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R S Atlas

Publications and source records attributed to R S Atlas.

8 recordsLinked to original sources
PubMed1999

Power of univariate and multivariate analyses of repeated measurements in controlled clinical trials.

The power of univariate and multivariate tests of significance is compared in relation to linear and nonlinear patterns of treatment effects in a repeated measurement design. Bonferroni correction was used to control the experiment-wise error rate in combining results from univariate tests of significance accomplished separately on average level, linear, quadratic, and cubic trend components. Multivariate tests on these same components of the overall treatment effect, as well as a multivariate test for between-groups difference on the original repeated measurements, were also evaluated for power against the same representative patterns of treatment effects. Results emphasize the advantage of parsimony that is achieved by transforming multiple repeated measurements into a reduced set of mean ngful composite variables representing average levels and rates of change. The Bonferroni correction applied to the separate univariate tests provided experiment-wise protection against Type I error, produced slightly greater experiment-wise power than a multivariate test applied to the same components of the data patterns, and provided substantially greater power than a multivariate test on the complete set of original repeated measurements. The separate univariate tests provide interpretive advantage regarding locus of the treatment effects.

Analysis of Variance↗
PubMed1995

Tests that are robust against variance heterogeneity in k x 2 designs with unequal cell frequencies.

Heterogeneity of variance produces serious bias in conventional analysis of variance tests of significance when cell frequencies are unequal. Welch in 1938 and 1947 proposed an adjusted t test for the difference between two means when cell frequencies and population variances are both unequal. This article describes two ways to use the Welch t to evaluate the significance of the main effect for two treatments across k levels of a concomitant factor in a two-way design. Monte Carlo results document the bias in conventional analysis of variance tests and the stable and appropriately conservative results from applications of the Welch t to evaluation of treatment effects in the two-way design.

Analysis of Variance↗
PubMed1995

Power of a test that is robust against variance heterogeneity.

Welch (1947) proposed an adjusted t test that can be used to correct the serious bias in Type I error protection that is otherwise present when both sample sizes and variances are unequal. The implications of the Welch adjustment for power of tests for the difference between two treatments across k levels of a concomitant factor are evaluated in this article for k x 2 designs with unequal sample sizes and unequal variances. Analyses confirm that, although Type I error is uniformly controlled, power of the Welch test of significance for the main effect of treatments remains rather seriously dependent on direction of the correlation between unequal variances and unequal sample sizes. Nevertheless, considering the fact that analysis of variance is not an acceptable option in such cases, the Welch t test appears to have an important role to play in the analysis of experimental data.

Analysis of Variance↗
PubMed1993

Computer simulation of alternative sampling strategies to estimate risk of infection from Cryptosporidium.

Estimation of acceptably safe levels of biological contaminants in drinking water requires fitting a mathematical model to infection rates observed in small samples of human subjects. Because of obvious constraints on exposing human subjects to infective conditions, it is not feasible to compare the utilities of alternative sampling strategies and research designs using data from real experiments. Computer simulation methods were used to generate sample data having known probabilities of infection determined by an exponential or log-linear infectivity model. Experimental conditions that were examined included variations in the total available sample size, strategies for allocating subjects among different test concentrations, and methods for fitting a prediction model to the observed data. Results confirmed that data obtained by exposing most subjects to a concentration that produces an infection rate approximating 50% and calculating the sample regression coefficient for the log-linear model as the average infectivity-to-concentration ratio provided the best estimates of safe concentration. Exposing a single subject to each successively higher test level until an initial infection is observed, and exposing all remaining subjects at that level, or an adjacent log-concentration level is a tactic supported by the empirical results.

Animals↗
PubMed1993

Selecting an interim analysis procedure.

Motivations for undertaking interim analyses differ, as do the methods proposed by different authors. This article evaluates five interim analysis procedures with regard to different requirements. The five procedures differ with respect to concern for the presence or absence of a true treatment effect. One provides interim criteria only for accepting Ho (terminating due to insufficient evidence of a true treatment effect), two provide criteria only for rejecting H(o), and the others provide criteria for either accepting or rejecting H(o). A computer program was developed to simulate applications of the interim analyses to sampling data. Actual Type I error probabilities, power, probabilities of early termination, and expected sample sizes resulting from the different interim analysis procedures are compared. One-sided and two-sided tests, equal and unequal interim sample segments, and interim alterations in the sample size or research design are considered. Results should be helpful in selecting a method that satisfies particular interests.

Humans↗
PubMed1992

Comparison of a two-stage and three-stage interim-analysis procedure.

A statistical model for combining p values from multiple tests of significance is used to define rejection and acceptance regions for two-stage and three-stage sampling plans. Type I error rates, power, frequencies of early termination decisions, and expected sample sizes are compared. Both the two-stage and three-stage procedures provide appropriate protection against Type I errors. The two-stage sampling plan with its single interim analysis entails minimal loss in power and provides substantial reduction in expected sample size as compared with a conventional single end-of-study test of significance for which power is in the adequate range. The three-stage sampling plan with its two interim analyses introduces somewhat greater reduction in power, but it compensates with greater reduction in expected sample size. Either interim-analysis strategy is more efficient than a single end-of-study analysis in terms of power per unit of sample size.

Bias↗
PubMedDate not supplied

Survival analysis with unreliable endpoints.

There exists a current interest in the application of survival analysis methodology to evaluate differences in latencies of response to psychological or psychopharmacological treatment modalities. However, unreliability in the measurement of treatment responses in such research poses a problem. Two methods of defining the "discrete endpoint" that is required for survival analysis are compared regarding power of tests of significance for differences in survival curves. Discrete endpoints defined by regression equations fitted to all available data for each subject provided greater power when entered into survival analysis than did endpoints dependent only on individual measurements. While this may not surprise statisticians, no examples of the use of regression estimates for survival analysis endpoints have been identified in reports of previous clinical trials nor in discussions concerning potential applications of survival analysis methodology in psychiatric research.

Analysis of Variance↗