Individual Participant Data (IPD) Meta-analyses of Randomised Controlled Trials: Guidance on Their Use

Jayne Tierney and colleagues offer guidance on how to spot a well-designed and well-conducted individual participant data meta-analysis.

Summary Points 

• Systematic reviews are most commonly based on aggregate data extracted from publications or obtained from trial investigators. 

• Systematic reviews involving the central collection and analysis of individual participant data (IPD) usually are larger-scale, international, collaborative projects that can bring about substantial improvements to the quantity and quality of data, give greater scope in the analyses, and provide more detailed and robust results. 

• The process of collecting, checking, and analysing IPD is more complex than for aggregate data, and not all IPD meta-analyses are done to the same standard, making it difficult for researchers, clinicians, patients, policy makers, funders, and publishers to judge their quality. 

• Following our step-by-step guide will help reviewers and users of IPD meta-analyses to understand them better and recognise those that are well designed and conducted and so help ensure that policy, practice, and research are informed by robust evidence about the effects of interventions.

Recovery of Sleep or Recovery of Self? A Grounded Theory Study of Residents’ Decision Making Regarding How to Spend Their Nonclinical Postcall Time

Purpose: As resident work hours policies evolve, residents’ off-duty time remains poorly understood. Despite assumptions about how residents should be using their postcall, off-duty time, there is little research on how residents actually use this time and the reasoning underpinning their activities. This study sought to understand residents’ nonclinical postcall activities when they leave the hospital, their decision-making processes, and their perspectives on the relationship between these activities and their well-being or recovery.

Method: The study took place at a Liaison Committee on Medical Education–accredited Canadian medical school from 2012 to 2014. The authors recruited a purposive and convenience sample of postgraduate year 1–5 residents from six surgical and nonsurgical specialties at three hospitals affiliated with the medical school. Using a constructivist grounded theory approach, semistructured interviews were conducted, audio-taped, transcribed, anonymized, and combined with field notes. The authors analyzed interview transcripts using constant comparative analysis and performed post hoc member checking.

Results: Twenty-four residents participated. Residents characterized their predominant approach to postcall decision making as one of making trade-offs between multiple, competing, seemingly incompatible, but equally valuable, activities. Participants exhibited two different trade-off orientations: being oriented toward maintaining a normal life or toward mitigating fatigue.

Conclusions: The authors’ findings on residents’ trade-off orientations suggest a dual recovery model with postcall trade-offs motivated by the recovery of sleep or of self. This model challenges the dominant viewpoint in the current duty hours literature and suggests that the duty hours discussion must be broadened to include other recovery processes.

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

Influence of testing on permeability of compacted fine soils

The British standard constant-head triaxial test for measuring the permeability of fine-grained soils takes a relatively long time. A quicker test could provide savings to the construction industry, particularly for checking the quality of landfill clay liners. An accelerated permeability test has been developed, but the method often underestimates the permeability values compared owing to structural changes in the soil sample. This paper reports on an investigation
into the accelerated test to discover if the changes can be limited by using a revised procedure. The accelerated test is assessed and compared with the standard test and a ramp-accelerated permeability test. Four different finegrained materials are compacted at various water contents to produce analogous samples for testing using the three different methods. Fabric analysis is carried out on specimens derived from post-test samples using mercury intrusion porosimetry and scanning electron microscopy to assess the effects of testing on soil structure. The results show that accelerated testing in general underestimates permeability compared with values derived from the standard test, owing to changes in soil structure caused by testing. The ramp-accelerated test is shown to provide an improvement in terms of these structural changes.