Abstract Background There is growing interest in evaluating differences in healthcare interventions across routinely collected demographic characteristics. However, individual subgroup analyses in randomized controlled trials are often not prespecified, adjusted for multiple testing, or conducted using the appropriate statistical test for interaction, and therefore frequently lack credibility. Meta-analyses can be used to examine the validity of potential subgroup differences by collating evidence across trials. Here, we characterize the conduct and clinical translation of age-treatment subgroup analyses in Cochrane reviews. Methods For a random sample of 928 Cochrane intervention reviews of randomized trials, we determined how often subgroup analyses of age are reported, how often these analyses have a P < 0.05 from formal interaction testing, how frequently subgroup differences first observed in an individual trial are later corroborated by other trials in the same meta-analysis, and how often statistically significant results are included in commonly used clinical management resources (BMJ Best Practice, UpToDate, Cochrane Clinical Answers, Google Scholar, and Google search). Results Among 928 Cochrane intervention reviews, 189 (20.4%) included plans to conduct age-treatment subgroup analyses. The vast majority (162 of 189, 85.7%) of the planned analyses were not conducted, commonly because of insufficient trial data. There were 22 reviews that conducted their planned age-treatment subgroup analyses, and another 3 reviews appeared to perform unplanned age-treatment subgroup analyses. These 25 (25 of 928, 2.7%) reviews conducted a total of 97 age-treatment subgroup analyses, of which 65 analyses (in 20 reviews) had non-overlapping subgroup levels. Among the 65 age-treatment subgroup analyses, 14 (21.5%) did not report any formal interaction testing. Seven (10.8%) reported P < 0.05 from formal age-treatment interaction testing; however, none of these seven analyses were in reviews that discussed the potential biological rationale or clinical significance of the subgroup findings or had results that were included in common clinical practice resources. Conclusion Age-treatment subgroup analyses in Cochrane intervention reviews were frequently planned but rarely conducted, and implications of detected interactions were not discussed in the reviews or mentioned in common clinical resources. When subgroup analyses are performed, authors should report the findings, compare the results to previous studies, and outline any potential impact on clinical care.
Comparisons of large trials with meta-analyses may reach different conclusions depending on how trials and meta-analyses are selected and how end points and agreement are defined. Scrutiny of these 2 major research methods can enhance our appreciation of both for guiding medical practice.
Digital cameras are becoming increasingly cheap and ubiquitous, leading researchers to exploit multiple cameras and plentiful processing to create richer and more accurate representations of real settings. This thesis addresses issues of scale in large camera arrays. I present a scalable architecture that continuously streams color video from over 100 inexpensive cameras to disk using four PCs, creating a one gigasample-per-second photometer. It extends prior work in camera arrays by providing as much control over those samples as possible. For example, this system not only ensures that the cameras are frequency-locked, but also allows arbitrary, constant temporal phase shifts between cameras, allowing the application to control the temporal sampling. The flexible mounting system also supports many different configurations, from tightly packed to widely spaced cameras, so applications can specify camera placement. Even greater flexibility is provided by processing power at each camera, including an MPEG2 encoder for video compression, and FPGAs and embedded microcontrollers to perform low-level image processing for real-time applications. I present three novel applications for the camera array that highlight strengths of the architecture and the advantages and feasibility of working with many inexpensive cameras: synthetic aperture videography, high speed videography, and spatiotemporal view interpolation. Synthetic aperture videography uses numerous moderately spaced cameras to emulate a single large-aperture one. Such a camera can see through partially occluding objects like foliage or crowds. I show the first synthetic aperture images and videos of dynamic events, including live video accelerated by image warps performed at each camera. High-speed videography uses densely packed cameras with staggered trigger times to increase the effective frame rate of the system. I show how to compensate for artifacts induced by the electronic rolling shutter commonly used in inexpensive CMOS image sensors and present results streaming 1560 fps video using 52 cameras. Spatiotemporal view interpolation processes images from multiple video cameras to synthesize new views from times and positions not in the captured data. We simultaneously extend imaging performance along two axes by properly staggering the trigger times of many moderately spaced cameras, enabling a novel multiple-camera optical flow variant for spatiotemporal view interpolation.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Windows MediaPlayer or RealPlayer.
article Free Access Share on IP-based protocols for mobile internetworking Authors: John Ioannidis Department of Computer Science, Columbia University Department of Computer Science, Columbia UniversityView Profile , Dan Duchamp Department of Computer Science, Columbia University Department of Computer Science, Columbia UniversityView Profile , Gerald Q. Maguire Department of Computer Science, Columbia University Department of Computer Science, Columbia UniversityView Profile Authors Info & Claims ACM SIGCOMM Computer Communication ReviewVolume 21Issue 4Sept. 1991pp 235–245https://doi.org/10.1145/115994.116014Published:01 August 1991Publication History 287citation1,446DownloadsMetricsTotal Citations287Total Downloads1,446Last 12 Months84Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
EACH YEAR, RESEARCHERS IDENTIFY THOUSANDS OF potential new “tools” for predicting patients’ medical futures. There is heightened interest for discovering, validating, and incorporating into clinical practice predictors that improve treatment choices and outcomes thereof. Thousands of articles report on potential predictors. A search of PubMed clinical queries under prognosis (specific strategy) yields 165 746 articles for cancer, 72 354 for cardiovascular disease, and even 3749 for rheumatoid arthritis. These run the gamut, including genetic tests, biomarkers, and an increasing variety of imaging modes, lengthening the list of candidate predictors. However, very few of these proposed predictors eventually change practice. Why? What makes a good predictor? A good predictor is one that has a favorable risk-benefit ratio, reasonable cost, acceptability, and convenience. As for any intervention in health care, proper evidence ideally requires randomized trials demonstrating that using the predictor improves decision making and subsequent clinical outcomes without inordinate adverse events. It also requires formal cost-effectiveness analyses, integrating benefits, risks, and cost. However, hardly any of the predictors in the literature or even those routinely adopted in clinical practice have had their effectiveness proven in randomized trials. Only a few examples of such trials exist; eg, trials evaluating the benefits of screening for abdominal aneurysms or measuring brain-type natriuretic peptide in patients with dyspnea. Conversely, a comprehensive randomized trial agenda trying to evaluate every proposed predictor in each proposed disease application and population would require millions of trials, which is unrealistic. Which candidate predictors should be evaluated by randomized trials and how should they be chosen for best results? A commonsense checklist might be to, first, preferably test predictors for diseases with major morbidity. Second, some effective treatment should be available. Third, the treatment should not be equally effective (or equally risky) for all persons. Fourth, consideration of the predictor should allow more accurate classification of individuals into categories in which treatment is or is not indicated. Fifth, the incremental prediction should be accomplished beyond what can be achieved with information already available. Sixth, there should be consensus about and standardization of established, routine predictors. Seventh, the predictor should be unambiguously defined and measured. Most published research on predictors is irrelevant or tangential to this checklist. Almost all articles report statistically significant results, but this means little. Many investigators deal with whether a predictor in isolation has any ability to predict something. This, however, does not consider that many clinical facts and routine laboratory predictors may already inform prognosis. Thus, it is often not clear whether the new test adds incremental prognostic information beyond known factors. Much of the literature is chaotic, and data dredging and selective reporting abound. Strong studies with clear design, purpose, and knowledge are clearly needed. In this issue of JAMA, Polonsky et al present such a welldesigned study addressing coronary artery calcium score (CACS) as a predictor of coronary heart disease (CHD). Is this predictor good enough? In regard to the aforementioned checklist, first, CHD indeed carries major morbidity. Second, effective lipid-lowering treatments are available for preventive purposes. Third, the absolute effectiveness of the treatments (absolute risk reduction) varies at different categories of baseline risk. Patients at greater than 20% risk of CHD over 10 years should be treated, those with less than 10% should not, and those with 10% to 20% are in the gray zone of intermediate risk. Fourth, Polonsky et al suggest that CACS does allow for a better classification of patients into categories in which, seemingly, treatment is or not indicated. Fifth, this is accomplished in addition to the information available from established routine predictors,
This meta-analysis showed that (18)F-FDG PET has good, but not excellent, concordance with the results of BMB for the detection of bone marrow infiltration in the staging of patients with lymphoma. (18)F-FDG PET may complement the results of BMB and its performance may vary according to the type of lymphoma.
Memories contain large arrays with high capacitance bitlines and IO lines. To reduce the power of memory accesses we limit the swings on these by controlling the time the lines are driven by using a replica feedback. The swings are set to 10% of the supply over a wide range of process and operating conditions.
Objectives Evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like τ 2 or I 2 , but their clinical interpretation is not straightforward. A prediction interval is less complicated: it presents the expected range of true effects in similar studies. We aimed to show the advantages of having the prediction interval routinely reported in meta-analyses. Design We show how the prediction interval can help understand the uncertainty about whether an intervention works or not. To evaluate the implications of using this interval to interpret the results, we selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009–2013 with a dichotomous (n=2009) or continuous (n=1254) outcome, and generated 95% prediction intervals for them. Results In 72.4% of 479 statistically significant (random-effects p<0.05) meta-analyses in the Cochrane Database 2009–2013 with heterogeneity (I 2 >0), the 95% prediction interval suggested that the intervention effect could be null or even be in the opposite direction. In 20.3% of those 479 meta-analyses, the prediction interval showed that the effect could be completely opposite to the point estimate of the meta-analysis. We demonstrate also how the prediction interval can be used to calculate the probability that a new trial will show a negative effect and to improve the calculations of the power of a new trial. Conclusions The prediction interval reflects the variation in treatment effects over different settings, including what effect is to be expected in future patients, such as the patients that a clinician is interested to treat. Prediction intervals should be routinely reported to allow more informative inferences in meta-analyses.
