ANALYZING BINOMIAL DATA IN A SPLIT-PLOT DESIGN: CLASSICAL APPROACHES OR MODERN TECHNIQUES?

Binomial data are often generated in split-plot experimental designs in agricultural, biological, and environmental research. Modeling non-normality and random effects are the two major challenges in analyzing binomial data in split-plot designs. In this study, seven statistical methods for testing whole-plot and subplot treatment effects using mixed, generalized linear, or generalized linear mixed models are compared for the size and power of the tests. This study shows that analyzing random effects properly is more important than adjusting the analysis for non-normality. Methods based on mixed and generalized linear mixed models hold Type I error rates better than generalized linear models. Whole-plot tests tend to be conservative in some cases, but these tests can be improved by removing the lower bound of zero from variance parameter estimation or by increasing the number of whole-plot replications. Mixed model methods tend to have higher power than generalized linear mixed models when the sample size is small. However, they perform equally well as the sample size becomes large

Joint Identification of Location and Dispersion Effects in Unreplicated Two-Level Factorials

Most procedures that have been proposed to identify dispersion effects in unreplicated factorial designs assume that location effects have been identified correctly. Incorrect identi- fication of location effects may impair subsequent identification of dispersion effects. We develop a model for joint identification of location and dispersion effects that can reliably identify active effects of both types. The joint model is estimated using maximum likelihood, and hence effect selection is done using a specially derived information criterion. An exhaustive search through a limited version of the space of possible models is conducted. Both a single-model output and model averaging are considered. The method is shown to be capable of identifying sensible location-dispersion models that are missed by methods that rely on sequential estimation of location and dispersion effects

Statistical Modeling of Discrete Percentage Measurements With Application to Construction of Acceptance Bounds for Wood Failure in Structural Adhesive Testing

The goals of this paper are: (1) to provide a statistical analysis approach that is appropriate for data from an interlaboratory study where responses are measured in discrete percentages and are subject to multiple sources of random variability, and (2) to apply this model to data on wood-failure percentages from block-shear tests on structural wood adhesives. We treat percentage responses measured in 5-point intervals as having arisen from observing 20 independent binary responses on different parts of the observed wood blocks. The overdispersion that is likely to result from the practical inadequacy of this assumption is overcome empirically by the inclusion of a random effect for blocks. We propose an analysis based on a parametric bootstrap to provide sampling distributions for statistics that regulators might wish to use in setting standards for acceptance of wood adhesives. Similar computational methods are developed to assess the fit of the model. This model is shown to provide a reasonably good fit or actual data in many of the cases to which it was applied

Cardiovascular Responses to Orthostasis and Their Association With Falls in Older Adults

Background Orthostatic hypotension (OH) refers to a marked decline in blood pressure when upright. OH has a high incidence and prevalence in older adults and represents a potential intrinsic risk factor for falls in these individuals. Previous studies have not included more recent definitions for blood pressure responses to orthostasis, including initial, delayed, and recovery blood pressure responses. Furthermore, there is little research examining the relationships between cerebrovascular functioning and falling risk. Therefore, we aimed to: (i) test the association between different blood pressure responses to orthostatic stress and retrospective falling history and; (ii) test the association between cerebrovascular responses to orthostatic stress and falling history. Methods We tested 59 elderly residents in long term care facilities who underwent a passive seated orthostatic stress test. Beat-to-beat blood pressure and cerebral blood flow velocity (CBFV) responses were assessed throughout testing. Risk factors for falls and falling history were collected from facility records. Cardiovascular responses to orthostasis were compared between retrospective fallers (≥1 fall in the previous year) and non-fallers. Results Retrospective fallers had larger delayed declines in systolic arterial pressure (SAP) compared to non-fallers (p  = 0.015). Fallers also showed poorer early (2 min) and late (15 min) recovery of SAP. Fallers had a greater decline in systolic CBFV. Conclusions Older adults with a positive falling history have impaired orthostatic control of blood pressure and CBFV. With better identification and understanding of orthostatic blood pressure impairments earlier intervention and management can be implemented, potentially reducing the associated risk of morbidity and mortality. Future studies should utilize the updated OH definitions using beat-to-beat technology, rather than conventional methods that may offer less accurate detection

Brown-headed cowbird nestlings influence nestmate begging, but not parental feeding, in hosts of three distinct sizes

Keywords: begging brood parasitism brown-headed cowbird host-parasite interaction Molothrus ater offspring solicitation parent feeding provisioning behaviour Avian brood parasites typically depress the fitness of their hosts by reducing the number of host offspring produced, yet little is known about how parasitic nestlings influence the behaviour of host parents and host offspring. In this study, we used three hosts of the brown-headed cowbird, Molothrus ater, that varied in size (i.e. smaller, similar to and larger than cowbirds of a given age) to determine whether parasitic nestlings altered patterns of food provisioning by host parents and begging by host young under field conditions. Adult provisioning did not change in the presence of a cowbird but instead was influenced by feeding treatment and host size. In parasitized broods where nestlings differed in size (i.e. the small and large hosts), the larger nestling received the majority of food brought to the nest, regardless of whether it was the cowbird or host nestling. In contrast, similar-sized host nestlings received a similar amount of food in parasitized and unparasitized host broods. Relative to unparasitized broods, the presence of a cowbird led to increased begging intensity by the small host, had no clear effect on begging behaviour of the intermediate-sized host, and reduced begging intensity of the large host. Taken together, these results suggest the presence of a cowbird did not lead to changes in provisioning behaviour in parents, and the extent to which cowbirds influenced host begging behaviour depended on the size of the host.

EXPERIMENTAL ERROR IN AGRONOMIC FIELD TRIALS

Agronomic experiments often summarize work carried out in trials run in several locations over several years, referred to generically as environments. The appropriate statistical analyses for these experiments depend on definitions used for experimental error. The results of one such experiment, in which identical designs were used in each environment, illustrate the commonalities and differences in analyses that can result from using different definitions of experimental error

Predicting which children with juvenile idiopathic arthritis will not attain early remission with conventional treatment: Results from the Reacch-out cohort

Objective. To estimate the probability of early remission with conventional treatment for each child with juvenile idiopathic arthritis (JIA). Children with a low chance of remission may be candidates for initial treatment with biologics or triple disease-modifying antirheumatic drugs (DMARD). Methods. We used data from 1074 subjects in the Research in Arthritis in Canadian Children emphasizing Outcomes (ReACCh-Out) cohort. The predicted outcome was clinically inactive disease for ≥ 6 months starting within 1 year of JIA diagnosis in patients who did not receive early biologic agents or triple DMARD. Models were developed in 200 random splits of 75% of the cohort and tested on the remaining 25% of subjects, calculating expected and observed frequencies of remission and c-index values. Results. Our best Cox logistic model combining 18 clinical variables a median of 2 days after diagnosis had a c-index of 0.69 (95% CI 0.67-0.71), better than using JIA category alone (0.59, 95% CI 0.56-0.63). Children in the lowest probability decile had a 20% chance of remission and 21% attained remission; children in the highest decile had a 69% chance of remission and 73% attained remission. Compared to 5% of subjects identified by JIA category alone, the model identified 14% of subjects as low chance of remission (probability \u3c 0.25), of whom 77% failed to attain remission. Conclusion. Although the model did not meet our a priori performance threshold (c-index \u3e 0.70), it identified 3 times more subjects with low chance of remission than did JIA category alone, and it may serve as a benchmark for assessing value added by future laboratory/imaging biomarkers