Random-effects meta-analysis models for the odds ratio in the case of rare events under different data-generating models: A simulation study

Meta-analysis of binary data is challenging when the event under investigation is rare, and standard models for random-effects meta-analysis perform poorly in such settings. In this simulation study, we investigate the performance of different random-effects meta-analysis models in terms of point and interval estimation of the pooled log odds ratio in rare events meta-analysis. First and foremost, we evaluate the performance of a hypergeometric-normal model from the family of generalized linear mixed models (GLMMs), which has been recommended, but has not yet been thoroughly investigated for rare events meta-analysis. Performance of this model is compared to performance of the beta-binomial model, which yielded favorable results in previous simulation studies, and to the performance of models that are frequently used in rare events meta-analysis, such as the inverse variance model and the Mantel–Haenszel method. In addition to considering a large number of simulation parameters inspired by real-world data settings, we study the comparative performance of the meta-analytic models under two different data-generating models (DGMs) that have been used in past simulation studies. The results of this study show that the hypergeometric-normal GLMM is useful for meta-analysis of rare events when moderate to large heterogeneity is present. In addition, our study reveals important insights with regard to the performance of the beta-binomial model under different DGMs from the binomial-normal family. In particular, we demonstrate that although misalignment of the beta-binomial model with the DGM affects its performance, it shows more robustness to the DGM than its competitors.     

An 'unconditional-like' structure for the conditional estimator of odds ratio from 2 x 2 tables

In the estimation of the odds ratio (OR), the conditional maximum-likelihood estimate (cMLE) is preferred to the more readily computed unconditional one (uMLE). However, the exact cMLE does not have a closed form to help divine it from the uMLE or to understand in what circumstances the difference between the two is appreciable. Here, the cMLE is shown to have the same 'ratio of cross-products' structure as its unconditional counterpart, but with two of the cell frequencies augmented, so as to shrink the unconditional estimator towards unity. The augmentation involves a factor, similar to the finite population correction, derived from the minimum of the marginal totals.     

A randomized test for the conditional odds ratio of matched pairs in an inverse binomial sampling design

Matched case‐control paired data are commonly used to study the association between a disease and an exposure of interest. This work provides a consistent test for this association with respect to the conditional odds ratio (), which is a measure of association that is also valid in prospective studies. We formulate the test from the maximum likelihood (ML) estimate of by using data under inverse binomial sampling, in which individuals are selected sequentially to form matched pairs until for the first time one obtains either a prefixed number of index pairs with the case unexposed but the control exposed or with the case exposed but the control unexposed. We discuss the situation of possible early stopping. We compare numerically the performance of our procedure with a competitor proposed by Lui ( 1996 ) in terms of type I error rate, power, average sample number (ASN) and the corresponding standard error. Our numerical study shows a gain in sample size without loss in power as compared to the competitor. Finally, we use the data taken from a case‐control study on the use of X‐rays and the risk of childhood acute myeloid leukemia for illustration.     

混合稀土对作物出苗率的影响

用OECD方法,在红壤、黄潮土、黄褐土上进行了混合稀土对水稻、油菜、大豆相对出苗率影响的试验,从LC50（半致死浓度）值看,在供试条件下,稀土对水稻的毒性最小,稀土在黄褐土上对作物的毒性也较小,而稀土对黄潮土上的大豆以及红壤上的油菜的毒性则较大。     

Frameshift events associated with the lysyl-tRNA and the rare arginine codon, AGA, in Escherichia coli: a case study involving the human Relaxin 2 protein

Human Relaxin 2 is an insulin-related peptide hormone with a mass of 19,084 Da. The mRNA contains a number of arginine codons that are rarely used by Escherichia coli to produce highly expressed proteins. As a result, expressing this recombinant protein in E. coli is problematic. When human Relaxin 2 was expressed in E. coli BL21 (DE3), several forms of the protein were made. One species had the expected molecular weight (19,084 Da). A second species observed had a molecular weight of 21,244 Da. A third minor species had a molecular weight of 17,118 Da. These aberrant molecular weights can be explained as follows. First, a sequence CGA-AAA-AAG-AGA, containing the rare arginine codons CGA and AGA was the site of the +1 frameshift that generated the 21,244 Da species. Since there was a limited supply of this arginyl-tRNA, the peptidyl-tRNA moved +1 nucleotide to occupy the codon and resumed protein synthesis. Second, a -1 frameshift associated with 'slippery A' sequence XXA-AAA-AAG accounted for 10% of the product with a mass of 17,118 Da. Presumably, the shift to -1 also occurred because there was a paucity of the arginyl-tRNAArgucu. Introduction of a plasmid coding for the cognate tRNA for AGA and site directed mutagenesis prevented the formation of both frameshift species.     

Comparison of random-effects meta-analysis models for the relative risk in the case of rare events: A simulation study

Pooling the relative risk (RR) across studies investigating rare events, for example, adverse events, via meta-analytical methods still presents a challenge to researchers. The main reason for this is the high probability of observing no events in treatment or control group or both, resulting in an undefined log RR (the basis of standard meta-analysis). Other technical challenges ensue, for example, the violation of normality assumptions, or bias due to exclusion of studies and application of continuity corrections, leading to poor performance of standard approaches. In the present simulation study, we compared three recently proposed alternative models (random-effects [RE] Poisson regression, RE zero-inflated Poisson [ZIP] regression, binomial regression) to the standard methods in conjunction with different continuity corrections and to different versions of beta-binomial regression. Based on our investigation of the models' performance in 162 different simulation settings informed by meta-analyses from the Cochrane database and distinguished by different underlying true effects, degrees of between-study heterogeneity, numbers of primary studies, group size ratios, and baseline risks, we recommend the use of the RE Poisson regression model. The beta-binomial model recommended by Kuss (2015) also performed well. Decent performance was also exhibited by the ZIP models, but they also had considerable convergence issues. We stress that these recommendations are only valid for meta-analyses with larger numbers of primary studies. All models are applied to data from two Cochrane reviews to illustrate differences between and issues of the models. Limitations as well as practical implications and recommendations are discussed; a flowchart summarizing recommendations is provided.     

Alternative responses to rare selection events are differentially vulnerable to changes in the frequency,scope, and intensity of environmental extremes

Extreme weather events are becoming more frequent, severe, and/or widespread as a consequence of anthropogenic climate change. While the economic and ecological implications of these changes have received considerable attention, the role of evolutionary processes in determining organismal responses to these critical challenges is currently unknown. Here we develop a novel theoretical framework that explores how alternative pathways for adaptation to rare selection events can influence population‐level vulnerabilities to future changes in the frequency, scope, and intensity of environmental extremes. We begin by showing that different life histories and trait expression profiles can shift the balance between additive and multiplicative properties of fitness accumulation, favoring different evolutionary responses to identical environmental phenomena. We then demonstrate that these different adaptive outcomes lead to predictable differences in population‐level vulnerabilities to rapid increases in the frequency, intensity, or scope of extreme weather events. Specifically, we show that when the primary mode of fitness accumulation is additive, evolution favors ignoring environmental extremes and lineages become highly vulnerable to extinction if the frequency or scope of extreme weather events suddenly increases. Conversely, when fitness accumulates primarily multiplicatively, evolution favors bet‐hedging phenotypes that cope well with historical extremes and are instead vulnerable to sudden increases in extreme event intensity. Our findings address a critical gap in our understanding of the potential consequences of rare selection events and provide a relatively simple rubric for assessing the vulnerabilities of any population of interest to changes in a wide variety of extreme environmental phenomena.     

Applications of proportional odds ordinal logistic regression models and continuation ratio models in examining the association of physical inactivity with erectile dysfunction among type 2 diabetic patients

[Purpose]Many studies have observed a high prevalence of erectile dysfunction among individuals performing physical activity in less leisure-time. However, this relationship in patients with type 2 diabetic patients is not well studied. In exposure outcome studies with ordinal outcome variables, investigators often try to make the outcome variable dichotomous and lose information by collapsing categories. Several statistical models have been developed to make full use of all information in ordinal response data, but they have not been widely used in public health research. In this paper, we discuss the application of two statistical models to determine the association of physical inactivity with erectile dysfunction among patients with type 2 diabetes.[Methods]A total of 204 married men aged 20-60 years with a diagnosis of type 2 diabetes at the outpatient unit of the Department of Endocrinology at PSG hospitals during the months of May and June 2019 were studied. We examined the association between physical inactivity and erectile dysfunction using proportional odds ordinal logistic regression models and continuation ratio models.[Results]The proportional odds model revealed that patients with diabetes who perform leisure time physical activity for over 40 minutes per day have reduced odds of erectile dysfunction (odds ratio=0.38) across the severity categories of erectile dysfunction after adjusting for age and duration of diabetes.[Conclusion]The present study suggests that physical inactivity has a negative impact on erectile function. We observed that the simple logistic regression model had only 75% efficiency compared to the proportional odds model used here; hence, more valid estimates were obtained here.     

Hypothetical role of men's sexual activity in the regulation of human sex ratio: Analysis of the sex ratio of post-war abandoned children

The regulation of the sex ratio at birth in human species remains poorly understood. After wars, a shift of the sex ratio in favor of men is always observed. Among the different hypothesis to explain this observation, one is to consider that Y-bearing spermatozoids have a weight advantage following insemination and that X-bearing spermatozoids, heavier, are more time-resistant. Following these observations, frequent sex may favor the birth of boys, whether infrequent sex may favor the birth of girls.Sustaining this sperm weight hypothesis, I report here that in France, after the two world wars, there has been an increase of abandoned illegitimate children with a significant shift of the sex ratio in favor of men. These observations may reflect an increase in illegitimate birth and indirectly an increase of men paternity.     

Restoration ecology in the Anthropocene: learning from responses of tropical forests to extreme disturbance events

Extreme disturbance events denote another aspect of global environmental changes archetypal of the Anthropocene. These events of climatic or anthropic origin are challenging our perceived understanding about how forests respond to disturbance. I present a general framework of tropical forest responses to extreme disturbance events with specific examples from tropical dry forests. The linkage between level of disturbance severity and dominant mechanism of vegetation recovery is reflected on a variety of initial trajectories of forest succession. Accordingly, more realistic and cost‐effective restoration goals in many tropical forests likely consist in maintaining a mosaic of different successional trajectories while promoting landscape connectivity, rather than encouraging full‐ecosystem recovery to pre‐disturbance conditions. Incorporating extreme disturbance events into the global restoration ecology agenda will be essential to design well‐informed ecosystem management strategies in the coming decades.     

Response of sex ratio to timing of breeding in the small cyprinid Gnathopogon caerulescens

The influence of hatching date on the sex ratio of wild Gnathopogon caerulescens was examined. Cohorts reared from eggs collected in the early and middle parts of the spawning season showed almost balanced sex ratios, with female bias in some cohorts. Cohorts born later in the season mostly displayed male bias, and the mean proportion of males later in the season was significantly higher than in early‐ and mid‐season cohorts. These results indicate that the sex ratio of G. caerulescens changes with the time of breeding, increasing along with the ambient water temperature of the lake.     

Adaptation to experimental alterations of the operational sex ratio in populations of Drosophila melanogaster

Theory predicts that males adapt to sperm competition by increasing their investment in testis mass to transfer larger ejaculates. Experimental and comparative data support this prediction. Nevertheless, the relative importance of sperm competition in testis size evolution remains elusive, because experiments vary only sperm competition whereas comparative approaches confound it with other variables, in particular male mating rate. We addressed the relative importance of sperm competition and male mating rate by taking an experimental evolution approach. We subjected populations of Drosophila melanogaster to sex ratios of 1:1, 4:1, and 10:1 (female:male). Female bias decreased sperm competition but increased male mating rate and sperm depletion. After 28 generations of evolution, males from the 10:1 treatment had larger testes than males from other treatments. Thus, testis size evolved in response to mating rate and sperm depletion, not sperm competition. Furthermore, our experiment demonstrated that drift associated with sex ratio distortion limits adaptation; testis size only evolved in populations in which the effect of sex ratio bias on the effective population size had been compensated by increasing the numerical size. We discuss these results with respect to reproductive evolution, genetic drift in natural and experimental populations, and consequences of natural sex ratio distortion.     

An accurate and non-labor intensive method for the determination of syringyl to guaiacyl ratio in lignin

The syringyl to guaiacyl (S:G) ratio of hardwood lignin has long been identified as a significant parameter in delignification processes and more recent results have shown that it is also important in determining the amount of ethanol that can be obtained from fermentation of hydrolyzed wood. Acidolysis of Klason or acid insoluble lignin in dioxane/water/HCl was being investigated when syringyl and guaiacyl nuclei with a diketone-containing sidechain were observed as the major products. The area ratio of the two gas chromatogram peaks appeared to be indicative of the S:G ratio. After optimization of the method the relative standard deviation was found to be in the range of 0.3–3.76% for Klason lignin from a wide range of Eucalyptus grandis grown in South Africa. The method was then compared to nitrobenzene oxidation (NBO) using 13 poplars in a double-blind study. The respective S:G ratios were used to calculate percentages of S units and when these values were plotted against each other a linear correlation was obtained with a slope of approximately 1.0 (R² = 0.86). The largest discrepancy for any poplar was 6.9% (62% vs. 58% S units). Both methods convincingly demonstrated a significant decrease in lignin content with an increase in the S:G ratio. Discussion is presented on a series of reaction that could lead to the formation of the two diketones.