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Suejane Wang - One of the best experts on this subject based on the ideXlab platform.
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modified toxicity probability interval design a safer and more reliable method than the 3 3 design for practical phase i trials
Journal of Clinical Oncology, 2013Co-Authors: Yuan Ji, Suejane WangAbstract:The 3!3 design is the most common choice among clinicians for phase I dose-escalation oncology trials. In recent reviews, more than 95% of phase I trials have been based on the 3!3 design. Given that it is intuitive and its implementation does not require a computer program, clinicians can conduct 3!3 dose escalations in practice with virtually no logistic cost, and trial protocols based on the 3!3 design pass institutional review board and biostatistics reviews quickly. However, the performance of the 3!3 design has rarely been compared with model-based designs in simulation studies with Matched Sample sizes. In the vast majority of statistical literature, the 3!3 design has been shown to be inferior in identifying true maximum-tolerated doses (MTDs), although the Sample size required by the 3!3 design is often orders-of-magnitude smaller than model-based designs. In this article, through comparative simulation studies with Matched Sample sizes, we demonstrate that the 3!3 design has higher risks of exposing patients to toxic doses above the MTD than the modified toxicity probability interval (mTPI) design, a newly developed adaptive method. In addition, compared with the mTPI design, the 3!3 design does not yield higher probabilities in identifying the correct MTD, even when the Sample size is Matched. Given that the mTPI design is equally transparent, costless to implement with free software, and more flexible in practical situations, we highly encourage its adoption in early dose-escalation studies whenever the 3!3 design is also considered. We provide free software to allow direct comparisons of the 3!3 design with other model-based designs in simulation studies with Matched Sample sizes. J Clin Oncol 31:1785-1791. © 2013 by American Society of Clinical Oncology
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modified toxicity probability interval design a safer and more reliable method than the 3 3 design for practical phase i trials
Journal of Clinical Oncology, 2013Co-Authors: Suejane WangAbstract:The 3 + 3 design is the most common choice among clinicians for phase I dose-escalation oncology trials. In recent reviews, more than 95% of phase I trials have been based on the 3 + 3 design. Given that it is intuitive and its implementation does not require a computer program, clinicians can conduct 3 + 3 dose escalations in practice with virtually no logistic cost, and trial protocols based on the 3 + 3 design pass institutional review board and biostatistics reviews quickly. However, the performance of the 3 + 3 design has rarely been compared with model-based designs in simulation studies with Matched Sample sizes. In the vast majority of statistical literature, the 3 + 3 design has been shown to be inferior in identifying true maximum-tolerated doses (MTDs), although the Sample size required by the 3 + 3 design is often orders-of-magnitude smaller than model-based designs. In this article, through comparative simulation studies with Matched Sample sizes, we demonstrate that the 3 + 3 design has higher risks of exposing patients to toxic doses above the MTD than the modified toxicity probability interval (mTPI) design, a newly developed adaptive method. In addition, compared with the mTPI design, the 3 + 3 design does not yield higher probabilities in identifying the correct MTD, even when the Sample size is Matched. Given that the mTPI design is equally transparent, costless to implement with free software, and more flexible in practical situations, we highly encourage its adoption in early dose-escalation studies whenever the 3 + 3 design is also considered. We provide free software to allow direct comparisons of the 3 + 3 design with other model-based designs in simulation studies with Matched Sample sizes.
Peter C Austin - One of the best experts on this subject based on the ideXlab platform.
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propensity score matching with competing risks in survival analysis
Statistics in Medicine, 2019Co-Authors: Peter C Austin, Jason P FineAbstract:Propensity-score matching is a popular analytic method to remove the effects of confounding due to measured baseline covariates when using observational data to estimate the effects of treatment. Time-to-event outcomes are common in medical research. Competing risks are outcomes whose occurrence precludes the occurrence of the primary time-to-event outcome of interest. All non-fatal outcomes and all cause-specific mortality outcomes are potentially subject to competing risks. There is a paucity of guidance on the conduct of propensity-score matching in the presence of competing risks. We describe how both relative and absolute measures of treatment effect can be obtained when using propensity-score matching with competing risks data. Estimates of the relative effect of treatment can be obtained by using cause-specific hazard models in the Matched Sample. Estimates of absolute treatment effects can be obtained by comparing cumulative incidence functions (CIFs) between Matched treated and Matched control subjects. We conducted a series of Monte Carlo simulations to compare the empirical type I error rate of different statistical methods for testing the equality of CIFs estimated in the Matched Sample. We also examined the performance of different methods to estimate the marginal subdistribution hazard ratio. We recommend that a marginal subdistribution hazard model that accounts for the within-pair clustering of outcomes be used to test the equality of CIFs and to estimate subdistribution hazard ratios. We illustrate the described methods by using data on patients discharged from hospital with acute myocardial infarction to estimate the effect of discharge prescribing of statins on cardiovascular death.
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the use of bootstrapping when using propensity score matching without replacement a simulation study
Statistics in Medicine, 2014Co-Authors: Peter C Austin, Dylan S SmallAbstract:Propensity-score matching is frequently used to estimate the effect of treatments, exposures, and interventions when using observational data. An important issue when using propensity-score matching is how to estimate the standard error of the estimated treatment effect. Accurate variance estimation permits construction of confidence intervals that have the advertised coverage rates and tests of statistical significance that have the correct type I error rates. There is disagreement in the literature as to how standard errors should be estimated. The bootstrap is a commonly used resampling method that permits estimation of the sampling variability of estimated parameters. Bootstrap methods are rarely used in conjunction with propensity-score matching. We propose two different bootstrap methods for use when using propensity-score matching without replacementand examined their performance with a series of Monte Carlo simulations. The first method involved drawing bootstrap Samples from the Matched pairs in the propensity-score-Matched Sample. The second method involved drawing bootstrap Samples from the original Sample and estimating the propensity score separately in each bootstrap Sample and creating a Matched Sample within each of these bootstrap Samples. The former approach was found to result in estimates of the standard error that were closer to the empirical standard deviation of the sampling distribution of estimated effects.
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type i error rates coverage of confidence intervals and variance estimation in propensity score Matched analyses
The International Journal of Biostatistics, 2009Co-Authors: Peter C AustinAbstract:Propensity-score matching is frequently used in the medical literature to reduce or eliminate the effect of treatment selection bias when estimating the effect of treatments or exposures on outcomes using observational data. In propensity-score matching, pairs of treated and untreated subjects with similar propensity scores are formed. Recent systematic reviews of the use of propensity-score matching found that the large majority of researchers ignore the Matched nature of the propensity-score Matched Sample when estimating the statistical significance of the treatment effect. We conducted a series of Monte Carlo simulations to examine the impact of ignoring the Matched nature of the propensity-score Matched Sample on Type I error rates, coverage of confidence intervals, and variance estimation of the treatment effect. We examined estimating differences in means, relative risks, odds ratios, rate ratios from Poisson models, and hazard ratios from Cox regression models. We demonstrated that accounting for the Matched nature of the propensity-score Matched Sample tended to result in type I error rates that were closer to the advertised level compared to when matching was not incorporated into the analyses. Similarly, accounting for the Matched nature of the Sample tended to result in confidence intervals with coverage rates that were closer to the nominal level, compared to when matching was not taken into account. Finally, accounting for the Matched nature of the Sample resulted in estimates of standard error that more closely reflected the sampling variability of the treatment effect compared to when matching was not taken into account.
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a critical appraisal of propensity score matching in the medical literature between 1996 and 2003
Statistics in Medicine, 2008Co-Authors: Peter C AustinAbstract:Propensity-score methods are increasingly being used to reduce the impact of treatment-selection bias in the estimation of treatment effects using observational data. Commonly used propensity-score methods include covariate adjustment using the propensity score, stratification on the propensity score, and propensity-score matching. Empirical and theoretical research has demonstrated that matching on the propensity score eliminates a greater proportion of baseline differences between treated and untreated subjects than does stratification on the propensity score. However, the analysis of propensity-score-Matched Samples requires statistical methods appropriate for Matched-pairs data. We critically evaluated 47 articles that were published between 1996 and 2003 in the medical literature and that employed propensity-score matching. We found that only two of the articles reported the balance of baseline characteristics between treated and untreated subjects in the Matched Sample and used correct statistical methods to assess the degree of imbalance. Thirteen (28 per cent) of the articles explicitly used statistical methods appropriate for the analysis of Matched data when estimating the treatment effect and its statistical significance. Common errors included using the log-rank test to compare Kaplan-Meier survival curves in the Matched Sample, using Cox regression, logistic regression, chi-squared tests, t-tests, and Wilcoxon rank sum tests in the Matched Sample, thereby failing to account for the Matched nature of the data. We provide guidelines for the analysis and reporting of studies that employ propensity-score matching.
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propensity score matching in the cardiovascular surgery literature from 2004 to 2006 a systematic review and suggestions for improvement
The Journal of Thoracic and Cardiovascular Surgery, 2007Co-Authors: Peter C AustinAbstract:Objective I conducted a systematic review of the use of propensity score matching in the cardiovascular surgery literature. I examined the adequacy of reporting and whether appropriate statistical methods were used. Methods I examined 60 articles published in the Annals of Thoracic Surgery , European Journal of Cardio-thoracic Surgery , Journal of Cardiovascular Surgery , and the Journal of Thoracic and Cardiovascular Surgery between January 1, 2004, and December 31, 2006. Results Thirty-one of the 60 studies did not provide adequate information on how the propensity score–Matched pairs were formed. Eleven (18%) of studies did not report on whether matching on the propensity score balanced baseline characteristics between treated and untreated subjects in the Matched Sample. No studies used appropriate methods to compare baseline characteristics between treated and untreated subjects in the propensity score–Matched Sample. Eight (13%) of the 60 studies explicitly used statistical methods appropriate for the analysis of Matched data when estimating the effect of treatment on the outcomes. Two studies used appropriate methods for some outcomes, but not for all outcomes. Thirty-nine (65%) studies explicitly used statistical methods that were inappropriate for Matched-pairs data when estimating the effect of treatment on outcomes. Eleven studies did not report the statistical tests that were used to assess the statistical significance of the treatment effect. Conclusions Analysis of propensity score–Matched Samples tended to be poor in the cardiovascular surgery literature. Most statistical analyses ignored the Matched nature of the Sample. I provide suggestions for improving the reporting and analysis of studies that use propensity score matching.
George Serafeim - One of the best experts on this subject based on the ideXlab platform.
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the impact of corporate sustainability on organizational processes and performance
Social Science Research Network, 2014Co-Authors: Robert G. Eccles, Ioannis Ioannou, George SerafeimAbstract:We investigate the effect of a corporate culture of sustainability on multiple facets of corporate behavior and performance outcomes. Using a Matched Sample of 180 companies, we find that corporations that voluntarily adopted environmental and social policies many years ago – termed as High Sustainability companies – exhibit fundamentally different characteristics from a Matched Sample of firms that adopted almost none of these policies – termed as Low Sustainability companies. In particular, we find that the boards of directors of these companies are more likely to be responsible for sustainability and top executive incentives are more likely to be a function of sustainability metrics. Moreover, they are more likely to have organized procedures for stakeholder engagement, to be more long-term oriented, and to exhibit more measurement and disclosure of nonfinancial information. Finally, we provide evidence that High Sustainability companies significantly outperform their counterparts over the long-term, both in terms of stock market and accounting performance. The outperformance is stronger in sectors where the customers are individual consumers instead of companies, companies compete on the basis of brands and reputations, and products significantly depend upon extracting large amounts of natural resources.
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the impact of corporate sustainability on organizational processes and performance
Management Science, 2014Co-Authors: Robert G. Eccles, Ioannis Ioannou, George SerafeimAbstract:We investigate the effect of corporate sustainability on organizational processes and performance. Using a Matched Sample of 180 U.S. companies, we find that corporations that voluntarily adopted sustainability policies by 1993-termed as high sustainability companies-exhibit by 2009 distinct organizational processes compared to a Matched Sample of companies that adopted almost none of these policies-termed as low sustainability companies. The boards of directors of high sustainability companies are more likely to be formally responsible for sustainability, and top executive compensation incentives are more likely to be a function of sustainability metrics. High sustainability companies are more likely to have established processes for stakeholder engagement, to be more long-term oriented, and to exhibit higher measurement and disclosure of nonfinancial information. Finally, high sustainability companies significantly outperform their counterparts over the long term, both in terms of stock market and accounting performance. This paper was accepted by Bruno Cassiman, business strategy.
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the impact of corporate sustainability on organizational processes and performance
National Bureau of Economic Research, 2012Co-Authors: Robert G. Eccles, Ioannis Ioannou, George SerafeimAbstract:We investigate the effect of corporate sustainability on organizational processes and performance. Using a Matched Sample of 180 US companies, we find that corporations that voluntarily adopted sustainability policies by 1993 - termed as High Sustainability companies - exhibit by 2009 distinct organizational processes compared to a Matched Sample of companies that adopted almost none of these policies - termed as Low Sustainability companies. The boards of directors of High Sustainability companies are more likely to be formally responsible for sustainability and top executive compensation incentives are more likely to be a function of sustainability metrics. High Sustainability companies are more likely to have established processes for stakeholder engagement, to be more long-term oriented, and to exhibit higher measurement and disclosure of nonfinancial information. Finally, High Sustainability companies significantly outperform their counterparts over the long-term, both in terms of stock market and accounting performance.
Suzanne Zivnuska - One of the best experts on this subject based on the ideXlab platform.
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interactive effects of impression management and organizational politics on job performance
Journal of Organizational Behavior, 2004Co-Authors: Suzanne Zivnuska, Michele K Kacma, Daw S Carlso, Virginia K AttoAbstract:The purpose of this research was to explore the interactive effect of organizational politics and impression management on supervisor ratings of employee performance. We hypothesized that the negative relationship between organizational politics and supervisor-rated performance is weaker among employees who are high in impression management than among those low in impression management. Data were collected from a Matched Sample of 112 white-collar employees and their supervisors. Results indicated that the interaction of organizational politics and impression management explained a significant incremental amount of variance in supervisor ratings of employee performance. These findings demonstrated that the extent to which an individual engaged in impression management in a non-political atmosphere may have been a key component to receiving favorable performance ratings. Copyright © 2004 John Wiley & Sons, Ltd.
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interactive effects of personality and organizational politics on contextual performance
Journal of Organizational Behavior, 2002Co-Authors: L A Witt, Dawn S. Carlson, Michele K Kacmar, Suzanne ZivnuskaAbstract:The authors explored the process of evaluating contextual performance in the context of a politically charged atmosphere. They hypothesized that the negative relationship between perceptions of organizational politics and contextual performance is weaker among workers high in three of the Big Five model of personality dimensions—agreeableness, extraversion, and conscientiousness. Data were collected from a Matched Sample of 540 supervisors and subordinates employed in the private sector. Results indicated that the interaction of politics and the personality dimension of agreeableness explained a significant incremental amount of variance in the interpersonal facilitation facet of contextual performance. These findings demonstrate the need to consider both the situation and the person as antecedents of contextual performance. Copyright © 2002 John Wiley & Sons, Ltd.
Yuan Ji - One of the best experts on this subject based on the ideXlab platform.
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modified toxicity probability interval design a safer and more reliable method than the 3 3 design for practical phase i trials
Journal of Clinical Oncology, 2013Co-Authors: Yuan Ji, Suejane WangAbstract:The 3!3 design is the most common choice among clinicians for phase I dose-escalation oncology trials. In recent reviews, more than 95% of phase I trials have been based on the 3!3 design. Given that it is intuitive and its implementation does not require a computer program, clinicians can conduct 3!3 dose escalations in practice with virtually no logistic cost, and trial protocols based on the 3!3 design pass institutional review board and biostatistics reviews quickly. However, the performance of the 3!3 design has rarely been compared with model-based designs in simulation studies with Matched Sample sizes. In the vast majority of statistical literature, the 3!3 design has been shown to be inferior in identifying true maximum-tolerated doses (MTDs), although the Sample size required by the 3!3 design is often orders-of-magnitude smaller than model-based designs. In this article, through comparative simulation studies with Matched Sample sizes, we demonstrate that the 3!3 design has higher risks of exposing patients to toxic doses above the MTD than the modified toxicity probability interval (mTPI) design, a newly developed adaptive method. In addition, compared with the mTPI design, the 3!3 design does not yield higher probabilities in identifying the correct MTD, even when the Sample size is Matched. Given that the mTPI design is equally transparent, costless to implement with free software, and more flexible in practical situations, we highly encourage its adoption in early dose-escalation studies whenever the 3!3 design is also considered. We provide free software to allow direct comparisons of the 3!3 design with other model-based designs in simulation studies with Matched Sample sizes. J Clin Oncol 31:1785-1791. © 2013 by American Society of Clinical Oncology
