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A01 Power analysis of cutoff-based randomized clinical trials
Abstracts
Fourteenth Annual Meeting of the Society for Clinical Trials Hilton Hotel, Orlando, Florida May 23-26, 1993
A01 POWER ANALYSIS OF CUTOFF-BASED RANDOMIZED CLINICAL TRIALS Joseph C. Cappelleri, Richard B. Darlington, and Willi~n3 M . K . Trochim
Harvard University School of Public Health Boston, Massachusetts A recent article in Controlled Clinical Trials presented a class of cutoff-based randomized clinical trials (RCTs) that are designed to help balance ethical and scientific concerns in certain situations. An algorithm and a program based on the Fisher Z method are developed that are particular to and inclusive of cutoff-based RCTs, as well as of the single cutoff-point (regression discontinuity) design, and a general methodology is developed that compares their power and sample size estimates relative to the conventional RCT. This paper quantifies power and sample size estimates for varying levels of randomization and cutoff-based assignment. While more randomization engenders greater statistical power, less randomization requires a much larger increase in sample size for small treatment effects. A02 CONFIDENCE INTERVALS FOR POWER PROBABILITIES: H O W P O W E R F U L WAS T H A T H Y P O T H E S I S T E S T ? Ralph G. O ' B r i e n
University of Florida GainesviUe, Florida Empirical retrospectivepower analysis uses data to estimate and assess the power of statistical tests already performed. This addresses such queries as: This test is nonsignificant. What was its statistical power? If I had studied 50% more subjects, what would have been the power? Similarly, empirical prospective power analysis uses data from a previous study to assess the power of a proposed similar study, perhaps a near replication. Let ), be the unknown noncentrality underlying an F or X2 test statistic for, say, some A N O V A or logit analysis based on N cases total. Using a strategy adapted from Venables (JRSS A, 1975), we find point estimates and 1 - ~¢ confidence intervals for theprimary noncentrality (X * = k/N) and then transform them to estimates and intervals for the power for N ' cases total. In other situations, a "clinically meaningful" or "conjectured" average treatment effect is specified. Intervals for * still require confidence intervals for the variance, and for this we ~_d~_pt the robust variance methodology of O'Brien (JASA, 1979). All methods are illustrated using data collected in a placebo-controlled cross-over study designed to test whether grapefruit pectin, a potential general dietary additive, improves serum lipid profiles in humans. (NIH:GCRC grant RR00046).
ControUed Clinical Trials 14:399--465(1993) © Elsevier SciencePublishing Co., Inc. 1993 655 Avenue of the Americas, New York, New York 10010
399 0197-2456/93/$6.00
Fourteenth Annual Meeting of the Society for Clinical Trials Hilton Hotel, Orlando, Florida May 23-26, 1993
A01 POWER ANALYSIS OF CUTOFF-BASED RANDOMIZED CLINICAL TRIALS Joseph C. Cappelleri, Richard B. Darlington, and Willi~n3 M . K . Trochim
Harvard University School of Public Health Boston, Massachusetts A recent article in Controlled Clinical Trials presented a class of cutoff-based randomized clinical trials (RCTs) that are designed to help balance ethical and scientific concerns in certain situations. An algorithm and a program based on the Fisher Z method are developed that are particular to and inclusive of cutoff-based RCTs, as well as of the single cutoff-point (regression discontinuity) design, and a general methodology is developed that compares their power and sample size estimates relative to the conventional RCT. This paper quantifies power and sample size estimates for varying levels of randomization and cutoff-based assignment. While more randomization engenders greater statistical power, less randomization requires a much larger increase in sample size for small treatment effects. A02 CONFIDENCE INTERVALS FOR POWER PROBABILITIES: H O W P O W E R F U L WAS T H A T H Y P O T H E S I S T E S T ? Ralph G. O ' B r i e n
University of Florida GainesviUe, Florida Empirical retrospectivepower analysis uses data to estimate and assess the power of statistical tests already performed. This addresses such queries as: This test is nonsignificant. What was its statistical power? If I had studied 50% more subjects, what would have been the power? Similarly, empirical prospective power analysis uses data from a previous study to assess the power of a proposed similar study, perhaps a near replication. Let ), be the unknown noncentrality underlying an F or X2 test statistic for, say, some A N O V A or logit analysis based on N cases total. Using a strategy adapted from Venables (JRSS A, 1975), we find point estimates and 1 - ~¢ confidence intervals for theprimary noncentrality (X * = k/N) and then transform them to estimates and intervals for the power for N ' cases total. In other situations, a "clinically meaningful" or "conjectured" average treatment effect is specified. Intervals for * still require confidence intervals for the variance, and for this we ~_d~_pt the robust variance methodology of O'Brien (JASA, 1979). All methods are illustrated using data collected in a placebo-controlled cross-over study designed to test whether grapefruit pectin, a potential general dietary additive, improves serum lipid profiles in humans. (NIH:GCRC grant RR00046).
ControUed Clinical Trials 14:399--465(1993) © Elsevier SciencePublishing Co., Inc. 1993 655 Avenue of the Americas, New York, New York 10010
399 0197-2456/93/$6.00