The Experts below are selected from a list of 207 Experts worldwide ranked by ideXlab platform
Oliver N Keene - One of the best experts on this subject based on the ideXlab platform.
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alternatives to the hazard ratio in summarizing efficacy in time to event studies an example from influenza trials
Statistics in Medicine, 2002Co-Authors: Oliver N KeeneAbstract:Estimates of the efficacy of new medicines are key to the investigation of their clinical effectiveness. The most widely recommended approach to summarizing time-to-event data from clinical trials is to use a hazard ratio. When the proportional hazards assumption is questionable, a hazard ratio depends on the length of patient follow-up. Hazard ratios do not directly translate into differences in times to events and therefore can present difficulties in interpretation. This paper describes an area where summary by hazard ratio would seem unsuitable and explores alternative Estimates of efficacy. In particular, the difference in median time to event between treatments can provide a useful and consistent measure of efficacy. Methods of calculating confidence intervals for differences in medians for censored time-to-event will be described. Accelerated failure time models provide a useful alternative approach to proportional hazards modelling. Estimates of the ratio of the median time to event between treatments are directly available from these models. One of the reasons given for summarizing time-to-event studies by a hazard ratio is to facilitate meta-analyses. The Bootstrap Estimate of standard error for difference in median in each trial can provide a method for combining results based on summary statistics. Copyright © 2002 John Wiley & Sons, Ltd.
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Alternatives to the hazard ratio in summarizing efficacy in time‐to‐event studies: an example from influenza trials
Statistics in Medicine, 2002Co-Authors: Oliver N KeeneAbstract:Estimates of the efficacy of new medicines are key to the investigation of their clinical effectiveness. The most widely recommended approach to summarizing time-to-event data from clinical trials is to use a hazard ratio. When the proportional hazards assumption is questionable, a hazard ratio depends on the length of patient follow-up. Hazard ratios do not directly translate into differences in times to events and therefore can present difficulties in interpretation. This paper describes an area where summary by hazard ratio would seem unsuitable and explores alternative Estimates of efficacy. In particular, the difference in median time to event between treatments can provide a useful and consistent measure of efficacy. Methods of calculating confidence intervals for differences in medians for censored time-to-event will be described. Accelerated failure time models provide a useful alternative approach to proportional hazards modelling. Estimates of the ratio of the median time to event between treatments are directly available from these models. One of the reasons given for summarizing time-to-event studies by a hazard ratio is to facilitate meta-analyses. The Bootstrap Estimate of standard error for difference in median in each trial can provide a method for combining results based on summary statistics. Copyright © 2002 John Wiley & Sons, Ltd.
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alternatives to the hazard ratio in summarizing efficacy in time to event studies an example from influenza trials
Statistics in Medicine, 2002Co-Authors: Oliver N KeeneAbstract:Estimates of the efficacy of new medicines are key to the investigation of their clinical effectiveness. The most widely recommended approach to summarizing time-to-event data from clinical trials is to use a hazard ratio. When the proportional hazards assumption is questionable, a hazard ratio depends on the length of patient follow-up. Hazard ratios do not directly translate into differences in times to events and therefore can present difficulties in interpretation. This paper describes an area where summary by hazard ratio would seem unsuitable and explores alternative Estimates of efficacy. In particular, the difference in median time to event between treatments can provide a useful and consistent measure of efficacy. Methods of calculating confidence intervals for differences in medians for censored time-to-event will be described. Accelerated failure time models provide a useful alternative approach to proportional hazards modelling. Estimates of the ratio of the median time to event between treatments are directly available from these models. One of the reasons given for summarizing time-to-event studies by a hazard ratio is to facilitate meta-analyses. The Bootstrap Estimate of standard error for difference in median in each trial can provide a method for combining results based on summary statistics. Copyright © 2002 John Wiley & Sons, Ltd.
Michael Falk - One of the best experts on this subject based on the ideXlab platform.
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A note on the inverse Bootstrap process for large quantiles
Stochastic Processes and their Applications, 1991Co-Authors: Michael FalkAbstract:Abstract It is shown that for q n = 1 − j(n) n with j(n) → n→∞ ∞, j(n) n → n→∞ 0 , the resulting Bootstrap Estimate of the quantile function of the sample qn-quantile has exact accuracy O p ( 1 j(n) 1 4 ) . This is a consequence of a functional central limit theorem for the corresponding inverse Bootstrap processes.
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Functional limit theorems for inverse Bootstrap processes of sample quantiles
Statistics & Probability Letters, 1991Co-Authors: Michael FalkAbstract:Abstract It is shown that the accuracy of the Bootstrap Estimate of the quantile function pertaining to the distribution of the sample q-quantile based on n independent and identically distributed observations is exactly Op(l/n 1 4 ), q ϵ (0, 1) fixed. Thi improved considerably by applying smoothed Bootstrap Estimates. Our results are formulated in terms of functional central limit theorems for the corresponding inverse Bootstrap processes.
Serge Uzan - One of the best experts on this subject based on the ideXlab platform.
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Prospective multicenter comparison of models to predict four or more involved axillary lymph nodes in patients with breast cancer with one to three metastatic sentinel lymph nodes
Journal of Clinical Oncology, 2009Co-Authors: Gabrielle Werkoff, Eric Lambaudie, Eric Fondrinier, Jean Levêque, Frédéric Marchal, Michèle Uzan, Emmanuel Barranger, François Guillemin, Emile Daraï, Serge UzanAbstract:PURPOSE: Three models have been developed to predict four or more involved axillary lymph nodes (ALNs) in patients with breast cancer with one to three involved sentinel lymph nodes (SLNs). Two scores were developed by Chagpar et al (Louisville scores excluding or including method of detection), and a nomogram was developed by Katz et al. The purpose of our investigation was to compare these models in a prospective, multicenter study. PATIENTS AND METHODS: Our study involved a cohort of 536 patients having one to three involved SLNs who underwent ALN dissection. We evaluated the area under the receiver operating characteristic curve (AUC), calibration (for the Katz nomogram only), false-negative (FN) rate, and clinical utility of the three models. Results were compared with the optimal logistic regression (OLR) model that was developed from the validation cohort. RESULTS: Among the 536 patients, 57 patients (10.6%) had >/= four involved ALNs. The AUC for the Katz nomogram was 0.84 (95% CI, 0.81 to 0.86). The Louisville score excluding method of detection was 0.75 (95% CI, 0.72 to 0.78). The Louisville score including method of detection was 0.77 (95% CI, 0.74 to 0.79). The FN rates were 2.5% (eight of 321 patients), 1.8% (two of 109 patients), and 0% (zero of 27 patients) for the Katz nomogram and the Louisville scores excluding and including method of detection, respectively. The Katz nomogram was well calibrated. Optimism-corrected Bootstrap Estimate AUC of the OLR model was 0.86. Using this result as a reasonable target for an external model, the performance of the Katz nomogram was remarkable. CONCLUSION: We validated the three models for their use in clinical practice. The Katz nomogram outperformed the two other models.
Stephen M. S. Lee - One of the best experts on this subject based on the ideXlab platform.
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Estimation of the Distribution Function of a Standardized Statistic
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 1997Co-Authors: Stephen M. S. Lee, G. Alastair YoungAbstract:For estimating the distribution of a standardized statistic, the Bootstrap Estimate is known to be local asymptotic minimax. Various computational techniques have been developed to improve on the simulation efficiency of uniform resampling, the standard Monte Carlo approach to approximating the Bootstrap Estimate. Two new approaches are proposed which give accurate yet simple approximations to the Bootstrap Estimate. The second of the approaches even improves the convergence rate of the simulation error. A simulation study examines the performance of these two approaches in comparison with other modified Bootstrap Estimates.
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Optimal choice between parametric and non-parametric Bootstrap Estimates
Mathematical Proceedings of the Cambridge Philosophical Society, 1994Co-Authors: Stephen M. S. LeeAbstract:A parametric Bootstrap Estimate (PB) may be more accurate than its nonparametric version (NB) if the parametric model upon which it is based is, at least approximately, correct. Construction of an optimal estimator based on both PB and NB is pursued with the aim of minimizing the mean squared error. Our approach is to pick an empirical Estimate of the optimal tuning parameter ee[0,1] which minimizes the mean square error of eNB + (1 — e) PB. The resulting hybrid estimator is shown to be more reliable than either PB or NB uniformly over a rich class of distributions. Theoretical asymptotic results show that the asymptotic error of this hybrid estimator is quite close in distribution to the smaller of the errors of PB and NB. All these errors typically have the same convergence rate of order O(n~~%). A particular example is also presented to illustrate the fact that this hybrid Estimate can indeed be strictly better than either of the pure Bootstrap Estimates in terms of minimizing mean squared error. Two simulation studies were conducted to verify the theoretical results and demonstrate the good practical performance of the hybrid method.
Gabrielle Werkoff - One of the best experts on this subject based on the ideXlab platform.
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Prospective multicenter comparison of models to predict four or more involved axillary lymph nodes in patients with breast cancer with one to three metastatic sentinel lymph nodes
Journal of Clinical Oncology, 2009Co-Authors: Gabrielle Werkoff, Eric Lambaudie, Eric Fondrinier, Jean Levêque, Frédéric Marchal, Michèle Uzan, Emmanuel Barranger, François Guillemin, Emile Daraï, Serge UzanAbstract:PURPOSE: Three models have been developed to predict four or more involved axillary lymph nodes (ALNs) in patients with breast cancer with one to three involved sentinel lymph nodes (SLNs). Two scores were developed by Chagpar et al (Louisville scores excluding or including method of detection), and a nomogram was developed by Katz et al. The purpose of our investigation was to compare these models in a prospective, multicenter study. PATIENTS AND METHODS: Our study involved a cohort of 536 patients having one to three involved SLNs who underwent ALN dissection. We evaluated the area under the receiver operating characteristic curve (AUC), calibration (for the Katz nomogram only), false-negative (FN) rate, and clinical utility of the three models. Results were compared with the optimal logistic regression (OLR) model that was developed from the validation cohort. RESULTS: Among the 536 patients, 57 patients (10.6%) had >/= four involved ALNs. The AUC for the Katz nomogram was 0.84 (95% CI, 0.81 to 0.86). The Louisville score excluding method of detection was 0.75 (95% CI, 0.72 to 0.78). The Louisville score including method of detection was 0.77 (95% CI, 0.74 to 0.79). The FN rates were 2.5% (eight of 321 patients), 1.8% (two of 109 patients), and 0% (zero of 27 patients) for the Katz nomogram and the Louisville scores excluding and including method of detection, respectively. The Katz nomogram was well calibrated. Optimism-corrected Bootstrap Estimate AUC of the OLR model was 0.86. Using this result as a reasonable target for an external model, the performance of the Katz nomogram was remarkable. CONCLUSION: We validated the three models for their use in clinical practice. The Katz nomogram outperformed the two other models.