Current Statistical Procedures Research Articles

Identification of traits for the improvement of grain yield in bread wheat under nitrogen stress and non-stress conditions

Abstract Correlation, path coefficient and step-wise regression analyses were used to identify important traits that affect grain yield in bread wheat. Thirty bread wheat varieties were field tested under with (N+) and without nitrogen (N0) conditions at New Developmental Research Farm, The University of Agriculture, Peshawar-Pakistan. Experimental materials were planted in randomized complete block (RCB) design with three replications. Correlation analysis revealed significant positive association of grain yield with number of spikes m-2, biological yield and harvest index under both production systems. For the 1000-grain weight, positive association was observed under N+ condition only. Furthermore, path coefficient analysis confirmed the true relationship of harvest index, biological yield, number of spikes m-2, and 1000-grain weight with grain yield in wheat. Similarly, step-wise regression analysis identified spikes m-2, grains spike-1, 1000-grain weight, harvest index and biological yield as important traits affecting grain yield in wheat under respective production system. Overall, on the basis of current statistical procedures spikes m-2, grains spike-1, 1000 grain weight, harvest index and biological yield needs due weightage in breeding program for further improvement in grain yield of bread wheat. Keywords: Biological Yield; Correlation; Path analysis; Step-wise regression http://dx.doi.org/10.19045/bspab.2019.80011

CopulaDTA: An R Package for Copula-Based Bivariate Beta-Binomial Models for Diagnostic Test Accuracy Studies in a Bayesian Framework

The current statistical procedures implemented in statistical software packages for pooling of diagnostic test accuracy data include HSROC regression (Rutter and Gatsonis 2001) and the bivariate random-effects meta-analysis model (BRMA; Reitsma et al. 2005; Arends et al. 2008; Chu and Cole 2006; Riley et al. 2007b). However, these models do not report the overall mean but rather the mean for a central study with random-effect equal to zero and have difficulties estimating the correlation between sensitivity and specificity when the number of studies in the meta-analysis is small and/or when the between-study variance is relatively large (Riley et al. 2007a). This tutorial on advanced statistical methods for meta-analysis of diagnostic accuracy studies discusses and demonstrates Bayesian modeling using the R package CopulaDTA (Nyaga 2017) to fit different models to obtain the meta-analytic parameter estimates. The focus is on the joint modeling of sensitivity and specificity using a copula based bivariate beta distribution. Essentially, we extend the work of Nikoloulopoulos (2015) by: (i) presenting the Bayesian approach which offers the flexibility and ability to perform complex statistical modeling even with small data sets and (ii) including covariate information, and (iii) providing an easy to use code. The statistical methods are illustrated by re-analyzing data of two published meta-analyses. Modeling sensitivity and specificity using the bivariate beta distribution provides marginal as well as study-specific parameter estimates as opposed to using the bivariate normal distribution (e.g., in BRMA) which only yields study-specific parameter estimates. Moreover, copula based models offer greater flexibility in modeling different correlation structures in contrast to the normal distribution which allows for only one correlation structure.

A tutorial on testing the race model inequality.

When participants respond in the same way to stimuli of two categories, responses are often observed to be faster when both stimuli are presented together (redundant signals) relative to the response time obtained when they are presented separately. This effect is known as the redundant signals effect. Several models have been proposed to explain this effect, including race models and coactivation models of information processing. In race models, the two stimulus components are processed in separate channels, and the faster channel determines the processing time. This mechanism leads, on average, to faster responses to redundant signals. In contrast, coactivation models assume integrated processing of the combined stimuli. To distinguish between these two accounts, Miller (Cognitive Psychology, 14, 247-279, 1982) derived the well-known race model inequality, which has become a routine test for behavioral data in experiments with redundant signals. In this tutorial, we review the basic properties of redundant signals experiments and current statistical procedures used to test the race model inequality during the period between 2011 and 2014. We highlight and discuss several issues concerning study design and the test of the race model inequality, such as inappropriate control of Type I error, insufficient statistical power, wrong treatment of omitted responses or anticipations, and the interpretation of violations of the race model inequality. We make detailed recommendations on the design of redundant signals experiments and on the statistical analysis of redundancy gains. We describe a number of coactivation models that may be considered when the race model has been shown to fail.

Marginal models for meta-analysis of diagnostic accuracy studies in frequentist and Bayesian framework using rstan and CopulaREMADA

Current statistical procedures implemented in general software packages for pooling of diagnostic test accuracy data include hSROC (Rutter and Gatsonis 2001) regression and the bivariate normal model (BRMA) (Reitsma et al. (2005), Arends (2006) and Chu and Cole (2006)). However, these models do not report the overall mean. The hSROC model is less intuitive and therefore less popular while the bivariate normal model has difficulties estimating the correlation parameter when the number studies in the meta-analysis are small and/or when the between-study variances are relatively large. As a result, the between study variance estimates from the BRMA are upwardly biased as they are inflated to compensate for the restriction on correlation parameter (Riley et al. 2007). We present advanced statistical methods for meta-analysis of diagnostic accuracy studies and demonstrate the use of different software packages in R or with an R interface together with code, to apply different model strategies for obtaining appropriate meta-analytic parameter estimates. The focus is on the joint modelling of sensitivity and specificity using copulas as well as using the concept of sharing random components. The methods are applied to two datasets: the classical example of Glas et al. (2003) on diagnostic accuracy of telomerase as urinary tumour marker for diagnosing primary bladder cancer and a second dataset from a systematic review by Arbyn et al. (2013) that aimed at comparing sensitivity and specificity of human papillomavirus testing versus repeat cytology for triage of minor cytological cervical lesions.

MULTI-CENTER CLINICAL TRIALS: RANDOMIZATION AND ANCILLARY STATISTICS.

The purpose of this paper is to investigate and develop methods for analysis of multi-center randomized clinical trials which only rely on the randomization process as a basis of inference. Our motivation is prompted by the fact that most current statistical procedures used in the analysis of randomized multi-center studies are model based. The randomization feature of the trials is usually ignored. An important characteristic of model based analysis is that it is straightforward to model covariates. Nevertheless in nearly all model based analyses, the effects due to different centers and, in general, the design of the clinical trials are ignored. An alternative to a model based analysis is to have analyses guided by the design of the trial. Our development of design based methods allows the incorporation of centers as well as other features of the trial design. The methods make use of conditioning on the ancillary statistics in the sample space generated by the randomization process. We have investigated the power of the methods and have found that, in the presence of center variation, there is a significant increase in power. The methods have been extended to group sequential trials with similar increases in power.

Measuring Poverty in a Growing World (or Measuring Growth in a Poor World)

The extent to which growth reduces global poverty has been disputed for 30 years. Although there are better data than ever before, controversies are not resolved. A major problem is that consumption measured from household surveys, which is used to measure poverty, grows less rapidly than consumption measured in national accounts, in the world as a whole and in large countries, particularly India, China, and the United States. In consequence, measured poverty has fallen less rapidly than appears warranted by measured growth in poor countries. One plausible cause is that richer households are less likely to participate in surveys. But growth in the national accounts is also upward biased, and consumption in the national accounts contains large and rapidly growing items that are not consumed by the poor and not included in surveys. So it is possible for consumption of the poor to grow less rapidly than national consumption, without any increase in measured inequality. Current statistical procedures in poor ...

Statistical evaluation of individual bioequivalence

Drug switchability requires the evidence of individual bioequivalence which -refers to the comparison of the closeness between the two distributions of the pharmacokinetic (PK) responses from the same subject obtained under the repeated administrations of the test and reference formulations. Advantages and drawbacks of the current statistical procedures for assessment of individual bioequivalence are discussed with emphasis on the aggregate-based criteria, An intersection-union test based on disaggregate criteria is proposed for the evaluation of individual bioequivalence. In addition, a modified aggregated criterion is suggested to overcome the drawbacks suffered by aggregate criteria. The relationships among different criteria are examined, and the performance of the procedures will be compared. A numerical example is given to illustrate the proposed procedures.

Interrupted time-series analysis with brief single-subject data.

Assessing change with short time-series data is difficult because visual inference is unreliable with such data, and current statistical procedures cannot control Type I error because they underestimate positive autocorrelation. This article describes these problems and shows how they can be solved with a new interrupted time-series analysis procedure (ITSACORR) that uses a more accurate estimate of autocorrelation. Monte Carlo analyses show that, with short series, ITSACORR provides better control of Type I error than all previous procedures and has acceptable power. Clinical examples also show that ITSACORR is easy to use and functions well with real data.

A Statistical Approach to the Construction and Analysis of Isobolograms

Current statistical procedures used in the construction of isobolograms do not use recent advances in mathematical statistics. The variability in the experimental data is either ignored or incompletely accounted for in the analyses. The decision procedures currently used to characterize the type of interaction between two agents do not permit the determination of the level of statistical significance associated with a given conclusion. Furthermore, the often formidable sample size is not exploited in the current isobologram methodology. Statistical techniques exist that may be used to construct isobolograms and decision procedures with a reliable level of significance. An isobologram is a contour of constant response of the underlying dose-response surface. Consequently, response surface methods (RSM) are useful in the estimation and analysis of isobolograms. The interaction between ethanol and chloral hydrate in female ICR mice was evaluated using the RSM approach by fitting the logistic model to quantal data. The loss of righting reflex was quantitated in mice 30 min after coadministration of the two drugs, injected IP on a body weight basis (mg/kg). The study consisted of 39 groups with 6 animals per group. Analysis of our data supports the conclusion of synergy between the drugs as reported by others; however, our results were obtained with a significantly smaller number of animals. These studies also demonstrate that response surface modeling can be used for determining additivity, synergism, and antagonism at a given preselected level of significance.

Some aspects of adaptation and adaptability of barley in European conditions.

After a review of different approaches found in the literature to problems of adaptation and adaptability of barley, attention is paid to the ecophysiological reasons which may explain the recent extension of winter barley cultivation in NW Europe. A brief account is given of cooperative trials carried out in Europe to define spring barley varietal adaptability ("ESBAN" and "JESBT" trials). A general view of current statistical procedures to analyse adaptability and genotype environment interaction patterns is presented. Some indications are given of the use that the breeder can make of ecophysiological methodology and statistical models in breeding barley for wider adaptation. (Abstract retrieved from CAB Abstracts by CABI’s permission)