The Experts below are selected from a list of 2145 Experts worldwide ranked by ideXlab platform
Xinlei Wang - One of the best experts on this subject based on the ideXlab platform.
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an integrative shrinkage estimator for random effects meta analysis of rare binary events
Contemporary clinical trials communications, 2018Co-Authors: Ou Bai, Xinlei WangAbstract:Meta-analysis has been a powerful tool for inferring the treatment effect between two experimental conditions from multiple studies of rare binary events. Recently, under a random-effects (RE) model, Bhaumik et al. developed a simple average (SA) estimator and showed that with the continuity correction factor 0.5, the SA estimator was the least biased among a set of commonly used estimators. In this paper, under various RE models that allow for treatment groups with equal and Unequal Variability (in either direction), we develop an integrative shrinkage (iSHRI) estimator based on the SA estimator, which aims to improve estimation efficiency in terms of mean squared error (MSE) that accounts for the bias-variance tradeoff. Through simulation, we find that iSHRI has better performance in general when compared with existing methods, in terms of bias, MSE, type I error and confidence interval coverage. Data examples of rosiglitazone meta-analysis are provided as well, where iSHRI yields competitive results.
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An integrative shrinkage estimator for random-effects meta-analysis of rare binary events
Elsevier, 2018Co-Authors: Ou Bai, Xinlei WangAbstract:Meta-analysis has been a powerful tool for inferring the treatment effect between two experimental conditions from multiple studies of rare binary events. Recently, under a random-effects (RE) model, Bhaumik et al. developed a simple average (SA) estimator and showed that with the continuity correction factor 0.5, the SA estimator was the least biased among a set of commonly used estimators. In this paper, under various RE models that allow for treatment groups with equal and Unequal Variability (in either direction), we develop an integrative shrinkage (iSHRI) estimator based on the SA estimator, which aims to improve estimation efficiency in terms of mean squared error (MSE) that accounts for the bias-variance tradeoff. Through simulation, we find that iSHRI has better performance in general when compared with existing methods, in terms of bias, MSE, type I error and confidence interval coverage. Data examples of rosiglitazone meta-analysis are provided as well, where iSHRI yields competitive results. Keywords: Bias, Estimation efficiency, Log odds ratio, Mean squared error, Sparse binary dat
Ou Bai - One of the best experts on this subject based on the ideXlab platform.
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an integrative shrinkage estimator for random effects meta analysis of rare binary events
Contemporary clinical trials communications, 2018Co-Authors: Ou Bai, Xinlei WangAbstract:Meta-analysis has been a powerful tool for inferring the treatment effect between two experimental conditions from multiple studies of rare binary events. Recently, under a random-effects (RE) model, Bhaumik et al. developed a simple average (SA) estimator and showed that with the continuity correction factor 0.5, the SA estimator was the least biased among a set of commonly used estimators. In this paper, under various RE models that allow for treatment groups with equal and Unequal Variability (in either direction), we develop an integrative shrinkage (iSHRI) estimator based on the SA estimator, which aims to improve estimation efficiency in terms of mean squared error (MSE) that accounts for the bias-variance tradeoff. Through simulation, we find that iSHRI has better performance in general when compared with existing methods, in terms of bias, MSE, type I error and confidence interval coverage. Data examples of rosiglitazone meta-analysis are provided as well, where iSHRI yields competitive results.
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An integrative shrinkage estimator for random-effects meta-analysis of rare binary events
Elsevier, 2018Co-Authors: Ou Bai, Xinlei WangAbstract:Meta-analysis has been a powerful tool for inferring the treatment effect between two experimental conditions from multiple studies of rare binary events. Recently, under a random-effects (RE) model, Bhaumik et al. developed a simple average (SA) estimator and showed that with the continuity correction factor 0.5, the SA estimator was the least biased among a set of commonly used estimators. In this paper, under various RE models that allow for treatment groups with equal and Unequal Variability (in either direction), we develop an integrative shrinkage (iSHRI) estimator based on the SA estimator, which aims to improve estimation efficiency in terms of mean squared error (MSE) that accounts for the bias-variance tradeoff. Through simulation, we find that iSHRI has better performance in general when compared with existing methods, in terms of bias, MSE, type I error and confidence interval coverage. Data examples of rosiglitazone meta-analysis are provided as well, where iSHRI yields competitive results. Keywords: Bias, Estimation efficiency, Log odds ratio, Mean squared error, Sparse binary dat
Gail Mckoon - One of the best experts on this subject based on the ideXlab platform.
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Evaluating the Unequal-variance and dual-process explanations of zROC slopes with response time data and the diffusion model.
Cognitive Psychology, 2012Co-Authors: Jeffrey J. Starns, Roger Ratcliff, Gail MckoonAbstract:We tested two explanations for why the slope of the z-transformed receiver operating characteristic (zROC) is less than 1 in recognition memory: the Unequal-variance account (target evidence is more variable than lure evidence) and the dual-process account (responding reflects both a continuous familiarity process and a threshold recollection process). These accounts are typically implemented in signal detection models that do not make predictions for response time (RT) data. We tested them using RT data and the diffusion model. Participants completed multiple study/test blocks of an "old"/"new" recognition task with the proportion of targets and the test varying from block to block (.21, .32, .50, .68, or .79 targets). The same participants completed sessions with both speed-emphasis and accuracy-emphasis instructions. zROC slopes were below one for both speed and accuracy sessions, and they were slightly lower for speed. The extremely fast pace of the speed sessions (mean RT=526) should have severely limited the role of the slower recollection process relative to the fast familiarity process. Thus, the slope results are not consistent with the idea that recollection is responsible for slopes below 1. The diffusion model was able to match the empirical zROC slopes and RT distributions when between-trial Variability in memory evidence was greater for targets than for lures, but missed the zROC slopes when target and lure Variability were constrained to be equal. Therefore, Unequal Variability in continuous evidence is supported by RT modeling in addition to signal detection modeling. Finally, we found that a two-choice version of the RTCON model could not accommodate the RT distributions as successfully as the diffusion model.
Jeffrey J. Starns - One of the best experts on this subject based on the ideXlab platform.
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Evaluating the Unequal-variance and dual-process explanations of zROC slopes with response time data and the diffusion model.
Cognitive Psychology, 2012Co-Authors: Jeffrey J. Starns, Roger Ratcliff, Gail MckoonAbstract:We tested two explanations for why the slope of the z-transformed receiver operating characteristic (zROC) is less than 1 in recognition memory: the Unequal-variance account (target evidence is more variable than lure evidence) and the dual-process account (responding reflects both a continuous familiarity process and a threshold recollection process). These accounts are typically implemented in signal detection models that do not make predictions for response time (RT) data. We tested them using RT data and the diffusion model. Participants completed multiple study/test blocks of an "old"/"new" recognition task with the proportion of targets and the test varying from block to block (.21, .32, .50, .68, or .79 targets). The same participants completed sessions with both speed-emphasis and accuracy-emphasis instructions. zROC slopes were below one for both speed and accuracy sessions, and they were slightly lower for speed. The extremely fast pace of the speed sessions (mean RT=526) should have severely limited the role of the slower recollection process relative to the fast familiarity process. Thus, the slope results are not consistent with the idea that recollection is responsible for slopes below 1. The diffusion model was able to match the empirical zROC slopes and RT distributions when between-trial Variability in memory evidence was greater for targets than for lures, but missed the zROC slopes when target and lure Variability were constrained to be equal. Therefore, Unequal Variability in continuous evidence is supported by RT modeling in addition to signal detection modeling. Finally, we found that a two-choice version of the RTCON model could not accommodate the RT distributions as successfully as the diffusion model.
Roger Ratcliff - One of the best experts on this subject based on the ideXlab platform.
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Evaluating the Unequal-variance and dual-process explanations of zROC slopes with response time data and the diffusion model.
Cognitive Psychology, 2012Co-Authors: Jeffrey J. Starns, Roger Ratcliff, Gail MckoonAbstract:We tested two explanations for why the slope of the z-transformed receiver operating characteristic (zROC) is less than 1 in recognition memory: the Unequal-variance account (target evidence is more variable than lure evidence) and the dual-process account (responding reflects both a continuous familiarity process and a threshold recollection process). These accounts are typically implemented in signal detection models that do not make predictions for response time (RT) data. We tested them using RT data and the diffusion model. Participants completed multiple study/test blocks of an "old"/"new" recognition task with the proportion of targets and the test varying from block to block (.21, .32, .50, .68, or .79 targets). The same participants completed sessions with both speed-emphasis and accuracy-emphasis instructions. zROC slopes were below one for both speed and accuracy sessions, and they were slightly lower for speed. The extremely fast pace of the speed sessions (mean RT=526) should have severely limited the role of the slower recollection process relative to the fast familiarity process. Thus, the slope results are not consistent with the idea that recollection is responsible for slopes below 1. The diffusion model was able to match the empirical zROC slopes and RT distributions when between-trial Variability in memory evidence was greater for targets than for lures, but missed the zROC slopes when target and lure Variability were constrained to be equal. Therefore, Unequal Variability in continuous evidence is supported by RT modeling in addition to signal detection modeling. Finally, we found that a two-choice version of the RTCON model could not accommodate the RT distributions as successfully as the diffusion model.
