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We consider a covariate-adjusted interventional estimand that resembles the binary treatment-specific mean estimand from the causal inference literature obtained by dichotomizing the continuous biomarker or therapy as above or below a threshold. For the case of educational outcome missingness, we recommend a new nonparametric effective estimator that uses machine learning and targeted minimum-loss estimation. The concept of simultaneous 95% confidence bands for the threshold-specific estimation over a set of thresholds is discussed. We discuss how to adjust our estimate when the biomarker is missing at random in clinical trials with biased sampling techniques, using inverse probability weighting. In the CYD14 and CYD15 dengue vaccine trials, the methods were used to estimate neutralizing antibody thresholds for virologically confirmed dengue risk.
Source link: https://europepmc.org/article/MED/35526218
This work includes a detailed review of a stochastic delayed model that regulates the transmission of the Hepatitis B virus from the genetic organs of the disease, as well as considering the effects of vaccinations and white noises. The disturbances are thought to be nonlinear, and an individual may lose his/her immunity after vaccination, implying that the vaccination will provide temporal protection. The model was updated to a stochastic framework, and it is well-supported by proving that the model solution exists globally, bounded stochastically, and is positive. We fitted the model against the available HBV data in Pakistan from March 2018 to February 2019, using the standard curve fitting techniques, and accordingly, the model's parameters were estimated.
Source link: https://europepmc.org/article/MED/35542829
A Kalman filtering algorithm with correlated noises can be used to build a Kalman filtering with correlated noises that can simultaneously estimate parameters and system states.
Source link: https://europepmc.org/article/PPR/PPR489220
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