Estimating structural mean models with multiple instrumental variables using the generalised method of moments

Instrumental variables analysis using genetic markers as instruments is now a widely used technique in epidemiology and biostatistics. As single markers tend to explain only a small proportion of phenotypical variation, there is increasing interest in using multiple genetic markers to obtain more precise estimates of causal parameters. Structural mean models (SMMs) are semi-parametric models that use instrumental variables to identify causal parameters, but there has been little work on using these models with multiple instruments, particularly for multiplicative and logistic SMMs. In this paper, we show how additive, multiplicative and logistic SMMs with multiple discrete instrumental variables can be estimated efficiently using the generalised method of moments (GMM) estimator, how the Hansen J-test can be used to test for model mis-specification, and how standard GMM software routines can be used to fit SMMs. We further show that multiplicative SMMs, like the additive SMM, identify a weighted average of local causal effects if selection is monotonic. We use these methods to reanalyse a study of the relationship between adiposity and hypertension using SMMs with two genetic markers as instruments for adiposity. We find strong effects of adiposity on hypertension, but no evidence of unobserved confounding.

Estimating Structural Mean Models with Multiple Instrumental Variables using the Generalised Method of Moments

Instrumental variables analysis using genetic markers as instruments is now a widely used technique in epidemiology and biostatistics. As single markers tend to explain only a small proportion of phenotypical variation, there is increasing interest in using multiple genetic markers to obtain more precise estimates of causal parameters. Structural mean models (SMMs) are semi-parametric models that use instrumental variables to identify causal parameters, but there has been little work on using these models with multiple instruments, particularly for multiplicative and logistic SMMs. In this paper, we show how additive, multiplicative and logistic SMMs with multiple discrete instrumental variables can be estimated efficiently using the generalised method of moments (GMM) estimator, how the Hansen J-test can be used to test for model mis-specification, and how standard GMM software routines can be used to fit SMMs. We further show that multiplicative SMMs, like the additive SMM, identify a weighted average of local causal effects if selection is monotonic. We use these methods to reanalyse a study of the relationship between adiposity and hypertension using SMMs with two genetic markers as instruments for adiposity. We find strong effects of adiposity on hypertension, but no evidence of unobserved confounding.Structural Mean Models, Multiple Instrumental Variables, Generalised Method of Moments, Mendelian Randomisation, Local Average Treatment Effects

A model study of enhanced oil recovery by flooding with aqueous surfactant solution and comparison with theory

With the aim of elucidating the details of enhanced oil recovery by surfactant solution flooding, we have determined the detailed behavior of model systems consisting of a packed column of calcium carbonate particles as the porous rock, n-decane as the trapped oil, and aqueous solutions of the anionic surfactant sodium bis(2-ethylhexyl) sulfosuccinate (AOT). The AOT concentration was varied from zero to above the critical aggregation concentration (cac). The salt content of the aqueous solutions was varied to give systems of widely different, post-cac oil–water interfacial tensions. The systems were characterized in detail by measuring the permeability behavior of the packed columns, the adsorption isotherms of AOT from the water to the oil–water interface and to the water–calcium carbonate interface, and oil–water–calcium carbonate contact angles. Measurements of the percent oil recovery by pumping surfactant solutions into calcium carbonate-packed columns initially filled with oil were analyzed in terms of the characterization results. We show that the measured contact angles as a function of AOT concentration are in reasonable agreement with those calculated from values of the surface energy of the calcium carbonate–air surface plus the measured adsorption isotherms. Surfactant adsorption onto the calcium carbonate–water interface causes depletion of its aqueous-phase concentration, and we derive equations which enable the concentration of nonadsorbed surfactant within the packed column to be estimated from measured parameters. The percent oil recovery as a function of the surfactant concentration is determined solely by the oil–water–calcium carbonate contact angle for nonadsorbed surfactant concentrations less than the cac. For surfactant concentrations greater than the cac, additional oil removal occurs by a combination of solubilization and emulsification plus oil mobilization due to the low oil–water interfacial tension and a pumping pressure increase

Local superfluid densities probed via current-induced superconducting phase gradients

We have developed a superconducting phase gradiometer consisting of two parallel DNA-templated nanowires connecting two thin-film leads. We have ramped the cross current flowing perpendicular to the nanowires, and observed oscillations in the lead-to-lead resistance due to cross-current-induced phase differences. By using this gradiometer we have measured the temperature and magnetic field dependence of the superfluid density and observed an amplification of phase gradients caused by elastic vortex displacements. We examine our data in light of Miller-Bardeen theory of dirty superconductors and a microscale version of Campbell's model of field penetration.Comment: 5 pages, 6 figure

Estimating Structural Mean Models with Multiple Instrumental Variables Using the Generalised Method of Moments

Instrumental variables analysis using genetic markers as instruments is now a widely used technique in epidemiology and biostatistics. As single markers tend to explain only a small proportion of phenotypic variation, there is increasing interest in using multiple genetic markers to obtain more precise estimates of causal parameters. Structural mean models (SMMs) are semiparametric models that use instrumental variables to identify causal parameters. Recently, interest has started to focus on using these models with multiple instruments, particularly for multiplicative and logistic SMMs. In this paper we show how additive, multiplicative and logistic SMMs with multiple orthogonal binary instrumental variables can be estimated efficiently in models with no further (continuous) covariates, using the generalised method of moments (GMM) estimator. We discuss how the Hansen J-test can be used to test for model misspecification, and how standard GMM software routines can be used to fit SMMs. We further show that multiplicative SMMs, like the additive SMM, identify a weighted average of local causal effects if selection is monotonic. We use these methods to reanalyse a study of the relationship between adiposity and hypertension using SMMs with two genetic markers as instruments for adiposity. We find strong effects of adiposity on hypertension

Total hip replacement for the treatment of end stage arthritis of the hip : a systematic review and meta-analysis

Background: Evolvements in the design, fixation methods, size, and bearing surface of implants for total hip replacement (THR) have led to a variety of options for healthcare professionals to consider. The need to determine the most optimal combinations of THR implant is warranted. This systematic review evaluated the clinical effectiveness of different types of THR used for the treatment of end stage arthritis of the hip. Methods: A comprehensive literature search was undertaken in major health databases. Randomised controlled trials (RCTs) and systematic reviews published from 2008 onwards comparing different types of primary THR in patients with end stage arthritis of the hip were included. Results: Fourteen RCTs and five systematic reviews were included. Patients experienced significant post-THR improvements in Harris Hip scores, but this did not differ between impact types. There was a reduced risk of implant dislocation after receiving a larger femoral head size (36 mm vs. 28 mm; RR = 0.17, 95% CI: 0.04, 0.78) or cemented cup (vs. cementless cup; pooled odds ratio: 0.34, 95% CI: 0.13, 0.89). Recipients of cross-linked vs. conventional polyethylene cup liners experienced reduced femoral head penetration and revision. There was no impact of femoral stem fixation and cup shell design on implant survival rates. Evidence on mortality and complications (aseptic loosening, femoral fracture) was inconclusive. Conclusions: The majority of evidence was inconclusive due to poor reporting, missing data, or uncertainty in treatment estimates. The findings warrant cautious interpretation given the risk of bias (blinding, attrition), methodological limitations (small sample size, low event counts, short follow-up), and poor reporting. Long-term pragmatic RCTs are needed to allow for more definitive conclusions. Authors are encouraged to specify the minimal clinically important difference and power calculation for their primary outcome(s) as well CONSORT, PRISMA and STROBE guidelines to ensure better reporting and more reliable production and assessment of evidence

Applying virtual reality to teach the software development process to novice software engineers

Software development is a complicated process that requires experienced human resources to produce successful software products. Although this process needs experience from the individuals, it is hard to provide this experience without encountering real incidents during the software development process. To fill this gap, this study proposes a Virtual Reality Based Software Development Framework (VR-SODEF), which provides an interactive virtual reality experience for individuals learning about the tasks of software development starting from requirement analysis through software testing. In the VR-SODEF, the participant takes on the role of a novice software developer being recruited into a virtual software development organisation who should work alongside five virtual characters, played by artificial intelligence. This exclusive viewpoint draws participants from the 2D separation of the classical experience and virtually into the world of the software development itself. Participants experience the intense dramatic elements created for simulation and confront the challenges of virtual software practitioners in a somewhat uncompromising virtual simulation environment. To examine the efficiency of the VR-SODEF, it was tested on 32 computing students, with results indicating that virtual reality can be an effective educational medium, especially for skills that might traditionally be acquired through experience rather than traditional classroom-based teaching

Active authentication for mobile devices utilising behaviour profiling.

With nearly 6 billion subscribers around the world, mobile devices have become an indispensable component in modern society. The majority of these devices rely upon passwords and personal identification numbers as a form of user authentication, and the weakness of these point-of-entry techniques is widely documented. Active authentication is designed to overcome this problem by utilising biometric techniques to continuously assess user identity. This paper describes a feasibility study into a behaviour profiling technique that utilises historical application usage to verify mobile users in a continuous manner. By utilising a combination of a rule-based classifier, a dynamic profiling technique and a smoothing function, the best experimental result for a users overall application usage was an equal error rate of 9.8 %. Based upon this result, the paper proceeds to propose a novel behaviour profiling framework that enables a user’s identity to be verified through their application usage in a continuous and transparent manner. In order to balance the trade-off between security and usability, the framework is designed in a modular way that will not reject user access based upon a single application activity but a number of consecutive abnormal application usages. The proposed framework is then evaluated through simulation with results of 11.45 and 4.17 % for the false rejection rate and false acceptance rate, respectively. In comparison with point-of-entry-based approaches, behaviour profiling provides a significant improvement in both the security afforded to the device and user convenience

Belimumab : a technological advance for systemic lupus erythematosus patients? Report of a systematic review and meta-analysis

Objectives: To undertake a systematic review and meta-analysis to investigate clinical effectiveness of belimumab for patients with systemic lupus erythematosus (SLE) and antinuclear and/or anti-double-stranded DNA (dsDNA) autoantibodies. Methods: We searched eight electronic databases and reference lists for randomised controlled trials (RCTs) of belimumab against placebo or best supportive care. Quality assessment and random effects meta-analysis were undertaken. Design: A meta-analysis of RCTs. Participants: 2133 SLE patients. Primary and secondary outcome measures: SLE Responder Index (SRI) at week 52. Results: Three double-blind placebo-controlled RCTs (L02, BLISS-52 BLISS-76) investigated 2133 SLE patients. BLISS-52 and BLISS-76 trials recruited patients with antinuclear and/or anti-dsDNA autoantibodies and demonstrated belimumab effectiveness for the SRI at week 52. Ethnicity and geographical location of participants varied considerably between BLISS trials. Although tests for statistical heterogeneity were negative, BLISS-52 results were systematically more favourable for all measured outcomes. Meta-analysis of pooled 52-week SRI BLISS results showed benefit for belimumab (OR 1.63, 95% CI 1.27 to 2.09). By week 76, the primary SRI outcome in BLISS-76 was not statistically significant (OR 1.31, 95% CI 0.919 to 1.855)

Aspirin for prophylactic use in the primary prevention of cardiovascular disease and cancer : a systematic review and overview of reviews

Background: Prophylactic aspirin has been considered to be beneficial in reducing the risks of heart disease and cancer. However, potential benefits must be balanced against the possible harm from side effects, such as bleeding and gastrointestinal (GI) symptoms. It is particularly important to know the risk of side effects when aspirin is used as primary prevention - that is when used by people as yet free of, but at risk of developing, cardiovascular disease (CVD) or cancer. In this report we aim to identify and re-analyse randomised controlled trials (RCTs), systematic reviews and meta-analyses to summarise the current scientific evidence with a focus on possible harms of prophylactic aspirin in primary prevention of CVD and cancer. Objectives: To identify RCTs, systematic reviews and meta-analyses of RCTs of the prophylactic use of aspirin in primary prevention of CVD or cancer. To undertake a quality assessment of identified systematic reviews and meta-analyses using meta-analysis to investigate study-level effects on estimates of benefits and risks of adverse events; cumulative meta-analysis; exploratory multivariable meta-regression; and to quantify relative and absolute risks and benefits. Methods: We identified RCTs, meta-analyses and systematic reviews, and searched electronic bibliographic databases (from 2008 September 2012) including MEDLINE, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects, NHS Centre for Reviews and Dissemination, and Science Citation Index. We limited searches to publications since 2008, based on timing of the most recent comprehensive systematic reviews. Results: In total, 2572 potentially relevant papers were identified and 27 met the inclusion criteria. Benefits of aspirin ranged from 6% reduction in relative risk (RR) for all-cause mortality [RR 0.94, 95% confidence interval (CI) 0.88 to 1.00] and 10% reduction in major cardiovascular events (MCEs) (RR 0.90, 95% CI 0.85 to 0.96) to a reduction in total coronary heart disease (CHD) of 15% (RR 0.85, 95% CI 0.69 to 1.06). Reported pooled odds ratios (ORs) for total cancer mortality ranged between 0.76 (95% CI 0.66 to 0.88) and 0.93 (95% CI 0.84 to 1.03). Inclusion of the Women's Health Study changed the estimated OR to 0.82 (95% CI 0.69 to 0.97). Aspirin reduced reported colorectal cancer (CRC) incidence (OR 0.66, 95% CI 0.90 to 1.02). However, including studies in which aspirin was given every other day raised the OR to 0.91 (95% CI 0.74 to 1.11). Reported cancer benefits appeared approximately 5 years from start of treatment. Calculation of absolute effects per 100,000 patient-years of follow-up showed reductions ranging from 33 to 46 deaths (all-cause mortality), 60-84 MCEs and 47-64 incidents of CHD and a possible avoidance of 34 deaths from CRC. Reported increased RRs of adverse events from aspirin use were 37% for GI bleeding (RR 1.37, 95% CI 1.15 to 1.62), between 54% (RR 1.54, 95% CI 1.30 to 1.82) and 62% (RR 1.62, 95% CI 1.31 to 2.00) for major bleeds, and between 32% (RR 1.32, 95% CI 1.00 to 1.74) and 38% (RR 1.38, 95% CI 1.01 to 1.82) for haemorrhagic stroke. Pooled estimates of increased RR for bleeding remained stable across trials conducted over several decades. Estimates of absolute rates of harm from aspirin use, per 100,000 patient-years of follow-up, were 99-178 for non-trivial bleeds, 46-49 for major bleeds, 68-117 for GI bleeds and 8-10 for haemorrhagic stroke. Meta-analyses aimed at judging risk of bleed according to sex and in individuals with diabetes were insufficiently powered for firm conclusions to be drawn. Limitations: Searches were date limited to 2008 because of the intense interest that this subject has generated and the cataloguing of all primary research in so many previous systematic reviews. A further limitation was our potential over-reliance on study-level systematic reviews in which the person-years of follow-up were not accurately ascertainable. However, estimates of number of events averted or incurred through aspirin use calculated from data in study-level meta-analyses did not differ substantially from estimates based on individual patient data-level meta-analyses, for which person-years of follow-up were more accurate (although based on less-than-complete assemblies of currently available primary studies). Conclusions: We have found that there is a fine balance between benefits and risks from regular aspirin use in primary prevention of CVD. Effects on cancer prevention have a long lead time and are at present reliant on post hoc analyses. All absolute effects are relatively small compared with the burden of these diseases. Several potentially relevant ongoing trials will be completed between 2013 and 2019, which may clarify the extent of benefit of aspirin in reducing cancer incidence and mortality. Future research considerations include expanding the use of IPD meta-analysis of RCTs by pooling data from available studies and investigating the impact of different dose regimens on cardiovascular and cancer outcomes