Efficiency of two-phase methods with focus on a planned population-based case-control study on air pollution and stroke

<p>Abstract</p> <p>Background</p> <p>We plan to conduct a case-control study to investigate whether exposure to nitrogen dioxide (NO₂) increases the risk of stroke. In case-control studies, selective participation can lead to bias and loss of efficiency. A two-phase design can reduce bias and improve efficiency by combining information on the non-participating subjects with information from the participating subjects. In our planned study, we will have access to individual disease status and data on NO₂exposure on group (area) level for a large population sample of Scania, southern Sweden. A smaller sub-sample will be selected to the second phase for individual-level assessment on exposure and covariables. In this paper, we simulate a case-control study based on our planned study. We develop a two-phase method for this study and compare the performance of our method with the performance of other two-phase methods.</p> <p>Methods</p> <p>A two-phase case-control study was simulated with a varying number of first- and second-phase subjects. Estimation methods: <it>Method 1</it>: Effect estimation with second-phase data only. <it>Method 2</it>: Effect estimation by adjusting the first-phase estimate with the difference between the adjusted and unadjusted second-phase estimate. The first-phase estimate is based on individual disease status and residential address for all study subjects that are linked to register data on NO₂-exposure for each geographical area. <it>Method 3</it>: Effect estimation by using the expectation-maximization (EM) algorithm without taking area-level register data on exposure into account. <it>Method 4</it>: Effect estimation by using the EM algorithm and incorporating group-level register data on NO₂-exposure.</p> <p>Results</p> <p>The simulated scenarios were such that, unbiased or marginally biased (< 7%) odds ratio (OR) estimates were obtained with all methods. The efficiencies of method 4, are generally higher than those of methods 1 and 2. The standard errors in method 4 decreased further when the case/control ratio is above one in the second phase. For all methods, the standard errors do not become substantially reduced when the number of first-phase controls is increased.</p> <p>Conclusion</p> <p>In the setting described here, method 4 had the best performance in order to improve efficiency, while adjusting for varying participation rates across areas.</p

Measures and models for causal inference in cross-sectional studies: arguments for the appropriateness of the prevalence odds ratio and related logistic regression

<p>Abstract</p> <p>Background</p> <p>Several papers have discussed which effect measures are appropriate to capture the contrast between exposure groups in cross-sectional studies, and which related multivariate models are suitable. Although some have favored the Prevalence Ratio over the Prevalence Odds Ratio -- thus suggesting the use of log-binomial or robust Poisson instead of the logistic regression models -- this debate is still far from settled and requires close scrutiny.</p> <p>Discussion</p> <p>In order to evaluate how accurately true causal parameters such as Incidence Density Ratio (IDR) or the Cumulative Incidence Ratio (CIR) are effectively estimated, this paper presents a series of scenarios in which a researcher happens to find a preset ratio of prevalences in a given cross-sectional study. Results show that, provided essential and non-waivable conditions for causal inference are met, the CIR is most often inestimable whether through the Prevalence Ratio or the Prevalence Odds Ratio, and that the latter is the measure that consistently yields an appropriate measure of the Incidence Density Ratio.</p> <p>Summary</p> <p>Multivariate regression models should be avoided when assumptions for causal inference from cross-sectional data do not hold. Nevertheless, if these assumptions are met, it is the logistic regression model that is best suited for this task as it provides a suitable estimate of the Incidence Density Ratio.</p

Process evaluation of a randomised pilot trial of home-based rehabilitation compared to usual care in patients with heart failure with preserved ejection fraction and their caregiver’s

Background: Whilst heart failure (HF) with preserved ejection fraction (HFpEF) affects almost 50 percent of the HF population, evidence-based treatment options remain limited. However, there is growing evidence of the potential value of exercise-based cardiac rehabilitation. This study reports the process evaluation of the Rehabilitation Enablement in Chronic Heart Failure (REACH-HF) intervention for HFpEF patients and their caregivers conducted as part of the REACH-HFpEF pilot trial. Methods: Process evaluation sub-study parallel to a single centre (Tayside, Scotland) randomised controlled pilot trial with qualitative assessment of both intervention fidelity delivery and HFpEF patients’ and caregivers’ experiences. The REACH-HF intervention consisted of self-help manual for patients and caregivers, facilitated over 12 weeks by trained healthcare professionals. Interviews were conducted following completion of intervention in a purposeful sample of 15 HFpEF patients and 7 caregivers. Results: Qualitative information from the facilitator interactions and interviews identified three key themes for patients and caregivers: (1) understanding their condition, (2) emotional consequences of HF, and (3) patients’ and caregivers’ responses to the REACH-HF intervention. The differing professional backgrounds demonstrate the possibility of delivering REACH-HF by either existing HF or cardiac rehabilitation services of a combination of the two. Conclusions: The REACH-HF home-based facilitated intervention for HFpEF appears feasible and well accepted model for delivery of a cardiac rehabilitation intervention, with the potential to address key unmet needs of patients and their caregivers who are often excluded from service provision and current CR programmes. Results of this study will inform a recently funded full multicentre randomised clinical trial

Is time-variant information stickiness state-dependent?

This paper estimates information stickiness with regard to inflation expectations in the United States and the Eurozone for the 1981/06–2015/12 and 1998/Q4–2015/Q2 periods, respectively, and further investigates whether such information stickiness is state- dependent. Based on a bootstrap sub-sample rolling-window estimation, we find that information stickiness varies over time, which contradicts the strict time dependency implied under sticky-information theory. We provide evidence that information stickiness depends on inflation volatility, which indicates that information stickiness is state-dependent and that it has a time trend. Using a threshold model, we estimate structural changes in the state- dependence and time-trend of information stickiness. The results show that information stickiness has been more dependent on inflation volatility and has had a higher time-trend in both regions following the 2008 financial crisis.info:eu-repo/semantics/publishedVersio

Time-dynamic effects on the global temperature when harvesting logging residues for bioenergy

The climate mitigation potential of using logging residues (tree tops and branches) for bioenergy has been debated. In this study, a time-dependent life cycle assessment (LCA) was performed using a single-stand perspective. Three forest stands located in different Swedish climate zones were studied in order to assess the global temperature change when using logging residues for producing district heating. These systems were compared with two fossil reference systems in which the logging residues were assumed to remain in the forest to decompose over time, while coal or natural gas was used for energy. The results showed that replacing coal with logging residues gave a direct climate benefit from a single-stand perspective, while replacing natural gas gave a delayed climate benefit of around 8-12 years depending on climate zone. A sensitivity analysis showed that the time was strongly dependent on the assumptions for extraction and combustion of natural gas. The LCA showed that from a single-stand perspective, harvesting logging residues for bioenergy in the south of Sweden would give the highest temperature change mitigation potential per energy unit. However, the differences between the three climate zones studied per energy unit were relatively small. On a hectare basis, the southern forest stand would generate more biomass compared to the central and northern locations, which thereby could replace more fossil fuel and give larger climate benefits

Neurobeachin, a Regulator of Synaptic Protein Targeting, Is Associated with Body Fat Mass and Feeding Behavior in Mice and Body-Mass Index in Humans

Neurobeachin (Nbea) regulates neuronal membrane protein trafficking and is required for the development and functioning of central and neuromuscular synapses. In homozygous knockout (KO) mice, Nbea deficiency causes perinatal death. Here, we report that heterozygous KO mice haploinsufficient for Nbea have higher body weight due to increased adipose tissue mass. In several feeding paradigms, heterozygous KO mice consumed more food than wild-type (WT) controls, and this consumption was primarily driven by calories rather than palatability. Expression analysis of feeding-related genes in the hypothalamus and brainstem with real-time PCR showed differential expression of a subset of neuropeptide or neuropeptide receptor mRNAs between WT and Nbea+/− mice in the sated state and in response to food deprivation, but not to feeding reward. In humans, we identified two intronic NBEA single-nucleotide polymorphisms (SNPs) that are significantly associated with body-mass index (BMI) in adult and juvenile cohorts. Overall, data obtained in mice and humans suggest that variation of Nbea abundance or activity critically affects body weight, presumably by influencing the activity of feeding-related neural circuits. Our study emphasizes the importance of neural mechanisms in body weight control and points out NBEA as a potential risk gene in human obesity

Abrasive, Silica Phytoliths and the Evolution of Thick Molar Enamel in Primates, with Implications for the Diet of Paranthropus boisei

Background: Primates—including fossil species of apes and hominins—show variation in their degree of molar enamel thickness, a trait long thought to reflect a diet of hard or tough foods. The early hominins demonstrated molar enamel thickness of moderate to extreme degrees, which suggested to most researchers that they ate hard foods obtained on or near the ground, such as nuts, seeds, tubers, and roots. We propose an alternative hypothesis—that the amount of phytoliths in foods correlates with the evolution of thick molar enamel in primates, although this effect is constrained by a species ’ degree of folivory. Methodology/Principal Findings: From a combination of dietary data and evidence for the levels of phytoliths in plant families in the literature, we calculated the percentage of plant foods rich in phytoliths in the diets of twelve extant primates with wide variation in their molar enamel thickness. Additional dietary data from the literature provided the percentage of each primate’s diet made up of plants and of leaves. A statistical analysis of these variables showed that the amount of abrasive silica phytoliths in the diets of our sample primates correlated positively with the thickness of their molar enamel, constrained by the amount of leaves in their diet (R 2 = 0.875; p,.0006). Conclusions/Significance: The need to resist abrasion from phytoliths appears to be a key selective force behind the evolution of thick molar enamel in primates. The extreme molar enamel thickness of the teeth of the East African homini