Sequential analysis3/31/2023 ![]() ![]() Further, in order to ensure data are still shared, the sequential correction procedures should not be antagonistic with current data-sharing incentives and infrastructure. Sequential correction procedures are harder to implement than simultaneous procedures as they require keeping track of the total number of tests that have been performed by others. Here we will also propose a third, α -debt, which does not maintain a constant false positive rate but allows it to grow controllably. There are several proposed solutions to address multiple sequential analyses, namely α -spending and α -investing procedures ( Aharoni and Rosset, 2014 Foster and Stine, 2008), which strictly control false positive rate. ![]() Simultaneous procedures correct for all tests at once, while sequential procedures correct for the latest in a non-simultaneous series of tests. However, as we discuss in this article, when performing hypothesis testing it is important to take into account all of the statistical tests that have been performed on the datasets.Ī distinction can be made between simultaneous and sequential correction procedures when correcting for multiple tests. At present, researchers reusing datasets tend to correct for the number of statistical tests that they perform on the datasets. However, researchers re-analyzing these datasets will need to exercise caution if they intend to perform hypothesis testing. The availability of open datasets will increase over time as funders mandate and reward data sharing and other open research practices ( McKiernan et al., 2016). The ability to explore pre-existing datasets in new ways should make research more efficient and has the potential to yield new discoveries ( Weston et al., 2019). Making data open will allow other researchers to both reproduce published analyses and ask new questions of existing datasets ( Molloy, 2011 Pisani et al., 2016). Thus our results provide the formal basis for extending the scope of standard group-sequential methods to a wide range of problems.In recent years, there has been a push to make the scientific datasets associated with published papers openly available to other researchers ( Nosek et al., 2015). In all cases, the joint distribution of the sequence of parameter estimates has the same form, exactly or asymptotically, as that of the sequence of means of an increasing number of independent, identically distributed normal variables. The asymptotic results are derived using standard methods for the nonsequential case, and they hold as long as these nonsequential techniques are applicable at each individual analysis. Our theory covers normal linear models, including the case of correlated observations, and asymptotic results extend to generalized linear models and the proportional hazards regression model for survival data. In this article we survey existing results concerning the joint distribution of the sequence of estimates of the parameter vector when a model is fitted to accumulating data and provide a unified theory that explains the "independent increments" structure commonly seen in group-sequential test statistics. ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |