Using a mixture model for multiple imputation in the presence of outliers: the ‘Healthy for life’ project

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74845/1/j.1467-9876.2007.00565.x.pd

Nested Markov Compliance Class Model in the Presence of Time-Varying Noncompliance

We consider a Markov structure for partially unobserved time-varying compliance classes in the Imbens-Rubin (1997) compliance model framework. The context is a longitudinal randomized intervention study where subjects are randomized once at baseline, outcomes and patient adherence are measured at multiple follow-ups, and patient adherence to their randomized treatment could vary over time. We propose a nested latent compliance class model where we use time-invariant subject-specific compliance principal strata to summarize longtudinal trends of subject-specific time-varying compliance patterns. The principal strata are formed using Markov models that related current compliance behavior to compliance history. Treatment effects are estimated as intent-to -treat effects within the compliance principal strata

Cash operating income and liquidity management for feeder cattle operations

Net cash flow measures the amount of cash remaining after all cash expense obligations are satisfied. This cash is available for additional farm investment, off-farm investment, family living, and additional debt repayment. A 5-year, average, monthly, cash-flow statement was used to determine net cash flow for 18 feeder cattle farms .Results indicate that excess cash was used primarily to invest in equipment, vehicles, and nonfarm assets. Investments in buildings increased moderately over the study period, while investment in land was minimal

Modeling Menstrual Cycle Length and Variability at the Approach of Menopause Using Bayesian Changepoint Models

As women approach menopause, the patterns of their menstruation cycle lengths change. To study these changes, we need to jointly model both the mean and variability of the cycle length. The model incorporates separate mean and variance change points for each woman and a hierarchical model to link them together, along with regression components to include predictors of menopausal onset such as age at menarche and parity. Data are from TREMIN, an ongoing 70-year old longitudinal study that has obtained menstrual calendar data of women throughout their reproductive life course. An additional complexity arises from the fact that these calendars have substantial missingness due to hormone use, surgery, failure to report, and loss of contact. We integrate multiple imputation and time-to event modeling in our Bayesian estimation procedure to deal with different forms of the missingness. Posterior predictive model checks are applied to evaluate the model fit. Our method successfully modeled patterns of women’s menstrual cycle trajectories throughout their late reproductive life and identified the change points for mean and variability of segment length, which provides insight into the menopausal process. More generally, our model points the way toward increasing use of joint mean-variance models to predict health outcomes and better understand disease processes

A two‐step semiparametric method to accommodate sampling weights in multiple imputation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142493/1/biom12413_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142493/2/biom12413.pd

The Effects of the Transcription Factor IRF-3 in Pam2ODN Microbial Resistance

https://openworks.mdanderson.org/sumexp23/1092/thumbnail.jp