A Better Approach to Resolving Variable Selection Uncertainty in Meta Analysis for Benefits Transfer

Applying Bayesian Model Averaging to mechanistic models: An example and comparison of methods

The strength and relevance of a meta-analysis depends on the validity of the statistical methods used. Of special importance is appropriately assessing different sources of variability. Many studies including meta-analyses have evaluated the efficacy and safety of vitamin E and have yielded varying results. Illuminating and resolving these disparities requires addressing study variability and model uncertainty. To describe Bayesian meta-analysis methods for combining data from clinical trials, using recent studies that analyzed the relationship between vitamin E dose and all-cause mortality. Studies used in a previously published meta-analysis appended by studies identified by a search of MEDLINE from August 2004 to December 2005 using the MeSH terms vitamin e and alpha tocopherol. men and nonpregnant women; use of vitamin E alone or in combination with other vitamins or minerals; random allocation of participants to either vitamin E or a placebo or other control group; intervention and follow-up duration greater than 1 year; 10 or more deaths. Independent data extraction by one author was reviewed and confirmed by a second author. Corresponding authors of the original publications were contacted when questions arose. Data collection included the number of patients and deaths, percent men, use of other vitamins or minerals, mean age, and length of follow-up. We combined study results using Bayesian hierarchical model averaging. Analyses used Markov chain Monte Carlo computational techniques. Vitamin E intake is unlikely to affect mortality regardless of dose. The Bayesian meta-analyses presented here are ideal for incorporating disparate sources of variability, including trial effect and model uncertainty.

A method to estimate the number of workers in Myrmica ant nests on abandoned meadows was developed based on removal of workers. Ant workers have a tendency to climb up on wooden sticks put into their nests, therefore, assuming that the number of workers removed on sticks is related to the total number of workers within the nests, regression models for Myrmica rubra, M. ruginodis and M. scabrinodis may be built. We used a general regression model to perform a backward stepwise elimination of explanatory variables. These were the number of workers removed on sticks, temperature at the nest and site (a categorical variable). In case of each species the final model contained only the number of workers removed as a significant variable. The method is apparently non-destructive as we did not observe decreased survival of nests surveyed as compared to control nests. The method can be a very useful tool in population studies of ants as well as in biodiversity projects, where ants are used as bioinidcators.

Research that connects the effects of urbanization on biodiversity and ecosystem services is lacking. Ants perform multifarious ecological functions that stabilize ecosystems and contribute to a number of ecosystem services. We studied responses of ant communities to urbanization in the Lake Tahoe basin by sampling sites along a gradient of urban land development. We sampled ant communities, measured vegetation characteristics, quantified human activities, and evaluated ant-community responses by grouping ants into service-providing units (SPUs), defined as a group of organisms and their populations that perform specific ecosystem services, to provide an understanding of urbanization impacts on biodiversity and their delivery of ecosystem services. Species richness and abundance peaked at intermediate levels of urban development, as did the richness of 3 types of ant SPUs (aerators, decomposers, and compilers). With increasing land development aerator and decomposer ants significantly declined in abundance, whereas compiler ants significantly increased in abundance. Competing models demonstrated that precipitation was frequently among the strongest influences on ant community structure; however, urban development and human activities also had a strong, negative influence on ants, appearing in most models with DeltaAIC(c) < 2 for species richness and abundance patterns of SPUs and generalists. Response diversity was observed within SPUs, which suggests that the corresponding ecosystem services were maintained until development reached 30-40%. Our data provide evidence that ecosystem functions, such as water infiltration and soil productivity, may be diminished at sites subject to greater levels of urbanization and that conserving ant communities and the ecosystem services they provide could be an important target in land-use planning and conservation efforts.

DOI: 10.5935/abc.20130120 Dear Editor, We would like to present a few considerations pursuant to the statistical analysis of the predictors of death in the article by Caluza et al1, even though observing its nature of initial/pilot project. It has not remained clear which variables were included in the initial model and which method was used to select those significant variables in the final adjusted model (backward, forward, full model or variants). By virtue of the fine progression of the patients (in-hospital mortality of 6.8%) and of the relatively small sample size (n = 205), the number of deaths was low (n = 14). That is an excellent clinical result, but it imposes caution upon the logistic regression. The final model has presented six significant variables (perhaps the initial model might have had even more co-variables), with a maximum Events Per Variable (EPV) ratio of 2.3 (14/6). A low ratio of EPV results in an unstable model, with an increase in bias, variability, and overrating of the coefficients of regression and unverisimilar Confidence Intervals (CI)2,3. The worst case scenario4 would exactly be with 2 to 4 EPV, ≤ 30 outcomes, and low prevalence/incidence (< 10%) of the predictors (previous Cerebral Vascular Accident – CVA – [7.8%], Total Atrioventricular Block – total AV block – [6.8%], intra-aortic balloon pump – IABP – [5.8%], and cardiogenic shock [7.3%]). The occluded artery related to the infarction and the zero tissue flow grade (blush) upon the initial injection in the artery related to the infarction were variables inserted in a parallel fashion in the model. Has multi-co-linearity been verified among these? Except in the filling by collaterals, the entire occluded artery will have zero tissue flow grade, resulting in a high redundancy, with a reduction in the reliability of the coefficients of regression and amplification of the standard errors5.

To verify the association between quality of life, socioenvironmental aspects and general health status of users of an Auditory Health Care Service. Cross-sectional observational study with simple random sample. A semi-structured interview was conducted with 114 adults and elderly, aged 19-92, Individual Hearing Aids (IHA) users in an auditory health service. Participants answered questions regarding characterization, sociodemographic data and quality of life. To study the aspects of care, a search was carried out in the medical records. Mann Whitney, Chi-square and Fisher Exact tests were performed for association. The analyses were performed in the STATA software considering a 5% significance level. For the multivariate analysis, the full model was started with all significant variables at 20%. A logistic regression model was used to repeat the variables with p <5%. The majority of the participants was female, aged over 60 years and had their first consultation with the speech-language therapist. As for WHOQOL-bref, more than half of the sample reported good quality of life and health satisfaction. Regarding the complaints associated with the majority of those who did not present dizziness, ear fullness and depression reported good quality of life. It was observed an association with statistical significance between health satisfaction, higher mean age and lower schooling. In the multivariate analysis, the effective use of hearing aids and no history of dizziness were associated with good quality of life. For the health variable it was found that not having dizziness increased the chances of being satisfied with health while each year the more schooling the chance to be satisfied reduced. The use of IHA is related to a good perception of the quality of Life and health status of individuals with hearing loss.

Introduction: Standard analysis of clinical trials with composite endpoints compare time-to-first event. Subsequent events are not counted and therefore not taken into account in treatment comparisons. We compared standard analyses to analyses that take into account all the events a patient may have experienced. Methods: The Trial to Assess Chelation Therapy (TACT) enrolled 1708 post-MI pts in a factorial trial comparing edetate disodium infusions versus placebo infusions and oral vitamins and minerals versus oral placebo. TACT had a composite primary endpoint: death, MI, stroke, coronary revascularization, and hospitalization for angina. Pre-specified primary analysis for TACT was based on the standard approach of considering time until the occurrence of the first event. Two different approaches for multiple events analyses were compared to the standard approach: 1] a generalization of the Cox model to handle recurrent events, developed by Andersen &amp; Gill (AG), and 2] a marginal modeling approach developed by Wie, Lin, and Weissfeld (WLW). Each considers the correlation among multiple events occurring within a pt. Results: There were 743 events in TACT. 483/1708 (28%) of the patients had at least 1 event. Time-to-first-event analysis did not use information on 260 (35%) events. The standard analysis for edetate disodium vs placebo produced a hazard ratio (HR) 0.82, 95% confidence intervals 0.69,0.99, p=0.035. Both multiple events analyses demonstrated narrower confidence intervals and lower P values. AG: HR 0.80 (0.66,0.96), p=0.017; and WLW: HR 0.79 (0.66, 0.95), p=0.010. In the comparison of the active edetate + active MVM vs placebo edetate + placebo MVM, there were 247/858 (29%) pts with at least 1 event, but there were 371 events. Therefore, 124 (33%) were not used. Results of the standard analysis for edetate + MVM showed HR 0.74 (0.57,0.95), p=0.016; for AG HR 0.66 (0.51,0.85),p=0.002, and for WLW HR 0.38 (0.30,0.49), p&lt;0.001. Conclusion: In this post-MI trial, a standard time to first event analysis did not make use of over 1/3 of events. Multiple events analysis can improve the precision of the estimate of benefit by producing narrower confidence intervals. These methods could be considered to reduce sample size and budgets in future clinical trials.

The rise and fall of mean sea level are non-uniform around the global oceans. Their long-term regional trend and variability are intimately linked to the fluctuations and changes in the climate system. In this study, geographical patterns of sea level change derived from altimetric data over the period 1993-2015 were partitioned into large-scale oscillations allied with prevailing climatic factors after an empirical orthogonal function analysis. Taking into account the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillations (PDO), the sea level change deduced from the multiple regression showed a better estimate than the simple linear regression thanks to significantly larger coefficients of determination and narrower confidence intervals. Regional patterns associated with climatic factors varied greatly in different basins, notably in the eastern and western regions of the Pacific Ocean. The PDO exhibited a stronger impact on long-term spatial change in mean sea level than the ENSO in various parts of the Indian and Pacific Oceans, as well as of the subtropics and along the equator. Further improvements in the signal decomposition technique and physical understanding of the climate system are needed to better attain the signature of climatic factors on regional mean sea level.

This book presents the results of an eight-year research project (1998–2006) in the Great Lakes region of the US to develop methods and estimates of costs and benefits of water management. Many different types of water management and river restoration issues are addressed, including dam removal, mining problems and pesticide pollution. The book discusses the political economy of water management, the use of economic valuation results for market and non-market goods and services in political decision-making surrounding river management, including benefits capture and internalization, implementation of the beneficiary and polluter pays principle, and other equity issues related to the distribution of costs and benefits across various stakeholders. So, the book provides a wide variety of practical examples of economic assessments of river management projects. The first part of the book provides an introduction to ecological engineering and the basic economic principles of environmental valuation of river systems. The impact of engineering works on ecosystems are described, and the scale at which, for instance, dams have ecologic and economic impacts due to ecosystem connectivity. One of the case studies illustrates how ecological modelling can improve the reliability of scenario building in valuation studies. The next chapters present different case studies of surface water quality management and dam removal. In the contingent valuation (CV) studies, important topics pass in review, such as hypothetical bias, the effect of elicitation method, sensitivity to scope, order and context effects. Furthermore, illustrations are provided of the application of different marginal utility of income weights in the aggregation of willingness-to-pay (WTP) over the entire population and other distributional effects that policy scenarios can cause. Other studies combine revealed and stated preference methods, or apply benefits transfer to estimate a wide set of possible benefits. The book also includes an example of land-use modelling of non-point source pollution that accounts for upstream causes and downstream consequences. Although the studies employ a rich array of cost-based, hedonic pricing, travel cost methods, and benefits transfer approaches, the overall scientific contribution of the book

The value of environmental goods to individuals often depends on spatial features such as distance. The most common approach of accounting for distance decay is to model utility as some function of distance. It has been suggested to instead model the value as a function of the quantity of an environmental good within a certain distance. We develop three novel quantity-within-distance models that may be more suited for evaluating quantity changes in an environmental good. We argue that these models could capture spatial patterns better than distance-based models when i) secondary benefits are a relevant source of welfare, ii) the environmental change is spatially scattered, iii) the distribution of the endowment, i.e. the present availability of the environmental good, matters. Using data from choice experiments on the extension of green space and trees in two urban areas, we compare required assumptions, model fit, and size and precision of aggregated welfare estimates. Our results indicate limited differences in model fit. However, the quantity-within-distance models consistently produce aggregate welfare estimates roughly half of common distance decay models and have narrower confidence intervals. While it is not possible to infer which is more accurate, the large differences can have considerable policy implications.

Application of Refined Exposure Surrogates in Ambient PM2.5 Epidemiology Studies: Accounting for Temporal/Spatial Variations in Particulate Matter Infiltration

Summary In this study, we show significant improvements over conventional pressure-transient analysis on the basis of nonlinear regression by using total least squares (TLS), which minimizes errors in both pressure and time simultaneously. To our knowledge, TLS has not been applied to pressure-transient analysis before in this sense. TLS regression is not an easy problem to solve mathematically, especially for nonlinear pressure-transient-model functions. In this work, we compare four different versions of TLS. We formulate a robust approximation of the TLS solution, which can handle a variety of tests and reservoir models yet does not compromise the performance of the analysis. We show that our technique reduces ambiguity in the estimation of parameters to a large extent, especially in the presence of noise in time. Using our TLS algorithm, we obtain much narrower confidence intervals on the parameter estimates of a variety of real data sets, compared to the conventional least squares (LS) approach. For synthetic data sets, we observe that the TLS estimates are often closer to the true values than estimates made with LS, especially for poorly determined problems. When the deviation is in pressure only, TLS and LS results are comparable. However, in the presence of deviations in time in addition to pressure, the performance of TLS algorithms is substantially better. We, therefore, expect that our technique will provide more accurate estimation of reservoir parameters, allowing for better forecasting of reservoir performance.

In many randomized multi-visit studies, some response variables have an ordinal scale for outcomes. The win odds (accounts for ties) and the win ratio (ignores ties) are useful for treatment comparisons for ordinal outcomes. This paper discusses the application of randomization-based covariance and stratification adjustment of the win odds (and win ratio) to enable their more convenient use. Adjustment for strata is through the weighted average of within stratum two-sample U statistics for numerators and denominators for the stratified win odds (or win ratio). As randomization-based, invocation of covariance adjustment is through constraints to zeros for baseline covariate differences in the joint vector with logarithms of stratified win odds (or win ratios) for the respective visits. Such adjustment has no formal assumptions about the distributions of response variables or co-variates or the relationships of covariates to response variables; but the resulting adjusted stratified win odds (or win ratios) have narrower confidence intervals than their unadjusted counterparts when covariates have at least moderately strong associations with response variables. There is illustration of such results for the stratified win odds (and win ratio) for a randomized multi-visit clinical trial with an ordinal outcome for a chronic respiratory disorder. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 33-48

The aim of this study was to investigate the use of nonlinear mixed effects (NLME) models in a real bioequivalence study and compare it to noncompartmental analysis (NCA) which is proposed by regulatory agencies. NCA requires few hypotheses but a large number of samples per subject. On the other hand, NLME approach is more complex than NCA but it has some advantages such as it requires few samples per subject. A real data application was provided for the study, which was get from Ege University Drug Development and Pharmacokinetics Research Center. For NLME, we used the stochastic approach to expectation-maximization (SAEM) algorithm, whereas linear trapezoidal rule was used for NCA. We estimate pharmacokinetic parameters, area under the curve (AUC0-∞) and maximum concentration (Cmax), and perform a bioequivalence tests using NCA and NLME. According to real data analysis, NLME approach has smaller within subject error, narrower confidence intervals than non-compartmental analysis. However, NLME models have some limitations because of increasing type I error. Therefore, caution is needed for small sample size and data with high variability.

We are interested in the risk of large losses of certain common financial portfolios (e.g., credit portfolios with default risk). In these cases, we would like to estimate a risk statistic called Value-at-Risk (VaRα) at an extremely high risk level α (typically 99.99%). This high risk level corresponds to rare events for which it is difficult or impossible to obtain data. We first use Monte Carlo simulation to compute a probability distribution for the portfolio loss. We then use Extreme Value Theory (EVT) to study the tail of this loss distribution. Finally we compute the VaR and associated confidence intervals using bootstrap techniques. For the portfolios under consideration, we have observed that the EVT-based approach results in narrower confidence intervals and hence less sampling uncertainty in computing the VaR. We have also observed that the bootstrap replicate's distribution for the EVT-based method demonstrates a better shape than the empirical method (which is typically very noisy).