The Experts below are selected from a list of 66258 Experts worldwide ranked by ideXlab platform
Felipe Fregni - One of the best experts on this subject based on the ideXlab platform.
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Sample Size Calculation in physical medicine and rehabilitation a systematic review of reporting characteristics and results in randomized controlled trials
Archives of Physical Medicine and Rehabilitation, 2011Co-Authors: Lydia Abdul Latif, João Eduardo Daud Amadera, Daniel C. Pimentel, Thais Valéria Costa De Andrade Pimentel, Felipe FregniAbstract:OBJECTIVE: To assess systematically the reporting of Sample Size Calculation in randomized controlled trials (RCTs) in 5 leading journals in the field of physical medicine and rehabilitation (PM&R). DATA SOURCES: The data source was full reports of RCTs in 5 leading PM&R journals (Journal of Rehabilitation Medicine, Archives of Physical Medicine and Rehabilitation, American Journal of Physical Medicine and Rehabilitation, Clinical Rehabilitation, and Disability and Rehabilitation) between January and December of 1998 and 2008. Articles were identified in Medline. STUDY SELECTION: A total of 111 articles met our inclusion criteria, which include RCTs of human studies in the 5 selected journals. DATA EXTRACTION: Sample Size Calculation reporting and trial characteristics were collected for each trial by independent investigators. DATA SYNTHESIS: In 2008, 57.3% of articles reported Sample Size Calculation as compared with only 3.4% in 1998. The parameters that were commonly used were a power of 80% and alpha of 5%. Articles often failed to report effect Size or effect estimates for Sample Size Calculation. Studies reporting Sample Size Calculation were more likely to describe the main outcome and to have a Sample Size greater than 50 subjects. The study outcome (positive vs negative) was not associated with the likelihood of Sample Size reporting. Trial characteristics of the 2 periods (1998 vs 2008) were similar except that in 1998 there were more negative studies compared with 2008. CONCLUSIONS: Although Sample Size Calculation reporting has improved dramatically in 10 years and is comparable with other fields in medicine, it is still not adequate given current publication guidelines.
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Sample Size Calculation in Physical Medicine and Rehabilitation: A Systematic Review of Reporting, Characteristics, and Results in Randomized Controlled Trials
Archives of physical medicine and rehabilitation, 2011Co-Authors: Lydia Abdul Latif, João Eduardo Daud Amadera, Daniel C. Pimentel, Thais Valéria Costa De Andrade Pimentel, Felipe FregniAbstract:Abstract Abdul Latif L, Daud Amadera JE, Pimentel D, Pimentel T, Fregni F. Sample Size Calculation in physical medicine and rehabilitation: a systematic review of reporting, characteristics, and results in randomized controlled trials. Objective To assess systematically the reporting of Sample Size Calculation in randomized controlled trials (RCTs) in 5 leading journals in the field of physical medicine and rehabilitation (PM&R). Data Sources The data source was full reports of RCTs in 5 leading PM&R journals ( Journal of Rehabilitation Medicine , Archives of Physical Medicine and Rehabilitation , American Journal of Physical Medicine and Rehabilitation , Clinical Rehabilitation , and Disability and Rehabilitation ) between January and December of 1998 and 2008. Articles were identified in Medline. Study Selection A total of 111 articles met our inclusion criteria, which include RCTs of human studies in the 5 selected journals. Data Extraction Sample Size Calculation reporting and trial characteristics were collected for each trial by independent investigators. Data Synthesis In 2008, 57.3% of articles reported Sample Size Calculation as compared with only 3.4% in 1998. The parameters that were commonly used were a power of 80% and alpha of 5%. Articles often failed to report effect Size or effect estimates for Sample Size Calculation. Studies reporting Sample Size Calculation were more likely to describe the main outcome and to have a Sample Size greater than 50 subjects. The study outcome (positive vs negative) was not associated with the likelihood of Sample Size reporting. Trial characteristics of the 2 periods (1998 vs 2008) were similar except that in 1998 there were more negative studies compared with 2008. Conclusions Although Sample Size Calculation reporting has improved dramatically in 10 years and is comparable with other fields in medicine, it is still not adequate given current publication guidelines.
Lydia Abdul Latif - One of the best experts on this subject based on the ideXlab platform.
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Sample Size Calculation in physical medicine and rehabilitation a systematic review of reporting characteristics and results in randomized controlled trials
Archives of Physical Medicine and Rehabilitation, 2011Co-Authors: Lydia Abdul Latif, João Eduardo Daud Amadera, Daniel C. Pimentel, Thais Valéria Costa De Andrade Pimentel, Felipe FregniAbstract:OBJECTIVE: To assess systematically the reporting of Sample Size Calculation in randomized controlled trials (RCTs) in 5 leading journals in the field of physical medicine and rehabilitation (PM&R). DATA SOURCES: The data source was full reports of RCTs in 5 leading PM&R journals (Journal of Rehabilitation Medicine, Archives of Physical Medicine and Rehabilitation, American Journal of Physical Medicine and Rehabilitation, Clinical Rehabilitation, and Disability and Rehabilitation) between January and December of 1998 and 2008. Articles were identified in Medline. STUDY SELECTION: A total of 111 articles met our inclusion criteria, which include RCTs of human studies in the 5 selected journals. DATA EXTRACTION: Sample Size Calculation reporting and trial characteristics were collected for each trial by independent investigators. DATA SYNTHESIS: In 2008, 57.3% of articles reported Sample Size Calculation as compared with only 3.4% in 1998. The parameters that were commonly used were a power of 80% and alpha of 5%. Articles often failed to report effect Size or effect estimates for Sample Size Calculation. Studies reporting Sample Size Calculation were more likely to describe the main outcome and to have a Sample Size greater than 50 subjects. The study outcome (positive vs negative) was not associated with the likelihood of Sample Size reporting. Trial characteristics of the 2 periods (1998 vs 2008) were similar except that in 1998 there were more negative studies compared with 2008. CONCLUSIONS: Although Sample Size Calculation reporting has improved dramatically in 10 years and is comparable with other fields in medicine, it is still not adequate given current publication guidelines.
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Sample Size Calculation in Physical Medicine and Rehabilitation: A Systematic Review of Reporting, Characteristics, and Results in Randomized Controlled Trials
Archives of physical medicine and rehabilitation, 2011Co-Authors: Lydia Abdul Latif, João Eduardo Daud Amadera, Daniel C. Pimentel, Thais Valéria Costa De Andrade Pimentel, Felipe FregniAbstract:Abstract Abdul Latif L, Daud Amadera JE, Pimentel D, Pimentel T, Fregni F. Sample Size Calculation in physical medicine and rehabilitation: a systematic review of reporting, characteristics, and results in randomized controlled trials. Objective To assess systematically the reporting of Sample Size Calculation in randomized controlled trials (RCTs) in 5 leading journals in the field of physical medicine and rehabilitation (PM&R). Data Sources The data source was full reports of RCTs in 5 leading PM&R journals ( Journal of Rehabilitation Medicine , Archives of Physical Medicine and Rehabilitation , American Journal of Physical Medicine and Rehabilitation , Clinical Rehabilitation , and Disability and Rehabilitation ) between January and December of 1998 and 2008. Articles were identified in Medline. Study Selection A total of 111 articles met our inclusion criteria, which include RCTs of human studies in the 5 selected journals. Data Extraction Sample Size Calculation reporting and trial characteristics were collected for each trial by independent investigators. Data Synthesis In 2008, 57.3% of articles reported Sample Size Calculation as compared with only 3.4% in 1998. The parameters that were commonly used were a power of 80% and alpha of 5%. Articles often failed to report effect Size or effect estimates for Sample Size Calculation. Studies reporting Sample Size Calculation were more likely to describe the main outcome and to have a Sample Size greater than 50 subjects. The study outcome (positive vs negative) was not associated with the likelihood of Sample Size reporting. Trial characteristics of the 2 periods (1998 vs 2008) were similar except that in 1998 there were more negative studies compared with 2008. Conclusions Although Sample Size Calculation reporting has improved dramatically in 10 years and is comparable with other fields in medicine, it is still not adequate given current publication guidelines.
Sin-ho Jung - One of the best experts on this subject based on the ideXlab platform.
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stratified fisher s exact test and its Sample Size Calculation
Biometrical Journal, 2014Co-Authors: Sin-ho JungAbstract:Chi-squared test has been a popular approach to the analysis of a 2 × 2 table when the Sample Sizes for the four cells are large. When the large Sample assumption does not hold, however, we need an exact testing method such as Fisher's test. When the study population is heterogeneous, we often partition the subjects into multiple strata, so that each stratum consists of homogeneous subjects and hence the stratified analysis has an improved testing power. While Mantel-Haenszel test has been widely used as an extension of the chi-squared test to test on stratified 2 × 2 tables with a large-Sample approximation, we have been lacking an extension of Fisher's test for stratified exact testing. In this paper, we discuss an exact testing method for stratified 2 × 2 tables that is simplified to the standard Fisher's test in single 2 × 2 table cases, and propose its Sample Size Calculation method that can be useful for designing a study with rare cell frequencies.
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On Sample Size Calculation for Comparing Survival Curves Under General Hypothesis Testing
Journal of biopharmaceutical statistics, 2012Co-Authors: Sin-ho Jung, Shein-chung ChowAbstract:The log-rank test is commonly used to test the equivalence of two survival distributions under right censoring. Jung et al. (2005) proposed a modified log-rank test for noninferiority trials and its corresponding Sample Size Calculation. In this article, we extend the use of the modified log-rank test for clinical trials with various types of nonconventional study objectives and propose its Sample Size Calculation under general null and alternative hypotheses. The proposed formula is so flexible that we can specify any survival distributions and accrual pattern. The proposed methods are illustrated with designing real clinical trials. Through simulations, the modified log-rank test and the derived formula for Sample Size Calculation are shown to have satisfactory small Sample performance.
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Sample Size Calculation for the weighted rank statistics with paired survival data
Statistics in Medicine, 2008Co-Authors: Sin-ho JungAbstract:This paper introduces a Sample Size Calculation method for the weighted rank test statistics with paired two-Sample survival data. Our Sample Size formula requires specification of joint survival and censoring distributions. For modelling the distribution of paired survival variables, we may use a paired exponential survival distribution that is specified by the marginal hazard rates and a measure of dependency. Also, in most trials randomizing paired subjects, the subjects of each pair are accrued and censored at the same time over an accrual period and an additional follow-up period, so that the paired subjects have a common censoring time. Under these practical settings, the design parameters include type I and type II error probabilities, marginal hazard rates under the alternative hypothesis, correlation coefficient, accrual period (or accrual rate) and follow-up period. If pilot data are available, we may estimate the survival distributions from them, but we specify the censoring distribution based on the specified accrual trend and the follow-up period planned for the new study. Through simulations, the formula is shown to provide accurate Sample Sizes under practical settings. Real studies are taken to demonstrate the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.
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Sample Size Calculation for paired survival data a simulation method
Journal of Statistical Computation and Simulation, 2008Co-Authors: Sin-ho JungAbstract:This paper proposes a Sample Size Calculation method for rank tests for paired two-Sample survival data using simulation method. A paired exponential survival model is specified by the marginal hazard rates and the correlation coefficient. Input variables include type I and II error probabilities, marginal hazard rates under the alternative hypothesis, accrual rate and follow-up period. Using efficient algorithms, our simulation method provides required Sample Size within minutes. The method is easily extended to Sample Size Calculation for K-Sample tests under dependence.
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Sample Size Calculation for weighted rank tests comparing survival distributions under cluster randomization: a simulation method.
Journal of biopharmaceutical statistics, 2007Co-Authors: Sin-ho JungAbstract:We propose a Sample Size Calculation method for rank tests comparing two survival distributions under cluster randomization with possibly variable cluster Sizes. Here, Sample Size refers to number of clusters. Our method is based on simulation procedure generating clustered exponential survival variables whose distribution is specified by the marginal hazard rate and the intracluster correlation coefficient. Sample Size is calculated given significance level, power, marginal hazard rates (or median survival times) under the alternative hypothesis, intracluster correlation coefficient, accrual rate, follow-up period, and cluster Size distribution.
Yu Shyr - One of the best experts on this subject based on the ideXlab platform.
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Sample Size Calculation based on exact test for assessing differential expression analysis in RNA-seq data
BMC bioinformatics, 2013Co-Authors: Yu ShyrAbstract:Background Sample Size Calculation is an important issue in the experimental design of biomedical research. For RNA-seq experiments, the Sample Size Calculation method based on the Poisson model has been proposed; however, when there are biological replicates, RNA-seq data could exhibit variation significantly greater than the mean (i.e. over-dispersion). The Poisson model cannot appropriately model the over-dispersion, and in such cases, the negative binomial model has been used as a natural extension of the Poisson model. Because the field currently lacks a Sample Size Calculation method based on the negative binomial model for assessing differential expression analysis of RNA-seq data, we propose a method to calculate the Sample Size.
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Sample Size Calculation for differential expression analysis of rna seq data under poisson distribution
International Journal of Computational Biology and Drug Design, 2013Co-Authors: Yan Guo, Yu ShyrAbstract:Sample Size determination is an important issue in the experimental design of biomedical research. Because of the complexity of RNA-seq experiments, however, the field currently lacks a Sample Size method widely applicable to differential expression studies utilising RNA-seq technology. In this report, we propose several methods for Sample Size Calculation for single-gene differential expression analysis of RNA-seq data under Poisson distribution. These methods are then extended to multiple genes, with consideration for addressing the multiple testing problem by controlling false discovery rate. Moreover, most of the proposed methods allow for closed-form Sample Size formulas with specification of the desired minimum fold change and minimum average read count, and thus are not computationally intensive. Simulation studies to evaluate the performance of the proposed Sample Size formulas are presented; the results indicate that our methods work well, with achievement of desired power. Finally, our Sample Size Calculation methods are applied to three real RNA-seq data sets.
João Eduardo Daud Amadera - One of the best experts on this subject based on the ideXlab platform.
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Sample Size Calculation in physical medicine and rehabilitation a systematic review of reporting characteristics and results in randomized controlled trials
Archives of Physical Medicine and Rehabilitation, 2011Co-Authors: Lydia Abdul Latif, João Eduardo Daud Amadera, Daniel C. Pimentel, Thais Valéria Costa De Andrade Pimentel, Felipe FregniAbstract:OBJECTIVE: To assess systematically the reporting of Sample Size Calculation in randomized controlled trials (RCTs) in 5 leading journals in the field of physical medicine and rehabilitation (PM&R). DATA SOURCES: The data source was full reports of RCTs in 5 leading PM&R journals (Journal of Rehabilitation Medicine, Archives of Physical Medicine and Rehabilitation, American Journal of Physical Medicine and Rehabilitation, Clinical Rehabilitation, and Disability and Rehabilitation) between January and December of 1998 and 2008. Articles were identified in Medline. STUDY SELECTION: A total of 111 articles met our inclusion criteria, which include RCTs of human studies in the 5 selected journals. DATA EXTRACTION: Sample Size Calculation reporting and trial characteristics were collected for each trial by independent investigators. DATA SYNTHESIS: In 2008, 57.3% of articles reported Sample Size Calculation as compared with only 3.4% in 1998. The parameters that were commonly used were a power of 80% and alpha of 5%. Articles often failed to report effect Size or effect estimates for Sample Size Calculation. Studies reporting Sample Size Calculation were more likely to describe the main outcome and to have a Sample Size greater than 50 subjects. The study outcome (positive vs negative) was not associated with the likelihood of Sample Size reporting. Trial characteristics of the 2 periods (1998 vs 2008) were similar except that in 1998 there were more negative studies compared with 2008. CONCLUSIONS: Although Sample Size Calculation reporting has improved dramatically in 10 years and is comparable with other fields in medicine, it is still not adequate given current publication guidelines.
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Sample Size Calculation in Physical Medicine and Rehabilitation: A Systematic Review of Reporting, Characteristics, and Results in Randomized Controlled Trials
Archives of physical medicine and rehabilitation, 2011Co-Authors: Lydia Abdul Latif, João Eduardo Daud Amadera, Daniel C. Pimentel, Thais Valéria Costa De Andrade Pimentel, Felipe FregniAbstract:Abstract Abdul Latif L, Daud Amadera JE, Pimentel D, Pimentel T, Fregni F. Sample Size Calculation in physical medicine and rehabilitation: a systematic review of reporting, characteristics, and results in randomized controlled trials. Objective To assess systematically the reporting of Sample Size Calculation in randomized controlled trials (RCTs) in 5 leading journals in the field of physical medicine and rehabilitation (PM&R). Data Sources The data source was full reports of RCTs in 5 leading PM&R journals ( Journal of Rehabilitation Medicine , Archives of Physical Medicine and Rehabilitation , American Journal of Physical Medicine and Rehabilitation , Clinical Rehabilitation , and Disability and Rehabilitation ) between January and December of 1998 and 2008. Articles were identified in Medline. Study Selection A total of 111 articles met our inclusion criteria, which include RCTs of human studies in the 5 selected journals. Data Extraction Sample Size Calculation reporting and trial characteristics were collected for each trial by independent investigators. Data Synthesis In 2008, 57.3% of articles reported Sample Size Calculation as compared with only 3.4% in 1998. The parameters that were commonly used were a power of 80% and alpha of 5%. Articles often failed to report effect Size or effect estimates for Sample Size Calculation. Studies reporting Sample Size Calculation were more likely to describe the main outcome and to have a Sample Size greater than 50 subjects. The study outcome (positive vs negative) was not associated with the likelihood of Sample Size reporting. Trial characteristics of the 2 periods (1998 vs 2008) were similar except that in 1998 there were more negative studies compared with 2008. Conclusions Although Sample Size Calculation reporting has improved dramatically in 10 years and is comparable with other fields in medicine, it is still not adequate given current publication guidelines.
