Sample size and power estimation for studies with health related quality of life outcomes: a comparison of four methods using the SF-36

We describe and compare four different methods for estimating sample size and power, when the primary outcome of the study is a Health Related Quality of Life (HRQoL) measure. These methods are: 1. assuming a Normal distribution and comparing two means; 2. using a non-parametric method; 3. Whitehead's method based on the proportional odds model; 4. the bootstrap. We illustrate the various methods, using data from the SF-36. For simplicity this paper deals with studies designed to compare the effectiveness (or superiority) of a new treatment compared to a standard treatment at a single point in time. The results show that if the HRQoL outcome has a limited number of discrete values (< 7) and/or the expected proportion of cases at the boundaries is high (scoring 0 or 100), then we would recommend using Whitehead's method (Method 3). Alternatively, if the HRQoL outcome has a large number of distinct values and the proportion at the boundaries is low, then we would recommend using Method 1. If a pilot or historical dataset is readily available (to estimate the shape of the distribution) then bootstrap simulation (Method 4) based on this data will provide a more accurate and reliable sample size estimate than conventional methods (Methods 1, 2, or 3). In the absence of a reliable pilot set, bootstrapping is not appropriate and conventional methods of sample size estimation or simulation will need to be used. Fortunately, with the increasing use of HRQoL outcomes in research, historical datasets are becoming more readily available. Strictly speaking, our results and conclusions only apply to the SF-36 outcome measure. Further empirical work is required to see whether these results hold true for other HRQoL outcomes. However, the SF-36 has many features in common with other HRQoL outcomes: multi-dimensional, ordinal or discrete response categories with upper and lower bounds, and skewed distributions, so therefore, we believe these results and conclusions using the SF-36 will be appropriate for other HRQoL measures

What is the relationship between the minimally important difference and health state utility values? The case of the SF-6D

BACKGROUND: The SF-6D is a new single summary preference-based measure of health derived from the SF-36. Empirical work is required to determine what is the smallest change in SF-6D scores that can be regarded as important and meaningful for health professionals, patients and other stakeholders. OBJECTIVES: To use anchor-based methods to determine the minimally important difference (MID) for the SF-6D for various datasets. METHODS: All responders to the original SF-36 questionnaire can be assigned an SF-6D score provided the 11 items used in the SF-6D have been completed. The SF-6D can be regarded as a continuous outcome scored on a 0.29 to 1.00 scale, with 1.00 indicating "full health". Anchor-based methods examine the relationship between an health-related quality of life (HRQoL) measure and an independent measure (or anchor) to elucidate the meaning of a particular degree of change. One anchor-based approach uses an estimate of the MID, the difference in the QoL scale corresponding to a self-reported small but important change on a global scale. Patients were followed for a period of time, then asked, using question 2 of the SF-36 as our global rating scale, (which is not part of the SF-6D), if there general health is much better (5), somewhat better (4), stayed the same (3), somewhat worse (2) or much worse (1) compared to the last time they were assessed. We considered patients whose global rating score was 4 or 2 as having experienced some change equivalent to the MID. In patients who reported a worsening of health (global change of 1 or 2) the sign of the change in the SF-6D score was reversed (i.e. multiplied by minus one). The MID was then taken as the mean change on the SF-6D scale of the patients who scored (2 or 4). RESULTS: This paper describes the MID for the SF-6D from seven longitudinal studies that had previously used the SF-36. CONCLUSIONS: From the seven reviewed studies (with nine patient groups) the MID for the SF-6D ranged from 0.010 to 0.048, with a weighted mean estimate of 0.033 (95% CI: 0.029 to 0.037). The corresponding Standardised Response Means (SRMs) ranged from 0.11 to 0.48, with a mean of 0.30 and were mainly in the "small to moderate" range using Cohen's criteria, supporting the MID results. Using the half-standard deviation (of change) approach the mean effect size was 0.051 (range 0.033 to 0.066). Further empirical work is required to see whether or not this holds true for other patient groups and populations

Bicategories of spans as cartesian bicategories

Bicategories of spans are characterized as cartesian bicategories in which every comonad has an Eilenberg-Moore ob ject and every left adjoint arrow is comonadic

The use of bootstrap methods for analysing health-related quality of life outcomes (particularly the SF-36)

Health-Related Quality of Life (HRQoL) measures are becoming increasingly used in clinical trials as primary outcome measures. Investigators are now asking statisticians for advice on how to analyse studies that have used HRQoL outcomes. HRQoL outcomes, like the SF-36, are usually measured on an ordinal scale. However, most investigators assume that there exists an underlying continuous latent variable that measures HRQoL, and that the actual measured outcomes (the ordered categories), reflect contiguous intervals along this continuum. The ordinal scaling of HRQoL measures means they tend to generate data that have discrete, bounded and skewed distributions. Thus, standard methods of analysis such as the t-test and linear regression that assume Normality and constant variance may not be appropriate. For this reason, conventional statistical advice would suggest that non-parametric methods be used to analyse HRQoL data. The bootstrap is one such computer intensive non-parametric method for analysing data. We used the bootstrap for hypothesis testing and the estimation of standard errors and confidence intervals for parameters, in four datasets (which illustrate the different aspects of study design). We then compared and contrasted the bootstrap with standard methods of analysing HRQoL outcomes. The standard methods included t-tests, linear regression, summary measures and General Linear Models. Overall, in the datasets we studied, using the SF-36 outcome, bootstrap methods produce results similar to conventional statistical methods. This is likely because the t-test and linear regression are robust to the violations of assumptions that HRQoL data are likely to cause (i.e. non-Normality). While particular to our datasets, these findings are likely to generalise to other HRQoL outcomes, which have discrete, bounded and skewed distributions. Future research with other HRQoL outcome measures, interventions and populations, is required to confirm this conclusion

Teaching the Grid: Learning Distributed Computing with the M-grid Framework

A classic challenge within Computer Science is to distribute data and processes so as to take advantage of multiple computers tackling a single problem in a simultaneous and coordinated way. This situation arises in a number of different scenarios, including Grid computing which is a secure, service-based architecture for tackling massively parallel problems and creating virtual organizations. Although the Grid seems destined to be an important part of the future computing landscape, it is very difficult to learn how to use as real Grid software requires extensive setting up and complex security processes. M-grid mimics the core features of the Grid, in a much simpler way, enabling the rapid prototyping of distributed applications. We describe m-grid and explore how it may be used to teach foundation Grid computing skills at the Higher Education level and report some of our experiences of deploying it as an exercise within a programming course

The Dynamic Proliferation of CanSINEs Mirrors the Complex Evolution of Feliforms

Background: Repetitive short interspersed elements (SINEs) are retrotransposons ubiquitous in mammalian genomes and are highly informative markers to identify species and phylogenetic associations. Of these, SINEs unique to the order Carnivora (CanSINEs) yield novel insights on genome evolution in domestic dogs and cats, but less is known about their role in related carnivores. In particular, genome-wide assessment of CanSINE evolution has yet to be completed across the Feliformia (cat-like) suborder of Carnivora. Within Feliformia, the cat family Felidae is composed of 37 species and numerous subspecies organized into eight monophyletic lineages that likely arose 10 million years ago. Using the Felidae family as a reference phylogeny, along with representative taxa from other families of Feliformia, the origin, proliferation and evolution of CanSINEs within the suborder were assessed. Results: We identified 93 novel intergenic CanSINE loci in Feliformia. Sequence analyses separated Feliform CanSINEs into two subfamilies, each characterized by distinct RNA polymerase binding motifs and phylogenetic associations. Subfamily I CanSINEs arose early within Feliformia but are no longer under active proliferation. Subfamily II loci are more recent, exclusive to Felidae and show evidence for adaptation to extant RNA polymerase activity. Further, presence/absence distributions of CanSINE loci are largely congruent with taxonomic expectations within Feliformia and the less resolved nodes in the Felidae reference phylogeny present equally ambiguous CanSINE data. SINEs are thought to be nearly impervious to excision from the genome. However, we observed a nearly complete excision of a CanSINEs locus in puma (Puma concolor). In addition, we found that CanSINE proliferation in Felidae frequently targeted existing CanSINE loci for insertion sites, resulting in tandem arrays. Conclusions: We demonstrate the existence of at least two SINE families within the Feliformia suborder, one of which is actively involved in insertional mutagenesis. We find SINEs are powerful markers of speciation and conclude that the few inconsistencies with expected patterns of speciation likely represent incomplete lineage sorting, species hybridization and SINE-mediated genome rearrangement

Correlates of Bird Collisions With Buildings Across Three North American Countries

Collisions with buildings cause up to 1 billion bird fatalities annually in North America. Bird-building collisions have recently received increased conservation, research, and policy attention. However, efforts to reduce collisions would benefit from studies conducted at large spatial scales across multiple study sites, with standardized methods, and with consideration of species- and life history-related variation and correlates of collisions. We addressed these research needs with a coordinated data collection effort at 40 sites across North America. We estimated collision vulnerability for 40 bird species by accounting for their North American population abundance, distribution overlap with study sites, and sampling effort. Of 10 species we identified as most vulnerable to collisions, some have been identified in past studies (e.g., Black-throated Blue Warbler [Setophaga caerulescens]) while others emerged for the first time (e.g., White-breasted Nuthatch [Sitta carolinensis]), possibly because we used a more standardized sampling approach than past studies. Analyses of species-specific collision correlates revealed that building size and glass area were positively associated with numbers of collisions for 5 of 8 species with enough observations to analyze independently. Vegetation around buildings influenced collisions for only 1 of those 8 species (Swainson\u27s Thrush [Catharus fuscescens]). We also found that life history predicted collisions; numbers of collisions were greatest for migratory, insectivorous, and woodland-inhabiting species. This coordinated, continent-wide study provides new insight into the species most vulnerable to building collisions, making them potentially in greatest need of conservation attention to reduce collisions. This study also lends insight into species- and life history-related variation and correlates of building collisions, information that can help refine collision management efforts

Role of habit in treatment adherence among adults with cystic fibrosis

Among adults with cystic fibrosis (CF), medication adherence is low and reasons for low adherence are poorly understood. Our previous exploratory study showed that stronger 'habit' (ie, automatically experiencing an urge to use a nebuliser) was associated with higher nebuliser adherence. We performed a secondary analysis of pilot trial data (n=61) to replicate the earlier study and determine whether habit-adherence association exists in other cohorts of adults with CF. In this study, high adherers also reported stronger habit compared with low adherers. Habit may be a promising target for self-management interventions. ACtiF pilot, ISRCTN13076797. [Abstract copyright: © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.