Bayesian regression discontinuity designs: Incorporating clinical knowledge in the causal analysis of primary care data

The regression discontinuity (RD) design is a quasi-experimental design that estimates the causal effects of a treatment by exploiting naturally occurring treatment rules. It can be applied in any context where a particular treatment or intervention is administered according to a pre-specified rule linked to a continuous variable. Such thresholds are common in primary care drug prescription where the RD design can be used to estimate the causal effect of medication in the general population. Such results can then be contrasted to those obtained from randomised controlled trials (RCTs) and inform prescription policy and guidelines based on a more realistic and less expensive context. In this paper we focus on statins, a class of cholesterol-lowering drugs, however, the methodology can be applied to many other drugs provided these are prescribed in accordance to pre-determined guidelines. NHS guidelines state that statins should be prescribed to patients with 10 year cardiovascular disease risk scores in excess of 20%. If we consider patients whose scores are close to this threshold we find that there is an element of random variation in both the risk score itself and its measurement. We can thus consider the threshold a randomising device assigning the prescription to units just above the threshold and withholds it from those just below. Thus we are effectively replicating the conditions of an RCT in the area around the threshold, removing or at least mitigating confounding. We frame the RD design in the language of conditional independence which clarifies the assumptions necessary to apply it to data, and which makes the links with instrumental variables clear. We also have context specific knowledge about the expected sizes of the effects of statin prescription and are thus able to incorporate this into Bayesian models by formulating informative priors on our causal parameters.Comment: 21 pages, 5 figures, 2 table

Survival extrapolation using the poly-Weibull model.

Recent studies of (cost-) effectiveness in cardiothoracic transplantation have required estimation of mean survival over the lifetime of the recipients. In order to calculate mean survival, the complete survivor curve is required but is often not fully observed, so that survival extrapolation is necessary. After transplantation, the hazard function is bathtub-shaped, reflecting latent competing risks which operate additively in overlapping time periods. The poly-Weibull distribution is a flexible parametric model that may be used to extrapolate survival and has a natural competing risks interpretation. In addition, treatment effects and subgroups can be modelled separately for each component of risk. We describe the model and develop inference procedures using freely available software. The methods are applied to two problems from cardiothoracic transplantation

PART-FARM GENERAL CROPLAND RETIREMENT: EFFECTS OF SOME ALTERNATIVE PROGRAM SPECIFICATIONS

Land Economics/Use,

Inter and intra-rater reliability of head posture assessment through observation

The purpose of this study is to assess inter and intra-rater reliability of head posture (HP) assessment through observation

Survival extrapolation in the presence of cause specific hazards.

Health economic evaluations require estimates of expected survival from patients receiving different interventions, often over a lifetime. However, data on the patients of interest are typically only available for a much shorter follow-up time, from randomised trials or cohorts. Previous work showed how to use general population mortality to improve extrapolations of the short-term data, assuming a constant additive or multiplicative effect on the hazards for all-cause mortality for study patients relative to the general population. A more plausible assumption may be a constant effect on the hazard for the specific cause of death targeted by the treatments. To address this problem, we use independent parametric survival models for cause-specific mortality among the general population. Because causes of death are unobserved for the patients of interest, a polyhazard model is used to express their all-cause mortality as a sum of latent cause-specific hazards. Assuming proportional cause-specific hazards between the general and study populations then allows us to extrapolate mortality of the patients of interest to the long term. A Bayesian framework is used to jointly model all sources of data. By simulation, we show that ignoring cause-specific hazards leads to biased estimates of mean survival when the proportion of deaths due to the cause of interest changes through time. The methods are applied to an evaluation of implantable cardioverter defibrillators for the prevention of sudden cardiac death among patients with cardiac arrhythmia. After accounting for cause-specific mortality, substantial differences are seen in estimates of life years gained from implantable cardioverter defibrillators

The experiences of Portuguese physiotherapists when they assess head posture for patients with neck pain: A focus group study

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Age-related differences in head posture between patients with neck pain and pain-free individuals

Head posture and neck pain of chronic nontraumatic origin: a comparison between patients and pain-free persons.SFRH/BD/30735/20

Orientation imaging of macro-sized polysilicon grains on wafers using spatially resolved acoustic spectroscopy

Due to its economical production process polysilicon, or multicrystalline silicon, is widely used to produce solar cell wafers. However, the conversion efficiencies are often lower than equivalent monocrystalline or thin film cells, with the structure and orientation of the silicon grains strongly linked to the efficiency. We present a non-destructive laser ultrasonic inspection technique, capable of characterising large (52 x 76 mm2) photocell's microstructure – measurement times, sample surface preparation and system upgrades for silicon scanning are discussed. This system, known as spatially resolved acoustic spectroscopy (SRAS) could be used to optimise the polysilicon wafer production process and potentially improve efficiency

The First Detailed Abundances for M giants in Baade's Window from Infrared Spectroscopy

We report the first abundance analysis of 14 M giant stars in the Galactic bulge, based on R=25,000 infrared spectroscopy (1.5-1.8um) using NIRSPEC at the Keck II telescope. Because some of the bulge M giants reach high luminosities and have very late spectral type, it has been suggested that they are the progeny of only the most metal rich bulge stars, or possibly members of a younger bulge population. We find the iron abundance and composition of the M giants are similar to those of the K giants that have abundances determined from optical high resolution spectroscopy: =-0.190 +/- 0.020 with a 1-sigma dispersion of 0.08 +/- 0.015. Comparing our bulge M giants to a control sample of local disk M giants in the Solar vicinity, we find the bulge stars are enhanced in alpha elements at the level of +0.3 dex relative to the Solar composition stars, consistent with other studies of bulge globular clusters and field stars. This small sample shows no dependence of spectral type on metallicity, nor is there any indication that the M giants are the evolved members of a subset of the bulge population endowed with special characteristics such as relative youth or high metallicity. We also find low 12C/13C < 10, confirming the prsence of extra-mixing processes during the red gaint phase of evolutionComment: 19 pages, 7 figures, accepted for publication in the Astrophysical Journa