scholarly journals Robust Confidence Intervals for Effect Size in the Two-Group Case

2005 ◽  
Vol 4 (2) ◽  
pp. 353-371 ◽  
Author(s):  
H. J. Keselman ◽  
James Algina ◽  
Katherine Fradette
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
Group Case

Abstract 3789: Nessun Dorma : Have the Risks of Rosiglitazone been Exaggerated?

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
George A Diamond ◽  
Sanjay Kaul
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Cardiovascular Death ◽  
Sensitivity Analyses ◽  
Odds Ratios ◽  
True Effect Size ◽  
Index Study ◽  
The Stability

Background A highly publicized meta-analysis of 42 clinical trials comprising 27,844 diabetics ignited a firestorm of controversy by charging that treatment with rosiglitazone was associated with a “…worrisome…” 43% greater risk of myocardial infarction ( p =0.03) and a 64% greater risk of cardiovascular death ( p =0.06). Objective The investigators excluded 4 trials from the infarction analysis and 19 trials from the mortality analysis in which no events were observed. We sought to determine if these exclusions biased the results. Methods We compared the index study to a Bayesian meta-analysis of the entire 42 trials (using odds ratio as the measure of effect size) and to fixed-effects and random-effects analyses with and without a continuity correction that adjusts for values of zero. Results The odds ratios and confidence intervals for the analyses are summarized in the Table . Odds ratios for infarction ranged from 1.43 to 1.22 and for death from 1.64 to 1.13. Corrected models resulted in substantially smaller odds ratios and narrower confidence intervals than did uncorrected models. Although corrected risks remain elevated, none are statistically significant (*p<0.05). Conclusions Given the fragility of the effect sizes and confidence intervals, the charge that roziglitazone increases the risk of adverse events is not supported by these additional analyses. The exaggerated values observed in the index study are likely the result of excluding the zero-event trials from analysis. Continuity adjustments mitigate this error and provide more consistent and reliable assessments of true effect size. Transparent sensitivity analyses should therefore be performed over a realistic range of the operative assumptions to verify the stability of such assessments especially when outcome events are rare. Given the relatively wide confidence intervals, additional data will be required to adjudicate these inconclusive results.

Using confidence intervals to estimate the response of salmon populations (Oncorhynchus spp.) to experimental habitat alterations

2005 ◽  
Vol 62 (12) ◽  
pp. 2716-2726 ◽  
Author(s):  
Michael J Bradford ◽  
Josh Korman ◽  
Paul S Higgins
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
Null Hypothesis ◽  
Statistical Power ◽  
Habitat Restoration ◽  
Coho Salmon ◽  
Fish Habitat ◽  
Monitoring Program ◽  
Considerable Uncertainty ◽  
Monitoring Programs

There is considerable uncertainty about the effectiveness of fish habitat restoration programs, and reliable monitoring programs are needed to evaluate them. Statistical power analysis based on traditional hypothesis tests are usually used for monitoring program design, but here we argue that effect size estimates and their associated confidence intervals are more informative because results can be compared with both the null hypothesis of no effect and effect sizes of interest, such as restoration goals. We used a stochastic simulation model to compare alternative monitoring strategies for a habitat alteration that would change the productivity and capacity of a coho salmon (Oncorhynchus kisutch) producing stream. Estimates of the effect size using a freshwater stock–recruit model were more precise than those from monitoring the abundance of either spawners or smolts. Less than ideal monitoring programs can produce ambiguous results, which are cases in which the confidence interval includes both the null hypothesis and the effect size of interest. Our model is a useful planning tool because it allows the evaluation of the utility of different types of monitoring data, which should stimulate discussion on how the results will ultimately inform decision-making.

Null Hypothesis Significance Testing: Ramifications, Ruminations and Recommendations

2005 ◽  
Vol 35 (1) ◽  
pp. 1-20 ◽  
Author(s):  
G. K. Huysamen
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Effect Size ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Population Parameter ◽  
Size Estimation ◽  
Null Hypothesis Significance Testing ◽  
Point Estimates ◽  
Size Estimates

Criticisms of traditional null hypothesis significance testing (NHST) became more pronounced during the 1960s and reached a climax during the past decade. Among others, NHST says nothing about the size of the population parameter of interest and its result is influenced by sample size. Estimation of confidence intervals around point estimates of the relevant parameters, model fitting and Bayesian statistics represent some major departures from conventional NHST. Testing non-nil null hypotheses, determining optimal sample size to uncover only substantively meaningful effect sizes and reporting effect-size estimates may be regarded as minor extensions of NHST. Although there seems to be growing support for the estimation of confidence intervals around point estimates of the relevant parameters, it is unlikely that NHST-based procedures will disappear in the near future. In the meantime, it is widely accepted that effect-size estimates should be reported as a mandatory adjunct to conventional NHST results.

Quantifying External Load in Australian Football Matches and Training Using Accelerometers

2013 ◽  
Vol 8 (1) ◽  
pp. 44-51 ◽  
Author(s):  
Luke J. Boyd ◽  
Kevin Ball ◽  
Robert J. Aughey
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
External Load ◽  
Percentage Change ◽  
Accelerometer Data ◽  
Elite Group ◽  
Australian Football ◽  
And Training

Purpose:To describe the external load of Australian football matches and training using accelerometers.Methods:Nineteen elite and 21 subelite Australian footballers wore accelerometers during matches and training. Accelerometer data were expressed in 2 ways: from all 3 axes (player load; PL) and from all axes when velocity was below 2 m/s (PLSLOW). Differences were determined between 4 playing positions (midfielders, nomadics, deeps, and ruckmen), 2 playing levels (elite and subelite), and matches and training using percentage change and effect size with 90% confidence intervals.Results:In the elite group, midfielders recorded higher PL than nomadics and deeps did (8.8%, 0.59 ± 0.24; 34.2%, 1.83 ± 0.39 respectively), and ruckmen were higher than deeps (37.2%, 1.27 ± 0.51). Elite midfielders, nomadics, and ruckmen recorded higher PLSLOW than deeps (13.5%, 0.65 ± 0.37; 11.7%, 0.55 ± 0.36; and 19.5%, 0.83 ± 0.50, respectively). Subelite midfielders were higher than nomadics, deeps, and ruckmen (14.0%, 1.08 ± 0.30; 31.7%, 2.61 ± 0.42; and 19.9%, 0.81 ± 0.55, respectively), and nomadics and ruckmen were higher than deeps for PL (20.6%, 1.45 ± 0.38; and 17.4%, 0.57 ± 0.55, respectively). Elite midfielders, nomadics, and ruckmen recorded higher PL (7.8%, 0.59 ± 0.29; 12.9%, 0.89 ± 0.25; and 18.0%, 0.67 ± 0.59, respectively) and PLSLOW (9.4%, 0.52 ± 0.30; 11.3%, 0.68 ± 0.25; and 14.1%, 0.84 ± 0.61, respectively) than subelite players. Small-sided games recorded the highest PL and PLSLOW and were the only training drill to equal or exceed the load from matches across positions and playing levels.Conclusion:PL differed between positions, with midfielders the highest, and between playing levels, with elite higher. Differences between matches and training were also evident, with PL from small-sided games equivalent to or higher than matches.

Increased High-Intensity Activity in Elite Australian Football Finals Matches

2011 ◽  
Vol 6 (3) ◽  
pp. 367-379 ◽  
Author(s):  
Robert J. Aughey
Keyword(s):  
Confidence Intervals ◽  
Physical Condition ◽  
Effect Size ◽  
High Velocity ◽  
High Intensity ◽  
Anecdotal Evidence ◽  
Total Distance ◽  
Competitive Season ◽  
Australian Football

Background:Australian football (AF) is a highly intermittent sport, requiring athletes to accelerate hundreds of times with repeated bouts of high-intensity running (HIR). Players aim to be in peak physical condition for finals, with anecdotal evidence of increased speed and pressure of these games.Purpose:However, no data exists on the running demands of finals games, and therefore the aim of this study was to compare the running demands of finals to regular season games with matched players and opponents.Methods:Player movement was recorded by GPS at 5 Hz and expressed per period of the match (rotation), for total distance, high-intensity running (HIR, 4.17-10.00 m·s-1) and maximal accelerations (2.78-10.00 m·s–2). All data was compared for regular season and finals games and the magnitude of effects was analyzed with the effect size (ES) statistic and expressed with confidence intervals.Results:Each of the total distance (11%; ES: 0.78 ± 0.30), high-intensity running distance (9%; ES: 0.29 ± 0.25) and number of maximal accelerations (97%; ES: 1.30 ± 0.20) increased in finals games. The largest percentage increases in maximal accelerations occurred from a commencement velocity of between 3–4 (47%; ES: 0.56 ± 0.21) and 4–5 m·s-1 (51%; ES: 0.72 ± 0.26), and with <19 s between accelerations (53%; ES: 0.63 ± 0.27).Conclusion:Elite AF players nearly double the number of maximal accelerations in finals compared with regular season games. This large increase is superimposed on requirements to cover a greater total distance and spend more time at high velocity during finals games. Players can be effectively conditioned to cope with these increased demands, even during a long competitive season.

Conceptual and Practical Implications for Rehabilitation Research: Effect Size Estimates, Confidence Intervals, and Power

2007 ◽  
Vol 21 (2) ◽  
pp. 87-100 ◽  
Author(s):  
James M. Ferrin ◽  
Malachy Bishop ◽  
Timothy N. Tansey ◽  
Michael Frain ◽  
Elizabeth A. Swett ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
Rehabilitation Research ◽  
Size Estimates ◽  
Practical Implications

Meta-analysis of the impacts of water management on aquatic communities

2008 ◽  
Vol 65 (3) ◽  
pp. 437-447 ◽  
Author(s):  
Tim J Haxton ◽  
C Scott Findlay
Keyword(s):  
Water Management ◽  
Confidence Intervals ◽  
Effect Size ◽  
Meta Analysis ◽  
Ecological Impacts ◽  
Aquatic Communities ◽  
Flow Modification ◽  
Meta Analyses ◽  
Fail Safe ◽  
Habitat Generalists

Systematic meta-analyses were conducted on the ecological impacts of water management, including effects of (i) dewatering on macroinvertebrates, (ii) a hypolimnetic release on downstream aquatic fish and macro invertebrate communities, and (iii) flow modification on fluvial and habitat generalists. Our meta-analysis indicates, in general, that (i) macroinvertebrate abundance is lower in zones or areas that have been dewatered as a result of water fluctuations or low flows (overall effect size, –1.64; 95% confidence intervals (CIs), –2.51, –0.77), (ii) hypolimnetic draws are associated with reduced abundance of aquatic (fish and macroinvertebrates) communities (overall effect size, –0.84; 95% CIs, –1.38, –0.33) and macroinvertebrates (overall effect size, –0.73; 95% CIs, –1.24, –0.22) downstream of a dam, and (iii) altered flows are associated with reduced abundance of fluvial specialists (–0.42; 95% CIs, –0.81, –0.02) but not habitat generalists (overall effect size, –0.14; 95% CIs, –0.61, 0.32). Publication bias is evident in several of the meta-analyses; however, multiple experiments from a single study may be contributing to this bias. Fail-safe Ns suggest that many (>100) studies showing positive or no effects of water management on the selected endpoints would be required to qualitatively change the results of the meta-analysis, which in turn suggests that the conclusions are reasonably robust.

scholarly journals Confidence intervals for the overall effect size in random-effects meta-analysis.

2008 ◽  
Vol 13 (1) ◽  
pp. 31-48 ◽  
Author(s):  
Julio Sánchez-Meca ◽  
Fulgencio Marín-Martínez
Keyword(s):  
Confidence Intervals ◽  
Random Effects ◽  
Effect Size ◽  
Meta Analysis
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