Sieve_NPMLE_Switch: Sieve_NPMLE_Switch function
In GSSE: Genotype-Specific Survival Estimation

Description Usage Arguments Details Value References See Also Examples

View source: R/Sieve_NPMLE_Switch.R

This function is used to estimate the genotype-specific distribution of time-to-event outcomes with the Sieve-NPMLE switch algorithm.

1	Sieve_NPMLE_Switch (Y, p0G, Delta, px, Grid, Knot, degree, maxiter=400, ep=1e-05)

`Y`	observed event times or censoring times.
`p0G`	probabilities of being a carrier.
`Delta`	indicators of event.
`px`	grid points at which the distribution function values are estimated.
`Grid`	grid points used for plots. `Grid` could be either exactly the same as `px` or different from `px`.
`Knot`	number of knots.
`degree`	degree of the B-spline base functions.
`maxiter`	maximum number of iterations.
`ep`	convergence criterion, default is `ep= 1e-05`.

This function is used to estimate the distribution of the age-at-onset of Parkinsons disease for carriers' and non-carriers' mutations in the leucine-rich repeat kinase 2, LRRK2, gene. Please refer to Wang et al. (2015) for more details.

This function returns a list of prediction values for classes

`lamb1.hat`	estimated values of hazard function for the carrier group.
`lamb2.hat`	estimated values of hazard function for the non-carrier group.
`Lamb1`	estimated values of cumulative hazard function for the carrier group.
`Lamb2`	estimated values of cumulative hazard function for the non-carrier group.
`Converge`	convergence status.

Wang, Y., Liang, B., Tong, X., Marder, K., Bressman, S., Orr-Urtreger, A., Giladi, N. & Zeng, D. (2015). Efficient estimation of nonparametric genetic risk function with censored data. Biometrika, 102(3), 515-532.

p0G_Func(), Sieve_NPMLE_Bootstrap() and Permutation_Test().

data("Simulated_data");

OY = Simulated_data[,2];
ind = order(OY);
ODelta = Simulated_data[,3];
Op0G = Simulated_data[,4];

Y = OY[ind];
Delta = ODelta[ind];
p0G = Op0G[ind];

Grid = seq(0.2, 3.65, 0.05);
fix_t1 = c(0.288, 0.693, 1.390);
fix_t2 = c(0.779, 1.860, 3.650);
px = seq(0.01, 3.65, 0.01);

SieveNPMLE_result = Sieve_NPMLE_Switch( Y=Y, Delta=Delta, p0G=p0G,
                                        px=px, Grid=Grid,
                                        Knot=6, degree=3  );

# 'Sieve_NPMLE_Switch' only returns values of hazard function 
# or cumulative hazard function of mutation groups, in practice, we only 
# need to do a little bit more as follows for calculating the 
# cumulative distribution values of mutation groups. 

Lambda_1.hat = cumsum( SieveNPMLE_result$lamb1.hat );
Lambda_2.hat = cumsum( SieveNPMLE_result$lamb2.hat );

F_carr_func = function(x){ 1 - exp(- max( Lambda_1.hat[Y <= x] ) ) }
F_non_func  = function(x){ 1 - exp(- max( Lambda_2.hat[Y <= x] ) ) }

est.f1 = apply(matrix(fix_t1, ncol=1), 1, F_carr_func );
est.f2 = apply(matrix(fix_t2, ncol=1), 1, F_non_func  );

estp = data.frame( fix_t1 = fix_t1, F_carr_t1 = est.f1,
                   fix_t2 = fix_t2, F_non_t2 = est.f2  );

print(estp);

# plot estimated curves

F_carr = apply( matrix(px, ncol=1), 1, F_carr_func );
F_non = apply( matrix(px, ncol=1), 1, F_non_func );

plot( px, F_carr, type='s', lty=1, ylim=c(0, 1), xlab="Y",
      ylab="Estimated Cumulative Distribution Function", col='blue' );
lines(px, F_non, type='s', lty=2, col='red');
legend("topleft", legend=c("Carrier group", "Non-Carrier group"),
       lty=c(1,2), col=c("blue","red") );