William J. Browne

Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research

animals used (i.e., species/strain, sex, and age/weight). Most of the papers surveyed did not report using randomisation (87%) or blinding (86%) to reduce bias in animal selection and outcome assessment. Only 70% of the publications that used statistical methods fully described them and presented the results with a measure of precision or variability [5]. These findings are a cause for concern and are consistent with reviews of many research areas, including clinical studies, published in recent years [2–22].

Animal research: Reporting in vivo experiments: The ARRIVE guidelines

2 Provide an accurate summary of the background, research objectives, including details of the species or strain of animal used, key methods, principal findings and conclusions of the study. INTRODUCTION Background 3 a. Include sufficient scientific background (including relevant references to previous work) to understand the motivation and context for the study, and explain the experimental approach and rationale. b. Explain how and why the animal species and model being used can address the scientific objectives and, where appropriate, the study’s relevance to human biology. Objectives 4 Clearly describe the primary and any secondary objectives of the study, or specific hypotheses being tested. METHODS Ethical statement 5 Indicate the nature of the ethical review permissions, relevant licences (e.g. Animal [Scientific Procedures] Act 1986), and national or institutional guidelines for the care and use of animals, that cover the research. Study design 6 For each experiment, give brief details of the study design including: a. The number of experimental and control groups. b. Any steps taken to minimise the effects of subjective bias when allocating animals to treatment (e.g. randomisation procedure) and when assessing results (e.g. if done, describe who was blinded and when). c. The experimental unit (e.g. a single animal, group or cage of animals). A time-line diagram or flow chart can be useful to illustrate how complex study designs were carried out. Experimental procedures 7 For each experiment and each experimental group, including controls, provide precise details of all procedures carried out. For example: a. How (e.g. drug formulation and dose, site and route of administration, anaesthesia and analgesia used [including monitoring], surgical procedure, method of euthanasia). Provide details of any specialist equipment used, including supplier(s). b. When (e.g. time of day). c. Where (e.g. home cage, laboratory, water maze). d. Why (e.g. rationale for choice of specific anaesthetic, route of administration, drug dose used). BJP British Journal of Pharmacology DOI:10.1111/j.1476-5381.2010.00872.x www.brjpharmacol.org British Journal of Pharmacology (2010) 16

Imaging of amyloid β in Alzheimer's disease with 18F-BAY94-9172, a novel PET tracer: proof of mechanism

BACKGROUND Amyloid-beta (Abeta) plaque formation is a hallmark of Alzheimers disease (AD) and precedes the onset of dementia. Abeta imaging should allow earlier diagnosis, but clinical application is hindered by the short decay half-life of current Abeta-specific ligands. (18)F-BAY94-9172 is an Abeta ligand that, due to the half-life of (18)F, is suitable for clinical use. We thus studied the effectiveness of this ligand in identifying patients with AD. METHODS 15 patients with mild AD, 15 healthy elderly controls, and five individuals with frontotemporal lobar degeneration (FTLD) were studied. (18)F-BAY94-9172 binding was quantified by use of the standardised uptake value ratio (SUVR), which was calculated for the neocortex by use of the cerebellum as reference region. SUVR images were visually rated as normal or AD. FINDINGS (18)F-BAY94-9172 binding matched the reported post-mortem distribution of Abeta plaques. All AD patients showed widespread neocortical binding, which was greater in the precuneus/posterior cingulate and frontal cortex than in the lateral temporal and parietal cortex. There was relative sparing of sensorimotor, occipital, and medial temporal cortex. Healthy controls and FTLD patients showed only white-matter binding, although three controls and one FTLD patient had mild uptake in frontal and precuneus cortex. At 90-120 min after injection, higher neocortical SUVR was observed in AD patients (2.0 [SD 0.3]) than in healthy controls (1.3 [SD 0.2]; p<0.0001) or FTLD patients (1.2 [SD 0.2]; p=0.009). Visual interpretation was 100% sensitive and 90% specific for detection of AD. INTERPRETATION (18)F-BAY94-9172 PET discriminates between AD and FTLD or healthy controls and might facilitate integration of Abeta imaging into clinical practice.

Partitioning Variation in Multilevel Models

In multilevel modeling the residual variation in a response variable is split into component parts that are attributed to various levels. In applied work, much use is made of the percentage of variation that is attributable to the higher level sources of variation. Such a measure, however, makes sense only in simple variance components, Normal response, models where it is often referred to as the intra-unit correlation. In this article we describe how similar measures can be found for both more complex random variation in Normal response models and models with discrete responses. In these cases the variance partitions are dependent on predictors associated with the individual observation. We compare several computational techniques to compute the variance partitions.

A comparison of Bayesian and likelihood-based methods for fitting multilevel models

We use simulation studies, whose design is realistic for educational andmedicalresearch(aswellasotherfleldsofinquiry),tocompareBayesianand likelihood-basedmethodsforflttingvariance-components(VC)andrandom-efiects logistic regression (RELR) models. The likelihood (and approximate likelihood) approachesweexaminearebasedonthemethodsmostwidelyusedincurrentap- plied multilevel (hierarchical) analyses: maximum likelihood (ML) and restricted ML(REML)forGaussianoutcomes,andmarginalandpenalizedquasi-likelihood (MQL and PQL) for Bernoulli outcomes. Our Bayesian methods use Markov chain Monte Carlo (MCMC) estimation, with adaptive hybrid Metropolis-Gibbs sampling for RELR models, and several difiuse prior distributions (i i1 (†;†) and U(0; 1 ) priors for variance components). For evaluation criteria we consider bias of point estimates and nominal versus actual coverage of interval estimates in re- peated sampling. In two-level VC models we flnd that (a) both likelihood-based and Bayesian approaches can be made to produce approximately unbiased esti- mates, although the automatic manner in which REML accomplishes this is an advantage, but (b) both approaches had di-culty achieving nominal coverage in smallsamplesandwithsmallvaluesoftheintraclasscorrelation. Withthethree- levelRELRmodelsweexamineweflndthat(c)quasi-likelihoodmethodsforesti- mating random-efiects variances perform badly with respect to bias and coverage intheexamplewesimulated,and(d)Bayesiandifiuse-priormethodsleadtowell- calibratedpointandintervalRELRestimates. Whileitistruethatthelikelihood- based methods we study are considerably faster computationally than MCMC, (i) steady improvements in recent years in both hardware speed and e-ciency of MonteCarloalgorithmsand(ii)thelackofcalibrationoflikelihood-basedmethods insomecommonhierarchicalsettingscombinetomakeMCMC-basedBayesianflt- tingofmultilevelmodelsanattractiveapproach,evenwithratherlargedatasets. Other analytic strategies based on less approximate likelihood methods are also possible butwouldbeneflt fromfurtherstudy ofthe type summarized here.

Disease mapping with WinBUGS and MLwiN

Preface. Notation. 0.1 Standard notation for multilevel modelling. 0.2 Spatial multiple-membership models and the MMMC notation. 0.3 Standard notation for WinBUGS models. 1. Disease mapping basics. 1.1 Disease mapping and map reconstruction. 1.2 Disease map restoration. 2. Bayesian hierarchical modelling. 2.1 Likelihood and posterior distributions. 2.2 Hierarchical models. 2.3 Posterior inference. 2.4 Markov chain Monte Carlo methods. 2.5 Metropolis and Metropolis-Hastings algorithms. 2.6 Residuals and goodness of fit. 3. Multilevel modelling. 3.1 Continuous response models. 3.2 Estimation procedures for multilevel models. 3.3 Poisson response models. 3.4 Incorporating spatial information. 3.5 Discussion. 4. WinBUGS basics. 4.1 About WinBUGS. 4.2 Start using WinBUGS. 4.3 Specification of the model. 4.4 Model fitting. 4.5 Scripts. 4.6 Checking convergence. 4.7 Spatial modelling: GeoBUGS. 4 .8 Conclusions. 5. MLwiN basics. 5.1 About MLwiN. 5.2 Getting started. 5.3 Fitting statistical models. 5.4 MCMC estimation in MLwiN. 5.5 Spatial modelling. 5.6 Conclusions. 6. Relative risk estimation. 6.1 Relative risk estimation using WinBUGS. 6.2 Spatial prediction. 6.3 An analysis of the Ohio dataset using MLwiN. 7. Focused clustering: the analysis of putative health hazards. 7.1 Introduction. 7.2 Study design. 7.3 Problems of inference. 7.4 Modelling the hazard exposure risk. 7.5 Models for count data. 7.6 Bayesian models. 7.7 Focused clustering in WinBUGS. 7.8 Focused clustering in MLwiN. 8. Ecological analysis. 8.1 Introduction. 8.2 Statistical models. 8.3 WinBUGS analyses of ecological datasets. 8.4 MLwiN analyses of ecological datasets. 9. Spatially-correlated survival analysis. 9.1 Survival analysis in WinBUGS. 9.2 Survival analysis in MLwiN. 10. Epilogue. Appendix 1: WinBUGS code for focused clustering models. A.1: Falkirk example. A.2: Ohio example. Appendix 2: S-Plus function for conversion to GeoBUGS format. Bibliography. Index.

Animal research: reporting in vivo experiments--the ARRIVE guidelines.

The following guidelines are excerpted (as permitted under the Creative Commons Attribution License (CCAL), with the knowledge and approval of PLoS Biology and the authors) from Kilkenny et al (2010).

Bones' adaptive response to mechanical loading is essentially linear between the low strains associated with disuse and the high strains associated with the lamellar/woven bone transition

There is a widely held view that the relationship between mechanical loading history and adult bone mass/strength includes an adapted state or “lazy zone” where the bone mass/strength remains constant over a wide range of strain magnitudes. Evidence to support this theory is circumstantial. We investigated the possibility that the “lazy zone” is an artifact and that, across the range of normal strain experience, features of bone architecture associated with strength are linearly related in size to their strain experience. Skeletally mature female C57BL/6 mice were right sciatic neurectomized to minimize natural loading in their right tibiae. From the fifth day, these tibiae were subjected to a single period of external axial loading (40, 10‐second rest interrupted cycles) on alternate days for 2 weeks, with a peak dynamic load magnitude ranging from 0 to 14 N (peak strain magnitude: 0–5000 µε) and a constant loading rate of 500 N/s (maximum strain rate: 75,000 µε/s). The left tibiae were used as internal controls. Multilevel regression analyses suggest no evidence of any discontinuity in the progression of the relationships between peak dynamic load and three‐dimensional measures of bone mass/strength in both cortical and cancellous regions. These are essentially linear between the low‐peak locomotor strains associated with disuse (∼300 µε) and the high‐peak strains derived from artificial loading and associated with the lamellar/woven bone transition (∼5000 µε). The strain:response relationship and minimum effective strain are site‐specific, probably related to differences in the mismatch in strain distribution between normal and artificial loading at the locations investigated.

A general multilevel multistate competing risks model for event history data, with an application to a study of contraceptive use dynamics

We propose a general discrete time model for multilevel event history data. The model is developed for the analysis of longitudinal repeated episodes within individuals where there are multiple states and multiple types of event (competing risks) which may vary across states. The different transitions are modelled jointly to allow for correlation across transitions in unobserved individual risk factors. Implementation of the methodology using existing multilevel models for discrete response data is described. The model is applied in an analysis of contraceptive use dynamics in Indonesia where transitions from two states, contraceptive use and nonuse, are of interest. A distinction is made between two ways in which an episode of contraceptive use may end: a transition to nonuse or a switch to another method. Before adjusting for covariate effects, there is a strong negative residual correlation between the hazards of a transition from use to nonuse and from nonuse to use; this correlation is due to a tendency for short periods of nonuse after a birth to be followed by long periods of using the same contraceptive method.

Animal Research: Reporting in vivo experiments: The ARRIVE guidelines

†These guidelines are excerpted (as permitted under the Creative Commons Attribution License [CCAL], with the knowledge and approval of PLoS Biology and the authors) from Kilkenny C, Browne WJ, Cuthill IC, Emerson M, Altman DG. Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research. PLoS Biol 2010; 8: e1000412, DOI:10.1371/journal.pbio.1000412. ITEM RECOMMENDATION [1]