Even though precise evaluation of money demand function is essential for cen- tral banking and for the right determination of the transmission mechanism, economists have not reached a consensus about the underlying determinants of money demand function neither their magnitude and direction. Researchers differ even in the selection of measures used for the main variables - income, and interest rate. While the heterogeneity in elasticity estimates of the former one has been scrutinize in several quantitative surveys, to the best of our knowledge, there has not been compiled any meta-analysis focusing on differences among the interest rate elasticities of the money demand. Therefore, we collected 53 studies reporting 1 094 estimates of interest rate elasticity. Implementing both the state-of-the-art methods and those proposed only recently, we have found out that researches are prone to selective reporting. Firstly, our results shows that negative publication bias is present in empirical studies of the money de- mand and increases the average elasticity estimate approximately three times (in absolute terms). Secondly, negative highly precise estimates are more likely to be compared to their imprecise counterparts. Additionally, we scrutinize po- tential sources of heterogeneity among individual...

Peer reviewed literature was searched to identify research pertaining to the uptake of heavy metals (As, Cd, Pb and Zn) by vegetables (cabbage, carrot, lettuce, and radish). The objectives of this research were to 1) determine the relationship between heavy metal concentrations in the soil and heavy metal concentrations in vegetables, and 2) determine the level of risk associated with exposure to heavy metals through ingestion of contaminated vegetables. Highly variable estimates and biologically implausible regression equations resulted from this meta-analysis. Exposure to arsenic through the ingestion of lettuce grown on contaminated soil significantly increases cancer risk, especially in children. Highly variable hazard quotients prevent strong statements concerning toxic effects from exposure to Pb, Cd, or Zn. A more in-depth meta-analysis (multiple-regression and nonlinear curve-fitting) and an upgrade in data reporting standards are recommended.

No description available.

Understanding how psychological processes drive human energy choices is an urgent, and yet relatively under-investigated, need for contemporary society. A knowledge gap still persists on the links between psychological factors identified in earlier studies and people’s behaviors in the energy domain. This research applies a meta-analytical procedure to assess the strength of the associations between five different classes of individual variables (i.e.,: attitudes, intentions, values, awareness, and emotions) and energy-saving behavioral intentions and behaviors (self-reported and actual). Based on a systematic review of studies published between 2007 and 2017, we estimate the average effect size of predictor-criterion relations, and we assess relevant moderators and publication bias, drawing on data obtained from 102 independent samples reported in 67 published studies (N = 59.948). Results from a series of five single meta-analyses reveal a pattern of significant positive associations between the selected psychological determinants and energy-saving indicators: associations between individual-level predictors and energy-saving outcomes are positive and moderate in size, ranging from large effects for emotions to small-moderate effects for pro-environmental values. Interestingly, moderation analysis reveals, among other things, that attitude-behavior links are not statistically significant when actual behavior is considered as an outcome. Implications for policy interventions are discussed.

Children with ADHD are characterized frequently as possessing underdeveloped executive functions and sustained attentional abilities, and recent commercial claims suggest that computer-based cognitive training can remediate these impairments and provide significant and lasting improvement in their attention, impulse control, social functioning, academic performance, and complex reasoning skills. The present review critically evaluates these claims through meta-analysis of 25 studies of facilitative intervention training (i.e., cognitive training) for children with ADHD. Random effects models corrected for publication bias and sampling error revealed that studies training short-term memory alone resulted in moderate magnitude improvements in short-term memory (d= 0.63), whereas training attention did not significantly improve attention and training mixed executive functions did not significantly improve the targeted executive functions (both nonsignificant: 95% confidence intervals include 0.0). Far transfer effects of cognitive training on academic functioning, blinded ratings of behavior (both nonsignificant), and cognitive tests (d= 0.14) were nonsignificant or negligible. Unblinded raters (d= 0.48) reported significantly larger benefits relative to blinded raters and objective tests (both p < .05), indicating the likelihood of Hawthorne effects. Critical examination of training targets revealed incongruence with empirical evidence regarding the specific executive functions that are (a) most impaired in ADHD, and (b) functionally related to the behavioral and academic outcomes these training programs are intended to ameliorate. Collectively, meta-analytic results indicate that claims regarding the academic, behavioral, and cognitive benefits associated with extant cognitive training programs are unsupported in ADHD. The methodological limitations of the current evidence base, however, leaves open the possibility that cognitive training techniques iv designed to improve empirically documented executive function deficits may benefit children with ADHD.

The age-cutoff regression discontinuity design (RDD) has emerged as one of the most rigorous quasi-experimental approaches to determining program effects of prekindergarten on literacy and numeracy outcomes for children at kindergarten entry. However, few pre-K meta-analyses have focused attention on validity threats. The current random-effects meta-regression tests the moderating effects of prominent threats to validity, selection bias and sensitivity, on impact estimates generated from age-cutoff regression discontinuity studies from large-scale programs. Results from averaging dependent standardized mean difference effects suggested small positive moderating effects of total attrition and robust 3-month bandwidths on reading effects, but not on math. However, these results were not statistically significant. In contrast, results generated from robust variance estimation yielded a small statistically significant association between total attrition and math effects. These mixed results may warrant further research on prekindergarten evaluation methodology, evaluation estimation methods, and the totality of evidence used to inform policy.

Background: The knowledge of factors influencing disease progression in patients with established coronary heart disease (CHD) is still relatively limited. One potential pathway is related to peroxisome proliferator–activated receptor gamma coactivator-1 alpha (PPARGC1A), a transcription factor linked to energy metabolism which may play a role in the heart function. Thus, its associations with subsequent CHD events remain unclear. We aimed to investigate the effect of three different SNPs in the PPARGC1A gene on the risk of subsequent CHD in a population with established CHD. Methods: We employed an individual-level meta-analysis using 23 studies from the GENetIcs of sUbSequent Coronary Heart Disease (GENIUS-CHD) consortium, which included participants (n = 80,900) with either acute coronary syndrome, stable CHD, or a mixture of both at baseline. Three variants in the PPARGC1A gene (rs8192678, G482S; rs7672915, intron 2; and rs3755863, T528T) were tested for their associations with subsequent events during the follow-up using a Cox proportional hazards model adjusted for age and sex. The primary outcome was subsequent CHD death or myocardial infarction (CHD death/myocardial infarction). Stratified analyses of the participant or study characteristics as well as additional analyses for secondary outcomes of specific cardiovascular disease diagnoses and all-cause death were also performed. Results: Meta-analysis revealed no significant association between any of the three variants in the PPARGC1A gene and the primary outcome of CHD death/myocardial infarction among those with established CHD at baseline: rs8192678, hazard ratio (HR): 1.01, 95% confidence interval (CI) 0.98–1.05 and rs7672915, HR: 0.97, 95% CI 0.94–1.00; rs3755863, HR: 1.02, 95% CI 0.99–1.06. Similarly, no significant associations were observed for any of the secondary outcomes. The results from stratified analyses showed null results, except for significant inverse associations between rs7672915 (intron 2) and the primary outcome among 1) individuals aged ≥65, 2) individuals with renal impairment, and 3) antiplatelet users. Conclusion: We found no clear associations between polymorphisms in the PPARGC1A gene and subsequent CHD events in patients with established CHD at baseline.

Most statistical methods to design and analyze randomized controlled trials with time-to-event data, and synthesize their results in meta-analyses, use the hazard ratio (HR) as the measure of treatment effect. However, the HR relies on the proportional hazard assumption which is often violated, especially in cancer trials. In addition, the HR might be challenging to interpret and is frequently misinterpreted as a risk ratio (RR). In meta-analysis, conventional methods ignore that HRs are estimated over different time supports when the component trials have different follow-up durations. These issues also pertain to advanced statistical methods, such as network meta-analysis and surrogate endpoints validation. Novel methods that rely on the difference in restricted mean survival times (RMST) would help addressing these issues. In this dissertation, I first developed a Bayesian network meta-analysis model using the difference in RMST. This model synthesizes all the available evidence from multiple time points and treatment comparisons simultaneously through within-study covariance and between-study covariance for the differences in RMST. I proposed an estimator of the within-study covariance and estimated the model under the Bayesian framework. The simulation studies showed adequate performance in terms of mean bias and mean squared error. I illustrated the model on a network of randomized trials of second-line treatments of advanced non-small-cell lung cancer. Second, I introduced a novel two-stage meta-analytical model to evaluate trial-level surrogacy. I measured trial-level surrogacy by the coefficient of determination at multiple time points based on the differences in RMST. The model borrows strength across data available at multiple time points and enables assessing how the strength of surrogacy changes over time. Simulation studies showed that the estimates of coefficients of determination are unbiased and have high precision in almost all of the scenarios we examined. I demonstrated my model in two individual patient data meta-analyses in gastric cancer. Both methods, for network meta-analysis and surrogacy evaluation, have the advantage of not involving extrapolation beyond the observed time support in component trials and of not relying on the proportional hazard assumption. Finally, motivated by the common misinterpretation of the HR as a RR, I investigated the theoretical relationship between the HR and the RR and compared empirically the treatment effects measured by the HR and the RR in a large sample of oncology RCTs. When there is evidence of superiority for experimental group, misinterpreting the HR as the RR leads to overestimating the benefits by about 20%. / 2026-02-08T00:00:00Z

No description available.

No description available.