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在使用分类预测变量进行多变量 Coxph 模型时，并在重新调整所有预测变量（函数 = relevel（））之后，我的两个预测变量具有多个“参考”级别，从而消除了一些分析。具体来说，对于“条件”和“病原体”因素（如下）。这是已知解决方案的常见问题吗？我有太多的预测变量（n=6）吗？

下面的代码：

mydata<-read.csv("survivaldata.csv")

mydata$concentration1 <- relevel(mydata$concentration1, "1")#
mydata$concentration2 <- relevel(mydata$concentration2, "1")#
mydata$contaminant <- relevel(mydata$contaminant, "Control")#
mydata$pathogenpres <- relevel(mydata$pathogenpres, "2")#
mydata$fam <- relevel(mydata$fam, "1")#
mydata$condition <- relevel(mydata$condition, "gg")#

surob1<-Surv(time=mydata$days.surv,event = mydata$censored)

fit.coxph1<-coxph(surob1~concentration1+concentration2+contaminant+pathogenpres+condition+fam,data=mydata,conf.type="plain")

> summary(fit.coxph1)
Call:
coxph(surob1~concentration1+concentration2+contaminant+pathogenpres+condition+fam,data=mydata)

  n= 188, number of events= 83 
   (8 observations deleted due to missingness)

                 coef exp(coef) se(coef)      z Pr(>|z|)  
concentration12  0.62577   1.86968  0.35103  1.783   0.0746 .
concentration13  0.69556   2.00483  0.35593  1.954   0.0507 .
concentration22  -0.15399   0.85728  0.31970 -0.482   0.6300  
concentration23  -0.26729   0.76545  0.31970 -0.836   0.4031  
contaminant1  0.74756   2.11185  0.69261  1.079   0.2804  
contaminant2  0.40921   1.50563  0.69438  0.589   0.5556  
condition1        NA        NA  0.00000     NA       NA  
condition2        NA        NA  0.00000     NA       NA  
condition3        NA        NA  0.00000     NA       NA  
condition4   0.53134   1.70120  0.76529  0.694   0.4875  
pathogenpres1            NA        NA  0.00000     NA       NA  
fam2       0.07799   1.08112  0.26550  0.294   0.7689  
fam3      -0.03410   0.96647  0.28008 -0.122   0.9031  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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