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Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser. [ABSTRACT FROM AUTHOR]

This study assesses the accuracy of toll road traffic forecasts in Norway. This study contributes to extant literature regarding transportation economics and policy because few studies have analyzed the accuracy of toll road traffic forecasts and current studies on this topic do not use a succinct econometric framework to infer the forecast’s bias and efficiency, which are determinants of the accuracy of forecasts. The data for this study include 68 toll road projects where forecasts and actual out-turns were available. All projects that are included in this study were opened for traffic during 1975–2014. Transport models were made mandatory for forecasting toll traffic in 2006. The results of this study reveal the following: (i) Norwegian toll road traffic forecasts are underestimated but are close to accurate because the mean percentage error is a mere 4%. This result sharply contrasts international studies that resulted in large overestimations at more than −20%. (ii) The accuracy of forecasts has not improved since transport models became mandatory. (iii) The Norwegian toll road traffic forecasts are unbiased, which implies that they do not tend to be one-sided. (iv) Norwegian forecasts are efficient, which implies that available information is used adequately when making forecasts. Our conclusion is that the toll traffic forecasts in Norway perform fairly well compared to the forecasts of other countries. In addition, we recommend that the assessment of forecasts in the transportation sector should be based on succinct econometric frameworks; otherwise, the conclusions may lack important information regarding the bias and efficiency of the forecasts. [ABSTRACT FROM AUTHOR]

In this rejoinder the authors stipulate further for two challenging issues. First, if placebo non-responders are selected simply by their response meeting a threshold, this selection may have misclassification error and consequently the treatment effect estimate may be biased, regardless of whether the estimand at the second stage is the treatment effect in the entire population or placebo non-responders. Secondly, the weak null hypothesis considered in our article Statistical Inference Problems in Sequential Parallel Comparison Design (2019) is that the expected treatment effects in placebo non-responders and in the entire set of patients entering the trial are both zero, in contrast to the strong null hypothesis that the statistical distribution of the response variable is equal in the compared treatments. The impact of violating the assumption of equal moments other than the mean parameter on statistical operating characteristics in estimation and testing of treatment effect can be substantial. As an example, the ordinary least squares based test detects a treatment difference even if the expected treatment effects in placebo non-responders and the entire population are both zero. [ABSTRACT FROM AUTHOR]