Numbers of adequately powered studies (≥50% power) and median power within each meta-analysis (MA) with respect to a 30% relative risk reduction (<i>RRR30</i>), overall and by medical specialty, outcome type and intervention-comparison type.

1<p>Other medical specialties: Blood and immune system, Ear and nose, Eye, General health, Genetic disorders, Injuries, accidents and wounds, Mouth and dental, Skin.</p>2<p>Other semi-objective outcomes: External structure, Internal structure, Surgical/device related success/failure, Withdrawals/drop-outs.</p>3<p>Other subjective outcomes: Pain, Mental health outcomes, Quality of life/functioning, Consumption, Satisfaction with care, Composite (at least 1 non-mortality/morbidity).</p>4<p>Non-pharmacological interventions include interventions classified as medical devices, surgical, complex, resources and infrastructure, behavioural, psychological, physical, complementary, educational, radiotherapy, vaccines, cellular and gene, screening.</p

Distribution of power available to detect a relative risk reduction of 30%, across 77,237 studies.

<p>Distribution of power available to detect a relative risk reduction of 30%, across 77,237 studies.</p

Average differences in observed log odds ratios between underpowered () compared to adequately powered studies, in subset A of 1,107 meta-analyses, overall and within medical specialties, outcome types and intervention-comparison types.

1<p>Other medical specialties, semi-objective outcomes, subjective outcomes and non-pharmacological interventions defined in footnotes to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059202#pone-0059202-t002" target="_blank">Table 2</a>.</p>2<p>Comparison is less meaningful when comparing two active interventions since the a priori “better” active intervention is not taken into account.</p

The Impact of Study Size on Meta-analyses: Examination of Underpowered Studies in Cochrane Reviews

<div><p>Background</p><p>Most meta-analyses include data from one or more small studies that, individually, do not have power to detect an intervention effect. The relative influence of adequately powered and underpowered studies in published meta-analyses has not previously been explored. We examine the distribution of power available in studies within meta-analyses published in Cochrane reviews, and investigate the impact of underpowered studies on meta-analysis results.</p> <p>Methods and Findings</p><p>For 14,886 meta-analyses of binary outcomes from 1,991 Cochrane reviews, we calculated power per study within each meta-analysis. We defined adequate power as ≥50% power to detect a 30% relative risk reduction. In a subset of 1,107 meta-analyses including 5 or more studies with at least two adequately powered and at least one underpowered, results were compared with and without underpowered studies. In 10,492 (70%) of 14,886 meta-analyses, all included studies were underpowered; only 2,588 (17%) included at least two adequately powered studies. 34% of the meta-analyses themselves were adequately powered. The median of summary relative risks was 0.75 across all meta-analyses (inter-quartile range 0.55 to 0.89). In the subset examined, odds ratios in underpowered studies were 15% lower (95% CI 11% to 18%, P<0.0001) than in adequately powered studies, in meta-analyses of controlled pharmacological trials; and 12% lower (95% CI 7% to 17%, P<0.0001) in meta-analyses of controlled non-pharmacological trials. The standard error of the intervention effect increased by a median of 11% (inter-quartile range −1% to 35%) when underpowered studies were omitted; and between-study heterogeneity tended to decrease.</p> <p>Conclusions</p><p>When at least two adequately powered studies are available in meta-analyses reported by Cochrane reviews, underpowered studies often contribute little information, and could be left out if a rapid review of the evidence is required. However, underpowered studies made up the entirety of the evidence in most Cochrane reviews.</p> </div

Percentages of 14,886 meta-analyses including no studies adequately powered to detect a target effect or including at least two adequately powered studies, where adequate power is defined as 80% or 50% in turn; and summary of median power within each meta-analysis.

<p>Percentages of 14,886 meta-analyses including no studies adequately powered to detect a target effect or including at least two adequately powered studies, where adequate power is defined as 80% or 50% in turn; and summary of median power within each meta-analysis.</p

Meta-analytic power with respect to a 30% relative risk reduction (RRR30), based on the random-effects model, overall and by medical specialty, outcome type and intervention-comparison type.

1<p>Other medical specialties, semi-objective outcomes, subjective outcomes and non-pharmacological interventions defined in footnotes to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059202#pone-0059202-t002" target="_blank">Table 2</a>.</p

Ratios comparing results obtained from adequately powered studies only with results obtained from all studies, in subset A of 1,107 meta-analyses: results shown are percentiles of the distribution of such ratios across meta-analyses.

1<p> in the all-studies meta-analysis in 256/1107 meta-analyses. In 199/256 (78%), also in the meta-analysis including adequately powered studies only. In 57/256 (22%), increased, but trivially, when underpowered studies were removed.</p

Using dynamic analysis of AFP provides a methodology for identifying patients at high risk of HCC.

A) Workflow for the development of an algorithm for HCC detection using AFP. The HCC surveillance cohort refined to patients with specific characteristics prior to formal Bayesian analysis in static and dynamic modes. In static mode a trigger zone was established, which was then tested dynamically. Estimated patient-specific intercept and gradient parameters plotted against each other. Estimates were taken from `windowed' version B) `full-data' version C) of full-trajectory retrospective Bayesian analysis. Triangles denote confirmed early-diagnosed HCC cases. Diagonal lines define regions of parameter space (above the line) that might indicate emerging HCC cases: purple—passes through (x, y) = (-0.01, 1) and (0, log20); brown—passes through (x, y) = (-0.01, 0.5) and (0, 1); yellow—passes through (x, y) = (-0.01, 0.5) and (0, log20). The area to the above/left of the yellow line was used to represent the area of ‘high risk’ characteristics of AFP. D) Illustration of triggering across waves of prospective Bayesian analysis. All HCC patients from the HCV group are shown, along with an equal number of non-HCC cases from the same group. A point is plotted for each trigger (HCCs denoted by triangles and non-HCCs by circles); a horizontal line is shown for patients who did not trigger at all. Points of a lighter shade are used to indicate that the patient-specific data are the same as in the preceding wave due to that patient's data set having ceased to accrue more AFPs in the training data-set.</p

AFP as an HCC surveillance tool detects a significant number of treatable HCC in patients with satisfactory outcomes.

<p><b>A</b>) Survival for total HCC cohort diagnosed with HCC between 1/1/2009 and 31/12/2014. <b>B</b>) The role of AFP in HCC detection. Method of HCC detection for the 133 patients within HCC surveillance programme at the time of diagnosis, chequered area within AFP pickup group represents the 28/49 patients in whom a recent US had not been performed—see text for details. <b>C</b>) Individual AFP levels at time of diagnosis for patients diagnosed with HCC, AFP values plotted at log10; AFP = 6 (local ULN; yellow) and AFP = 20 (red) are shown. All columns p<0.0001 to one another by Kruskal Wallis test with Dunns multiple comparison. <b>D</b>) Survival of patients with HCC diagnosed through surveillance screening either through US or AFP mediated conversion to CT/MRI imaging, error bars = SEM, p value denotes Mantel Cox. <b>E</b>) Therapy offered to patients within each group (US detected n = 61 and AFP detected n = 49) of patients with HCC detected during surveillance; all p>0.05 by 2 way Anova. Of the 11 and 9 patients listed for liver transplantation, 2 (due to tumour growth) and 1 (due to frailty) were delisted from the waiting list whilst awaiting transplantation in US and AFP detected groups respectively.</p

Exploratory analysis of HCC surveillance cohort.

<p>Exploratory analysis of HCC surveillance cohort.</p