INTRODUCTION Regulatory Requirement Basic Considerations Procedures for Sample Size Calculation Aims and Structure of the Book CONSIDERATIONS PRIOR TO SAMPLE SIZE CALCULATION Confounding and Interaction One-Sided Test Versus Two-Sided Test Crossover Design Versus Parallel Design Subgroup/Interim Analyses Data Transformation Practical Issues COMPARING MEANS One-Sample Design Two-Sample Parallel Design Two-Sample Crossover Design Multiple-Sample One-Way ANOVA Multiple-Sample Williams Design Practical Issues LARGE SAMPLE TESTS FOR PROPORTIONS One-Sample Design Two-Sample Parallel Design Two-Sample Crossover Design One-Way Analysis of Variance Williams Design Relative Risk - Parallel Design Relative Risk - Crossover Design Practical Issues EXACT TESTS FOR PROPORTIONS Binomial Test Fisher's Exact Test Optimal Multiple-Stage Designs for Single Arm Trials Flexible Designs for Multiple-Arm Trials Remarks TESTS FOR GOODNESS-OF-FIT AND CONTINGENCY TABLES Tests for Goodness-of-Fit Test for Independence -Single Stratum Test for Independence -Multiple Strata Test for Categorical Shift Carry-Over Effect Test Practical Issues COMPARING TIME-TO-EVENT DATA Basic Concepts Exponential Model Cox's Proportional Hazards Model Weighted Log-Rank Test Practical Issues GROUP SEQUENTIAL METHODS Pocock's Test O'Brien and Fleming's Test Wang and Tsiatis' Test Inner Wedge Test Binary Variables Time-to-Event Data Alpha Spending Function Sample Size Re-Estimation Conditional Power Practical Issues COMPARING VARIABILITIES Comparing Intra-Subject Variabilities Comparing Intra-Subject CVs Comparing Inter-Subject Variabilities Comparing Total Variabilities Practical Issues BIOEQUIVALENCE TESTING Bioequivalence Criteria Average Bioequivalence Population Bioequivalence Individual Bioequivalence In Vitro Bioequivalence NONPARAMETRICS Violation of Assumptions One-Sample Location Problem Two-Sample Location Problem Test for Independence Practical Issues SAMPLE SIZE CALCULATION IN OTHER AREAS Dose Response Studies ANOVA with Repeated Measures Quality of Life Bridging Studies Vaccine Clinical Trials Appendix: Tables of Quantiles References Index

Sample Size Calculations in Clinical Research

Sample size calculations in human electrophysiology (EEG and ERP) studies: A systematic review and recommendations for increased rigor.

Clinical trials in the modern medical era began in the mid-20th century with the first randomized, double-blind assessment of an antibiotic, streptomycin, for the treatment of pulmonary tuberculosis.1 Over the course of the next 50 years, the randomized, controlled trial became the “gold standard” for testing the efficacy of new therapies and is generally required for their approval by regulatory agencies worldwide. To be sure, many effective cardiovascular therapies have been identified and approved for use following this generally accepted approach. In this editorial, I will review the many factors that can and do adversely affect the performance and interpretation of contemporary clinical trials in cardiovascular medicine, hoping to shed light on what I view as a series of growing and only partly remediable challenges to effective clinical investigation.

Clinical trials in cardiovascular medicine set the standard for randomized, controlled trial design owing to the population prevalence of the major cardiovascular diseases, the event rates in these patient populations, and the practicably achievable sample sizes sufficient to minimize type II errors. In the early studies of fibrinolytic therapy for myocardial infarction (ISIS [International Study of Infarct Survival] and TIMI [Thrombolysis In Myocardial Infarction]), for example, the expected 5-week mortality rate for conventionally treated patients was 12% to 13% at the time2,3 in Europe and the United States. Thus, with several thousand patients in each treatment arm, one could expect to detect confidently true reductions in absolute mortality of 3% to 4%, as was the case in these landmark trials.

Over the past three decades, mortality rates for highly prevalent cardiovascular diseases, including acute coronary syndromes, heart failure, and sudden death, have continuously improved owing to the clear benefits of therapies proved to be efficacious in double-blind, randomized, controlled trials. With these mounting, cumulative successes, however, the marginal benefit …

Clinical Trials in Cardiovascular Medicine in an Era of Marginal Benefit, Bias, and Hyperbole

A logarithmic curve fitting methodology for the calculation of badminton racket-shuttlecock impact locations from three-dimensional motion capture data was presented and validated. Median absolute ...

/pdf/effect-of-racket-shuttlecock-impact-location-on-shot-outcome-3mavu35cz4.pdf

Effect of racket-shuttlecock impact location on shot outcome for badminton smashes by elite players

Group testing is a procedure in which groups that contain several units (plants) are analysed without having to inspect individual plants, with the purpose of estimating the prevalence of genetically modified plants (adventitious presence of unwanted transgenic plants, AP) in a population at a low cost, without losing precision. When pool (group) testing is used to estimate the proportion of AP (p), there are several procedures that can be used for computing the confidence interval (CI); however, they usually do not ensure precision in the estimation of p. This research proposes a formula for determining the required number of pools (g), given a pool size (k), for estimating the proportion of AP plants using the Dorfman model. The proposed formula ensures precision in the estimated proportion of AP because it guarantees that the width (W) of the CI will be equal to, or narrower than, the desired width (ω), with a probability of γ. This probability accounts for the stochastic nature of the sample variance of p. We give examples to show how to use the proposed sample-size formula. Simulated data were created and tables are presented showing the different scenarios that a researcher may encounter. The Monte Carlo method was used to study the coverage and the level of assurance achieved by the proposed sample sizes. An R program that reproduces the results in the tables and makes it easy for the researcher to create other scenarios is given in the Appendix.

Optimal sample size for estimating the proportion of transgenic plants using the Dorfman model with a random confidence interval

In clinical research, parameters required for sample size calculation are usually unknown. A typical approach is to use estimates from some pilot studies as the true parameters in the calculation. This approach, however, does not take into consideration sampling error. Thus, the resulting sample size could be misleading if the sampling error is substantial. As an alternative, we suggest a Bayesian approach with noninformative prior to reflect the uncertainty of the parameters induced by the sampling error. Based on the informative prior and data from pilot samples, the Bayesian estimators based on appropriate loss functions can be obtained. Then, the traditional sample size calculation procedure can be carried out using the Bayesian estimates instead of the frequentist estimates. The results indicate that the sample size obtained using the Bayesian approach differs from the traditional sample size obtained by a constant inflation factor, which is purely determined by the size of the pilot study. An example is given for illustration purposes.

A Bayesian Approach on Sample Size Calculation for Comparing Means

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A Bayesian Approach on Sample Size Calculation for Comparing Means