P values based on standard hypothesis testing are commonly reported in articles published by the Journal of Forestry Research(JFR).However,effect sizes are barely used and reported,even if they are of direct relevance...P values based on standard hypothesis testing are commonly reported in articles published by the Journal of Forestry Research(JFR).However,effect sizes are barely used and reported,even if they are of direct relevance to the primary questions of many of the published studies.The incorporation of effect sizes in studies published by JFR should be encouraged and promoted.Inclusion of effect sizes as a requirement in the journal guidelines will facilitate a major change in the way data are tested and interpreted,with the ultimate goal to exempt researchers from the custom of drawing conclusions merely based upon a dichotomous statistical result(P value).Such a policy can also lead to more informed decisions of whether identified effects are of practical relevance to the forestry.展开更多
This paper discusses the nonlinearity of fish acoustic signals by using the surrogate data method. We compare the difference of three test statistics - time-irreversibility Trey, correlation dimension D2 and auto mutu...This paper discusses the nonlinearity of fish acoustic signals by using the surrogate data method. We compare the difference of three test statistics - time-irreversibility Trey, correlation dimension D2 and auto mutual information function I between the original data and the surrogate data. We come to the conclusion that there exists nonlinearity in the fish acoustic signals and there exist deterministic nonlinear components; therefore nonlinear dynamic theory can be used to analyze fish acoustic signals.展开更多
Family-based tests of association between a genetic marker and a disease constitute a common design to dissect the genetic architecture of complex traits. The FBAT software is one of the most popular tools to perform ...Family-based tests of association between a genetic marker and a disease constitute a common design to dissect the genetic architecture of complex traits. The FBAT software is one of the most popular tools to perform such studies. However, researchers are also often interested in the genetic contribution to a more specific manifestation of the phenotype (e.g. severe vs. non-severe form) known as a secondary outcome. Here, what we demonstrate is the limited power of the classical formulation of the FBAT statistic to detect the effect of genetic variants that influence a secondary outcome, in particular when these variants also impact on the onset of the disease, the primary outcome. We prove that this loss of power is driven by an implicit hypothesis, and we propose a derivation of the original FBAT statistic, free from this implicit hypothesis. Finally, we demonstrate analytically that our new statistic is robust and more powerful than FBAT for the detection of association between a genetic variant and a secondary outcome.展开更多
In randomized clinical trials with right-censored time-to-event outcomes,the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of t...In randomized clinical trials with right-censored time-to-event outcomes,the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of treatments but is too conservative under covariate-adaptive random-ization.The stratified log-rank test,which adjusts baseline covariates in the test procedure by stratification,is asymptotically valid regardless of what treatment randomization is applied.In the literature,however,under simple randomization there is no affirmative conclusion about whether the stratified log-rank test is asymptotically more powerful than the unstratified log-rank test.In this article we show when the stratified and unstratified log-rank tests aim for the same null hypothesis and that,under simple randomization,the stratified log-rank test is asymp-totically more powerful than the unstratified log-rank test in the region of alternative hypothesis that is specified by a Cox proportional hazards model.We also provide some discussion about why we do not have an affirmative conclusion in general.展开更多
基金co-supported by the Outstanding Action Plan of Chinese Sci-tech Journals(Grant No.OAP–C–077)the Startup Foundation for Introducing Talent of Nanjing University of Information Science&Technology(NUIST),Nanjing,China(Grant No.003080)the Jiangsu Distinguished Professor Program of the People’s Government of Jiangsu Province。
文摘P values based on standard hypothesis testing are commonly reported in articles published by the Journal of Forestry Research(JFR).However,effect sizes are barely used and reported,even if they are of direct relevance to the primary questions of many of the published studies.The incorporation of effect sizes in studies published by JFR should be encouraged and promoted.Inclusion of effect sizes as a requirement in the journal guidelines will facilitate a major change in the way data are tested and interpreted,with the ultimate goal to exempt researchers from the custom of drawing conclusions merely based upon a dichotomous statistical result(P value).Such a policy can also lead to more informed decisions of whether identified effects are of practical relevance to the forestry.
文摘This paper discusses the nonlinearity of fish acoustic signals by using the surrogate data method. We compare the difference of three test statistics - time-irreversibility Trey, correlation dimension D2 and auto mutual information function I between the original data and the surrogate data. We come to the conclusion that there exists nonlinearity in the fish acoustic signals and there exist deterministic nonlinear components; therefore nonlinear dynamic theory can be used to analyze fish acoustic signals.
基金supported by the Programme Blanc de l’Agence National de la Recherche.
文摘Family-based tests of association between a genetic marker and a disease constitute a common design to dissect the genetic architecture of complex traits. The FBAT software is one of the most popular tools to perform such studies. However, researchers are also often interested in the genetic contribution to a more specific manifestation of the phenotype (e.g. severe vs. non-severe form) known as a secondary outcome. Here, what we demonstrate is the limited power of the classical formulation of the FBAT statistic to detect the effect of genetic variants that influence a secondary outcome, in particular when these variants also impact on the onset of the disease, the primary outcome. We prove that this loss of power is driven by an implicit hypothesis, and we propose a derivation of the original FBAT statistic, free from this implicit hypothesis. Finally, we demonstrate analytically that our new statistic is robust and more powerful than FBAT for the detection of association between a genetic variant and a secondary outcome.
文摘In randomized clinical trials with right-censored time-to-event outcomes,the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of treatments but is too conservative under covariate-adaptive random-ization.The stratified log-rank test,which adjusts baseline covariates in the test procedure by stratification,is asymptotically valid regardless of what treatment randomization is applied.In the literature,however,under simple randomization there is no affirmative conclusion about whether the stratified log-rank test is asymptotically more powerful than the unstratified log-rank test.In this article we show when the stratified and unstratified log-rank tests aim for the same null hypothesis and that,under simple randomization,the stratified log-rank test is asymp-totically more powerful than the unstratified log-rank test in the region of alternative hypothesis that is specified by a Cox proportional hazards model.We also provide some discussion about why we do not have an affirmative conclusion in general.
