A panel of kallikrein markers can predict outcome of prostate biopsy following clinical work-up: an independent validation study from the European Randomized Study of Prostate Cancer screening, France

<p>Abstract</p> <p>Background</p> <p>We have previously shown that a panel of kallikrein markers - total prostate-specific antigen (PSA), free PSA, intact PSA and human kallikrein-related peptidase 2 (hK2) - can predict the outcome of prostate biopsy in men with elevated PSA. Here we investigate the properties of our panel in men subject to clinical work-up before biopsy.</p> <p>Methods</p> <p>We applied a previously published predictive model based on the kallikrein panel to 262 men undergoing prostate biopsy following an elevated PSA (≥ 3 ng/ml) and further clinical work-up during the European Randomized Study of Prostate Cancer screening, France. The predictive accuracy of the model was compared to a "base" model of PSA, age and digital rectal exam (DRE).</p> <p>Results</p> <p>83 (32%) men had prostate cancer on biopsy of whom 45 (54%) had high grade disease (Gleason score 7 or higher). Our model had significantly higher accuracy than the base model in predicting cancer (area-under-the-curve [AUC] improved from 0.63 to 0.78) or high-grade cancer (AUC increased from 0.77 to 0.87). Using a decision rule to biopsy those with a 20% or higher risk of cancer from the model would reduce the number of biopsies by nearly half. For every 1000 men with elevated PSA and clinical indication for biopsy, the model would recommend against biopsy in 61 men with cancer, the majority (≈80%) of whom would have low stage <it>and </it>low grade disease at diagnosis.</p> <p>Conclusions</p> <p>In this independent validation study, the model was highly predictive of prostate cancer in men for whom the decision to biopsy is based on both elevated PSA and clinical work-up. Use of this model would reduce a large number of biopsies while missing few cancers.</p

Data and programming code from the studies on the learning curve for radical prostatectomy

Our group analyzed a multi-institutional data set to address the question of how the outcomes of surgery for prostate cancer are affected by surgeon-specific factors. The cohort consists of 9076 patients treated by open radical prostatectomy at one of four US academic institutions 1987 - 2003. The primary analyses focused on 7765 patients without neoadjuvant therapy. The most well-known finding is that of a surgical "learning curve", with rates of prostate cancer cure strongly dependent on surgeon experience. In this "data note", we provide the raw data set, as well as well-annotated programming code for the main analyses. Data include markers of cancer severity (stage, grade and prostate-specific antigen level), cancer outcome, and surgeon variables such as training and experience

Empirical Study of Data Sharing by Authors Publishing in PLoS Journals

Many journals now require authors share their data with other investigators, either by depositing the data in a public repository or making it freely available upon request. These policies are explicit, but remain largely untested. We sought to determine how well authors comply with such policies by requesting data from authors who had published in one of two journals with clear data sharing policies.We requested data from ten investigators who had published in either PLoS Medicine or PLoS Clinical Trials. All responses were carefully documented. In the event that we were refused data, we reminded authors of the journal's data sharing guidelines. If we did not receive a response to our initial request, a second request was made. Following the ten requests for raw data, three investigators did not respond, four authors responded and refused to share their data, two email addresses were no longer valid, and one author requested further details. A reminder of PLoS's explicit requirement that authors share data did not change the reply from the four authors who initially refused. Only one author sent an original data set.We received only one of ten raw data sets requested. This suggests that journal policies requiring data sharing do not lead to authors making their data sets available to independent investigators

Feasibility study of a clinically-integrated randomized trial of modifications to radical prostatectomy

<p>Abstract</p> <p>Background</p> <p>Numerous technical modifications to radical prostatectomy have been proposed. Such modifications are likely to lead to only slight improvements in outcomes. Although small differences would be worthwhile, an appropriately powered randomized trial would need to be very large, and thus of doubtful feasibility given the expense, complexity and regulatory burden of contemporary clinical trials. We have proposed a novel methodology, the clinically-integrated randomized trial, which dramatically streamlines trial procedures in order to reduce the marginal cost of an additional patient towards zero. We aimed to determine the feasibility of implementing such a trial for radical prostatectomy.</p> <p>Methods</p> <p>Patients undergoing radical prostatectomy as initial treatment for prostate cancer were randomized in a factorial design to involvement of the fascia during placement of the anastomotic sutures, urethral irrigation, both or neither. Endpoint data were obtained from routine clinical documentation. Accrual and compliance rates were monitored to determine the feasibility of the trial.</p> <p>Results</p> <p>From a total of 260 eligible patients, 154 (59%) consented; 56 patients declined to participate, 20 were not approached on recommendation of the treating surgeon, and 30 were not approached for logistical reasons. Although recording by surgeons of the procedure used was incomplete (~80%), compliance with randomization was excellent when it was recorded, with only 6% of procedures inconsistent with allocation. Outcomes data was received from 71% of patients at one year. This improved to 83% as the trial progressed.</p> <p>Conclusions</p> <p>A clinically-integrated randomized trial was conducted at low cost, with excellent accrual, and acceptable compliance with treatment allocation and outcomes reporting. This demonstrates the feasibility of the methodology. Improved methods to ensure documentation of surgical procedures would be required before wider implementation.</p> <p>Trial registration</p> <p>ClinicalTrials.gov <a href="http://www.clinicaltrials.gov/ct2/show/NCT00928850">NCT00928850</a></p

Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents

<p>Abstract</p> <p>Background</p> <p>Quantiles are a staple of epidemiologic research: in contemporary epidemiologic practice, continuous variables are typically categorized into tertiles, quartiles and quintiles as a means to illustrate the relationship between a continuous exposure and a binary outcome.</p> <p>Discussion</p> <p>In this paper we argue that this approach is highly problematic and present several potential alternatives. We also discuss the perceived drawbacks of these newer statistical methods and the possible reasons for their slow adoption by epidemiologists.</p> <p>Summary</p> <p>The use of quantiles is often inadequate for epidemiologic research with continuous variables.</p

A call for BMC Research Notes contributions promoting best practice in data standardization, sharing and publication

BMC Research Notes aims to ensure that data files underlying published articles are made available in standard, reusable formats, and the journal is calling for contributions from the scientific community to achieve this goal. Educational Data Notes included in this special series should describe a domain-specific data standard and provide an example data set with the article, or a link to data that are permanently hosted elsewhere. The contributions should also provide some evidence of the data standard's application and preparation guidance that could be used by others wishing to conduct similar experiments. The journal is also keen to receive contributions on broader aspects of scientific data sharing, archiving, and open data

The Integrated Calibration Index (ICI) and related metrics for quantifying the calibration of logistic regression models

Assessing the calibration of methods for estimating the probability of the occurrence of a binary outcome is an important aspec

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

<p>Abstract</p> <p>Background</p> <p>Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques.</p> <p>Methods</p> <p>In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques.</p> <p>Results</p> <p>Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve.</p> <p>Conclusion</p> <p>Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.</p

Message to complementary and alternative medicine: evidence is a better friend than power

BACKGROUND: Evidence-based medicine (EBM) is being embraced by an increasing number of practitioners and advocates of complementary and alternative medicine (CAM). A significant constituency within CAM, however, appears to have substantive doubts about EBM and some are expressly hostile. DISCUSSION: Many of the arguments raised against EBM within the CAM community are based on a caricature radically at odds with established, accepted and published principles of EBM practice. Contrary to what has sometimes been argued, EBM is not cookbook medicine that ignores individual needs. Neither does EBM mandate that only proven therapies should be used. Before EBM, decisions on health care tended to be based on tradition, power and influence. Such modes usually act to the disadvantage of marginal groups. CONCLUSION: By placing CAM on an equal footing with conventional medicine - what matters for both is evidence of effectiveness - EBM provides an opportunity for CAM to find an appropriate and just place in health care