R/ll_flexrsurv_fromto_gamma0_bh.R

In flexrsurv: Flexible Relative Survival Analysis

ll_flexrsurv_fromto_gamma0_bh<-function(gamma0, alpha0, beta0, alpha, beta, Y, X0, X, Z, 
		expected_rate,
		weights=NULL,
		step, Nstep,
		intTD=intTD_NC, intweightsfunc=intweights_CAV_SIM,
		Spline_t0=BSplineBasis(knots=NULL,  degree=3,   keep.duplicates=TRUE), Intercept_t0=FALSE,
		nT0basis,
		nX0,
		nX, 
		nTbasis,
		Spline_t =BSplineBasis(knots=NULL,  degree=3,   keep.duplicates=TRUE),
		Intercept_t_NPH=rep(TRUE, nX),
		debug=FALSE, ...){
	# compute log likelihood of the relative survival model model
	# rate = (f(t)%*%gamma) * exp( X0%*%alpha0 + X%*%beta0(t) + sum( alphai(zi)betai(t) ))
	# gamma0 : vector of coef for baseline hazard
	# alpha0 ; vector of all coefs for non time dependant variables (may contain non-loglinear terms such as spline)
	# beta0 ; matrix of all coefs for log-linear but  time dependant variables  X%*%beta0(t)
	# beta  : matrix of coefs for beta(t) nTbasis * nTDvars for NLG and NPH
	# alpha : vector of coef for alpha(z) for NLG and NPH
	# Y : object of class Surv with beginning and end of interval
	#
	# X0 : non-time dependante variable (may contain spline bases expended for non-loglinear terms)
	# X : log lineair but time dependante variable 
	# Z : object of class "DesignMatrixNPHNLL" time dependent variables (spline basis expended)
	# expected_rate : expected rate at event time T
	# weights : vector of weights  : LL = sum_i w_i ll_i
	# Step : object of class "NCLagParam" or "GLMLagParam"
	# intTD : function to perform numerical integration 
	# intweightfunc : function to compute weightsfor numerical integration
	#  Spline_t0, spline object for baseline hazard, with evaluate() method
	#  Intercept_t0=FALSE, option for evaluate, = TRUE all the basis, =FALSE all but first basis 
	#  Spline_t, spline object for time dependant effects,  with evaluate() method
	# Intercept_t_NPH vector of intercept option for NPH spline (=FALSE when X is NLL too, ie in case of remontet additif NLLNPH)
	# nT0basis : number of spline basis for NPHLIN effects
	# nX0   : nb of PH variables dim(X0)=c(nobs, nX0)
	# nX    : nb of NPHLIN variables dim(X)=c(nobs, nX)
	# nTbasis : number of time spline basis
	#  ... not used args
	# the function do not check the concorcance between length of parameter vectors and the number of knots and the Z.signature
	# returned value : the log liikelihood of the model
	
	
	if(is.null(Z)){
		nZ <- 0
	} else {
		nZ <- Z@nZ
	}
	
	if(Intercept_t0){
		tmpgamma0 <- gamma0
	}
	else {
		tmpgamma0 <- c(0, gamma0)
	}
	
	# baseline hazard at the end of the interval
	
	YT0Gamma0 <- predictSpline(Spline_t0*tmpgamma0, Y[,1], intercept=Intercept_t0)
	
	
	
	# contribution of non time dependant variables
	if(nX0) PHterm <-exp(X0 %*% alpha0)
	else PHterm <- 1
	
	# contribution of time d?pendant effect
	# parenthesis are important for efficiency
	if(nZ) {
		Zalphabeta <- Z@DM %*%( diag(alpha) %*% Z@signature  %*% t(ExpandAllCoefBasis(beta, ncol=nZ,  value=1)) )
		if(nX) {
			Zalphabeta <- Zalphabeta +  X %*% t(ExpandCoefBasis(beta0,
							ncol=nX,
							splinebasis=Spline_t,
							expand=!Intercept_t_NPH,
							value=0))
		}
	}
	else {
		if(nX) {
			Zalphabeta <-  X %*% t(ExpandCoefBasis(beta0,
							ncol=nX,
							splinebasis=Spline_t,
							expand=!Intercept_t_NPH,
							value=0))
		}
		else {
			Zalphabeta <- NULL
		}
	}
	
	if(nX + nZ) {
		NPHterm <- intTD(func=rateTD_bh_alphabeta, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
				step=step, Nstep=Nstep,
				intweightsfunc=intweightsfunc, 
				gamma0=gamma0, Zalphabeta=Zalphabeta, 
				Spline_t0=Spline_t0*tmpgamma0, Intercept_t0=Intercept_t0,
				Spline_t = Spline_t, Intercept_t=TRUE,
				debug=debug)
	}
	else {
#    NPHterm <- intTD(func=rateTD_gamma0_bh, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
#                     step=step, Nstep=Nstep,
#                     intweightsfunc=intweightsfunc, 
#                     gamma0=gamma0,
#                     Spline_t0=Spline_t0*tmpgamma0, Intercept_t0=Intercept_t0,
#                     debug=debug)
		NPHterm <- predict(integrate(Spline_t0*tmpgamma0), Y[,2], intercep=Intercept_t0) -
				predict(integrate(Spline_t0*tmpgamma0), Y[,1], intercep=Intercept_t0)
	}
# spline bases for baseline hazard at the end of intervals
	YT0 <- evaluate(Spline_t0, Y[,2], intercept=Intercept_t0)
	# spline bases for each TD effect
	if(nX0){
		if(nX + nZ){
			# spline bases for each TD effect at the end of intervals
			YT <- evaluate(Spline_t, Y[,2], intercept=TRUE)
			eventterm <- ifelse(Y[,3] ,
					log( PHterm * (YT0Gamma0) * exp(apply(YT * Zalphabeta, 1, sum)) + expected_rate ), 
					0)
		} 
		else { 
			eventterm <- ifelse(Y[,3] , 
					log( PHterm * (YT0Gamma0) + expected_rate ), 
					0)
		}
	} 
	else {
		if(nX + nZ){
			# spline bases for each TD effect at the end of intervals
			YT <- evaluate(Spline_t, Y[,2], intercept=TRUE)
			eventterm <- ifelse(Y[,3] ,
					log( (YT0Gamma0) * exp(apply(YT * Zalphabeta, 1, sum)) + expected_rate ), 
					0)
		} else {
			eventterm <- ifelse(Y[,3] , 
					log( (YT0Gamma0) + expected_rate ), 
					0)
		}
	}
	
	if (!is.null(weights)) {
		if( nX0){
			ret <- crossprod(eventterm - PHterm * NPHterm , weights)
		} else {
			ret <- crossprod(eventterm - NPHterm , weights)
		}
	}
	else {
		if( nX0){
			ret <- sum( eventterm - PHterm * NPHterm )
		} else {
			ret <- sum( eventterm - NPHterm )
		}
	}
	
	ret 
}