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Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods
"... In meta-analysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs meta-analysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I 2 ..."
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In meta-analysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs meta-analysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I 2 index has been proposed to quantify the degree of heterogeneity in a meta-analysis. In this article, the performances of the Q test and the confidence interval around the I 2 index are compared by means of a Monte Carlo simulation. The results show the utility of the I 2 index as a complement to the Q test, although it has the same problems of power with a small number of studies.
Distribution-based aggregation for relational learning with identifier attributes
- Machine Learning
, 2004
"... Feature construction through aggregation plays an essential role in modeling relational domains with one-to-many relationships between tables. One-to-many relationships lead to bags (multisets) of related entities, from which predictive information must be captured. This paper focuses on aggregation ..."
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Cited by 42 (10 self)
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Feature construction through aggregation plays an essential role in modeling relational domains with one-to-many relationships between tables. One-to-many relationships lead to bags (multisets) of related entities, from which predictive information must be captured. This paper focuses on aggregation from categorical attributes that can take many values (e.g., object identifiers). We present a novel aggregation method as part of a relational learning system ACORA, that combines the use of vector distance and meta-data about the class-conditional distributions of attribute values. We provide a theoretical foundation for this approach deriving a “relational fixed-effect ” model within a Bayesian framework, and discuss the implications of identifier aggregation on the expressive power of the induced model. One advantage of using identifier attributes is the circumvention of limitations caused either by missing/unobserved object properties or by independence assumptions. Finally, we show empirically that the novel aggregators can generalize in the presence of identifier (and other high-dimensional) attributes, and also explore the limitations of the applicability of the methods. 1
A meta-analysis of smoking cessation interventions with individuals in substance abuse treatment or recovery
- Journal of Consulting and Clinical Psychology 72:1144–1156, 2004. PMID: 15612860 RICHTER, K.P.; CHOI, W.S.; AND ALFORD, D.P
"... This meta-analysis examined outcomes of smoking cessation interventions evaluated in 19 randomized controlled trials with individuals in current addictions treatment or recovery. Smoking and substance use outcomes at posttreatment and long-term follow-up ( 6 months) were summarized with random effe ..."
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This meta-analysis examined outcomes of smoking cessation interventions evaluated in 19 randomized controlled trials with individuals in current addictions treatment or recovery. Smoking and substance use outcomes at posttreatment and long-term follow-up ( 6 months) were summarized with random effects models. Intervention effects for smoking cessation were significant at posttreatment and comparable for participants in addictions treatment and recovery; however, intervention effects for smoking cessation were nonsignificant at long-term follow-up. Smoking cessation interventions provided during addictions treatment were associated with a 25 % increased likelihood of long-term abstinence from alcohol and illicit drugs. Short-term smoking cessation effects look promising, but innovative strategies are needed for long-term cessation. Contrary to previous concerns, smoking cessation interventions during addic-tions treatment appeared to enhance rather than compromise long-term sobriety. Cigarette smoking is endemic among individuals with substance abuse problems, with rates as high as 74 % to 88 % (Kalman, 1998), compared with 23 % in the general population (Centers for Disease Control and Prevention, 2002). Substance abusers tend to start smoking at a younger age and are more likely to be heavy smokers, nicotine dependent, and experience greater difficulty with quitting
The power of statistical tests in meta-analysis
- Psychological Methods
, 2001
"... Calculations of the power of statistical tests are important in planning research studies (including meta-analyses) and in interpreting situations in which a result has not proven to be statistically significant. The authors describe procedures to com-pute statistical power of fixed- and random-effe ..."
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Calculations of the power of statistical tests are important in planning research studies (including meta-analyses) and in interpreting situations in which a result has not proven to be statistically significant. The authors describe procedures to com-pute statistical power of fixed- and random-effects tests of the mean effect size, tests for heterogeneity (or variation) of effect size parameters across studies, and tests for contrasts among effect sizes of different studies. Examples are given using 2 published meta-analyses. The examples illustrate that statistical power is not always high in meta-analysis. The use of quantitative methods to summarize the results of several empirical research studies, or meta-analysis, is now widespread in psychology, medicine, and the social sciences. Meta-analysis involves de-scribing the results of each study using a numerical index (an estimate of effect size such as a correlation coefficient, a standardized mean difference, or an odds ratio) and then combining these estimates across studies to obtain a summary. Although inference procedures for meta-analysis have been available for well over a decade, there is little work on the calculation of the power of statisti-cal tests in meta-analysis. However, power calcula-tions are always part of sound statistical planning (Co-hen, 1977). Moreover, power calculations are often a required component of research grant proposals in primary research, and the requirement of providing some estimate of statistical power is increasingly an issue in evaluating research synthesis projects as well. Although meta-analyses with large numbers of studies investigating even medium-sized effects may have quite powerful tests, meta-analyses of smaller num-bers of studies and meta-analyses in areas in which effects are expected to be small do not necessarily have very powerful statistical tests. The purpose of this article is to provide procedures
Are racial and ethnic minorities less willing to participate
- in health research? PLoS Med 2006; 3: e19. 20 Anderson KM, Odell PM, Wislon PW, Kannell WB
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Online models for content optimization
- In Advances in Neural Information Processing Systems
"... We describe a new content publishing system that selects articles to serve to a user, choosing from an editorially programmed pool that is frequently refreshed. It is now deployed on a major Yahoo! portal, and selects articles to serve to hundreds of millions of user visits per day, significantly in ..."
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We describe a new content publishing system that selects articles to serve to a user, choosing from an editorially programmed pool that is frequently refreshed. It is now deployed on a major Yahoo! portal, and selects articles to serve to hundreds of millions of user visits per day, significantly increasing the number of user clicks over the original manual approach, in which editors periodically selected articles to display. Some of the challenges we face include a dynamic content pool, short article lifetimes, non-stationary click-through rates, and extremely high traffic volumes. The fundamental problem we must solve is to quickly identify which items are popular (perhaps within different user segments), and to exploit them while they remain current. We must also explore the underlying pool constantly to identify promising alternatives, quickly discarding poor performers. Our approach is based on tracking per article performance in near real time through online models. We describe the characteristics and constraints of our application setting, discuss our design choices, and show the importance and effectiveness of coupling online models with a randomization procedure. We discuss the challenges encountered in a production online content-publishing environment and highlight issues that deserve careful attention. Our analysis of this application also suggests a number of future research avenues. 1
Can Simply Answering Research Questions Change Behaviour? Systematic Review and Meta Analyses of Brief Alcohol Intervention Trials
, 2011
"... Background: Participant reports of their own behaviour are critical for the provision and evaluation of behavioural interventions. Recent developments in brief alcohol intervention trials provide an opportunity to evaluate longstanding concerns that answering questions on behaviour as part of resear ..."
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Background: Participant reports of their own behaviour are critical for the provision and evaluation of behavioural interventions. Recent developments in brief alcohol intervention trials provide an opportunity to evaluate longstanding concerns that answering questions on behaviour as part of research assessments may inadvertently influence it and produce bias. The study objective was to evaluate the size and nature of effects observed in randomized manipulations of the effects of answering questions on drinking behaviour in brief intervention trials. Methodology/Principal Findings: Multiple methods were used to identify primary studies. Between-group differences in total weekly alcohol consumption, quantity per drinking day and AUDIT scores were evaluated in random effects metaanalyses. Ten trials were included in this review, of which two did not provide findings for quantitative study, in which three outcomes were evaluated. Between-group differences were of the magnitude of 13.7 (20.17 to 27.6) grams of alcohol per week (approximately 1.5 U.K. units or 1 standard U.S. drink) and 1 point (0.1 to 1.9) in AUDIT score. There was no difference in quantity per drinking day. Conclusions/Significance: Answering questions on drinking in brief intervention trials appears to alter subsequent selfreported behaviour. This potentially generates bias by exposing non-intervention control groups to an integral component of the intervention. The effects of brief alcohol interventions may thus have been consistently under-estimated. These
A meta-analysis of teen cigarette smoking cessation. Health Psychol 2006; 25: 549–57
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Red and Processed Meat and Colorectal Cancer Incidence: Meta-Analysis of Prospective Studies
"... Background: The evidence that red and processed meat influences colorectal carcinogenesis was judged convincing in the 2007 World Cancer Research Fund/American Institute of Cancer Research report. Since then, ten prospective studies have published new results. Here we update the evidence from prospe ..."
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Background: The evidence that red and processed meat influences colorectal carcinogenesis was judged convincing in the 2007 World Cancer Research Fund/American Institute of Cancer Research report. Since then, ten prospective studies have published new results. Here we update the evidence from prospective studies and explore whether there is a non-linear association of red and processed meats with colorectal cancer risk. Methods and Findings: Relevant prospective studies were identified in PubMed until March 2011. For each study, relative risks and 95 % confidence intervals (CI) were extracted and pooled with a random-effects model, weighting for the inverse of the variance, in highest versus lowest intake comparison, and dose-response meta-analyses. Red and processed meats intake was associated with increased colorectal cancer risk. The summary relative risk (RR) of colorectal cancer for the highest versus the lowest intake was 1.22 (95 % CI = 1.1121.34) and the RR for every 100 g/day increase was 1.14 (95 % CI = 1.0421.24). Non-linear dose-response meta-analyses revealed that colorectal cancer risk increases approximately linearly with increasing intake of red and processed meats up to approximately 140 g/day, where the curve approaches its plateau. The associations were similar for colon and rectal cancer risk. When analyzed separately, colorectal cancer risk was related to intake of fresh red meat (RR for 100 g/day increase = 1.17, 95 % CI = 1.0521.31) and processed meat (RR for 50 g/day increase = 1.18, 95 % CI = 1.1021.28). Similar results were observed for colon cancer, but for rectal cancer, no significant associations were
