• TETRAD

  • Referenced in 440 articles [sw12177]
  • search when there may be unobserved confounders of measured variables, to search for models...
  • twilight

  • Referenced in 10 articles [sw06103]
  • Using filtered permutations, the influence of hidden confounders could be diminished...
  • EValue

  • Referenced in 4 articles [sw34331]
  • package EValue: Sensitivity Analyses for Unmeasured Confounding or Selection Bias in Observational Studies and Meta ... Analyses. Conducts sensitivity analyses for unmeasured confounding for either an observational study or a meta ... risk ratio scale that an unmeasured confounder would need to have with both the treatment ... meta-analysis, use the function confounded_meta to compute point estimates and inference...
  • MADNESS

  • Referenced in 7 articles [sw06887]
  • smoothness leads to large numerical cancellation and confounds the dynamically-adaptive, multiresolution algorithms inside...
  • RIATA-HGT

  • Referenced in 7 articles [sw17371]
  • bacteria. Further, HGT is a major confounding factor for any attempt to reconstruct bacterial phylogenies...
  • Flask

  • Referenced in 5 articles [sw09691]
  • networks. Severely resource-constrained devices present a confounding challenge to the functional programmer...
  • TASSEL

  • Referenced in 5 articles [sw11446]
  • studies, however, researchers must contend with the confounding effects of both population and family structure...
  • BACprior

  • Referenced in 3 articles [sw15385]
  • Hyperparameter Omega in the Bayesian Adjustment for Confounding (BAC) Algorithm. The BACprior package provides ... sensitivity analysis of the Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012) with...
  • gformula

  • Referenced in 3 articles [sw37467]
  • outcome in the presence of time-varying confounders that are themselves also affected ... intermediate variables, and in particular when confounders of the mediator–outcome relationships are them- selves...
  • ADJSURV

  • Referenced in 4 articles [sw27710]
  • method of direct adjustment controls for possible confounders due to an imbalance of patient characteristics...
  • coroICA

  • Referenced in 2 articles [sw34908]
  • group-wise stationary noise. We introduce coroICA, confounding-robust independent component analysis, a novel ... rendered dependent) by hidden group-wise stationary confounding. It extends the ordinary ICA model ... incorporate group-wise (or environment-wise) confounding. We show that our proposed general noise model...
  • deepTL

  • Referenced in 2 articles [sw41464]
  • deep learning semiparametric regression for adjusting complex confounding structures. Deep Treatment Learning (deepTL), a robust ... approach, is proposed to adjust the complex confounding structures in comparative effectiveness analysis of observational ... health record (EHR) data in which complex confounding structures are often embedded. Specifically, we develop...
  • episensr

  • Referenced in 3 articles [sw15562]
  • observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both...
  • StateTrace

  • Referenced in 3 articles [sw16861]
  • only ordinal assumptions and so, is not confounded by range effects that plague alternative methods...
  • conf.design

  • Referenced in 3 articles [sw24896]
  • simple tools for constructing and manipulating confounded and fractional factorial designs...
  • gma

  • Referenced in 3 articles [sw26339]
  • data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under...
  • CONN

  • Referenced in 3 articles [sw26510]
  • noise in order to avoid possible confounds such as spurious correlations based on non-neuronal...
  • SurvBoost

  • Referenced in 1 article [sw23772]
  • different areas. In practice, data may involve confounding variables that do not satisfy ... model can be adopted to control the confounding effects by stratification of the confounding variable ... without directly modeling the confounding effects. However, there is lack of computationally efficient statistical software ... with high-dimensional covariate variables and other confounders. Extensive simulation studies demonstrate that in many...
  • ccSVM

  • Referenced in 1 article [sw35176]
  • ccSVM: correcting Support Vector Machines for confounding factors in biological data classification. Motivation: Classifying biological ... unclear how to correct for confounding factors such as population structure, age or gender ... that can correct the prediction for observed confounding factors. This is achieved by minimizing ... statistical dependence between the classifier and the confounding factors. We prove that this formulation...
  • CURobustML

  • Referenced in 1 article [sw42172]
  • multilevel observational studies under cluster-level unmeasured confounding. Recently, machine learning (ML) methods have been ... However, many ML methods require that all confounders are measured to consistently estimate treatment effects ... presence of cluster-level unmeasured confounders, a type of unmeasured confounders that are shared within ... robust from biases from unmeasured cluster-level confounders in a variety of multilevel observational studies...