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Required Sample Size (required + sample_size)

Distribution by Scientific Domains

Medical Sciences	50%

Selected Abstracts

Reporting of minimum clinically important differences in surgical trials

ANZ JOURNAL OF SURGERY, Issue 4 2009
Irwin Kashani
Background:, The minimum clinically important difference (MCID) is the smallest difference in outcome between the groups that would be of clinical interest. It influences the estimates that are made to determine the required sample side. The aim of this study was to explore the reporting of the MCID in surgical trials. Method:, Surgical trials that were published between January 1981 and December 2006 in five prestigious surgical journals were evaluated. Selected for study were trials that studied two groups and reported the main outcome event as a proportion. Results:, Only 21% (100/486) of the admissible surgical trials mentioned a value for the MCID when estimating the sample size. There was a trend, however, for compliance with these factors to increase during the study period. The present post-hoc calculations of the required sample size, which were based on the observed differences between the groups at the end of the study, suggested that one-third of the trials should have accrued at least fivefold the number of patients. Although reporting an estimate of the sample size was associated with the study of more patients (median sample size 145 vs 100), it was not associated with the reporting of more positive results, that is, 61% (95/155) versus 65% (214/331). Conclusion:, There has been an improvement in the proportion of surgical trials reporting formal estimates of sample size during the last three decades. But the construct of these estimates is often suspect because of a failure to provide realistic values for the MCID. [source]

A Note on Comparing Exposure Data to a Regulatory Limit in the Presence of Unexposed and a Limit of Detection

BIOMETRICAL JOURNAL, Issue 6 2005
Haitao Chu
Abstract In some occupational health studies, observations occur in both exposed and unexposed individuals. If the levels of all exposed individuals have been detected, a two-part zero-inflated log-normal model is usually recommended, which assumes that the data has a probability mass at zero for unexposed individuals and a continuous response for values greater than zero for exposed individuals. However, many quantitative exposure measurements are subject to left censoring due to values falling below assay detection limits. A zero-inflated log-normal mixture model is suggested in this situation since unexposed zeros are not distinguishable from those exposed with values below detection limits. In the context of this mixture distribution, the information contributed by values falling below a fixed detection limit is used only to estimate the probability of unexposed. We consider sample size and statistical power calculation when comparing the median of exposed measurements to a regulatory limit. We calculate the required sample size for the data presented in a recent paper comparing the benzene TWA exposure data to a regulatory occupational exposure limit. A simulation study is conducted to investigate the performance of the proposed sample size calculation methods. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

A Comparison of Procedures for Adaptive Choice of Location Tests in Flexible Two-Stage Designs

BIOMETRICAL JOURNAL, Issue 3 2003
Tim Friede
Abstract Although linear rank statistics for the two-sample problem are distribution free tests, their power depends on the distribution of the data. In the planning phase of an experiment, researchers are often uncertain about the shape of this distribution and so the choice of test statistic for the analysis and the determination of the required sample size are based on vague information. Adaptive designs with interim analysis can potentially overcome both problems. And in particular, adaptive tests based on a selector statistic are a solution to the first. We investigate whether adaptive tests can be usefully implemented in flexible two-stage designs to gain power. In a simulation study, we compare several methods for choosing a test statistic for the second stage of an adaptive design based on interim data with the procedure that applies adaptive tests in both stages. We find that the latter is a sensible approach that leads to the best results in most situations considered here. The different methods are illustrated using a clinical trial example. [source]

Review: Neutrophil gelatinase-associated lipocalin: A troponin-like biomarker for human acute kidney injury

NEPHROLOGY, Issue 4 2010
PRASAD DEVARAJAN
ABSTRACT Acute kidney injury (AKI) is a common and serious condition, the diagnosis of which currently depends on functional markers such as serum creatinine measurements. Unfortunately, creatinine is a delayed and unreliable indicator of AKI. The lack of early biomarkers of structural kidney injury (akin to troponin in acute myocardial injury) has hampered our ability to translate promising experimental therapies to human AKI. Fortunately, understanding the early stress response of the kidney to acute injuries has revealed a number of potential biomarkers. The discovery, translation and validation of neutrophil gelatinase-associated lipocalin (NGAL), possibly the most promising novel AKI biomarker, is reviewed. NGAL is emerging as an excellent stand-alone troponin-like structural biomarker in the plasma and urine for the early diagnosis of AKI, and for the prediction of clinical outcomes such as dialysis requirement and mortality in several common clinical scenarios. The approach of using NGAL as a trigger to initiate and monitor therapies for AKI, and as a safety biomarker when using potentially nephrotoxic agents, is also promising. In addition, it is hoped that the use of sensitive and specific biomarkers such as NGAL as endpoints in clinical trials will result in a reduction in required sample sizes, and hence the cost incurred. Furthermore, predictive biomarkers like NGAL may play a critical role in expediting the drug development process. However, given the complexity of AKI, additional biomarkers (perhaps a panel of plasma and urinary biomarkers) may eventually need to be developed and validated for optimal progress to occur. [source]