Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations.

Cost-effectiveness analyses (CEAs) alongside randomised controlled trials (RCTs) are increasingly designed to collect resource use and preference-based health status data for the purpose of healthcare technology assessment. However, because of the way these measures are collected, they are prone to missing data, which can ultimately affect the decision of whether an intervention is good value for money. We examine how missing cost and effect outcome data are handled in RCT-based CEAs, complementing a previous review (covering 2003-2009, 88 articles) with a new systematic review (2009-2015, 81 articles) focussing on two different perspectives. First, we provide guidelines on how the information about missingness and related methods should be presented to improve the reporting and handling of missing data. We propose to address this issue by means of a quality evaluation scheme, providing a structured approach that can be used to guide the collection of information, elicitation of the assumptions, choice of methods and considerations of possible limitations of the given missingness problem. Second, we review the description of the missing data, the statistical methods used to deal with them and the quality of the judgement underpinning the choice of these methods. Our review shows that missing data in within-RCT CEAs are still often inadequately handled and the overall level of information provided to support the chosen methods is rarely satisfactory

Erratum to: Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations.

Reference 5, which reads: 5. Manca P, Palmer S. Handling missing values in cost effectiveness analyses that use data from cluster randomized trials. Appl Health Econ Health Policy. 2006;4:65–75. Should read: 5. Manca A, Palmer S. Handling missing data in patientlevel cost-effectiveness analysis alongside randomised clinical trials. Appl Health Econ Health Policy. 2005;4:65–75

Bayesian hierarchical model for the prediction of football results

The problem of modelling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. We propose a Bayesian hierarchical model to fulfil both these aims and test its predictive strength based on data about the Italian Serie A 1991-1992 championship. To overcome the issue of overshrinkage produced by the Bayesian hierarchical model, we specify a more complex mixture model that results in a better fit to the observed data. We test its performance using an example of the Italian Serie A 2007-2008 championship

Multi-Technique Investigation of a Biomimetic Insect Tarsal Adhesive Fluid

There is substantial motivation to develop novel adhesives which take advantage of the superior adhesive strength and adaptability of many natural animal adhesives; however, the tools typically used to study these mechanisms are incapable of determining the precise interactions of molecules at an adhesive interface. In this study, a surface specific, order sensitive vibrational spectroscopy called sum frequency generation (SFG) is, for the first time, combined with multiple bulk characterization techniques to examine a novel, simple biomimetic adhesive fluid inspired by tarsal fluid of insects. Insects perform complex adhesive demands, including sticking, climbing vertically and running upside-down with little difficulty. Thus, we hypothesize that both bulk and surface specific properties of the fluid contribute to the success of this wet adhesive mechanism. SFG spectra of biomimetic emulsion exhibited similar hydrocarbon organization on hydrophobic and hydrophilic substrates to natural beetle fluid previously studied with the same method. Bulk characterization techniques indicated that the emulsion had a shear-thinning profile with the ability to enhance traction forces during climbing and low surface tension ideal for surface wetting on the majority of natural surfaces. Multi-technique comparisons between emulsion and pure squalane revealed that a hydrocarbon only based fluid could not replicate the traction promoting properties of the emulsion. We conclude that the insect tarsal fluid adhesive mechanism relies upon contributions from both surface-specific properties optimizing traction force and bulk properties promoting rapid surface wetting and maintaining pull-off force for fast detachment

A bayesian nonparametric model for white blood cells in patients with lower urinary tract symptoms

Lower Urinary Tract Symptoms (LUTS) affect a significant proportion of the population and often lead to a reduced quality of life. LUTS overlap across a wide variety of diseases, which makes the diagnostic process extremely complicated. In this work we focus on the relation between LUTS and Urinary Tract Infection (UTI). The latter is detected through the number of White Blood Cells (WBC) in a sample of urine: WBC≥ 1 indicates UTI and high levels may indicate complications. The objective of this work is to provide the clinicians with a tool for supporting the diagnostic process, deepening the available knowledge about LUTS and UTI. We analyze data recording both LUTS profile and WBC count for each patient. We propose to model the WBC using a random partition model in which we specify a prior distribution over the partition of the patients which includes the clustering information contained in the LUTS profile. Then, within each cluster, the WBC counts are assumed to be generated by a zero-inflated Poisson distribution. The results of the predictive distribution allows to identify the symptoms configuration most associated with the presence of UTI as well as with severe infections

A Virtual Conversational Agent for Teens with Autism: Experimental Results and Design Lessons

We present the design of an online social skills development interface for teenagers with autism spectrum disorder (ASD). The interface is intended to enable private conversation practice anywhere, anytime using a web-browser. Users converse informally with a virtual agent, receiving feedback on nonverbal cues in real-time, and summary feedback. The prototype was developed in consultation with an expert UX designer, two psychologists, and a pediatrician. Using the data from 47 individuals, feedback and dialogue generation were automated using a hidden Markov model and a schema-driven dialogue manager capable of handling multi-topic conversations. We conducted a study with nine high-functioning ASD teenagers. Through a thematic analysis of post-experiment interviews, identified several key design considerations, notably: 1) Users should be fully briefed at the outset about the purpose and limitations of the system, to avoid unrealistic expectations. 2) An interface should incorporate positive acknowledgment of behavior change. 3) Realistic appearance of a virtual agent and responsiveness are important in engaging users. 4) Conversation personalization, for instance in prompting laconic users for more input and reciprocal questions, would help the teenagers engage for longer terms and increase the system's utility

Summarising salient information on historical controls: A structured assessment of validity and comparability across studies

BACKGROUND: While placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions. METHODS: We discuss the original 'Pocock' criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer. RESULTS: A number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be. CONCLUSION: Historical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly

Designing a stepped wedge trial: three main designs, carry-over effects and randomisation approaches.

BACKGROUND: There is limited guidance on the design of stepped wedge cluster randomised trials. Current methodological literature focuses mainly on trials with cross-sectional data collection at discrete times, yet many recent stepped wedge trials do not follow this design. In this article, we present a typology to characterise the full range of stepped wedge designs, and offer guidance on several other design aspects. METHODS: We developed a framework to define and report the key characteristics of a stepped wedge trial, including cluster allocation and individual participation. We also considered the relative strengths and weaknesses of trials according to this framework. We classified recently published stepped wedge trials using this framework and identified illustrative case studies. We identified key design choices and developed guidance for each. RESULTS: We identified three main stepped wedge designs: those with a closed cohort, an open cohort, and a continuous recruitment short exposure design. In the first two designs, many individuals experience both control and intervention conditions. In the final design, individuals are recruited in continuous time as they become eligible and experience either the control or intervention condition, but not both, and then provide an outcome measurement at follow-up. While most stepped wedge trials use simple randomisation, stratification and restricted randomisation are often feasible and may be useful. Some recent studies collect outcome information from individuals exposed a long time before or after the rollout period, but this contributes little to the primary analysis. Incomplete designs should be considered when the intervention cannot be implemented quickly. Carry-over effects can arise in stepped wedge trials with closed and open cohorts. CONCLUSIONS: Stepped wedge trial designs should be reported more clearly. Researchers should consider the use of stratified and/or restricted randomisation. Trials should generally not commit resources to collect outcome data from individuals exposed a long time before or after the rollout period. Though substantial carry-over effects are uncommon in stepped wedge trials, researchers should consider their possibility before conducting a trial with closed or open cohorts