<b>TPmsm</b>: Estimation of the Transition Probabilities in 3-State Models

Araújo, Artur; Meira‐Machado, Luís; Roca‐Pardiñas, Javier

doi:10.18637/jss.v062.i04

Cited by 13 publications

(8 citation statements)

References 35 publications

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“…In addition, the number of R packages focused on the estimation of statistical models using the Markov Chain Monte Carlo simulation approach is sensibly bigger. Finally, the msm (Jackson, 2011), heemod (Antoine Filipovi et al, 2017) and the TPmsm packages (Artur Araújo et al, 2014) focus on health applications of multi -state analysis using different kinds of models, including Markov-related ones among them.…”

Section: Analysis Of Existing Dtmc-related Softwarementioning

confidence: 99%

Discrete Time Markov Chains with R

Spedicato¹

2017

The R Journal

125

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Section: Analysis Of Existing Dtmc-related Softwarementioning

confidence: 99%

Discrete Time Markov Chains with R

Spedicato¹

2017

The R Journal

125

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“…The parameters of interest in the multistate model for this study are: (i) the relationship between covariates and time to event; (ii) the transition intensities (hazard rates) ( 31 , 32 ).…”

Section: Methodsmentioning

confidence: 99%

Modeling Transitional States of Drivers Yielding Right-Of-Way to Pedestrians at Signalized Midblock Crosswalks using a Hazard-Based Multistate Model

Kutela

Teng

2019

Transportation Research Record

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This study presents an analysis of transitional states of drivers yielding right-of-way to pedestrians at signalized midblock crosswalks. The study utilizes data from twenty signalized midblock crosswalks located in Las Vegas, Nevada, equipped with traffic control signals (TCSs), pedestrians hybrid beacons (PHBs), circular flashing beacons (CFBs), circular rapid flashing beacons (CRFBs), and rectangular rapid flashing beacons (RRFBs). It applies both descriptive analysis and multistate models to explore the variations in transition durations and factors associated with the states’ transition of drivers yielding right-of-way, namely non-yield, multiple-threats, and full yield. The study found that RRFBs are the signal types that have a high frequency of multiple-threats state. The duration to reach full yield varies between 0 s and 318 s; the duration of non-yield to multiple-threats varies between 1 s and 105 s; whereas multiple-threats to full yield duration varies between 1 s and 38 s. The multistate model results show that only the signal type factor has a clear pattern. For this factor, the RRFBs showed low hazard ratios for all three transitions, when compared with CFBs and CRFBs. In various ways, the states’ transition durations were also associated with the number of cross stages, number of lanes, vehicle density, incoming vehicle speeds, annual average daily traffic, and number of pedestrians crossing, as well as their crossing positions. The developed model can be used by engineers and planners to evaluate factors affecting the effectiveness of the crosswalk treatments.

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“…For small sample sizes, the presmoothed Aalen-Johansen estimator (Moreira et al, 2013) might be preferred. We will use the Aalen-Johansen estimator implemented in the R package TPmsm (Araújo et al, 2014) to carry out our estimation and therefore concentrate on evaluation of the loss function and optimization of the surveillance time points.…”

Section: The Loss Function Under the Nonhomogeneous Markov Modelmentioning

confidence: 99%

Tailored optimal posttreatment surveillance for cancer recurrence

Chen

2020

Biometrics

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A substantial rise in the number of cancer survivors has led to urgent management questions regarding effective posttreatment imaging-based surveillance strategies for cancer recurrence. Current surveillance guidelines provided by a number of professional societies all warn against overly aggressive surveillance, especially for low-risk patients, but all fail to provide more specific directions to accommodate underlying heterogeneity of cancer recurrence. Therefore it is imperative to develop data-driven strategies that can tailor the surveillance schedules to recurrence risk in this era of stricter insurance regulations, provider shortages, and rising costs of health care. Due to a lack of statistical methods for optimizing surveillance scheduling in presence of competing risks, we propose a general approach that uses an intuitive loss function for optimization of early detection of recurrence before death. The proposed strategies can tailor to patient risks of recurrence, in terms of both intensity and amount of surveillance. Using general three-state Markov models, our method is flexible and includes earlier works as special cases. We illustrate our method in both simulation studies and an application to breast cancer surveillance.

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TPmsm: Estimation of the Transition Probabilities in 3-State Models

Cited by 13 publications

References 35 publications

Discrete Time Markov Chains with R

Discrete Time Markov Chains with R

Modeling Transitional States of Drivers Yielding Right-Of-Way to Pedestrians at Signalized Midblock Crosswalks using a Hazard-Based Multistate Model

Tailored optimal posttreatment surveillance for cancer recurrence

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