342 research outputs found
Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure
No full textReply to : Steyerberg EW, Schemper M, Harrell FE. Logistic regression modeling and the number of events per variable: selection bias dominates. J Clin Epidemiol. 2011 Dec;64(12):1464-5; author reply 1463-4. doi: 10.1016/j.jclinepi.2011.06.016. PMID: 22032755. which is a comment on : Courvoisier DS, Combescure C, Agoritsas T, Gayet-Ageron A, Perneger TV. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. J Clin Epidemiol. 2011 Sep;64(9):993-1000. doi: 10.1016/j.jclinepi.2010.11.012. Epub 2011 Mar 16. PMID: 21411281. https://archive-ouverte.unige.ch/unige:25409</a
Tailored Thienopyridine therapy: no urgency for CYP2C19 genotyping
No full textBetween 20% and 50% of cardiovascular patients treated with clopidogrel, an anti-P2Y12 drug, display high on-treatment platelet reactivity (HTPR) and are not adequately protected from major adverse cardiovascular events (MACE). Despite a minor influence of the CYP2C19*2 genetic variant on the pharmacodynamic response to clopidogrel (5% to 12%) and a limited or absent value for predicting stent thrombosis and MACE, this latter polymorphism is currently considered an important candidate to tailor anti-P2Y12 therapy during percutaneous coronary intervention. Seven studies have examined the value of CYP2C19*2 for predicting HTPR in comparison to a specific pharmacodynamic assay (VASP assay). Overall, the summarized sensitivity of the CYP2C19*2 genotype for predicting HTPR was 37.6% (95% CI: 32.2 to 43.3%), yielding a negative likelihood ratio of only 0.77 (95% CI: 0.68 to 0.86) which confirms its limited value as a routine clinical aid. A tailored anti-P2Y12 treatment strategy restricted to CYP2C19*2 carriers may be of some help, but this restrictive approach leaves out noncarriers with HTPR. As for platelet function testing, there is currently no convincing data to support that using CYP2C19*2 genotyping as a tailored anti-P2Y12 treatment would be an effective strategy and there is no urgency for CYP2C19 genotyping in clinical practice. Strategies incorporating genotyping, phenotyping, and clinical data in a stratified and sequential approach may be more promising
Overview of model validation for survival regression model with competing risks using melanoma study data
No full textThe article introduces how to validate regression models in the analysis of competing risks. The prediction accuracy of competing risks regression models can be assessed by discrimination and calibration. The area under receiver operating characteristic curve (AUC) or Concordance-index, and calibration plots have been widely used as measures of discrimination and calibration, respectively. One-time splitting method can be used for randomly splitting original data into training and test datasets. However, this method reduces sample sizes of both training and testing datasets, and the results can be different by different splitting processes. Thus, the cross-validation method is more appealing. For time-to-event data, model validation is performed at each analysis time point. In this article, we review how to perform model validation using the riskRegression package in R, along with plotting a nomogram for competing risks regression models using the regplot() package
Post-test Probability According to Prevalence
No full textReply to : Galen BT. Post-test probability according to prevalence. J Gen Intern Med. 2011 Oct;26(10):1090. doi: 10.1007/s11606-011-1787-5. PMID: 21751056; PMCID: PMC3181292 which is a comment on : Agoritsas T, Courvoisier DS, Combescure C, Deom M, Perneger TV. Does prevalence matter to physicians in estimating post-test probability of disease ? A randomized trial. J Gen Intern Med. 2011 Apr;26(4):373-8. doi: 10.1007/s11606-010-1540-5. Epub 2010 Nov 4. PMID: 21053091; PMCID: PMC3055966. https://archive-ouverte.unige.ch/unige:25179</p
Clinical investigations to evaluate high-risk orthopaedic devices: systematic review and meta-analysis
No full textWe will systematically review the clinical studies that have been used to evaluate orthopaedic medical devices that are CE-marked for use in Europe. We will summarise the evidence available pre and post-CE mark (and FDA clearance if applicable) and conduct meta-analysis to compare to information in registry reports where possible.
Co-authors: Christophe Combescure, Christophe Barea, Anne Lübbeke (all University of Geneva)
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 96524
Thinker, Soldier, Scribe: cross-sectional study of researchers' roles and author order in the <i>Annals of Internal Medicine</i>
No full textHow researchers' contributions relate to author order on the byline remains unclear. We sought to identify researchers' contributions associated with author order, and to explore the existence of author profiles
Meta-analyses of survival rates: improving the extraction of the standard errors from reported 95% confidence intervals
No full textIn clinical research, meta-analyses are widely used to synthesize results from various studies. The advantage is to generalize a result to a larger population and to allow a more accurate data analysis by increasing the sample size. These objectives are achieved by assigning weights to different and sometimes contradictory studies, then combining their results by a weighted average of the estimates of interest, i.e. proportions, differences in risk or in mean between study arms, etc. The weight assigned to each study is derived from the variance of its estimate of interest (Shadish & Haddock, 1994). One issue in conducting a meta-analysis is the extraction of the needed data from the published articles. Indeed, data are sometimes reported graphically, incompletely, or not in a directly analyzable format (for instance, the median is reported while the mean is meant to be meta-analyzed). In studies reporting survival analyses, results are often communicated in the form of the Kaplan-Meier survival curves (Kaplan & Meier, 1958) or survival rates at specific time points in the text of the manuscript or in a table. For the meta-analysis of survival data, various approaches have been proposed to extract the needed information from articles. Most of them focused on the extraction of the hazard ratio since meta-analyses of randomized clinical trials are frequent in clinical research (Parmar et al. 1998; Williamson et al. 2002; Tierney et al. 2007). Some approaches have also been proposed to reconstruct individual data from published survival curves (Guyot et al. 2012; Wei & Royston 2017; Liu et al. 2021). However, when the purpose of a meta-analysis is to combine, across studies, the survival rates of patients with a specific disease condition and, those studies report survival only at specific time points, the previously mentioned approaches are not suitable. In these cases, since the needed data are the survival rates and their standard errors, the common approach is to assume the normality of the survival estimator and extract the standard error from its reported 95% confidence interval. However, this can be problematic because there are several methods to compute the 95% confidence interval of survival. In scientific medical articles, the transformation used is rarely clarified and the default method is not the same in all statistical software. Therefore, the meta-analyst needs to make an assumption about the transformation used by the authors of the article from which the standard error has to be extracted. This Master's thesis aims at assessing the impact of a wrong assumption about the method used to compute the 95% confidence interval of the survival on the results of the meta-analyses. It also aims at proposing an innovative approach to identify the method used to compute the 95% confidence interval of a survival rate thus and to correctly extract the standard error of the survival estimate from its 95% confidence interval.</p
A review of methods for meta-analysis of aggregated survival data
No full textIn most of meta-analyses of aggregated survival data, a pooled measure of the intervention's effect is obtained by combining reported hazard ratios. Advanced statistical methods have been proposed for a better characterization of the effect. With these methods, published survival curves can be synthesized in a single summary survival curve and a variation over follow-up time of the effect can be tested. Besides the pooled intervention's effect, the synthesis of survival in each arm is helpful for the appraisal of the intervention's benefits. Moreover, some meta-analyses aim to evaluate survival in a single group. In meta-analysis of comparative studies, testing a variation of the effect over time can avoid simplistic conclusions. These methods have been published in journals of statistics and may not be accessible to a large audience. The purpose of this article is to review novel methods for meta-analysis of aggregated survival data. The principle of each method, and its advantages and limitations are explained
A systematic review of glomerular hyperfiltration assessment and definition in the medical literature.
No full textBACKGROUND AND OBJECTIVES
Evaluation of glomerular hyperfiltration (GH) is difficult; the variable reported definitions impede comparisons between studies. A clear and universal definition of GH would help in comparing results of trials aimed at reducing GH. This study assessed how GH is measured and defined in the literature.
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS
Three databases (Embase, MEDLINE, CINAHL) were systematically searched using the terms "hyperfiltration" or "glomerular hyperfiltration". All studies reporting a GH threshold or studying the effect of a high GFR in a continuous manner against another outcome of interest were included.
RESULTS
The literature search was performed from November 2012 to February 2013 and updated in August 2014. From 2013 retrieved studies, 405 studies were included. Threshold use to define GH was reported in 55.6% of studies. Of these, 88.4% used a single threshold and 11.6% used numerous thresholds adapted to participant sex or age. In 29.8% of the studies, the choice of a GH threshold was not based on a control group or literature references. After 2004, the use of GH threshold use increased (P<0.001), but the use of a control group to precisely define that GH threshold decreased significantly (P<0.001); the threshold did not differ among pediatric, adult, or mixed-age studies. The GH threshold ranged from 90.7 to 175 ml/min per 1.73 m(2) (median, 135 ml/min per 1.73 m(2)).
CONCLUSION
Thirty percent of studies did not justify the choice of threshold values. The decrease of GFR in the elderly was rarely considered in defining GH. From a methodologic point of view, an age- and sex-matched control group should be used to define a GH threshold
The distribution of P -values in medical research articles suggested selective reporting associated with statistical significance
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