大佬救命：
在JMbayes中的函数如下：

lmeFit <- lme(y ~ 0 + group + group:ns(time, k = c(2.5, 6), B = c(0.5, 13)),
              data = train_data,
              random = list(id = pdDiag(form = ~ ns(time, k = c(2.5, 6),
                                                    B = c(0.5, 13)))))
survFit <- coxph(Surv(Time, event) ~ group ,
                 data = train_data.id, x = TRUE)
# current value
jointFit1 <- jointModelBayes(lmeFit, survFit, timeVar = "time",
                             n.iter = 2000L,
                             priors = list(priorA.tauBs = 1e-01, priorB.tauBs = 1e-01))

dForm <- list(fixed = ~ 0 + group:dns(time, k = c(2.5, 6), B = c(0.5, 13)),
              indFixed = 3:8,
              random = ~ 0 + dns(time, k = c(2.5, 6), B = c(0.5, 13)),
              indRandom = 2:4)
jointFit2 <- update(jointFit1, param = "td-both", extraForm = dForm)

如果把上述代码换成rstanarm包的stan_jm函数：
D1 <- stan_jm(formulaLong = y ~ 0 + group + group:ns(time, k = c(2.5, 6), B = c(0.5, 13))
              + (ns(time, k = c(2.5, 6),B = c(0.5, 13))|id),
              dataLong = train_data,
              formulaEvent = survival::Surv(Time, event) ~ group,
              dataEvent =train_data.id,
              assoc = "etavalue",
              time_var = "time",
              chains = 1,cores = 1,refresh = 500, iter=2000)

D2 <- stan_jm(formulaLong = y ~ 0 + group + group:ns(time, k = c(2.5, 6), B = c(0.5, 13))+
                group:dns(time,k=c(2.5,6),B=c(0.5,13))
              + ((ns(time, k = c(2.5, 6),B = c(0.5, 13))+dns(time,k=c(2.5,6),B=c(0.5,13)))|id),
              dataLong = train_data,
              formulaEvent = survival::Surv(Time, event) ~ group,
              dataEvent =train_data.id,
              assoc = c("etavalue", "etaslope"),
              time_var = "time",
              chains = 1,cores = 1, refresh = 500,  iter=1000)
这样好像是不对的，因为最后拟合出来的结果，rstanarm包的结果不理想。问题应该出在红色字体部分，但我不知道怎么修改，求大神指点。