#NONMEM MARKOV MODEL FULL#
#NONMEM MARKOV MODEL PRO#
#NONMEM MARKOV MODEL SOFTWARE#

#NONMEM MARKOV MODEL FULL#

All significant covariates reported by a prior primary statistical analysis of the same data were included in the full covariate model. The full covariate model was initially developed, followed by backward elimination process to reduce the model. A two-state, first-order, discrete time Markov model was developed with longitudinal adherence data characterized by вЂњdose taking (1)вЂќ and вЂњdose missing (0).вЂќ Covariate effects were linearly added in the logit domain of transition probability parameters (P01 and P10) in the model. Analyzed data reflect 12 months of follow-up per participant. Adherence was monitored electronically demographic and socio-behavioral data were collected during study visits. The uninfected partner received oral PrEP according to the вЂњbridge to antiretroviral therapy вЂќ strategy (i.e., until the infected partner had been on ART for в‰Ґ6 months).

Methods: The Partners Demonstration Project was a prospective, open-label, implementation science-driven study of HIV PrEP among heterosexual HIV serodiscordant couples in Kenya and Uganda. The objective of the current work is to assess the impact of multiple demographic and socio-behavioral factors on the adherence to tenofovir-based PrEP among HIV serodiscordant couples in East Africa using Markov mixed-effects modeling approach. Purpose: Adherence is important for the effectiveness of human immunodeficiency virus (HIV) preexposure prophylaxis (PrEP).

5Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.

4Johns Hopkins University School of Medicine, Baltimore, MD, United States.

2Trevena Inc, King of Prussia, PA, United States.

1UNT System College of Pharmacy, UNTHSC, Fort Worth, TX, United States.

Haberer 5* on behalf of the Partners Demonstration Project Team

#NONMEM MARKOV MODEL PRO#

The power to detect the drug effect was improved by utilizing a bivariate MHMM model over the univariate MHMM models where the number of subject required for 80% power was 25 with the bivariate MHMM model versus 63 in the univariate MHMM FEV1 model and > 100 in the univariate MHMM PRO model.Surulivelrajan Mallayasamy 1, Ayyappa Chaturvedula 1, Michael J.

A drug effect was included on the transition rate probability and the precision of the drug effect parameter improved with increasing magnitude of the parameter. Parameter precision was better with higher magnitudes of the transition probability parameters. Parameter precision was high for all parameters with the exception of the variance of the transition rate dictating the transition from remission to exacerbation (relative root mean squared error > 150%). A bivariate MHMM was developed for simulating and analysing hypothetical COPD data consisting of PRO and FEV1 measurements collected every week for 60 weeks. The influence of including random and covariate effects of varying magnitudes on the parameters in the model was quantified and a power analysis was performed to compare the power of a single bivariate MHMM with two separate univariate MHMMs.

#NONMEM MARKOV MODEL SOFTWARE#

Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example. Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data.

Nonmem markov model