF before and after RP was assessed by the International Index of Erectile Function 6 (IIEF-6), with recovery defined as IIEF-6 ≥24. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We analyzed EF recovery rates of patients treated with bilateral nerve-sparing surgery and free from adjuvant/salvage treatment at the time of EF assessment. Local polynomial regression analyses explored changes in the outcomes over time. Linear and logistic regression analyses were used to estimate the influence of year of surgery on baseline variables and EF recovery. RESULTS AND LIMITATIONS: We observed a significant decrease over time of the EF recovery rates at both 12 and 24mo post-RP (all p=0.01). However, patient's age at surgery increased over time (mean increase of 0.5 per year; p<0.01), with a resultant increase in risk of comorbidity (odds ratio [OR]=1.1, 95% confidence interval [CI]: 1.02-1.15; p=0.008) and thus decrease in baseline IIEF-6 score (0.35 points per year; p=0.0003). After accounting for baseline and pathological characteristics, urinary function, and type of surgery in a multivariable analysis, year of surgery was not associated with EF recovery (12mo: OR=0.97, 95% CI: 0.91-1.03, p=0.4; 24mo: OR=0.97, 95% CI: 0.91-1.03, p=0.3). CONCLUSIONS: Findings from a high-volume center suggest that, despite the advancements in surgical and postoperative care, EF outcomes after RP have not improved over the last decade. Additional strategies are required to improve EF recovery after RP. PATIENT SUMMARY: The probability of regaining potency after surgery for prostate cancer did not improve over the last decade; more efforts are needed to improve patient's care after radical prostatectomy
Decision curve analysis for personalized treatment choice between multiple options
Full text linkDecision curve analysis can be used to determine whether a personalized model
for treatment benefit would lead to better clinical decisions. Decision curve
analysis methods have been described to estimate treatment benefit using data
from a single RCT. Our main objective is to extend the decision curve analysis
methodology to the scenario where several treatment options exist and evidence
about their effects comes from a set of trials, synthesized using network
meta-analysis (NMA). We describe the steps needed to estimate the net benefit
of a prediction model using evidence from studies synthesized in an NMA. We
show how to compare personalized versus one-size-fit-all treatment
decision-making strategies, like "treat none" or "treat all patients with a
specific treatment" strategies. The net benefit per strategy can then be
plotted for a plausible range of threshold probabilities to reveal the most
clinically useful strategy. We applied our methodology to an NMA prediction
model for relapsing-remitting multiple sclerosis, which can be used to choose
between Natalizumab, Dimethyl Fumarate, Glatiramer Acetate, and placebo. We
illustrated the extended decision curve analysis methodology using several
threshold values combinations for each available treatment. For the examined
threshold values, the "treat patients according to the prediction model"
strategy performs either better than or close to the one-size-fit-all treatment
strategies. However, even small differences may be important in clinical
decision-making. As the advantage of the personalized model was not consistent
across all thresholds, an improved model may be needed before advocating its
applicability for decision-making. This novel extension of decision curve
analysis can be applied to NMA based prediction models to evaluate their use to
aid treatment decision-making
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
