Description: Programs for fitting Gaussian linear mixed models (LMM).

Packaged: 2016-05-12 13:28:32 UTC; denizakdemir

Description: This program can be used to fit Gaussian linear mixed models (LMM). Univariate and multivariate response models, multiple variance components, as well as, certain correlation and covariance structures are supported. In many occasions, the user can pick one of the several mixed model fitting algorithms, which are explained further in the details section. Some algorithms are specific to certain types of models (univariate or multivariate, diagonal or non-diagonal residual, one or multiple variance components, etc,...).

Packaged: 2018-11-25 04:26:01 UTC; denizakdemir

Title: Sensitivity Assessment to Unmeasured Confounding with Multiple
Description: A sensitivity analysis approach for unmeasured confounding in observational data with multiple treatments and a binary outcome. This approach derives the general bias formula and provides adjusted causal effect estimates in response to various assumptions about the degree of unmeasured confounding. Nested multiple imputation is embedded within the Bayesian framework to integrate   uncertainty about the sensitivity parameters and sampling variability.  Bayesian Additive Regression Model (BART) is used for outcome modeling. The causal estimands are the average treatment effects (ATE) based on the risk difference.  For more details, see paper: Hu L et al. (2020) A flexible sensitivity analysis approach for unmeasured confounding with a multiple treatments and a binary outcome <arXiv:2012.06093>.

Description: A sensitivity analysis approach for unmeasured confounding in observational data with multiple treatments and a binary outcome. This approach derives the general bias formula and provides adjusted causal effect estimates in response to various assumptions about the degree of unmeasured confounding. Nested multiple imputation is embedded within the Bayesian framework to integrate   uncertainty about the sensitivity parameters and sampling variability.  Bayesian Additive Regression Model (BART) is used for outcome modeling. The causal estimands are the conditional average treatment effects (CATE) based on the risk difference.  For more details, see paper: Hu L et al. (2020) A flexible sensitivity analysis approach for unmeasured confounding with a multiple treatments and a binary outcome <arXiv:2012.06093>.