## 【原】R数据分析：纵向分类结局的分析-马尔可夫多态模型的理解与实操

2022-03-25

Ward DD, Wallace LMK, Rockwood K

Cumulative health deficits, APOE genotype, and risk for later-life mild cognitive impairment and dementia

Journal of Neurology, Neurosurgery & Psychiatry 2021;92:136-142.

# 多态马尔可夫模型

The Markov models stand out as much simpler than other models from a probability point of view, and this simplifies the likelihood evaluation

A multi-state model describes how an individual moves between a series of states in continuous time

in which individuals can advance or recover between adjacent disease states, or die from any state.：

Fitting multi-state models to panel data generally relies on the Markov assumption, that future evolution only depends on the current state.

# 实例操练

observations from the same subject must be adjacent in the dataset, and observations must be ordered by time within subjects.

``statetable.msm(state, PTNUM, data=cav)``

``msm_model <- msm(state ~ years, subject = PTNUM, data = mydata,                    covariates = ~ dage + ihd,                     qmatrix = twoway4.q,                     death = 4,                    method = "BFGS", control = list(fnscale = 4000, maxit = 10000))``

# 小结

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