A method for exploratory repeated-measures analysis applied to a breast-cancer screening study

When a model may be fitted separately to each individual statistical unit, inspection of the point estimates may help the statistician to understand between-individual variability and to identify possible relationships. However, some information will be lost in such an approach because estimation uncertainty is disregarded. We present a comparative method for exploratory repeated-measures analysis to complement the point estimates that was motivated by and is demonstrated by analysis of data from the CADET II breast-cancer screening study. The approach helped to flag up some unusual reader behavior, to assess differences in performance, and to identify potential random-effects models for further analysis.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS481 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

RAZOR: A Phase II Open Randomized Trial of Screening Plus Goserelin and Raloxifene Versus Screening Alone in Premenopausal Women at Increased Risk of Breast Cancer.

Background: Ovarian suppression in premenopausal women is known to reduce breast cancer risk. This study aimed to assess uptake and compliance with ovarian suppression using the luteinizing hormone releasing hormone (LHRH) analogue, goserelin, with add-back raloxifene, as a potential regimen for breast cancer prevention.Methods: Women at ≥30% lifetime risk breast cancer were approached and randomized to mammographic screening alone (C-Control) or screening in addition to monthly subcutaneous injections of 3.6 mg goserelin and continuous 60 mg raloxifene daily orally (T-Treated) for 2 years. The primary endpoint was therapy adherence. Secondary endpoints were toxicity/quality of life, change in bone density, and mammographic density.Results: A total of 75/950 (7.9%) women approached agreed to randomization. In the T-arm, 20 of 38 (52%) of women completed the 2-year period of study compared with the C-arm (27/37, 73.0%). Dropouts were related to toxicity but also the wish to have established risk-reducing procedures and proven chemoprevention. As relatively few women completed the study, data are limited, but those in the T-arm reported significant increases in toxicity and sexual problems, no change in anxiety, and less cancer worry. Lumbar spine bone density declined by 7.0% and visually assessed mammographic density by 4.7% over the 2-year treatment period.Conclusions: Uptake is somewhat lower than comparable studies with tamoxifen for prevention with higher dropout rates. Raloxifene may preserve bone density, but reduction in mammographic density reversed after treatment was completed.Impact: This study indicates that breast cancer risk reduction may be possible using LHRH agonists, but reducing toxicity and preventing bone changes would make this a more attractive option. Cancer Epidemiol Biomarkers Prev; 27(1); 58-66. ©2017 AACR.Funding for the study was provided by Eli Lilly and drug supplies by Lilly and Astrazeneca

At the coalface and the cutting edge: general practitioners’ accounts of the rewards of engaging with HIV medicine

The interviews we conducted with GPs suggest that an engagement with HIV medicine enables clinicians to develop strong and long-term relationships with and expertise about the care needs of people living with HIV ‘at the coalface’, while also feeling connected with a broader network of medical practitioners and other professionals concerned with and contributing to the ever-changing world of science: ‘the cutting edge’. The general practice HIV prescriber is being modelled here as the interface between these two worlds, offering a rewarding opportunity for general practitioners to feel intimately connected to both community needs and scientific change

Using GWAS top hits to inform priors in Bayesian fine-mapping association studies

The default causal single‐nucleotide polymorphism (SNP) effect size prior in Bayesian fine‐mapping studies is usually the Normal distribution. This choice is often based on computational convenience, rather than evidence that it is the most suitable prior distribution. The choice of prior is important because previous studies have shown considerable sensitivity of causal SNP Bayes factors to the form of the prior. In some well‐studied diseases there are now considerable numbers of genome‐wide association study (GWAS) top hits along with estimates of the number of yet‐to‐be‐discovered causal SNPs. We show how the effect sizes of the top hits and estimates of the number of yet‐to‐be‐discovered causal SNPs can be used to choose between the Laplace and Normal priors, to estimate the prior parameters and to quantify the uncertainty in this estimation. The methodology can readily be applied to other priors. We show that the top hits available from breast cancer GWAS provide overwhelming support for the Laplace over the Normal prior, which has important consequences for variant prioritisation. This work in this paper enables practitioners to derive more objective priors than are currently being used and could lead to prioritisation of different variants