Mortality rates were calculated as defined in the text.Summary statistics for White cervical cancer mortality rates in 13 U.S. states from 1975 to 2010.

The Longitudinal Study of Ocular Complications of AIDS was a 15-year multi-center observational study which collected demographic, medical history, treatment, and vision-related data at quarterly visits from 2,392 patients with AIDS. Each SAS dataset in this collection relates to the cumulative patient-visits from a particular LSOCA form. For example, va.sas7bdat is the SAS dataset for the visual acuity data. Use the appropriate LSOCA form and SAS labels from the SAS PROC CONTENTS to decode each data item.

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. Reference: O. J. Mengshoel, S. Poll, and T. Kurtoglu. "Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft." Proc. of the IJCAI-09 Workshop on Self-* and Autonomous Systems (SAS): Reasoning and Integration Challenges, 2009 BibTex Reference: @inproceedings{mengshoel09developing, title = {Developing Large-Scale {Bayesian} Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft}, author = {Mengshoel, O. J. and Poll, S. and Kurtoglu, T.}, booktitle = {Proc. of the IJCAI-09 Workshop on Self-$\star$ and Autonomous Systems (SAS): Reasoning and Integration Challenges}, year={2009} }

Results from PROC MIXED (SAS) analysis of effects of inoculum origin on plant biomass production of mid-successional plant species relative to the sterilized control treatment.

Composition of the control diet and its ingredients1.

Biochemistry exams of the control group and obese group before and after weight loss.

Causal inference with observational longitudinal data and time-varying exposures is complicated due to the potential for time-dependent confounding and unmeasured confounding. Most causal inference methods that handle time-dependent confounding rely on either the assumption of no unmeasured confounders or the availability of an unconfounded variable that is associated with the exposure (e.g., an instrumental variable). Furthermore, when data are incomplete, validity of many methods often depends on the assumption of missing at random. We propose an approach that combines a parametric joint mixed-effects model for the study outcome and the exposure with g-computation to identify and estimate causal effects in the presence of time-dependent confounding and unmeasured confounding. G-computation can estimate participant-specific or population-average causal effects using parameters of the joint model. The joint model is a type of shared parameter model where the outcome and exposure-selection models share common random effect(s). We also extend the joint model to handle missing data and truncation by death when missingness is possibly not at random. We evaluate the performance of the proposed method using simulation studies and compare the method to both linear mixed-effects models and fixed-effects models combined with g-computation as well as to targeted maximum likelihood estimation. We apply the method to an epidemiologic study of vitamin D and depressive symptoms in older adults and include code using SAS PROC NLMIXED software to enhance the accessibility of the method to applied researchers.