![]() ![]() NeCamp, Timothy Kilbourne, Amy Almirall, DanielĬluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations. If you believe you should have access to that content, please contact your librarian.įor librarians and administrators, your personal account also provides access to institutional account management. The institutional subscription may not cover the content that you are trying to access. Oxford Academic is home to a wide variety of products. View the institutional accounts that are providing access.View your signed in personal account and access account management features.Some societies use Oxford Academic personal accounts to provide access to their members.Ĭlick the account icon in the top right to: See below.Ī personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions. Some societies use Oxford Academic personal accounts to provide access to their members. If you do not have a society account or have forgotten your username or password, please contact your society. Do not use an Oxford Academic personal account. When on the society site, please use the credentials provided by that society.If you see ‘Sign in through society site’ in the sign in pane within a journal: Many societies offer single sign-on between the society website and Oxford Academic. Society member access to a journal is achieved in one of the following ways: If you cannot sign in, please contact your librarian. If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.Įnter your library card number to sign in. Following successful sign in, you will be returned to Oxford Academic.When on the institution site, please use the credentials provided by your institution.Select your institution from the list provided, which will take you to your institution's website to sign in.Click Sign in through your institution.Shibboleth / Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.Ĭhoose this option to get remote access when outside your institution. ![]() ![]() Typically, access is provided across an institutional network to a range of IP addresses. If you are a member of an institution with an active account, you may be able to access content in one of the following ways: Get help with access Institutional accessĪccess to content on Oxford Academic is often provided through institutional subscriptions and purchases. We also apply the proposed method to Chinese famine sample data in order to show its performance when testing the significance of gene-environment interactions. Extensive simulations demonstrate the good performance of the proposed test and its robustness when certain sparsity assumptions are violated. We show that, under certain regularity conditions, the Type-I error of the proposed method is asymptotically correct, and we establish its power under high-dimensional alternatives. Here, we propose a computationally efficient test with a closed-form limiting distribution, which allows the parameter being tested to be either sparse or dense. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and are thus computationally expensive. In these scenarios, it is essential to test the significance of a high-dimensional subvector of the model’s coefficients. Generalized linear models often have high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. ![]()
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