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https://www.selleckchem.com/products/sitagliptin.html After variable selection, standard inferential procedures for regression parameters may not be uniformly valid; there is no finite-sample size at which a standard test is guaranteed to approximately attain its nominal size. This problem is exacerbated in high-dimensional settings, where variable selection becomes unavoidable. This has prompted a flurry of activity in developing uniformly valid hypothesis tests for a low-dimensional regression parameter (eg, the causal effect of an exposure A on an outcome Y) in high-dimensional models. So far there has been limited focus on model misspecification, although this is inevitable in high-dimensional settings. We propose tests of the null that are uniformly valid under sparsity conditions weaker than those typically invoked in the literature, assuming working models for the exposure and outcome are both correctly specified. When one of the models is misspecified, by amending the procedure for estimating the nuisance parameters, our tests continue to be valid; hence, they are doubly robust. Our proposals are straightforward to implement using existing software for penalized maximum likelihood estimation and do not require sample splitting. We illustrate them in simulations and an analysis of data obtained from the Ghent University intensive care unit. © 2020 The International Biometric Society.BACKGROUND In Parkinson's disease, mild cognitive impairment and dementia are associated with α-synuclein deposition and spread. However, coexistent Alzheimer's disease and cerebrovascular disease are common at autopsy, and may affect cognition. Our objective was to map cognitive impairment in Parkinson's disease to these different causes using clinical assessment. METHODS Neuropsychological testing was performed in a cross-sectional sample of cognitively impaired patients with Parkinson's disease. The pattern of deficits in varying cognitive domains was mapped to the presentation

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