It has been suggested that targeting prostate lesions identified on magnetic resonance imaging (MRI) will improve the sensitivity of prostate biopsy for high-grade disease. The clinical significance ...of high-grade tumors found on MRI but missed on systematic biopsy is open to question.
To determine the risk of mortality for high-grade cancers identified by MRI targeting in men who had benign systematic biopsy findings.
We used data from 999 men with negative systematic biopsy and concurrent MRI-targeted biopsy in the National Cancer Institute MRI study. The comparison group consisted of 3056 men followed for 11 yr after negative sextant biopsy in the European Randomized Trial of Screening for Prostate Cancer (ERSPC).
We calculated the number of patients needed to be diagnosed (NND) and treated (NNT) following targeted biopsy in order to prevent one prostate cancer death at 11 yr. We used a simple modeling approach that involved several assumptions, such as the proportion of the deaths in ERSPC preventable by earlier detection with MRI-guided biopsy. We then varied these assumptions to assess the effects on the results.
NND and NNT were 89 and 57 for the scenario involving assumptions favorable to MRI, and 169 and 127 for a more neutral set of assumptions, respectively. Results were only more encouraging for MRI targeting under unlikely scenarios, such as 100% sensitivity for MRI and a cure rate of 100% for treatment.
Although MRI may be of benefit overall, considering the decrease in overdiagnosis among men with negative MRI findings, targeting biopsy needles to MRI-detected lesions results in a large number of men diagnosed and treated per death prevented. Consideration should be given to changing guidelines on grading of MRI cores and those regarding treatment of MRI-detected high-grade prostate cancer.
We carried out a modeling study to assess how magnetic resonance imaging (MRI) scan results used to target prostate cancer lesions during biopsy can affect outcomes. The model results show that if MRI-visible tumors are targeted during prostate biopsy, a large number of men need to be diagnosed and treated for prostate cancer in order to avoid just one prostate cancer death.
Targeting magnetic resonance imaging–visible lesions during prostate biopsy leads to a large number of men being diagnosed and treated for prostate cancer in order for one prostate cancer death to be avoided.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
There is increased interest in the use of prediction models to guide clinical decision-making in orthopedics. Prediction models are typically evaluated in terms of their accuracy: discrimination ...(area-under-the-curve AUC or concordance index) and calibration (a plot of predicted vs. observed risk). But it can be hard to know how high an AUC has to be in order to be “high enough” to warrant use of a prediction model, or how much miscalibration would be disqualifying. Decision curve analysis was developed as a method to determine whether use of a prediction model in the clinic to inform decision-making would do more good than harm. Here we give a brief introduction to decision curve analysis, explaining the critical concepts of net benefit and threshold probability. We briefly review some prediction models reported in the orthopedic literature, demonstrating how use of decision curves has allowed conclusions as to the clinical value of a prediction model. Conversely, papers without decision curves were unable to address questions of clinical value. We recommend increased use of decision curve analysis to evaluate prediction models in the orthopedics literature.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Many decisions in medicine involve trade-offs, such as between diagnosing patients with disease versus unnecessary additional testing for those who are healthy. Net benefit is an increasingly ...reported decision analytic measure that puts benefits and harms on the same scale. This is achieved by specifying an exchange rate, a clinical judgment of the relative value of benefits (such as detecting a cancer) and harms (such as unnecessary biopsy) associated with models, markers, and tests. The exchange rate can be derived by asking simple questions, such as the maximum number of patients a doctor would recommend for biopsy to find one cancer. As the answers to these sorts of questions are subjective, it is possible to plot net benefit for a range of reasonable exchange rates in a “decision curve.” For clinical prediction models, the exchange rate is related to the probability threshold to determine whether a patient is classified as being positive or negative for a disease. Net benefit is useful for determining whether basing clinical decisions on a model, marker, or test would do more good than harm. This is in contrast to traditional measures such as sensitivity, specificity, or area under the curve, which are statistical abstractions not directly informative about clinical value. Recent years have seen an increase in practical applications of net benefit analysis to research data. This is a welcome development, since decision analytic techniques are of particular value when the purpose of a model, marker, or test is to help doctors make better clinical decisions.
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BFBNIB, CMK, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
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.
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Decision-analytic measures to assess clinical utility of prediction models and diagnostic tests incorporate the relative clinical consequences of true and false positives without the need for ...external information such as monetary costs. Net Benefit is a commonly used metric that weights the relative consequences in terms of the risk threshold at which a patient would opt for treatment. Theoretical results demonstrate that clinical utility is affected by a model’;s calibration, the extent to which estimated risks correspond to observed event rates. We analyzed the effects of different types of miscalibration on Net Benefit and investigated whether and under what circumstances miscalibration can make a model clinically harmful. Clinical harm is defined as a lower Net Benefit compared with classifying all patients as positive or negative by default. We used simulated data to investigate the effect of overestimation, underestimation, overfitting (estimated risks too extreme), and underfitting (estimated risks too close to baseline risk) on Net Benefit for different choices of the risk threshold. In accordance with theory, we observed that miscalibration always reduced Net Benefit. Harm was sometimes observed when models underestimated risk at a threshold below the event rate (as in underestimation and overfitting) or overestimated risk at a threshold above event rate (as in overestimation and overfitting). Underfitting never resulted in a harmful model. The impact of miscalibration decreased with increasing discrimination. Net Benefit was less sensitive to miscalibration for risk thresholds close to the event rate than for other thresholds. We illustrate these findings with examples from the literature and with a case study on testicular cancer diagnosis. Our findings strengthen the importance of obtaining calibrated risk models.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK, VSZLJ
Acupuncture for Chronic Pain Vickers, Andrew J; Linde, Klaus
JAMA : the journal of the American Medical Association,
03/2014, Volume:
311, Issue:
9
Journal Article
Peer reviewed
Open access
CLINICAL QUESTION Is acupuncture associated with reduced pain outcomes for patients with chronic pain compared with sham-acupuncture (placebo) or no-acupuncture control? BOTTOM LINE Acupuncture is ...associated with improved pain outcomes compared with sham-acupuncture and no-acupuncture control, with response rates of approximately 30% for no acupuncture, 42.5% for sham acupuncture, and 50% for acupuncture.
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.
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.
The use of quantiles is often inadequate for epidemiologic research with continuous variables.
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Urologists regularly develop clinical risk prediction models to support clinical decisions. In contrast to traditional performance measures, decision curve analysis (DCA) can assess the utility of ...models for decision making. DCA plots net benefit (NB) at a range of clinically reasonable risk thresholds.
To provide recommendations on interpreting and reporting DCA when evaluating prediction models.
We informally reviewed the urological literature to determine investigators’ understanding of DCA. To illustrate, we use data from 3616 patients to develop risk models for high-grade prostate cancer (n=313, 9%) to decide who should undergo a biopsy. The baseline model includes prostate-specific antigen and digital rectal examination; the extended model adds two predictors based on transrectal ultrasound (TRUS).
We explain risk thresholds, NB, default strategies (treat all, treat no one), and test tradeoff. To use DCA, first determine whether a model is superior to all other strategies across the range of reasonable risk thresholds. If so, that model appears to improve decisions irrespective of threshold. Second, consider if there are important extra costs to using the model. If so, obtain the test tradeoff to check whether the increase in NB versus the best other strategy is worth the additional cost. In our case study, addition of TRUS improved NB by 0.0114, equivalent to 1.1 more detected high-grade prostate cancers per 100 patients. Hence, adding TRUS would be worthwhile if we accept subjecting 88 patients to TRUS to find one additional high-grade prostate cancer or, alternatively, subjecting 10 patients to TRUS to avoid one unnecessary biopsy.
The proposed guidelines can help researchers understand DCA and improve application and reporting.
Decision curve analysis can identify risk models that can help us make better clinical decisions. We illustrate appropriate reporting and interpretation of decision curve analysis.
Decision curve analysis (DCA) is a method to evaluate whether risk prediction models can have utility for supporting treatment decisions. This guide for researchers explains what DCA is, how to interpret it, and how to report its results.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Testing for prostate-specific antigen (PSA) has profoundly affected the diagnosis and treatment of prostate cancer. PSA testing has enabled physicians to detect prostate tumours while they are still ...small, low-grade and localized. This very ability has, however, created controversy over whether we are now diagnosing and treating insignificant cancers. PSA testing has also transformed the monitoring of treatment response and detection of disease recurrence. Much current research is directed at establishing the most appropriate uses of PSA testing and at developing methods to improve on the conventional PSA test.
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