Correcting bias in the meta-analysis of correlations.

We demonstrate that all conventional meta-analyses of correlation coefficients are biased, explain why, and offer solutions. Because the standard errors of the correlation coefficients depend on the size of the coefficient, inverse-variance weighted averages will be biased even under ideal meta-analytical conditions (i.e., absence of publication bias, p-hacking, or other biases). Transformation to Fisher's z often greatly reduces these biases but still does not mitigate them entirely. Although all are small-sample biases (n < 200), they will often have practical consequences in psychology where the typical sample size of correlational studies is 86. We offer two solutions: the well-known Fisher's z-transformation and new small-sample adjustment of Fisher's that renders any remaining bias scientifically trivial. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Footprint of publication selection bias on meta-analyses in medicine, environmental sciences, psychology, and economics.

Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 68,000 meta-analyses containing over 700,000 effect size estimates from medicine (67,386/597,699), environmental sciences (199/12,707), psychology (605/23,563), and economics (327/91,421). Our results indicate that meta-analyses in economics are the most severely contaminated by publication selection bias, closely followed by meta-analyses in environmental sciences and psychology, whereas meta-analyses in medicine are contaminated the least. After adjusting for publication selection bias, the median probability of the presence of an effect decreased from 99.9% to 29.7% in economics, from 98.9% to 55.7% in psychology, from 99.8% to 70.7% in environmental sciences, and from 38.0% to 29.7% in medicine. The median absolute effect sizes (in terms of standardized mean differences) decreased from d = 0.20 to d = 0.07 in economics, from d = 0.37 to d = 0.26 in psychology, from d = 0.62 to d = 0.43 in environmental sciences, and from d = 0.24 to d = 0.13 in medicine.

Estimating the extent of selective reporting: An application to economics.

Using a sample of 70,399 published p-values from 192 meta-analyses, we empirically estimate the counterfactual distribution of p-values in the absence of any biases. Comparing observed p-values with counterfactually expected p-values allows us to estimate how many p-values are published as being statistically significant when they should have been published as non-significant. We estimate the extent of selectively reported p-values to range between 57.7% and 71.9% of the significant p-values. The counterfactual p-value distribution also allows us to assess shifts of p-values along the entire distribution of published p-values, revealing that particularly very small p-values (p < 0.001) are unexpectedly abundant in the published literature. Subsample analysis suggests that the extent of selective reporting is reduced in research fields that use experimental designs, analyze microeconomics research questions, and have at least some adequately powered studies.

Meta-analyses of partial correlations are biased: Detection and solutions.

We demonstrate that all meta-analyses of partial correlations are biased, and yet hundreds of meta-analyses of partial correlation coefficients (PCCs) are conducted each year widely across economics, business, education, psychology, and medical research. To address these biases, we offer a new weighted average, UWLS+3 . UWLS+3 is the unrestricted weighted least squares weighted average that makes an adjustment to the degrees of freedom that are used to calculate partial correlations and, by doing so, renders trivial any remaining meta-analysis bias. Our simulations also reveal that these meta-analysis biases are small-sample biases (n < 200), and a simple correction factor of (n - 2)/(n - 1) greatly reduces these small-sample biases along with Fisher's z. In many applications where primary studies typically have hundreds or more observations, partial correlations can be meta-analyzed in standard ways with only negligible bias. However, in other fields in the social and the medical sciences that are dominated by small samples, these meta-analysis biases are easily avoidable by our proposed methods.

Exploring open science practices in behavioural public policy research.

In their book 'Nudge: Improving Decisions About Health, Wealth and Happiness', Thaler & Sunstein (2009) argue that choice architectures are promising public policy interventions. This research programme motivated the creation of 'nudge units', government agencies which aim to apply insights from behavioural science to improve public policy. We closely examine a meta-analysis of the evidence gathered by two of the largest and most influential nudge units (DellaVigna & Linos (2022 Econometrica 90, 81-116 (doi:10.3982/ECTA18709))) and use statistical techniques to detect reporting biases. Our analysis shows evidence suggestive of selective reporting. We additionally evaluate the public pre-analysis plans from one of the two nudge units (Office of Evaluation Sciences). We identify several instances of excellent practice; however, we also find that the analysis plans and reporting often lack sufficient detail to evaluate (unintentional) reporting biases. We highlight several improvements that would enhance the effectiveness of the pre-analysis plans and reports as a means to combat reporting biases. Our findings and suggestions can further improve the evidence base for policy decisions.

Safety and Tolerability of GalNAc3-Conjugated Antisense Drugs Compared to the Same-Sequence 2'-O-Methoxyethyl-Modified Antisense Drugs: Results from an Integrated Assessment of Phase 1 Clinical Trial Data.

The triantennary N-acetylgalactosamine (GalNAc3) cluster has demonstrated the utility of receptor-mediated uptake of ligand-conjugated antisense drugs targeting RNA expressed by hepatocytes. GalNAc3-conjugated 2'-O-methoxyethyl (2'MOE) modified antisense oligonucleotides (ASOs) have demonstrated a higher potency than the unconjugated form to support lower doses for an equivalent pharmacological effect. We utilized the Ionis integrated safety database to compare four GalNAc3-conjugated and four same-sequence unconjugated 2'MOE ASOs. This assessment evaluated data from eight randomized placebo-controlled dose-ranging phase 1 studies involving 195 healthy volunteers (79 GalNAc3 ASO, 24 placebo; 71 ASO, 21 placebo). No safety signals were identified by the incidence of abnormal threshold values in clinical laboratory tests for either ASO group. However, there was a significant increase in mean alanine transaminase levels compared with placebo in the upper dose range of the unconjugated 2'MOE ASO group. The mean percentage of subcutaneous injections leading to local cutaneous reaction was 30-fold lower in the GalNAc3-conjugated ASO group compared with the unconjugated ASO group (0.9% vs. 28.6%), with no incidence of flu-like reactions (0.0% vs. 0.7%). Three subjects (4.2%) in the unconjugated ASO group discontinued dosing. An improvement in the overall safety and tolerability profile of GalNAc3-conjugated 2'MOE ASOs is evident in this comparison of short-term clinical data in healthy volunteers.

Redesigning crop varieties to win the race between climate change and food security.

Climate change poses daunting challenges to agricultural production and food security. Rising temperatures, shifting weather patterns, and more frequent extreme events have already demonstrated their effects on local, regional, and global agricultural systems. Crop varieties that withstand climate-related stresses and are suitable for cultivation in innovative cropping systems will be crucial to maximize risk avoidance, productivity, and profitability under climate-changed environments. We surveyed 588 expert stakeholders to predict current and novel traits that may be essential for future pearl millet, sorghum, maize, groundnut, cowpea, and common bean varieties, particularly in sub-Saharan Africa. We then review the current progress and prospects for breeding three prioritized future-essential traits for each of these crops. Experts predict that most current breeding priorities will remain important, but that rates of genetic gain must increase to keep pace with climate challenges and consumer demands. Importantly, the predicted future-essential traits include innovative breeding targets that must also be prioritized; for example, (1) optimized rhizosphere microbiome, with benefits for P, N, and water use efficiency, (2) optimized performance across or in specific cropping systems, (3) lower nighttime respiration, (4) improved stover quality, and (5) increased early vigor. We further discuss cutting-edge tools and approaches to discover, validate, and incorporate novel genetic diversity from exotic germplasm into breeding populations with unprecedented precision, accuracy, and speed. We conclude that the greatest challenge to developing crop varieties to win the race between climate change and food security might be our innovativeness in defining and boldness to breed for the traits of tomorrow.

Characterization of cooperative PS-oligo activation of human TLR9.

Single-stranded phosphorothioate oligonucleotides (PS-oligos) can activate TLR9, leading to an innate immune response. This can occur with PS-oligos containing unmethylated CpG sites, the canonical motif, or PS-oligos that do not contain those motifs (non-CpG). Structural evidence shows that TLR9 contains two PS-oligo binding sites, and recent data suggest that synergistic cooperative activation of TLR9 can be achieved by adding two separate PS-oligos to cells, each engaging with a separate site on TLR9 to enhance TLR9 activation as a pair. Here, we demonstrate and characterize this cooperativity phenomenon using PS-oligos in human cell lines, and we introduce several novel PS-oligo pairs (CpG and non-CpG pairs) that show cooperative activation. Indeed, we find that cooperative PS-oligos likely bind at different sites on TLR9. Interestingly, we find that PS-oligos that generate little TLR9 activation on their own can prime TLR9 to be activated by other PS-oligos. Finally, we determine that previous models of TLR9 activation cannot be used to fully explain data from systems using human TLR9 and PS-oligos. Overall, we reveal new details of TLR9 activation, but we also find that more work needs to be done to determine where certain PS-oligos are binding to TLR9.

Reversal of diabetic retinopathy in two patients following the use of physiologic insulin Resensitization.

We followed two patients with diabetic retinopathy over the course of their treatment with physiologic Insulin resensitization. Both patients showed improvement of their diabetic retinopathy, after treatment.

A Receptor Story: Insulin Resistance Pathophysiology and Physiologic Insulin Resensitization's Role as a Treatment Modality.

Physiologic insulin secretion consists of an oscillating pattern of secretion followed by distinct trough periods that stimulate ligand and receptor activation. Apart from the large postprandial bolus release of insulin, ß cells also secrete small amounts of insulin every 4-8 min independent of a meal. Insulin resistance is associated with a disruption in the normal cyclical pattern of insulin secretion. In the case of type-2 diabetes, ß-cell mass is reduced due to apoptosis and ß cells secrete insulin asynchronously. When ligand/receptors are constantly exposed to insulin, a negative feedback loop down regulates insulin receptor availability to insulin, creating a relative hyperinsulinemia. The relative excess of insulin leads to insulin resistance (IR) due to decreased receptor availability. Over time, progressive insulin resistance compromises carbohydrate metabolism, and may progress to type-2 diabetes (T2D). In this review, we discuss insulin resistance pathophysiology and the use of dynamic exogenous insulin administration in a manner consistent with more normal insulin secretion periodicity to reverse insulin resistance. Administration of insulin in such a physiologic manner appears to improve insulin sensitivity, lower HgbA1c, and, in some instances, has been associated with the reversal of end-organ damage that leads to complications of diabetes. This review outlines the rationale for how the physiologic secretion of insulin orchestrates glucose metabolism, and how mimicking this secretion profile may serve to improve glycemic control, reduce cellular inflammation, and potentially improve outcomes in patients with diabetes.

Meta-analyses in psychology often overestimate evidence for and size of effects.

Adjusting for publication bias is essential when drawing meta-analytic inferences. However, most methods that adjust for publication bias do not perform well across a range of research conditions, such as the degree of heterogeneity in effect sizes across studies. Sladekova et al. 2022 (Estimating the change in meta-analytic effect size estimates after the application of publication bias adjustment methods. Psychol. Methods) tried to circumvent this complication by selecting the methods that are most appropriate for a given set of conditions, and concluded that publication bias on average causes only minimal over-estimation of effect sizes in psychology. However, this approach suffers from a 'Catch-22' problem-to know the underlying research conditions, one needs to have adjusted for publication bias correctly, but to correctly adjust for publication bias, one needs to know the underlying research conditions. To alleviate this problem, we conduct an alternative analysis, robust Bayesian meta-analysis (RoBMA), which is not based on model-selection but on model-averaging. In RoBMA, models that predict the observed results better are given correspondingly larger weights. A RoBMA reanalysis of Sladekova et al.'s dataset reveals that more than 60% of meta-analyses in psychology notably overestimate the evidence for the presence of the meta-analytic effect and more than 50% overestimate its magnitude.

Correct standard errors can bias meta-analysis.

Partial correlation coefficients are often used as effect sizes in the meta-analysis and systematic review of multiple regression analysis research results. There are two well-known formulas for the variance and thereby for the standard error (SE) of partial correlation coefficients (PCC). One is considered the "correct" variance in the sense that it better reflects the variation of the sampling distribution of partial correlation coefficients. The second is used to test whether the population PCC is zero, and it reproduces the test statistics and the p-values of the original multiple regression coefficient that PCC is meant to represent. Simulations show that the "correct" PCC variance causes random effects to be more biased than the alternative variance formula. Meta-analyses produced by this alternative formula statistically dominate those that use "correct" SEs. Meta-analysts should never use the "correct" formula for partial correlations' standard errors.

Unrestricted weighted least squares represent medical research better than random effects in 67,308 Cochrane meta-analyses.

OBJECTIVES: To evaluate how well meta-analysis mean estimators represent reported medical research and establish which meta-analysis method is better using widely accepted model selection measures: Akaike information criterion (AIC) and Bayesian information criterion (BIC). STUDY DESIGN AND SETTING: We compiled 67,308 meta-analyses from the Cochrane Database of Systematic Reviews (CDSR) published between 1997 and 2020, collectively encompassing nearly 600,000 medical findings. We compared unrestricted weighted least squares (UWLS) vs. random effects (RE); fixed effect was also secondarily considered. RESULTS: The probability that a randomly selected systematic review from the CDSR would favor UWLS over RE is 79.4% (95% confidence interval [CI95%]: 79.1; 79.7). The odds ratio that a Cochrane systematic review would substantially favor UWLS over RE is 9.33 (CI95%: 8.94; 9.73) using the conventional criterion that a difference in AIC (or BIC) of two or larger represents a 'substantial' improvement. UWLS's advantage over RE is most prominent in the presence of low heterogeneity. However, UWLS also has a notable advantage in high heterogeneity research, across different sizes of meta-analyses and types of outcomes. CONCLUSION: UWLS frequently dominates RE in medical research, often substantially. Thus, the UWLS should be reported routinely in the meta-analysis of clinical trials.

Systematic Analysis of Chemical Modifications of Phosphorothioate Antisense Oligonucleotides that Modulate Their Innate Immune Response.

While rare, some gapmer phosphorothioate (PS) antisense oligonucleotides (ASOs) can induce a noncanonical TLR9-dependent innate immune response. In this study, we performed systematic analyses of the roles of PS ASO backbone chemistry, 2' modifications, and sequence in PS ASO induced TLR9 signaling. We found that each of these factors can contribute to altering PS ASO induced TLR9 signaling, and in some cases the effects are quite dramatic. We also found that the positioning (5' vs. 3') of a particular backbone or 2' modification within a PS ASO can affect its TLR9 signaling. Interestingly, medicinal chemical strategies that decrease TLR9 signaling for one sequence can have opposing effects on another sequence. Our results demonstrate that TLR9 signaling is highly PS ASO sequence dependent, the mechanism of which remains unknown. Despite this, we determined that placement of two mesyl phosphoramidate linkages within the PS ASO gap is the most promising strategy to mitigate PS ASO dependent TLR9 activation to enhance the therapeutic index and, therefore, further streamline PS ASO drug development.

SIDT2 Inhibits Phosphorothioate Antisense Oligonucleotide Activity by Regulating Cellular Localization of Lysosomes.

Phosphorothioate (PS)-modified antisense oligonucleotide (ASO) drugs enter cells through endocytic pathways where a majority are entrapped within membrane-bound endosomes and lysosomes, representing a limiting step for antisense activity. While late endosomes have been identified as a major site for productive PS-ASO release, how lysosomes regulate PS-ASO activity beyond macromolecule degradation remains not fully understood. In this study, we reported that SID1 transmembrane family, member 2 (SIDT2), a lysosome transmembrane protein, can robustly regulate PS-ASO activity. We showed that SIDT2 is required for the proper colocalization between PS-ASO and lysosomes, suggesting an important role of SIDT2 in the entrapment of PS-ASOs in lysosomes. Mechanistically, we revealed that SIDT2 regulates lysosome cellular location. Lysosome location is largely determined by its movement along microtubules. Interestingly, we also observed an enrichment of proteins involved in microtubule function among SIDT2-binding proteins, suggesting that SIDT2 regulates lysosome location via its interaction with microtubule-related proteins. Overall, our data suggest that lysosome protein SIDT2 inhibits PS-ASO activity potentially through its interaction with microtubule-related proteins to place lysosomes at perinuclear regions, thus, facilitating PS-ASO's localization to lysosomes for degradation.

Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods.

Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods' performance to depend on the true data generating process, and no method consistently outperforms the others across a wide range of conditions. Unfortunately, when different methods lead to contradicting conclusions, researchers can choose those methods that lead to a desired outcome. To avoid the condition-dependent, all-or-none choice between competing methods and conflicting results, we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1) selection models of p-values and (2) models adjusting for small-study effects. The resulting model ensemble weights the estimates and the evidence for the absence/presence of the effect from the competing approaches with the support they receive from the data. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of complementary publication bias adjustment methods.

Progress in molecular biology and translational science addressing the needs of nano-rare patients.

The healthcare systems in the developed economies were established primarily to address the more prevalent diseases and have been customized to support the provision of therapeutics to rare patients. However, with the ever-broader implementation of genomic sequencing, it is clear that there are significantly more disease-causing mutations in the human genome than realized and that many mutations are much rarer than current definitions of rare disease populations. Given this, I propose parsing patient populations and defining patient populations more precisely. Nano-rare patients are defined as patients having disease-causing mutations that are unique to a single patient or having a known worldwide prevalence of less than 30. These patient populations present unique challenges to healthcare systems that demand the development of novel models for delivery of therapeutics and novel, more efficient drug discovery technologies, such as antisense technology. The challenges presented by nano-rare patients, a novel non-profit model as a means of providing experimental treatments rather than the traditional commercial model, and progress in establishing a non-profit solution are discussed.

Bean Leaf Beetle (Ootheca spp.) (Coleoptera: Chrysomelidae) Management via Planting Timing and Insecticides.

Bean leaf beetles (Ootheca spp.) (Coleoptera: Chrysomelidae) are one of Africa's most important pests of the common bean (Phaseolus vulgaris L.). Roots, leaves, floral parts, and young pods are all attacked, leading to a considerable loss in grain yield. In Uganda, there are no comprehensive prescribed management strategies for bean leaf beetles, but farmers typically try to control the pest by delaying bean crop sowing, and to a lesser extent, using insecticides. Although farmers have consistently implemented the two approaches, there is no information on the effects of the approaches in Uganda. To assess the impact of planting timing and insecticide spray regimes on bean leaf beetle populations, concomitant foliar damage, and grain yield, we set up trials in three agro-ecological zones with known presence of the beetles during the second rainy season of 2016 (2016) and the first rainy season of 2017 (2017). The first planting, coinciding with early planting, was conducted within one week after the onset of rains. The second planting, coinciding with mid planting, followed two weeks later, while the third planting, considered late planting in this study, was conducted one month after the second planting. A foliar application of cypermethrin commencing at 7 days after emergence (DAE), 14 DAE, 21 DAE, 28 DAE, and 35 DAE; a soil drench of imidacloprid at planting combined with a foliar spray starting at 7 DAE; and an untreated control were among the insecticide spray regimes evaluated. Higher bean leaf beetle abundance was recorded from mid-planting, while higher foliar damage was recorded from late planting in two of the three agro-ecological zones. However, higher marketable grain yield was recorded from early planting in all agro-ecological zones, suggesting that delayed planting may not be beneficial. Insecticide application reduced foliar damage and increased marketable grain yield, with a combination of soil drench and foliar spray resulting in much less foliar damage and, as a result, higher grain yield. However, this did not result in economic benefits. Furthermore, marketable grain yield was higher when insecticide spray regimes were combined with early planting in all agro-ecological zones during both seasons. Our findings suggest that the common bean should be planted early and that the control of the bean leaf beetle should target both the adults and the juvenile stages in the soil. Therefore, there is a need for farmers to be able to access less-expensive soil treatments.