我正在使用survey包来分析纵向数据库。数据看起来像
personid spellid long.w Dur rc sex 1 10 age
1 1 278 6.4702295519 0 0 47 20 16
2 1 203 2.8175129012 1 1 126 87 62
3 1 398 6.1956669321 0 0 180 6 37
4 1 139 7.2791061847 1 0 104 192 20
7 1 10 3.6617503439 1 0 18 24 25
8 1 3 2.265464682 0 1 168 136 40
9 1 134 6.3180994022 0 1 116 194 35
10 1 272 6.9167936912 0 0 39 119 45
11 1 296 5.354798213 1 1 193 161 62
在变量SEX之后，我有10个自举权重，然后是变量Age。
纵向重量在列long.w
中给出我正在使用以下代码。
data.1 <- read.table("Panel.csv", sep = ",",header=T)
library(survey)
library(survival)
#### Unweigthed model
mod.1 <- summary(coxph(Surv(Dur, rc) ~ age + sex, data.1))
mod.1
coxph(formula = Surv(Dur, rc) ~ age + sex, data = data.1)
n= 36, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
age -4.992e-06 1.000e+00 2.291e-02 0.000 1.000
sex 5.277e-01 1.695e+00 5.750e-01 0.918 0.359
exp(coef) exp(-coef) lower .95 upper .95
age 1.000 1.00 0.9561 1.046
sex 1.695 0.59 0.5492 5.232
Concordance= 0.651 (se = 0.095 )
Rsquare= 0.024 (max possible= 0.858 )
### --- Weights
weights <- data.1[,7:16]*data.1$long.w
panel <-svrepdesign(data=data.1,
weights=data.1[,3],
type="BRR",
repweights=weights,
combined.weights=TRUE
)
#### Weighted model
mod.1.w <- svycoxph(Surv(Dur,rc)~ age+ sex ,design=panel)
summary(mod.1.w)
Balanced Repeated Replicates with 10 replicates.
Call:
svycoxph.svyrep.design(formula = Surv(Dur, rc) ~ age + sex, design = panel)
n= 36, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
age 0.0198 1.0200 0.0131 1.512 0.131
sex 1.0681 2.9098 0.2336 4.572 4.84e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
age 1.02 0.9804 0.9941 1.047
sex 2.91 0.3437 1.8407 4.600
Concordance= 0.75 (se = 0.677 )
Rsquare= NA (max possible= NA )
Likelihood ratio test= NA on 2 df, p=NA
Wald test = 28.69 on 2 df, p=5.875e-07
Score (logrank) test = NA on 2 df, p=NA
### ----
> panel.2 <-svrepdesign(data=data.1,
+ weights=data.1[,3],
+ type="BRR",
+ repweights=data.1[,7:16],
+ combined.weights=FALSE,
+ )
Warning message:
In svrepdesign.default(data = data.1, weights = data.1[, 3], type = "BRR", :
Data look like combined weights: mean replication weight is 101.291666666667 and mean sampling weight is 203.944444444444
mod.2.w <- svycoxph(Surv(Dur,rc)~ age+ sex ,design=panel.2)
> summary(mod.2.w)
Call: svrepdesign.default(data = data.1, weights = data.1[, 3], type = "BRR",
repweights = data.1[, 7:16], combined.weights = FALSE, )
Balanced Repeated Replicates with 10 replicates.
Call:
svycoxph.svyrep.design(formula = Surv(Dur, rc) ~ age + sex, design = panel.2)
n= 36, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
age 0.0198 1.0200 0.0131 1.512 0.131
sex 1.0681 2.9098 0.2336 4.572 4.84e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
age 1.02 0.9804 0.9941 1.047
sex 2.91 0.3437 1.8407 4.600
Concordance= 0.75 (se = 0.677 )
Rsquare= NA (max possible= NA )
Likelihood ratio test= NA on 2 df, p=NA
Wald test = 28.69 on 2 df, p=5.875e-07
Score (logrank) test = NA on 2 df, p=NA
纵向重量之和为7,342。事件总数必须在2,357左右，审查后的观察总数为4,985（＃34;＃34;共有7,342人
模型mod.1.w和mod.2.w是否考虑了纵向权重？如果这样做，为什么摘要报告只有n = 36，事件数= 14？
如果我采用其他统计数据，设计效果很好。例如，当我考虑data.1时，svymean(~Dur, panel.2)中不考虑抽样设计的Dur的平均值约为4.9和5.31。