id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-195082-7tnwkxuh	Oodally, Ajmal	Modeling dependent survival data through random effects with spatial correlation at the subject level	2020-10-12		.txt	text/plain	4801	342	61	Estimates are obtained through a stochastic approximation version of the Expectation Maximization algorithm combined with a Monte-Carlo Markov Chain, for which convergence is proven. Li and Ryan (2002) developed a semi-parametric spatial frailty model with Monte Carlo simulations and Laplace approximation of a rank based marginal likelihood. Along the same lines, Lin (2012) estimated parameters of a log-normal spatial frailty model using a two-iteration approach based on an approximate likelihood function, alternating between the estimation of the regression parameter and the variance components. For instance, we use as initial values for the regression parameter β and baseline components the estimated values obtained when fitting the data by a piecewise constant proportional hazards model. Furthermore, using the villages as clusters in the marginal and shared frailty models to analyse the malaria data set has serious impact on some of the parameter estimates. Convergent stochastic algorithm for parameter estimation in frailty models using integrated partial likelihood	./cache/cord-195082-7tnwkxuh.txt	./txt/cord-195082-7tnwkxuh.txt