BackgroundConfounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias.MethodsA Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects.ResultsThe simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect.ConclusionIn logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model.
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Background: In their research reports, scientists are expected to discuss limitations that their studies have. Previous research showed that often, such discussion is absent. Also, many journals emphasize the importance of avoiding overstatement of claims. We wanted to see to what extent editorial handling and peer review affects self-acknowledgment of limitations and hedging of claims.Methods: Using software that automatically detects limitation-acknowledging sentences and calculates the level of hedging in sentences, we compared the submitted manuscripts and their ultimate publications of all randomized trials published in 2015 in 27 BioMed Central (BMC) journals and BMJ Open. We used mixed linear and logistic regression models, accounting for clustering of manuscript-publication pairs within journals, to quantify before-after changes in the mean numbers of limitation-acknowledging sentences, in the probability that a manuscript with zero self-acknowledged limitations ended up as a publication with at least one and in hedging scores.Results: Four hundred forty-six manuscript-publication pairs were analyzed. The median number of manuscripts per journal was 10.5 (interquartile range 6-18). The average number of distinct limitation sentences increased by 1.39 (95% CI 1.09-1.76), from 2.48 in manuscripts to 3.87 in publications. Two hundred two manuscripts (45.3%) did not mention any limitations. Sixty-three (31%, 95% CI 25-38) of these mentioned at least one after peer review. Changes in mean hedging scores were negligible.Conclusions: Our findings support the idea that editorial handling and peer review lead to more self-acknowledgment of study limitations, but not to changes in linguistic nuance.
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Mexican oregano is a non-timber forest product harvested in natural vegetation and represents an important source of income for rural families. Recent reports have highlighted decreases in natural populations caused by increased harvest intensity. Oregano leaf harvesting is a complex problem, involving different components and views, and has a clear spatial dimension. We proposed an analytical framework based on multi-criteria-multi-objective analyses. GIS tools were used as the platform for managing, displaying and analyzing ecological and socioeconomic information from different sources in order to evaluate land suitability of three different management strategies for two competing land objectives: oregano Harvest and oregano Regeneration. The incorporation of environmental evaluation criteria in the analysis allowed the identification of new potential oregano harvesting areas which were neither reported by harvesters, nor registered during harvesting trips. Socio-economic criteria, such as land tenure, highlighted the fact that a substantial proportion of current oregano harvesting areas are located outside ejido limits resulting in potential conflicts for resource access. The proposed Balanced oregano management strategy, in which the same proportion of suitable area (50%) was assigned to both objectives, represents the most favorable management strategy. This option allows harvesters to continue earning an income from oregano leaf harvest; and at the same time helps in the selection of the best areas for oregano regeneration. It also represents a management strategy with a smaller impact on oregano populations and on the harvesters ́ income, as well as lower monitoring costs. The proposed analytical frame-work may contribute to advance the application of systematic approaches for solving decision-making problems in areas where oregano leaves and other NTFP are harvested.
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