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pro vyhledávání: '"Shepherd, Bryan E"'
Autor:
Barnatchez, Keith, Nethery, Rachel, Shepherd, Bryan E., Parmigiani, Giovanni, Josey, Kevin P.
Exposure measurement error is a ubiquitous but often overlooked challenge in causal inference with observational data. Existing methods accounting for exposure measurement error largely rely on restrictive parametric assumptions, while emerging data-
Externí odkaz:
http://arxiv.org/abs/2410.12590
The probability-scale residual (PSR) is defined as $E\{sign(y, Y^*)\}$, where $y$ is the observed outcome and $Y^*$ is a random variable from the fitted distribution. The PSR is particularly useful for ordinal and censored outcomes for which fitted v
Externí odkaz:
http://arxiv.org/abs/2409.11385
While a randomized control trial is considered the gold standard for estimating causal treatment effects, there are many research settings in which randomization is infeasible or unethical. In such cases, researchers rely on analytical methods for ob
Externí odkaz:
http://arxiv.org/abs/2402.12576
Clustered data are common in practice. Clustering arises when subjects are measured repeatedly, or subjects are nested in groups (e.g., households, schools). It is often of interest to evaluate the correlation between two variables with clustered dat
Externí odkaz:
http://arxiv.org/abs/2402.11341
Publikováno v:
Statistics in Medicine. 2023; 42(24): 4333-4348
Clustered data are common in biomedical research. Observations in the same cluster are often more similar to each other than to observations from other clusters. The intraclass correlation coefficient (ICC), first introduced by R. A. Fisher, is frequ
Externí odkaz:
http://arxiv.org/abs/2303.04880
Continuous response variables often need to be transformed to meet regression modeling assumptions; however, finding the optimal transformation is challenging and results may vary with the choice of transformation. When a continuous response variable
Externí odkaz:
http://arxiv.org/abs/2207.08325
Cumulative probability models (CPMs) are a robust alternative to linear models for continuous outcomes. However, they are not feasible for very large datasets due to elevated running time and memory usage, which depend on the sample size, the number
Externí odkaz:
http://arxiv.org/abs/2207.06562
Detection limits (DLs), where a variable is unable to be measured outside of a certain range, are common in research. Most approaches to handle DLs in the response variable implicitly make parametric assumptions on the distribution of data outside DL
Externí odkaz:
http://arxiv.org/abs/2207.02815
Regression models for continuous outcomes often require a transformation of the outcome, which the user either specify {\it a priori} or estimate from a parametric family. Cumulative probability models (CPMs) nonparametrically estimate the transforma
Externí odkaz:
http://arxiv.org/abs/2206.14426
Autor:
Amorim, Gustavo, Tao, Ran, Lotspeich, Sarah, Shaw, Pamela A., Lumley, Thomas, Patel, Rena C., Shepherd, Bryan E.
Validation studies are often used to obtain more reliable information in settings with error-prone data. Validated data on a subsample of subjects can be used together with error-prone data on all subjects to improve estimation. In practice, more tha
Externí odkaz:
http://arxiv.org/abs/2205.01743