Discovering what designers do

This paper presents selected aspects of the experience and the outcomes of two DTI - SERC/EPSRC funded projects in design research and discusses how they might be applicable to the teaching of engineering design in universities. The genesis of the two projects is discussed focusing on the concept of the Design History Editor and the development from this of the Online Design Journal and the Designer's Assistant. The way in which these two software tools work together to detect, record, and retrieve work according to the concerns addressed is indicated. Possibilities are suggested for using these tools for support in the teaching of design

Additional file 2 of Assessment of learning curves in complex surgical interventions: a consecutive case-series study

Profile likelihood (τ) for surgeon 3. Profile likelihood of τ from the two-phase model fitted on surgeon 3’s series. (PDF 12 kb

Additional file 1 of Assessment of learning curves in complex surgical interventions: a consecutive case-series study

Two-phase model formulation. A detailed description of the formulation and fitting process (via ML estimation) of the two-phase model for both a continuous and a binary outcome. (PDF 174 kb

Additional file 3 of Alternative empirical Bayes models for adjusting for batch effects in genomic studies

Comparison of P-values for applying robust and non-robust F tests on the four experimental datasets. (XLSX 12 kb

Additional file 2 of Alternative empirical Bayes models for adjusting for batch effects in genomic studies

Mean and variance of gene expression distributions estimated from the EGFR signature and the TCGA breast cancer patient datasets. In TCGA, we used proteomics data of the patients, and binned the EGFR protein expression into 6 gradually increasing levels, partitioning all patients into 6 equal-sized groups. Mean and variances are estimated within each group. Up- and down-regulated genes are both EGFR signature genes derived by ASSIGN. The design and parameters for our simulation studies resemble the real estimates in these tables. Batch 1 represents the EGFR signature dataset with small gene variances, and a clear separation between the two condition groups in the expression of up-regulated genes. Batch 2 resembles the TCGA patient data with much larger variances than Batch 1. (XLSX 10 kb

The inclusion of real world evidence in clinical development planning

BACKGROUND: When designing studies it is common to search the literature to investigate variability estimates to use in sample size calculations. Proprietary data of previously designed trials in a particular indication are also used to obtain estimates of variability. Estimates of treatment effects are typically obtained from randomised controlled clinical trials (RCTs). Based on the observed estimates of treatment effect, variability and the minimum clinical relevant difference to detect, the sample size for a subsequent trial is estimated. However, data from real world evidence (RWE) studies, such as observational studies and other interventional studies in patients in routine clinical practice, are not widely used in a systematic manner when designing studies. In this paper, we propose a framework for inclusion of RWE in planning of a clinical development programme. METHODS: In our proposed approach, all evidence, from both RCTs and RWE (i.e. from studies in routine clinical practice), available at the time of designing of a new clinical trial is combined in a Bayesian network meta-analysis (NMA). The results can be used to inform the design of the next clinical trial in the programme. The NMA was performed at key milestones, such as at the end of the phase II trial and prior to the design of key phase III studies. To illustrate the methods, we designed an alternative clinical development programme in multiple sclerosis using RWE through clinical trial simulations. RESULTS: Inclusion of RWE in the NMA and the resulting trial simulations demonstrated that 284 patients per arm were needed to achieve 90% power to detect effects of predetermined size in the TRANSFORMS study. For the FREEDOMS and FREEDOMS II clinical trials, 189 patients per arm were required. Overall there was a reduction in sample size of at least 40% across the three phase III studies, which translated to a time savings of at least 6 months for the undertaking of the fingolimod phase III programme. CONCLUSION: The use of RWE resulted in a reduced sample size of the pivotal phase III studies, which led to substantial time savings compared to the approach of sample size calculations without RWE

What factors predict length of stay in a neonatal unit: a systematic review

Objective In the UK, 1 in 10 babies require specialist neonatal care. This care can last from hours to months depending on the need of the baby. The increasing survival of very preterm babies has increased neonatal care resource use. Evidence from multiple studies is crucial to identify factors which may be important for predicting length of stay (LOS). The ability to predict LOS is vital for resource planning, decision-making and parent counselling. The objective of this review was to identify which factors are important to consider when predicting LOS in the neonatal unit. Design A systematic review was undertaken which searched MEDLINE, EMBASE and Scopus for papers from 1994 to 2016 (May) for research investigating prediction of neonatal LOS. Strict inclusion and exclusion criteria were applied. Quality of each study was discussed, but not used as a reason for exclusion from the review. Main outcome measure Prediction of LOS in the neonatal unit. Results 9 studies were identified which investigated the prediction of neonatal LOS indicating a lack of evidence in the area. Inherent factors, particularly birth weight, sex and gestational age allow for a simple and objective prediction of LOS, which can be calculated on the first day of life. However, other early occurring factors may well also be important and estimates may need revising throughout the baby's stay in hospital. Conclusions Predicting LOS is vital to aid the commissioning of services and to help clinicians in their counselling of parents. The lack of evidence in this area indicates a need for larger studies to investigate methods of accurately predicting LOS

Barriers, Facilitators and Motivators.

<p>Barriers, Facilitators and Motivators.</p

Interview Themes.

<p>Interview Themes.</p

Interview schedule.

<p>Interview schedule.</p