Sample size calculation for the Power Model for dose proportionality studies

There are several approaches to assess or demonstrate pharmacokinetic dose proportionality. One statistical method is the traditional ANOVA model, where dose proportionality is evaluated using the bioequivalence limits. A more informative method is the mixed effects Power Model, where dose proportionality is assessed using a decision rule for the estimated slope. Here we propose analytical derivations of sample sizes for various designs (including crossover, incomplete block and parallel group designs) to be analysed according to the Power Model.     

An Observational Investigation of Poker Style and the Five-Factor Personality Model

Little empirical investigation has been made of the relationship between personality and gambling play style. In an observation of on-table poker behavior, this study classified 44 players competing in an amateur league (43 male; mean age 32) according to two main dimensions of play style; tight or loose and aggressive or passive. Superstitious beliefs towards the game were additionally measured. The NEO-PI-R questionnaire (Costa and McCrae, Revised NEO personality inventory (NEO-PI-R) and NEO-five factor inventory (NEO-FFI) professional manual, 1992) was used to measure personality according to the five-factor model. Results showed players with superstitious beliefs to be significantly higher in neuroticism and lower in conscientiousness and than those with no superstitious beliefs. Results approaching significance showed players adopting an aggressive style to be higher in extraversion than passive players and superstitious players to be lower in extraversion than non-superstitious players. Analysis of play style according to age of player revealed a significantly higher mean age among loose compared to tight players. Implications of results concerning validity of these style classifications and potential development in future work are discussed.     

Using Model-Based Clustering to Improve Qualitative Inquiry: Computer-Aided Qualitative Data Analysis,Latent Class Analysis,and Interpretive Transparency

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - A combination of computer-aided qualitative data analysis (CAQDAS) and latent class analysis (LCA) can substantially...     

Community engagement in the Aboriginal Families Study: Strategies to promote participation

Background

Aboriginal women and families are under-represented in Australian research on pregnancy and childbirth. The Aboriginal Families Study aimed to investigate the views and experiences of a representative sample of women giving birth to an Aboriginal baby in South Australia between July 2011 and June 2013, using methods designed to respect Aboriginal culture and communities.

Modeling the Dose Response Relationship of Waterborne Acanthamoeba

This study developed dose response models for determining the probability of eye or central nervous system infections from previously conducted studies using different strains of Acanthamoeba spp. The data were a result of animal experiments using mice and rats exposed corneally and intranasally to the pathogens. The corneal inoculations of Acanthamoeba isolate Ac 118 included varied amounts of Corynebacterium xerosis and were best fit by the exponential model. Virulence increased with higher levels of C. xerosis. The Acanthamoeba culbertsoni intranasal study with death as an endpoint of response was best fit by the beta‐Poisson model. The HN‐3 strain of A. castellanii was studied with an intranasal exposure and three different endpoints of response. For all three studies, the exponential model was the best fit. A model based on pooling data sets of the intranasal exposure and death endpoint resulted in an LD₅₀ of 19,357 amebae. The dose response models developed in this study are an important step towards characterizing the risk associated with free‐living amoeba like Acanthamoeba in drinking water distribution systems. Understanding the human health risk posed by free‐living amoeba will allow for quantitative microbial risk assessments that support building design decisions to minimize opportunities for pathogen growth and survival.     

Potential Uncertainty Reduction in Model‐Averaged Benchmark Dose Estimates Informed by an Additional Dose Study

A methodology is presented for assessing the information value of an additional dosage experiment in existing bioassay studies. The analysis demonstrates the potential reduction in the uncertainty of toxicity metrics derived from expanded studies, providing insights for future studies. Bayesian methods are used to fit alternative dose‐response models using Markov chain Monte Carlo (MCMC) simulation for parameter estimation and Bayesian model averaging (BMA) is used to compare and combine the alternative models. BMA predictions for benchmark dose (BMD) are developed, with uncertainty in these predictions used to derive the lower bound BMDL. The MCMC and BMA results provide a basis for a subsequent Monte Carlo analysis that backcasts the dosage where an additional test group would have been most beneficial in reducing the uncertainty in the BMD prediction, along with the magnitude of the expected uncertainty reduction. Uncertainty reductions are measured in terms of reduced interval widths of predicted BMD values and increases in BMDL values that occur as a result of this reduced uncertainty. The methodology is illustrated using two existing data sets for TCDD carcinogenicity, fitted with two alternative dose‐response models (logistic and quantal‐linear). The example shows that an additional dose at a relatively high value would have been most effective for reducing the uncertainty in BMA BMD estimates, with predicted reductions in the widths of uncertainty intervals of approximately 30%, and expected increases in BMDL values of 5–10%. The results demonstrate that dose selection for studies that subsequently inform dose‐response models can benefit from consideration of how these models will be fit, combined, and interpreted.