In a recent individual patient data meta-analysis, acupuncture was found to be superior to both sham and non-sham controls in patients with chronic pain. In this paper we identify variations in types ...of sham and non-sham controls used and analyze their impact on the effect size of acupuncture.
Based on literature searches of acupuncture trials involving patients with headache and migraine, osteoarthritis, and back, neck and shoulder pain, 29 trials met inclusion criteria, 20 involving sham controls (n = 5,230) and 18 non-sham controls (n = 14,597). For sham controls, we analysed non-needle sham, penetrating sham needles and non-penetrating sham needles. For non-sham controls, we analysed non-specified routine care and protocol-guided care. Using meta-regression we explored impact of choice of control on effect of acupuncture.
Acupuncture was significantly superior to all categories of control group. For trials that used penetrating needles for sham control, acupuncture had smaller effect sizes than for trials with non-penetrating sham or sham control without needles. The difference in effect size was -0.45 (95% C.I. -0.78, -0.12; p = 0.007), or -0.19 (95% C.I. -0.39, 0.01; p = 0.058) after exclusion of outlying studies showing very large effects of acupuncture. In trials with non-sham controls, larger effect sizes associated with acupuncture vs. non-specified routine care than vs. protocol-guided care. Although the difference in effect size was large (0.26), it was not significant with a wide confidence interval (95% C.I. -0.05, 0.57, p = 0.1).
Acupuncture is significantly superior to control irrespective of the subtype of control. While the choice of control should be driven by the study question, our findings can help inform study design in acupuncture, particularly with respect to sample size. Penetrating needles appear to have important physiologic activity. We recommend that this type of sham be avoided.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Active surveillance is the preferred management of low risk prostate cancer. Cancer specific anxiety during active surveillance remains under studied. We evaluated long-term anxiety in men on active ...surveillance to determine whether interventions must be tailored to improve adherence.
A total of 413 men enrolled in active surveillance at a single tertiary care center completed quality of life surveys as part of routine care. A modified version of the MAX-PC (Memorial Anxiety Scale for Prostate Cancer) was used to determine cancer specific anxiety. Generalized estimating equations were applied to evaluate the association between anxiety and the duration on surveillance. Additionally, we examined associations between anxiety and patient age, marital status, Gleason score, the number of positive cores, family history and overall health.
Median patient age was 61 years, median prostate specific antigen at diagnosis was 4.4 ng/ml and 95% of the patients had Gleason 6 disease. Median time from the initiation of active surveillance to the last survey was 3.7 years. There was a 29% risk of reporting cancer specific anxiety within year 1. Anxiety significantly decreased with time (OR 0.87, 95% CI 0.79–0.95, p = 0.003). Pathological and demographic characteristics were not associated with anxiety after adjusting for time on surveillance.
In men undergoing active surveillance we observed a moderate risk of cancer specific anxiety which significantly decreases with time. Those considering conservative management can be informed that, although it is common to experience some anxiety initially, most patients rapidly adjust and report low anxiety levels within 2 years.
Abstract Background A statistical model based on four kallikrein markers (total prostate-specific antigen tPSA, free PSA fPSA, intact PSA, and human kallikrein-related peptidase 2) in blood can ...predict risk of Gleason score ≥7 (high-grade) cancer at prostate biopsy. Objective To determine the value of this model in predicting high-grade cancer at biopsy in a community-based setting in which referral criteria included percentage of fPSA to tPSA (%fPSA). Design, setting, and participants We evaluated the model, with or without adding blood levels of microseminoprotein-β (MSMB) in a cohort of 749 men referred for prostate biopsy due to elevated PSA (≥3 ng/ml), low %fPSA (<20%), or suspicious digital rectal examination at Skåne University Hospital, Malmö, Sweden. Outcome measurements and statistical analysis The kallikrein markers, with or without MSMB levels, measured in cryopreserved anticoagulated blood were combined with age in a published statistical model (Prostate Testing for Cancer and Treatment ProtecT) to predict high-grade cancer at biopsy. Predictive accuracy was compared with a base model. Results and limitations The %fPSA was low (median: 17; interquartile range: 13–22) in this cohort because this marker was used as a referral criterion. The ProtecT model improved discrimination over age and PSA for high-grade cancer (0.777 vs 0.720; p = 0.002). At one illustrative cut point, use of the panel would reduce the number of biopsies by 236 per 1000 and detect 195 of 208 (94%) but delay diagnosis of 13 of 208 high-grade cancers. MSMB levels in blood did not improve the accuracy of the panel ( p = 0.2). Conclusions The kallikrein model is predictive of high-grade cancer if criteria for biopsy referral also include %fPSA, and it can reduce unnecessary biopsies without missing an undue number of tumors. Patient summary We evaluated a published model to predict biopsy outcome in men biopsied due to low percentage of free to total prostate-specific antigen. The model helps reduce unnecessary biopsies without missing an undue number of high-grade cancers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
We have observed that the area under the receiver operating characteristic curve (AUC) is increasingly being used to evaluate whether a novel predictor should be incorporated in a multivariable model ...to predict risk of disease. Frequently, investigators will approach the issue in two distinct stages: first, by testing whether the new predictor variable is significant in a multivariable regression model; second, by testing differences between the AUC of models with and without the predictor using the same data from which the predictive models were derived. These two steps often lead to discordant conclusions.
We conducted a simulation study in which two predictors, X and X*, were generated as standard normal variables with varying levels of predictive strength, represented by means that differed depending on the binary outcome Y. The data sets were analyzed using logistic regression, and likelihood ratio and Wald tests for the incremental contribution of X* were performed. The patient-specific predictors for each of the models were then used as data for a test comparing the two AUCs. Under the null, the size of the likelihood ratio and Wald tests were close to nominal, but the area test was extremely conservative, with test sizes less than 0.006 for all configurations studied. Where X* was associated with outcome, the area test had much lower power than the likelihood ratio and Wald tests.
Evaluation of the statistical significance of a new predictor when there are existing clinical predictors is most appropriately accomplished in the context of a regression model. Although comparison of AUCs is a conceptually equivalent approach to the likelihood ratio and Wald test, it has vastly inferior statistical properties. Use of both approaches will frequently lead to inconsistent conclusions. Nonetheless, comparison of receiver operating characteristic curves remains a useful descriptive tool for initial evaluation of whether a new predictor might be of clinical relevance.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention.
Herein, we argue that this needs to ...change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice.
Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Recent evidence shows that acupuncture is effective for chronic pain. However we do not know whether there are characteristics of acupuncture or acupuncturists that are associated with better or ...worse outcomes.
An existing dataset, developed by the Acupuncture Trialists' Collaboration, included 29 trials of acupuncture for chronic pain with individual data involving 17,922 patients. The available data on characteristics of acupuncture included style of acupuncture, point prescription, location of needles, use of electrical stimulation and moxibustion, number, frequency and duration of sessions, number of needles used and acupuncturist experience. We used random-effects meta-regression to test the effect of each characteristic on the main effect estimate of pain. Where sufficient patient-level data were available, we conducted patient-level analyses.
When comparing acupuncture to sham controls, there was little evidence that the effects of acupuncture on pain were modified by any of the acupuncture characteristics evaluated, including style of acupuncture, the number or placement of needles, the number, frequency or duration of sessions, patient-practitioner interactions and the experience of the acupuncturist. When comparing acupuncture to non-acupuncture controls, there was little evidence that these characteristics modified the effect of acupuncture, except better pain outcomes were observed when more needles were used (p=0.010) and, from patient level analysis involving a sub-set of five trials, when a higher number of acupuncture treatment sessions were provided (p<0.001).
There was little evidence that different characteristics of acupuncture or acupuncturists modified the effect of treatment on pain outcomes. Increased number of needles and more sessions appear to be associated with better outcomes when comparing acupuncture to non-acupuncture controls, suggesting that dose is important. Potential confounders include differences in control group and sample size between trials. Trials to evaluate potentially small differences in outcome associated with different acupuncture characteristics are likely to require large sample sizes.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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