Choice Of Statistical Approach Research Articles

Modelling Local Attitudes to Protected Areas in Developing Countries

During a time of intensifying competition for land, Protected Areas (PAs) are coming under increasing pressure to justify their status. Positive local attitudes to a PA are a potentially important component of any such justification, especially in the developing world where human pressure on natural resources is often high. However, despite numerous studies our understanding of what drives positive attitudes to PAs is still exceedingly limited. Here, we review the literature on local attitudes towards PAs in developing countries. Our survey reveals a highly fragmented research area where studies typically lack an explicit conceptual basis, and where there is wide variation in choice of statistical approach, explanatory and response variables, and incorporation of contextual information. Nevertheless, there is a relatively high degree of concordance between studies, with certain variables showing strong associations with attitudes. We recommend that PA attitude researchers in developing countries adopt a more rigorous model building approach based on a clear conceptual framework and drawing on the extensive empirical literature. Such an approach would improve the quality of research, increase comparability, and provide a stronger basis to support conservation decision-making.

Time-Series Analyses of Air Pollution and Mortality in the United States: A Subsampling Approach

Background: Hierarchical Bayesian methods have been used in previous papers to estimate national mean effects of air pollutants on daily deaths in time-series analyses.Objectives: We obtained maximum likelihood estimates of the common national effects of the criteria pollutants on mortality based on time-series data from ≤ 108 metropolitan areas in the United States.Methods: We used a subsampling bootstrap procedure to obtain the maximum likelihood estimates and confidence bounds for common national effects of the criteria pollutants, as measured by the percentage increase in daily mortality associated with a unit increase in daily 24-hr mean pollutant concentration on the previous day, while controlling for weather and temporal trends. We considered five pollutants [PM10, ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2)] in single- and multipollutant analyses. Flexible ambient concentration–response models for the pollutant effects were considered as well. We performed limited sensitivity analyses with different degrees of freedom for time trends.Results: In single-pollutant models, we observed significant associations of daily deaths with all pollutants. The O3 coefficient was highly sensitive to the degree of smoothing of time trends. Among the gases, SO2 and NO2 were most strongly associated with mortality. The flexible ambient concentration–response curve for O3 showed evidence of nonlinearity and a threshold at about 30 ppb.Conclusions: Differences between the results of our analyses and those reported from using the Bayesian approach suggest that estimates of the quantitative impact of pollutants depend on the choice of statistical approach, although results are not directly comparable because they are based on different data. In addition, the estimate of the O3-mortality coefficient depends on the amount of smoothing of time trends.

STATISTICAL METHODS FOR COST‐EFFECTIVENESS ANALYSES THAT USE OBSERVATIONAL DATA: A CRITICAL APPRAISAL TOOL AND REVIEW OF CURRENT PRACTICE

Many cost-effectiveness analyses (CEAs) use data from observational studies. Statistical methods can only address selection bias if they make plausible assumptions. No quality assessment tool is available for appraising CEAs that use observational studies. We developed a new checklist to assess statistical methods for addressing selection bias in CEAs that use observational data. The checklist criteria were informed by a conceptual review and applied in a systematic review of economic evaluations. Criteria included whether the study assessed the 'no unobserved confounding' assumption, overlap of baseline covariates between the treatment groups and the specification of the regression models. The checklist also considered structural uncertainty from the choice of statistical approach. We found 81 studies that met the inclusion criteria: studies tended to use regression (51%), matching on individual covariates (25%) or matching on the propensity score (22%). Most studies (77%) did not assess the 'no observed confounding' assumption, and few studies (16%) fully considered structural uncertainty from the choice of statistical approach. We conclude that published CEAs do not assess the main assumptions behind statistical methods for addressing selection bias. This checklist can raise awareness about the assumptions behind statistical methods for addressing selection bias and can complement existing method guidelines for CEAs.

Protein differences between human trapezius and vastus lateralis muscles determined with a proteomic approach

BackgroundThe trapezius muscle is a neck muscle that is susceptible to chronic pain conditions associated with repetitive tasks, commonly referred to as chronic work-related myalgia, hence making the trapezius a muscle of clinical interest. To provide a basis for further investigations of the proteomic traits of the trapezius muscle in disease, two-dimensional difference gel electrophoresis (2D-DIGE) was performed on the healthy trapezius using vastus lateralis as a reference. To obtain as much information as possible from the vast proteomic data set, both one-way ANOVA, with and without false discovery rate (FDR) correlation, and partial least square projection to latent structures with discriminant analysis (PLS-DA) were combined to compare the outcome of the analysis.ResultsThe trapezius and vastus lateralis showed significant differences in metabolic, contractile and regulatory proteins, with different results depending on choice of statistical approach and pre-processing technique. Using the standard method, FDR correlated one-way ANOVA, 42 protein spots differed significantly in abundance between the two muscles. Complementary analysis using immunohistochemistry and western blot confirmed the results from the 2D-DIGE analysis.ConclusionsThe proteomic approach used in the present study combining 2D-DIGE and multivariate modelling provided a more comprehensive comparison of the protein profiles of the human trapezius and vastus lateralis muscle, than previously possible to obtain with immunohistochemistry or SDS-PAGE alone. Although 2D-DIGE has inherent limitations it is particularly useful to comprehensively screen for important structural and metabolic proteins, and appears to be a promising tool for future studies of patients suffering from chronic work related myalgia or other muscle diseases.

Analysing the SF‐36 in population‐based research. A comparison of methods of statistical approaches using chronic pain as an example

The Medical Outcomes Study 36 Item Short-Form (SF-36) questionnaire is one of the most widely used measures of health related quality of life in medical research, including studies on pain-related conditions. Although scores in each of its eight domains rarely conform to a normal distribution, it is most widely analysed using simple parametric statistical techniques. Some have suggested a need for more complex or non-parametric analytical approaches, and this quandary faces researchers recurrently when using the SF-36. In this study of chronic pain, we compared results arising from the SF-36 between three study sub-samples, using conventional parametric, non-parametric, bootstrapping and log transforming methods. Respondents to a postal survey conducted in Aberdeen, Leeds and London (n = 3002, response rate 52%) were categorized in three groups according to previously validated questionnaires: those with chronic pain of predominantly neuropathic origin (POPNO, n = 241), those with chronic pain (non-POPNO, n = 1179), and those with no chronic pain (n = 1537). SF-36 scores were compared between these groups, using: ANOVA and t-tests; Kruskall-Wallis and Mann-Whitney U-tests; bootstrapping methods; and log transformation with ANOVA. There were highly significant differences between the three groups, with lower scores in all SF-36 domains found those with chronic pain (P < 0.001). Those with chronic POPNO had lower scores in all domains than those with chronic pain (non-POPNO) (P < 0.001). These results were the same after applying each statistical method. In this study, the choice of statistical approach had no influence on the results. We conclude that the conventional approach, using straightforward parametric tests, is both simplest and the best for allowing comparison with other studies. We are likely to adopt this in future studies.

Analysis of rainfall derivatives using daily precipitation models: opportunities and pitfalls

This study examines rainfall variability and its implications for wheat production risk in northeast Germany. The hedging effectiveness of rainfall options and the role of geographical basis risk are analyzed using a daily precipitation model. Simpler pricing methods such as the burn analysis and the index value simulation serve as benchmarks for comparison. It is found that the choice of statistical approach may lead to considerable differences in the estimation results. Daily precipitation models should be used with some caution in the context of derivative pricing because they tend to underestimate rainfall variability. This is unexpected, because daily simulation models are usually preferred in the context of temperature‐based weather indexes.