如何检查 R 在 GEE 分析（使用 geepack）中用作二分法结果的参考水平？
How do I check what R used as reference level for my dichotomous outcome in a GEE analysis (using geepack)?
我有 700 名患者的聚类患者数据（针对两家不同的医院分为两组）。我想弄清楚某个心血管风险因素 exposure（具有 3 个级别的因素：下降、稳定 [参考] 和上升）是否与我的二进制 outcome_pres（数字为 0 表示没有结果和 1 表示是结果），针对 age 和 sex 进行了调整。为此，我在 R:
中使用了 geepack 包的 GEE 模型
library(tidyverse)
library(magrittr)
library(geepack)
data <- structure(list(id = c(23, 30, 92, 122, 132, 141, 157, 158, 167,
175, 200, 230, 237, 257, 283, 297, 336, 339, 357, 376, 379, 421,
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1797, 1806, 1810, 1821, 1822, 1875, 1879, 1890, 1903, 1917, 1964,
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69.9986310746064, 68.5831622176591, 62.7843942505133, 63.006160164271,
65.45106091718, 70.5845311430527, 88.9308692676249, 75.7399041752224,
62.2231348391513, 56.7775496235455, 61.3935660506502, 69.0239561943874,
65.3415468856947, 66.0232717316906, 71.7700205338809, 66.4120465434634,
68.8788501026694, 60.6379192334018, 67.0225872689938, 63.5537303216975,
64.1724845995893, 64.1670088980151, 67.8001368925394, 68.7200547570157,
66.4996577686516, 62.9267624914442, 79.1403148528405, 71.6659822039699,
62.3326488706366, 71.1019849418207, 62.3408624229979, 65.3579739904175,
78.2067077344285, 63.0171115674196, 66.6365503080082, 63.8740588637919,
68.6899383983573, 68.4188911704312, 66.0260095824778, 67.5482546201232,
65.9329226557153, 59.9534565366188, 71.9452429842574, 68.4161533196441,
70.6420260095825, 63.2991101984942, 71.6249144421629, 60.6789869952088,
65.6810403832991, 65.347022587269, 62.4558521560575, 67.5318275154004,
64.9281314168378, 67.129363449692, 67.3292265571526, 65.2375085557837,
59.2881587953457, 64.1396303901437, 65.7713894592745, 65.7166324435318,
60.4818617385353, 66.5215605749487, 72.8186173853525, 68.6789869952088,
65.678302532512, 74.5790554414784, 64.1505817932923, 65.7166324435318,
57.7494866529774, 62.0150581793292, 62.0752908966461, 74.135523613963,
67.64681724846, 72.4900752908966, 65.8891170431211, 76.9308692676249,
68.4709103353867, 66.3983572895277, 69.5605749486653, 66.6721423682409,
65.0403832991102, 67.6386036960986, 67.5318275154004, 62.54893908282,
78.0041067761807, 77.0184804928131, 66.4914442162902, 80.8049281314168,
65.3251197809719, 75.2087611225188, 66.7241615331964, 56.6078028747433,
65.7713894592745, 70.611909650924, 66.6721423682409, 72.227241615332,
77.3114305270363, 82.2450376454483, 65.0294318959617, 63.315537303217,
71.0499657768652, 62.4722792607803, 62.7186858316222, 63.5373032169747,
69.4866529774127, 66.839151266256, 65.4647501711157, 66.8911704312115,
78.403832991102, 65.8590006844627, 64.2765229295003, 79.9671457905544,
85.07871321013, 66.7515400410678, 75.211498973306, 71.3620807665982,
72.2628336755647, 64.9363449691992, 77.9493497604381, 58.5817932922656,
70.2258726899384, 76.0191649555099, 68.4928131416838, 69.1143052703628,
69.3388090349076, 71.7262149212868, 90.9650924024641, 67.7043121149897,
77.9301848049281), sex = structure(c(1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 1L), .Label = c("Women", "Men"), class = "factor"),
exposure = structure(c(1L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 1L,
3L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 3L,
1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 3L, 1L,
2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 3L, 1L, 2L, 1L, 3L, 1L, 2L, 1L, 3L, 1L, 2L, 2L,
2L, 2L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 3L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 3L, 3L, 2L,
1L, 2L, 1L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 3L, 1L, 1L, 3L, 1L,
2L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 3L, 1L,
2L, 2L, 3L, 2L, 1L, 3L, 1L, 2L, 1L, 3L, 3L, 2L, 1L, 1L, 3L,
2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 2L,
3L, 1L, 2L, 1L, 1L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 1L, 1L,
2L, 3L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 3L, 2L, 1L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 3L, 1L, 3L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 1L, 3L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 3L, 2L, 1L, 1L, 3L, 1L, 1L,
1L, 3L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 3L,
2L, 1L, 3L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 3L, 2L, 2L, 1L,
1L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 3L,
1L, 2L, 2L, 2L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 3L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 3L,
1L, 1L, 1L, 3L, 1L, 2L), .Label = c("Stable", "Fall", "Rise"
), class = "factor"), outcome_pres = c(1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1,
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), row.names = c(NA,
-570L), class = c("tbl_df", "tbl", "data.frame"))
和模型
model <- geeglm(formula=outcome_pres~exposure+sex+age, data=data, id=id, family=binomial("logit"), corstr="ar1")
如何检查 R 是否实际使用 outcome_pres 的 0 作为此分析中的参考类别？
我认为你误解了什么。 outcome_pres 是您的结果，您将其定义为二项分布，并且具有值 0 和 1：
str(data)
tibble [570 x 6] (S3: tbl_df/tbl/data.frame)
$ id : num [1:570] 23 30 92 122 132 141 157 158 167 175 ...
$ group : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
$ age : num [1:570] 61.7 63.3 66.6 64.4 63.7 ...
$ sex : Factor w/ 2 levels "Women","Men": 1 1 1 1 1 2 1 2 1 1 ...
$ exposure : Factor w/ 3 levels "Stable","Fall",..: 1 1 1 3 1 3 1 1 1 1 ...
$ outcome_pres: num [1:570] 1 1 1 1 1 0 1 1 1 1 ...
本案例中没有参考类别。
如果你的问题是我怎么知道我曝光的参考类别是什么，那么你可以直接在你模型的summary中看到：
summary(model)
Call:
geeglm(formula = outcome_pres ~ exposure + sex + age, family = binomial("logit"),
data = data, id = id, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -3.652686 1.826395 4.00 0.0455 *
exposureFall 0.718930 0.444144 2.62 0.1055
exposureRise -0.854571 0.325662 6.89 0.0087 **
sexMen 0.000538 0.242922 0.00 0.9982
age 0.080847 0.028230 8.20 0.0042 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
您有 exposureFall 和 exposureRise，这意味着您的参考是摘要中不存在的类别，即 stable:
data$exposure %>% unique()
[1] Stable Rise Fall
Levels: Stable Fall Rise
与 stable 相比，Rise 具有保护作用（您的奇数比为 exp(-0.854571)）
我有 700 名患者的聚类患者数据（针对两家不同的医院分为两组）。我想弄清楚某个心血管风险因素 exposure（具有 3 个级别的因素：下降、稳定 [参考] 和上升）是否与我的二进制 outcome_pres（数字为 0 表示没有结果和 1 表示是结果），针对 age 和 sex 进行了调整。为此，我在 R:
geepack 包的 GEE 模型
library(tidyverse)
library(magrittr)
library(geepack)
data <- structure(list(id = c(23, 30, 92, 122, 132, 141, 157, 158, 167,
175, 200, 230, 237, 257, 283, 297, 336, 339, 357, 376, 379, 421,
425, 431, 436, 437, 443, 449, 458, 505, 518, 521, 546, 547, 573,
613, 618, 644, 655, 672, 697, 730, 750, 755, 780, 786, 798, 853,
862, 874, 882, 916, 945, 948, 979, 982, 1002, 1003, 1006, 1022,
1059, 1069, 1092, 1095, 1116, 1127, 1133, 1162, 1178, 1188, 1201,
1210, 1239, 1242, 1258, 1280, 1281, 1297, 1307, 1318, 1331, 1353,
1369, 1383, 1407, 1463, 1473, 1477, 1485, 1519, 1555, 1567, 1573,
1611, 1636, 1659, 1686, 1700, 1712, 1744, 1766, 1767, 1771, 1778,
1797, 1806, 1810, 1821, 1822, 1875, 1879, 1890, 1903, 1917, 1964,
2007, 2010, 2018, 2028, 2067, 2071, 2077, 2078, 2086, 2090, 2103,
2128, 2148, 2185, 2223, 2239, 2257, 2267, 2283, 2300, 2304, 2332,
2395, 2399, 2407, 2427, 2431, 2434, 2440, 2445, 2449, 2480, 2515,
2533, 2536, 2546, 2552, 2635, 2660, 2697, 2705, 2724, 2725, 2728,
2748, 2778, 2794, 2798, 2830, 2843, 2895, 2902, 2907, 2915, 2924,
2929, 2952, 2955, 2963, 2982, 3016, 3018, 3036, 3065, 3083, 3087,
3177, 3178, 3186, 3196, 3199, 3230, 3272, 3278, 3292, 3302, 3304,
3311, 3312, 3351, 3378, 3399, 3401, 3403, 3445, 3447, 3449, 3457,
3472, 3477, 3486, 3490, 3515, 3516, 3519, 3521, 17001, 64001,
128001, 136001, 177001, 177002, 240001, 240001, 248002, 352001,
370002, 410001, 426001, 443002, 443002, 466002, 466002, 469002,
470001, 489002, 520002, 542001, 595001, 615001, 651001, 651002,
657001, 658002, 665001, 665001, 687002, 698002, 745001, 754001,
800002, 804001, 811002, 818001, 881001, 881001, 920001, 927001,
927001, 943001, 974001, 1015002, 1037001, 1081001, 1081002, 1133001,
1136001, 1141001, 1157001, 1175002, 1206001, 1247001, 1247002,
1283001, 1290002, 1296001, 1307001, 1344002, 1346001, 1346001,
1379001, 1419002, 1427001, 1438001, 1592002, 1652002, 1711001,
1754001, 1763001, 1811001, 1833001, 1878002, 1892001, 1915001,
1915002, 1921001, 1949002, 1961001, 1995001, 2014001, 2022002,
2102001, 2102002, 2138002, 2141001, 2141002, 2193002, 2240001,
2281001, 2281002, 2432001, 2493001, 2517001, 2558001, 2558002,
2588001, 2588001, 2590001, 2601001, 2620002, 2653001, 2678001,
2714001, 2721001, 2721002, 2721002, 2731001, 2777001, 2806001,
2813001, 2862001, 2862002, 2872001, 2872002, 2902001, 2959001,
2967001, 3026002, 3026002, 3037001, 3063002, 3094002, 3126002,
3139001, 3196001, 3233001, 3252001, 3282002, 3290002, 3314001,
3334001, 3334001, 3377002, 3420001, 3427001, 3458001, 3532002,
3558001, 3653001, 3663002, 3675002, 3678002, 3692001, 3757002,
3764002, 3764002, 3774001, 3780001, 3823002, 3823002, 3836001,
3862001, 3916001, 3953001, 4032001, 4042001, 4063001, 4077001,
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1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 1L), .Label = c("Women", "Men"), class = "factor"),
exposure = structure(c(1L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 1L,
3L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 3L,
1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 3L, 1L,
2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 3L, 1L, 2L, 1L, 3L, 1L, 2L, 1L, 3L, 1L, 2L, 2L,
2L, 2L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 3L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 3L, 3L, 2L,
1L, 2L, 1L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 3L, 1L, 1L, 3L, 1L,
2L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 3L, 1L,
2L, 2L, 3L, 2L, 1L, 3L, 1L, 2L, 1L, 3L, 3L, 2L, 1L, 1L, 3L,
2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 2L,
3L, 1L, 2L, 1L, 1L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 1L, 1L,
2L, 3L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 3L, 2L, 1L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 3L, 1L, 3L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 1L, 3L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 3L, 2L, 1L, 1L, 3L, 1L, 1L,
1L, 3L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 3L,
2L, 1L, 3L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 3L, 2L, 2L, 1L,
1L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 3L,
1L, 2L, 2L, 2L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 3L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 3L,
1L, 1L, 1L, 3L, 1L, 2L), .Label = c("Stable", "Fall", "Rise"
), class = "factor"), outcome_pres = c(1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1,
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), row.names = c(NA,
-570L), class = c("tbl_df", "tbl", "data.frame"))
和模型
model <- geeglm(formula=outcome_pres~exposure+sex+age, data=data, id=id, family=binomial("logit"), corstr="ar1")
如何检查 R 是否实际使用 outcome_pres 的 0 作为此分析中的参考类别？
我认为你误解了什么。 outcome_pres 是您的结果，您将其定义为二项分布，并且具有值 0 和 1：
str(data)
tibble [570 x 6] (S3: tbl_df/tbl/data.frame)
$ id : num [1:570] 23 30 92 122 132 141 157 158 167 175 ...
$ group : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
$ age : num [1:570] 61.7 63.3 66.6 64.4 63.7 ...
$ sex : Factor w/ 2 levels "Women","Men": 1 1 1 1 1 2 1 2 1 1 ...
$ exposure : Factor w/ 3 levels "Stable","Fall",..: 1 1 1 3 1 3 1 1 1 1 ...
$ outcome_pres: num [1:570] 1 1 1 1 1 0 1 1 1 1 ...
本案例中没有参考类别。
如果你的问题是我怎么知道我曝光的参考类别是什么，那么你可以直接在你模型的summary中看到：
summary(model)
Call:
geeglm(formula = outcome_pres ~ exposure + sex + age, family = binomial("logit"),
data = data, id = id, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -3.652686 1.826395 4.00 0.0455 *
exposureFall 0.718930 0.444144 2.62 0.1055
exposureRise -0.854571 0.325662 6.89 0.0087 **
sexMen 0.000538 0.242922 0.00 0.9982
age 0.080847 0.028230 8.20 0.0042 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
您有 exposureFall 和 exposureRise，这意味着您的参考是摘要中不存在的类别，即 stable:
data$exposure %>% unique()
[1] Stable Rise Fall
Levels: Stable Fall Rise
stable 相比，Rise 具有保护作用（您的奇数比为 exp(-0.854571)）