README.md

In JiyuanHu/JointMM: A joint modeling tool for analyzing zero-inflated longitudinal proportions and time-to-event data

JointMM

A joint modeling framework for analyzing zero-inflated longitudinal proportions and time to event data

This package is developed to investigate the association between zero-inflated longitudinal proportions and time to an event,e.g., disease onset. It is in particular applicable to microbiome proportion data (or called relative abundance data) from prospective studies. The model JointMM is specifically designed to handle the zero-inflated and highly skewed longitudinal microbial proportion data to examine whether the temporal pattern of microbial presence and/or the non-zero microbial proportions are associated with differences in the time to an event.

Installation of JointMM in R:

library("devtools"); install_github("JiyuanHu/JointMM");

Example code:

require(JointMM)

data(dat) #load in the long format of longitudinal and survival data

zero.prop = mean(dat$Y==0)

1. full model; joint modeling

res1 = JointMM.func(data=dat,cov.name.long=c('obstime','treatment'),cov.name.surv='treatment',threshold.zero.prop = 0.1,is.longi.model.only = FALSE, quad.n=10)

2. full model; modeling of the longitudinal part only - this is applicable when only longitudinal data is available

res2 = JointMM.func(data=dat,cov.name.long=c('obstime','treatment'),cov.name.surv='treatment', threshold.zero.prop = 0.1,is.longi.model.only = TRUE, quad.n=10)

3. reduced model-- joint modeling analysis when the proportion of zero RA is lower than the threshold.zero.prop; Here the threshold of 0.6 is just for illustration. For > sample size such like in our demo dataset, a threshold of 10\% is recommended.

res3 = JointMM.func(data=dat,cov.name.long=c('obstime','treatment'),cov.name.surv='treatment',threshold.zero.prop = 0.6,is.longi.model.only = FALSE, quad.n=10)

4. reduced model-- used when the proportion of zero RA is lower than the threshold.zero.prop; modeling of the longitudinal part only - this is applicable when only longitudinal data is available

res4 = JointMM.func(data=dat,cov.name.long=c('obstime','treatment'),cov.name.surv='treatment', threshold.zero.prop = 0.6,is.longi.model.only = TRUE, quad.n=10)

names(res1) and names(res3)

c("par.est.Wald","SEs","est.hessian","Wald.Ts","pvals.Wald","status.Wald")

names(res2) and names(res4)

c("par.est.Longonly","SEs","est.hessian","Wald.Ts.Longonly","pvals.Wald.Longonly","status.Wald.Longonly")

5. Evaluate the cross-part correlation in the longitudinal sub-model

res5 = JointMM.func(data=dat,cov.name.long=c('obstime','treatment'),cov.name.surv='treatment', threshold.zero.prop = 0.1,is.longi.model.only = TRUE, cross.part.corr.eval = TRUE,quad.n=10)

Reference: Hu J, Wang C, Blaser M, Li H (2021). Joint modeling of zero-inflated longitudinal proportions and time-to-event data with application to a gut microbiome study. Biometrics (Accepted)

JiyuanHu/JointMM documentation built on Dec. 18, 2021, 1:37 a.m.