Independent contrasts and regression through the origin

Following the pioneering work of Felsenstein and Garland, phylogeneticists have been using regression through the origin to analyze comparative data using independent contrasts. The reason why regression through the origin must be used with such data was revisited. The demonstration led to the formulation of a permutation test for the coefficient of determination and the regression coefficient estimates in regression through the origin. Simulations were carried out to measure type I error and power of the parametric and permutation tests under two models of data generation: regression models I and II (correlation model). Although regression through the origin assumes model I data, in independent contrast data error is present in the explanatory as well as the response variables. Two forms of permutations were investigated to test the regression coefficients: permutation of the values of the response variable y, and permutation of the residuals of the regression model. The simulations showed that the parametric tests or any of the permutation tests can be used when the error is normal, which is the usual assumption in independent contrast studies; only the test by permutation of y should be used when the error is highly asymmetric; and the parametric tests should be used when extreme values are present in covariables. Two examples are presented. The first one concerns non-specificity in fish parasites of the genus Lamellodiscus, the second the richness in parasites in 78 species of mammals.     

A Group Sequential Method for one Standard Control and more than one Experimental Treatment

Many group-sequential test procedures have been proposed to meet the ethical need for interim analyses. All of these papers, however, focus their discussion on the situation where there are only one standard control and one experimental treatment. In this paper, we consider a trial with one standard control, but with more than one experimental treatment. We have developed a group-sequential test procedure to accommodate any finite number of experimental treatments. To facilitate the practical application of the proposed test procedure, on the basis of Monte Carlo simulation, we have derived the critical values of α-levels equal to 0.01, 0.05 and 0.10 for the number of experimental treatments ranging from 2 to 4 and the number of multiple group sequential analysis ranging from 1 to 10. Comparing with a single non-sequential analysis that has a reasonable power (say, 0.80), we have demonstrated that the application of the proposed test procedure may substantially reduce the required sample size without seriously sacrificing the original power.     

Hypothesis Testing Procedures for a Series of Independent Fourfold Tables under Inverse Sampling

This paper considers four summary test statistics, including the one recently proposed by Bennett (1986, Biometrical Journal 28, 859–862), for hypothesis testing of association in a series of independent fourfold tables under inverse sampling. This paper provides a systematic and quantitative evaluation of the small-sample performance for these summary test statistics on the basis of a Monte Carlo simulation. This paper notes that the test statistic developed by Bennett (1986) can be conservative and thereby possibly lose the power when the underlying disease is not rare. This paper also finds that for given a fixed total number of cases in each table, the conditional test statistic is the best in controlling type I error among all test statistics considered here.     

Comparisons of Some Chi-Squared Goodness-of-Fit Test Statistics in Sparse One-Way Multinomials

The behavior of Pearson's X² test, the likelihood ratio test Y² and the two of its derivatives, G² and G_k², the Freeman-Tukey test (FT) and the Cressie and Read test Statistic I(2/3) are examined in this study. Estimated attained α levels based on 1000 simulated samples when the approximating distribution is χ_k-1², are computed for these tests for the various values of k, n and seven null hypotheses. Results from estimated power computations indicate that none of the test statistics has a clear advantage over any others, and that the choice of which test to use must therefore rest mainly on the performances with regards to the attained α levels when the χ² approximation is invoked. In this respect, the log-normal approximation proposed by Lawal and Upton (1980) is strongly recommended. This is closely followed by the I(2/3).     

Power, precaution, Type II error and sampling design in assessment of environmental impacts

Increasingly, environmental managers attempt to incorporate precautionary principles into decision making. In any quantitative analysis of impacts, precaution is closely related to the power of the analysis to detect an impact. Designs of sampling to detect impacts are, however, complex because of natural spatial and temporal variability and the intrinsic nature of the statistical interactions which define impacts. Here, pulse and press responses and impacts that affect time courses (temporal variance) were modelled to determine the influences of increasing temporal replication—sampling more times in each of several longer periods before and again after an impact.Increasing the number of control or reference locations and number of replicate sample units at each time and place of sampling investigated the influence of spatial replication on power. From numerous scenarios of impacts, with or without natural spatial and temporal interactions (i.e. not caused by an impact), general recommendations are possible. Detecting press impacts requires maximal numbers of control locations. Shorter-term pulse impacts are best detected when the number of periods sampled is maximized. Impacts causing changes in temporal variance are most likely to be detected by sampling with the greatest possible number of periods or times within periods.To allow precautionary decision making, the type of predicted impact should be specified with its magnitude and duration. Only then can sampling be designed to be powerful, thereby allowing precautionary concepts to be invoked.     

Understanding on the Pooled Test for Controlled Clinical Trialsn

Several independent clinical trials are usually conducted to demonstrate and support the evidence of the efficacy of a new drug. When not all the trials demonstrate a treatment effect because of a lack of statistical significant finding, the sponsor sometimes conducts a post hoc pooled test and uses the pooled result as extra statistical evidence. In this paper, we study the extent of type I error rate inflation with the post hoc pooled analysis and the power of interaction test in assessing the homogeneity of the trials with respect to treatment effect size. We also compare the power of several test procedures with or without pooled test involved and discuss the appropriateness of pooled tests under different alternative hypotheses.     

Statistical analysis of structural compensatory growth: how can we reduce the rate of false detection?

Compensatory growth (CG) is a key issue in work aiming at a full understanding of the adaptive significance of growth plasticity and its carryover effects on life-history. The number of studies addressing evolutionary explanations for CG has increased rapidly during the last few years, but there has not been a parallel gain in our understanding of the methodological difficulties associated with the analysis of CG. We point out two features of growth that can have serious consequences for detecting CG: (1) size dependence of growth rates, which causes nonlinearity of growth trajectories, and; (2) temporal overlapping of structural growth and replenishment of energy reserves after a period of famine. We show that the currently used methods can be prone to spurious detection of CG (Type I error) under conditions of nonlinear growth, and therefore lead to the accumulation of a significant amount of false “empirical support.” True and simulated growth data provided consistent results suggesting that a substantial fraction of the existing evidence for CG may be spurious. A small curvature in the growth trajectory can lead to spurious “detection” of CG when control and manipulated trajectories are compared over the same time interval (the “simultaneous” approach). We present a novel, robust method (the “asynchronous” approach) based on the accurate selection of control trajectories and comparison of control and treatment growth rates at different times. This method enables a reliable test to be performed for compensation under asymptotic growth. While the general results of our simulations do not support the application of conventional methods to the general case of nonlinear growth trajectories under the simultaneous approach, simple methods may prove valid if the experimental design allows for asynchronous comparisons. We advocate an alternative approach to deal with “safe” detection of CG that overcomes the problems associated with the occurrence of nonlinear and asymptotic growth, and provide recommendations for improving CG study designs. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Testing Equality between Two Diagnostic Procedures in Paired‐Sample Ordinal Data

When a new diagnostic procedure is developed, it is important to assess whether the diagnostic accuracy of the new procedure is different from that of the standard procedure. For paired‐sample ordinal data, this paper develops two test statistics for testing equality of the diagnostic accuracy between two procedures without assuming any parametric models. One is derived on the basis of the probability of correctly identifying the case for a randomly selected pair of a case and a non‐case over all possible cutoff points, and the other is derived on the basis of the sensitivity and specificity directly. To illustrate the practical use of the proposed test procedures, this paper includes an example regarding the use of digitized and plain films for screening breast cancer. This paper also applies Monte Carlo simulation to evaluate the finite sample performance of the two statistics developed here and notes that they can perform well in a variety of situations. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)