Analysing change based on two measures taken under different conditions.

Consider an analysis of change using two measurements on each individual taken from two periods of a longitudinal study, where the measurement conditions were different at each study period. In such situations, 'conditions effects' will necessarily be confounded with change between periods. An example of a conditions effect is a practice or learning effect, where a participant is tested at each period but learns to complete the test more effectively on the second occasion. If the conditions effect mechanism is associated with change and other factors in the analysis then biased model estimates will result. Unfortunately, as with classical age-period-cohort problems, estimating the conditions effect is impossible without modelling assumptions. In this paper, we show that the conditions effect is identifiable given empirically unverifiable assumptions about: (1) the sources of confounding bias in the study; (2) the functional form of age-related change; and (3) factors related to the conditions-effect. We develop the conditions-effect adjustment model (CEAM) for estimating change effects under different sets of assumptions. While none of these assumptions can be verified using the data alone, it is argued that assumptions 1 and 2 are always required when analysing change-even in the absence of conditions effects-and that robustness to all these assumptions can be assessed via sensitivity analysis. The CEAM is illustrated in an application to cognitive test data from the Whitehall II study of British civil servants

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

An extension theorem for conformal gauge singularities

We analyse conformal gauge, or isotropic, singularities in cosmological models in general relativity. Using the calculus of tractors, we find conditions in terms of tractor curvature for a local extension of the conformal structure through a cosmological singularity and prove a local extension theorem.Comment: 43 pages, no figures, version as published in JMP, small changes, updated reference

The Long Wavelength Array Software Library

The Long Wavelength Array Software Library (LSL) is a Python module that provides a collection of utilities to analyze and export data collected at the first station of the Long Wavelength Array, LWA1. Due to the nature of the data format and large-N ($\gtrsim$100 inputs) challenges faced by the LWA, currently available software packages are not suited to process the data. Using tools provided by LSL, observers can read in the raw LWA1 data, synthesize a filter bank, and apply incoherent de-dispersion to the data. The extensible nature of LSL also makes it an ideal tool for building data analysis pipelines and applying the methods to other low frequency arrays.Comment: accepted to the Journal of Astronomical Instrumentation; 24 pages, 4 figure

Parafermionic conformal field theory on the lattice

Finding the precise correspondence between lattice operators and the continuum fields that describe their long-distance properties is a largely open problem for strongly interacting critical points. Here we solve this problem essentially completely in the case of the three-state Potts model, which exhibits a phase transition described by a strongly interacting 'parafermion' conformal field theory. Using symmetry arguments, insights from integrability, and extensive simulations, we construct lattice analogues of nearly all the relevant and marginal physical fields governing this transition. This construction includes chiral fields such as the parafermion. Along the way we also clarify the structure of operator product expansions between order and disorder fields, which we confirm numerically. Our results both suggest a systematic methodology for attacking non-free field theories on the lattice and find broader applications in the pursuit of exotic topologically ordered phases of matter.Comment: 27 pages, 4 figures; v2 added reference

Modeling within-household associations in household panel studies

Household panel data provide valuable information about the extent of similarity in coresidents' attitudes and behaviours. However, existing analysis approaches do not allow for the complex association structures that arise due to changes in household composition over time. We propose a flexible marginal modeling approach where the changing correlation structure between individuals is modeled directly and the parameters estimated using second-order generalized estimating equations (GEE2). A key component of our correlation model specification is the 'superhousehold', a form of social network in which pairs of observations from different individuals are connected (directly or indirectly) by coresidence. These superhouseholds partition observations into clusters with nonstandard and highly variable correlation structures. We thus conduct a simulation study to evaluate the accuracy and stability of GEE2 for these models. Our approach is then applied in an analysis of individuals' attitudes towards gender roles using British Household Panel Survey data. We find strong evidence of between-individual correlation before, during and after coresidence, with large differences among spouses, parent-child, other family, and unrelated pairs. Our results suggest that these dependencies are due to a combination of non-random sorting and causal effects of coresidence

Strong Authentication for Web Services using Smartcards

The popularity of the Internet and the variety of services it provides has been immense. Unfortunately, many of these services require the user to register and subsequently login to the system in order to access them. This has resulted in the user having to remember a multitude of username and password combinations in order to use the service securely. However, literature has clearly demonstrated this is not an effective approach, as users will frequently choose simple passwords, write them down, share them or use the same password for multiple systems. This paper proposes a novel concept where Internet users authenticate to web services (service providers) by the use of a smartcard – taking away any requirement for the user to provide credentials. The smartcard is useful in this context as it is a trusted device that is capable of applying cryptography in a tamper resistant environment. The development of the concept is based upon an extension to Authentication Authorisation Infrastructure (AAI) models, where a trusted authority (Identity Provider) will provide and manage the smart card to end-users. In devices such as mobile phones, a smartcard is already present (e.g. the SIM) to facilitate this and it is envisaged such a card could also be produced for desktop environments – similarly to what many banks are currently implementing

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 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