我如何檢查 R 在 GEE 分析（使用 geepack）中用作我的二分結果的參考水平？
[英]How do I check what R used as reference level for my dichotomous outcome in a GEE analysis (using geepack)?
問題描述
我有 700 名患者的聚類患者數據（針對兩家不同的醫院分為兩組）。 我試圖弄清楚某個心血管風險因素exposure （具有 3 個級別的因素：下降、穩定 [參考] 和上升）是否與我的二進制outcome_pres （數字為 0 表示無結果，1 表示有結果）有關，調整age和sex 。 為此，我使用了 GEE model，並在 R 中使用了geepack R ：
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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9591001), group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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68.2518822724162, 70.2094455852156, 70.2532511978097, 65.347022587269,
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 model
model <- geeglm(formula=outcome_pres~exposure+sex+age, data=data, id=id, family=binomial("logit"), corstr="ar1")
如何檢查 R 是否實際使用outcome_pres的 0 作為此分析中的參考類別？
1 個解決方案
解決方案1
2 已采納 2022-05-04 14:08:44
我認為你在這里誤解了一些東西。 outcome_pres是您的結果，您將其定義為二項分布，並且具有值 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 ...
在這種情況下沒有參考類別。
如果你的問題是我怎么知道我曝光的參考類別是什么，那么你可以直接在你的model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 ，這意味着您的參考是摘要中不存在的類別，即stable的：
data$exposure %>% unique()
[1] Stable Rise Fall
Levels: Stable Fall Rise
因此，與stable相比， Rise具有保護作用（您的奇數比為 exp(-0.854571)）
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