The Experts below are selected from a list of 111966 Experts worldwide ranked by ideXlab platform

Arne C Bathke - One of the best experts on this subject based on the ideXlab platform.

  • sample size calculation and blinded recalculation for Analysis of Covariance models with multiple random covariates
    Journal of Biopharmaceutical Statistics, 2020
    Co-Authors: Georg Zimmermann, Meinhard Kieser, Arne C Bathke
    Abstract:

    When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates is usually recommended. For this purpose, the Analysis of Covariance is frequently used, and recently several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and pitfalls. Therefore, we propose a method, which allows for blinded re-estimation of the sample size during the course of the trial. Simulations confirm that the proposed method provides reliable results in many practically relevant situations, and applicability is illustrated by a real-life data example.

  • small sample performance and underlying assumptions of a bootstrap based inference method for a general Analysis of Covariance model with possibly heteroskedastic and nonnormal errors
    Statistical Methods in Medical Research, 2019
    Co-Authors: Georg Zimmermann, Arne C Bathke, Markus Pauly
    Abstract:

    It is well known that the standard F test is severely affected by heteroskedasticity in unbalanced Analysis of Covariance models. Currently available potential remedies for such a scenario are based on heteroskedasticity-consistent Covariance matrix estimation (HCCME). However, the HCCME approach tends to be liberal in small samples. Therefore, in the present paper, we propose a combination of HCCME and a wild bootstrap technique, with the aim of improving the small-sample performance. We precisely state a set of assumptions for the general Analysis of Covariance model and discuss their practical interpretation in detail, since this issue may have been somewhat neglected in applied research so far. We prove that these assumptions are sufficient to ensure the asymptotic validity of the combined HCCME-wild bootstrap Analysis of Covariance. The results of our simulation study indicate that our proposed test remedies the problems of the Analysis of Covariance F test and its heteroskedasticity-consistent alternatives in small to moderate sample size scenarios. Our test only requires very mild conditions, thus being applicable in a broad range of real-life settings, as illustrated by the detailed discussion of a dataset from preclinical research on spinal cord injury. Our proposed method is ready-to-use and allows for valid hypothesis testing in frequently encountered settings (e.g., comparing group means while adjusting for baseline measurements in a randomized controlled clinical trial).

  • sample size calculation and blinded recalculation for Analysis of Covariance models with multiple random covariates
    arXiv: Methodology, 2018
    Co-Authors: Georg Zimmermann, Meinhard Kieser, Arne C Bathke
    Abstract:

    When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates (e.g., baseline measurements) is usually recommended, in order to reduce bias and increase power. For this purpose, the Analysis of Covariance (ANCOVA) is frequently used, and recently, several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and surprising pitfalls, which have not been recognized so far. Moreover, since a considerable number of parameters have to be specified in advance, the risk of making erroneous initial assumptions, leading to substantially over- or underpowered studies, is increased. Therefore, we propose a method, which allows for re-estimating the sample size at a prespecified time point during the course of the trial. Extensive simulations for a broad range of settings, including unbalanced designs, confirm that the proposed method provides reliable results in many practically relevant situations. An advantage of the reassessment procedure is that it does not require unblinding of the data. In order to facilitate the application of the proposed method, we provide some R code and discuss a real-life data example.

  • small sample performance and underlying assumptions of a bootstrap based inference method for a general Analysis of Covariance model with possibly heteroskedastic and nonnormal errors
    arXiv: Methodology, 2017
    Co-Authors: Georg Zimmermann, Arne C Bathke, Markus Pauly
    Abstract:

    It is well known that the standard F test is severely affected by heteroskedasticity in unbalanced Analysis of Covariance (ANCOVA) models. Currently available potential remedies for such a scenario are based on heteroskedasticity-consistent Covariance matrix estimation (HCCME). However, the HCCME approach tends to be liberal in small samples. Therefore, in the present manuscript, we propose a combination of HCCME and a wild bootstrap technique, with the aim of improving the small-sample performance. We precisely state a set of assumptions for the general ANCOVA model and discuss their practical interpretation in detail, since this issue may have been somewhat neglected in applied research so far. We prove that these assumptions are sufficient to ensure the asymptotic validity of the combined HCCME-wild bootstrap ANCOVA. The results of our simulation study indicate that our proposed test remedies the problems of the ANCOVA F test and its heteroskedasticity-consistent alternatives in small to moderate sample size scenarios. Our test only requires very mild conditions, thus being applicable in a broad range of real-life settings, as illustrated by the detailed discussion of a dataset from preclinical research on spinal cord injury. Our proposed method is ready-to-use and allows for valid hypothesis testing in frequently encountered settings (e.g., comparing group means while adjusting for baseline measurements in a randomized controlled clinical trial).

Georg Zimmermann - One of the best experts on this subject based on the ideXlab platform.

  • sample size calculation and blinded recalculation for Analysis of Covariance models with multiple random covariates
    Journal of Biopharmaceutical Statistics, 2020
    Co-Authors: Georg Zimmermann, Meinhard Kieser, Arne C Bathke
    Abstract:

    When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates is usually recommended. For this purpose, the Analysis of Covariance is frequently used, and recently several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and pitfalls. Therefore, we propose a method, which allows for blinded re-estimation of the sample size during the course of the trial. Simulations confirm that the proposed method provides reliable results in many practically relevant situations, and applicability is illustrated by a real-life data example.

  • small sample performance and underlying assumptions of a bootstrap based inference method for a general Analysis of Covariance model with possibly heteroskedastic and nonnormal errors
    Statistical Methods in Medical Research, 2019
    Co-Authors: Georg Zimmermann, Arne C Bathke, Markus Pauly
    Abstract:

    It is well known that the standard F test is severely affected by heteroskedasticity in unbalanced Analysis of Covariance models. Currently available potential remedies for such a scenario are based on heteroskedasticity-consistent Covariance matrix estimation (HCCME). However, the HCCME approach tends to be liberal in small samples. Therefore, in the present paper, we propose a combination of HCCME and a wild bootstrap technique, with the aim of improving the small-sample performance. We precisely state a set of assumptions for the general Analysis of Covariance model and discuss their practical interpretation in detail, since this issue may have been somewhat neglected in applied research so far. We prove that these assumptions are sufficient to ensure the asymptotic validity of the combined HCCME-wild bootstrap Analysis of Covariance. The results of our simulation study indicate that our proposed test remedies the problems of the Analysis of Covariance F test and its heteroskedasticity-consistent alternatives in small to moderate sample size scenarios. Our test only requires very mild conditions, thus being applicable in a broad range of real-life settings, as illustrated by the detailed discussion of a dataset from preclinical research on spinal cord injury. Our proposed method is ready-to-use and allows for valid hypothesis testing in frequently encountered settings (e.g., comparing group means while adjusting for baseline measurements in a randomized controlled clinical trial).

  • sample size calculation and blinded recalculation for Analysis of Covariance models with multiple random covariates
    arXiv: Methodology, 2018
    Co-Authors: Georg Zimmermann, Meinhard Kieser, Arne C Bathke
    Abstract:

    When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates (e.g., baseline measurements) is usually recommended, in order to reduce bias and increase power. For this purpose, the Analysis of Covariance (ANCOVA) is frequently used, and recently, several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and surprising pitfalls, which have not been recognized so far. Moreover, since a considerable number of parameters have to be specified in advance, the risk of making erroneous initial assumptions, leading to substantially over- or underpowered studies, is increased. Therefore, we propose a method, which allows for re-estimating the sample size at a prespecified time point during the course of the trial. Extensive simulations for a broad range of settings, including unbalanced designs, confirm that the proposed method provides reliable results in many practically relevant situations. An advantage of the reassessment procedure is that it does not require unblinding of the data. In order to facilitate the application of the proposed method, we provide some R code and discuss a real-life data example.

  • small sample performance and underlying assumptions of a bootstrap based inference method for a general Analysis of Covariance model with possibly heteroskedastic and nonnormal errors
    arXiv: Methodology, 2017
    Co-Authors: Georg Zimmermann, Arne C Bathke, Markus Pauly
    Abstract:

    It is well known that the standard F test is severely affected by heteroskedasticity in unbalanced Analysis of Covariance (ANCOVA) models. Currently available potential remedies for such a scenario are based on heteroskedasticity-consistent Covariance matrix estimation (HCCME). However, the HCCME approach tends to be liberal in small samples. Therefore, in the present manuscript, we propose a combination of HCCME and a wild bootstrap technique, with the aim of improving the small-sample performance. We precisely state a set of assumptions for the general ANCOVA model and discuss their practical interpretation in detail, since this issue may have been somewhat neglected in applied research so far. We prove that these assumptions are sufficient to ensure the asymptotic validity of the combined HCCME-wild bootstrap ANCOVA. The results of our simulation study indicate that our proposed test remedies the problems of the ANCOVA F test and its heteroskedasticity-consistent alternatives in small to moderate sample size scenarios. Our test only requires very mild conditions, thus being applicable in a broad range of real-life settings, as illustrated by the detailed discussion of a dataset from preclinical research on spinal cord injury. Our proposed method is ready-to-use and allows for valid hypothesis testing in frequently encountered settings (e.g., comparing group means while adjusting for baseline measurements in a randomized controlled clinical trial).

George F Borm - One of the best experts on this subject based on the ideXlab platform.

  • a simple sample size formula for Analysis of Covariance in cluster randomized trials
    Statistics in Medicine, 2012
    Co-Authors: Steven Teerenstra, Sandra Eldridge, Maud Graff, Esther De Hoop, George F Borm
    Abstract:

    For cluster randomized trials with a continuous outcome, the sample size is often calculated as if an Analysis of the outcomes at the end of the treatment period (follow-up scores) would be performed. However, often a baseline measurement of the outcome is available or feasible to obtain. An Analysis of Covariance (ANCOVA) using both the baseline and follow-up score of the outcome will then have more power. We calculate the efficiency of an ANCOVA Analysis using the baseline scores compared with an Analysis on follow-up scores only. The sample size for such an ANCOVA Analysis is a factor r(2) smaller, where r is the correlation of the cluster means between baseline and follow-up. This correlation can be expressed in clinically interpretable parameters: the correlation between baseline and follow-up of subjects (subject autocorrelation) and that of clusters (cluster autocorrelation). Because of this, subject matter knowledge can be used to provide (range of) plausible values for these correlations, when estimates from previous studies are lacking. Depending on how large the subject and cluster autocorrelations are, Analysis of Covariance can substantially reduce the number of clusters needed. Copyright (c) 2012 John Wiley & Sons, Ltd.

  • a simple sample size formula for Analysis of Covariance in randomized clinical trials
    Journal of Clinical Epidemiology, 2007
    Co-Authors: George F Borm, Jaap Fransen, Wim A J G Lemmens
    Abstract:

    OBJECTIVE: Randomized clinical trials that compare two treatments on a continuous outcome can be analyzed using Analysis of Covariance (ANCOVA) or a t-test approach. We present a method for the sample size calculation when ANCOVA is used. STUDY DESIGN AND SETTING: We derived an approximate sample size formula. Simulations were used to verify the accuracy of the formula and to improve the approximation for small trials. The sample size calculations are illustrated in a clinical trial in rheumatoid arthritis. RESULTS: If the correlation between the outcome measured at baseline and at follow-up is rho, ANCOVA comparing groups of (1-rho(2))n subjects has the same power as t-test comparing groups of n subjects. When on the same data, ANCOVA is used instead of t-test, the precision of the treatment estimate is increased, and the length of the confidence interval is reduced by a factor 1-rho(2). CONCLUSION: ANCOVA may considerably reduce the number of patients required for a trial.

Wim A J G Lemmens - One of the best experts on this subject based on the ideXlab platform.

  • a simple sample size formula for Analysis of Covariance in randomized clinical trials
    Journal of Clinical Epidemiology, 2007
    Co-Authors: George F Borm, Jaap Fransen, Wim A J G Lemmens
    Abstract:

    OBJECTIVE: Randomized clinical trials that compare two treatments on a continuous outcome can be analyzed using Analysis of Covariance (ANCOVA) or a t-test approach. We present a method for the sample size calculation when ANCOVA is used. STUDY DESIGN AND SETTING: We derived an approximate sample size formula. Simulations were used to verify the accuracy of the formula and to improve the approximation for small trials. The sample size calculations are illustrated in a clinical trial in rheumatoid arthritis. RESULTS: If the correlation between the outcome measured at baseline and at follow-up is rho, ANCOVA comparing groups of (1-rho(2))n subjects has the same power as t-test comparing groups of n subjects. When on the same data, ANCOVA is used instead of t-test, the precision of the treatment estimate is increased, and the length of the confidence interval is reduced by a factor 1-rho(2). CONCLUSION: ANCOVA may considerably reduce the number of patients required for a trial.

Jaap Fransen - One of the best experts on this subject based on the ideXlab platform.

  • a simple sample size formula for Analysis of Covariance in randomized clinical trials
    Journal of Clinical Epidemiology, 2007
    Co-Authors: George F Borm, Jaap Fransen, Wim A J G Lemmens
    Abstract:

    OBJECTIVE: Randomized clinical trials that compare two treatments on a continuous outcome can be analyzed using Analysis of Covariance (ANCOVA) or a t-test approach. We present a method for the sample size calculation when ANCOVA is used. STUDY DESIGN AND SETTING: We derived an approximate sample size formula. Simulations were used to verify the accuracy of the formula and to improve the approximation for small trials. The sample size calculations are illustrated in a clinical trial in rheumatoid arthritis. RESULTS: If the correlation between the outcome measured at baseline and at follow-up is rho, ANCOVA comparing groups of (1-rho(2))n subjects has the same power as t-test comparing groups of n subjects. When on the same data, ANCOVA is used instead of t-test, the precision of the treatment estimate is increased, and the length of the confidence interval is reduced by a factor 1-rho(2). CONCLUSION: ANCOVA may considerably reduce the number of patients required for a trial.