Analisis exploratorio base de datos

# Establecer el directorio de trabajo 

library(readxl)
library(summarytools)

DATA_SET <- read_excel("DATA SET 14-10-2025.xlsx") #cargue base de datos
print(dfSummary(DATA_SET), method = 'render')

Data Frame Summary

DATA_SET

Dimensions: 200 x 180
Duplicates: 0
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 ID [numeric]
Mean (sd) : 100.5 (57.9)
min ≤ med ≤ max:
1 ≤ 100.5 ≤ 200
IQR (CV) : 99.5 (0.6)
 200 distinct values 200 (100.0%) 0 (0.0%)
2 CE_MUJER [character] 1. Si
200(100.0%)
 200 (100.0%) 0 (0.0%)
3 CE_EDAD_18 [character] 1. Si
200(100.0%)
 200 (100.0%) 0 (0.0%)
4 CE_DX_mama [character] 1. Si
200(100.0%)
 200 (100.0%) 0 (0.0%)
5 CE_TRAT [character] 1. Si
200(100.0%)
 200 (100.0%) 0 (0.0%)
6 CE_TRAT_2 [character]
1. No
2. Si
140(70.0%)
60(30.0%)
 200 (100.0%) 0 (0.0%)
7 CE_libre_cancer [character] 1. Si
200(100.0%)
 200 (100.0%) 0 (0.0%)
8 CE_CJO [character] 1. Si
200(100.0%)
 200 (100.0%) 0 (0.0%)
9 CE_COM_mental [character] 1. No
200(100.0%)
 200 (100.0%) 0 (0.0%)
10 CE_CAMA [character] 1. No
200(100.0%)
 200 (100.0%) 0 (0.0%)
11 CE_PARTICIPACION [character] 1. Si
200(100.0%)
 200 (100.0%) 0 (0.0%)
12 CE_CONSENTIMIENTO [character] 1. Si
200(100.0%)
 200 (100.0%) 0 (0.0%)
13 CE_COMPLETO [character] 1. Complete
200(100.0%)
 200 (100.0%) 0 (0.0%)
14 qlq_c30_1 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
42(21.0%)
17(8.5%)
80(40.0%)
61(30.5%)
 200 (100.0%) 0 (0.0%)
15 qlq_c30_2 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
20(10.0%)
10(5.0%)
132(66.0%)
38(19.0%)
 200 (100.0%) 0 (0.0%)
16 qlq_c30_3 [character]
1. Bastante
2. Para nada
3. Un poco
6(3.0%)
172(86.0%)
22(11.0%)
 200 (100.0%) 0 (0.0%)
17 qlq_c30_4 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
8(4.0%)
1(0.5%)
171(85.5%)
20(10.0%)
 200 (100.0%) 0 (0.0%)
18 qlq_c30_5 [character]
1. Bastante
2. Para nada
3. Un poco
1(0.5%)
194(97.0%)
5(2.5%)
 200 (100.0%) 0 (0.0%)
19 qlq_c30_6 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
16(8.0%)
4(2.0%)
144(72.0%)
36(18.0%)
 200 (100.0%) 0 (0.0%)
20 qlq_c30_7 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
6(3.0%)
4(2.0%)
179(89.5%)
11(5.5%)
 200 (100.0%) 0 (0.0%)
21 qlq_c30_8 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
8(4.0%)
1(0.5%)
158(79.0%)
33(16.5%)
 200 (100.0%) 0 (0.0%)
22 qlq_c30_9 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
29(14.5%)
15(7.5%)
79(39.5%)
77(38.5%)
 200 (100.0%) 0 (0.0%)
23 qlq_c30_10 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
21(10.5%)
5(2.5%)
101(50.5%)
73(36.5%)
 200 (100.0%) 0 (0.0%)
24 qlq_c30_11 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
23(11.5%)
15(7.5%)
94(47.0%)
68(34.0%)
 200 (100.0%) 0 (0.0%)
25 qlq_c30_12 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
17(8.5%)
3(1.5%)
138(69.0%)
42(21.0%)
 200 (100.0%) 0 (0.0%)
26 qlq_c30_13 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
8(4.0%)
3(1.5%)
174(87.0%)
15(7.5%)
 200 (100.0%) 0 (0.0%)
27 qlq_c30_14 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
2(1.0%)
3(1.5%)
175(87.5%)
20(10.0%)
 200 (100.0%) 0 (0.0%)
28 qlq_c30_15 [character]
1. Para nada
2. Un poco
194(97.0%)
6(3.0%)
 200 (100.0%) 0 (0.0%)
29 qlq_c30_16 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
11(5.5%)
2(1.0%)
148(74.0%)
39(19.5%)
 200 (100.0%) 0 (0.0%)
30 qlq_c30_17 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
5(2.5%)
2(1.0%)
179(89.5%)
14(7.0%)
 200 (100.0%) 0 (0.0%)
31 qlq_c30_18 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
21(10.5%)
9(4.5%)
90(45.0%)
80(40.0%)
 200 (100.0%) 0 (0.0%)
32 qlq_c30_19 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
24(12.0%)
9(4.5%)
120(60.0%)
47(23.5%)
 200 (100.0%) 0 (0.0%)
33 qlq_c30_20 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
18(9.0%)
4(2.0%)
137(68.5%)
41(20.5%)
 200 (100.0%) 0 (0.0%)
34 qlq_c30_21 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
23(11.5%)
6(3.0%)
125(62.5%)
46(23.0%)
 200 (100.0%) 0 (0.0%)
35 qlq_c30_22 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
26(13.0%)
14(7.0%)
88(44.0%)
72(36.0%)
 200 (100.0%) 0 (0.0%)
36 qlq_c30_23 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
15(7.5%)
8(4.0%)
135(67.5%)
42(21.0%)
 200 (100.0%) 0 (0.0%)
37 qlq_c30_24 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
15(7.5%)
13(6.5%)
118(59.0%)
54(27.0%)
 200 (100.0%) 0 (0.0%)
38 qlq_c30_25 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
26(13.0%)
3(1.5%)
97(48.5%)
74(37.0%)
 200 (100.0%) 0 (0.0%)
39 qlq_c30_26 [character]
1. Bastante
2. Para nada
3. Un poco
8(4.0%)
170(85.0%)
22(11.0%)
 200 (100.0%) 0 (0.0%)
40 qlq_c30_27 [character]
1. Bastante
2. Para nada
3. Un poco
10(5.0%)
169(84.5%)
21(10.5%)
 200 (100.0%) 0 (0.0%)
41 qlq_c30_28 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
11(5.5%)
4(2.0%)
160(80.0%)
25(12.5%)
 200 (100.0%) 0 (0.0%)
42 qlq_c30_29 [numeric]
Mean (sd) : 5.6 (1.1)
min ≤ med ≤ max:
1 ≤ 6 ≤ 7
IQR (CV) : 1 (0.2)
1:1(0.5%)
2:1(0.5%)
3:8(4.0%)
4:20(10.0%)
5:61(30.5%)
6:63(31.5%)
7:46(23.0%)
 200 (100.0%) 0 (0.0%)
43 qlq_c30_30 [numeric]
Mean (sd) : 5.8 (1.2)
min ≤ med ≤ max:
2 ≤ 6 ≤ 7
IQR (CV) : 2 (0.2)
2:4(2.0%)
3:7(3.5%)
4:12(6.0%)
5:51(25.5%)
6:64(32.0%)
7:62(31.0%)
 200 (100.0%) 0 (0.0%)
44 qlq_c30_31 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
31(15.5%)
10(5.0%)
97(48.5%)
62(31.0%)
 200 (100.0%) 0 (0.0%)
45 qlq_c30_32 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
3(1.5%)
1(0.5%)
179(89.5%)
17(8.5%)
 200 (100.0%) 0 (0.0%)
46 qlq_c30_33 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
21(10.5%)
8(4.0%)
103(51.5%)
68(34.0%)
 200 (100.0%) 0 (0.0%)
47 qlq_c30_34 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
32(16.0%)
12(6.0%)
114(57.0%)
42(21.0%)
 200 (100.0%) 0 (0.0%)
48 qlq_c30_35 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
18(9.6%)
12(6.4%)
135(72.2%)
22(11.8%)
 187 (93.5%) 13 (6.5%)
49 qlq_c30_36 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
26(13.0%)
5(2.5%)
124(62.0%)
45(22.5%)
 200 (100.0%) 0 (0.0%)
50 qlq_c30_37 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
35(17.5%)
19(9.5%)
100(50.0%)
46(23.0%)
 200 (100.0%) 0 (0.0%)
51 qlq_c30_38 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
26(13.0%)
3(1.5%)
119(59.5%)
52(26.0%)
 200 (100.0%) 0 (0.0%)
52 qlq_c30_39 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
12(6.0%)
7(3.5%)
151(75.5%)
30(15.0%)
 200 (100.0%) 0 (0.0%)
53 qlq_c30_40 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
4(2.0%)
2(1.0%)
175(87.5%)
19(9.5%)
 200 (100.0%) 0 (0.0%)
54 qlq_c30_41 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
4(2.0%)
4(2.0%)
163(81.5%)
29(14.5%)
 200 (100.0%) 0 (0.0%)
55 qlq_c30_42 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
1(0.5%)
5(2.5%)
162(81.0%)
32(16.0%)
 200 (100.0%) 0 (0.0%)
56 qlq_c30_43 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
48(24.0%)
13(6.5%)
74(37.0%)
65(32.5%)
 200 (100.0%) 0 (0.0%)
57 qlq_c30_44 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
13(6.5%)
3(1.5%)
140(70.0%)
44(22.0%)
 200 (100.0%) 0 (0.0%)
58 qlq_c30_45 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
11(5.5%)
2(1.0%)
140(70.0%)
47(23.5%)
 200 (100.0%) 0 (0.0%)
59 qlq_c30_46 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
23(12.9%)
2(1.1%)
124(69.7%)
29(16.3%)
 178 (89.0%) 22 (11.0%)
60 qlq_c30_47 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
26(13.0%)
11(5.5%)
85(42.5%)
78(39.0%)
 200 (100.0%) 0 (0.0%)
61 qlq_c30_48 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
15(7.5%)
6(3.0%)
155(77.5%)
24(12.0%)
 200 (100.0%) 0 (0.0%)
62 qlq_c30_49 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
12(6.0%)
5(2.5%)
134(67.0%)
49(24.5%)
 200 (100.0%) 0 (0.0%)
63 qlq_c30_50 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
12(6.0%)
4(2.0%)
127(63.5%)
57(28.5%)
 200 (100.0%) 0 (0.0%)
64 qlq_c30_51 [character]
1. Bastante
2. Para nada
3. Un poco
4(2.0%)
181(90.5%)
15(7.5%)
 200 (100.0%) 0 (0.0%)
65 qlq_c30_52 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
15(7.5%)
6(3.0%)
128(64.0%)
51(25.5%)
 200 (100.0%) 0 (0.0%)
66 qlq_c30_53 [character]
1. Bastante
2. Mucho
3. Para nada
4. Un poco
9(4.5%)
4(2.0%)
151(75.5%)
36(18.0%)
 200 (100.0%) 0 (0.0%)
67 EDAD [numeric]
Mean (sd) : 65.1 (9.5)
min ≤ med ≤ max:
37 ≤ 65 ≤ 93
IQR (CV) : 13 (0.1)
 44 distinct values 200 (100.0%) 0 (0.0%)
68 ESTRATO [numeric]
Mean (sd) : 2.7 (0.9)
min ≤ med ≤ max:
1 ≤ 3 ≤ 6
IQR (CV) : 1 (0.3)
1:12(6.0%)
2:70(35.0%)
3:84(42.0%)
4:27(13.5%)
5:5(2.5%)
6:2(1.0%)
 200 (100.0%) 0 (0.0%)
69 AFILIACION_SALUD [character]
1. Contributivo
2. Subsidiado
194(97.0%)
6(3.0%)
 200 (100.0%) 0 (0.0%)
70 EDUCACION [character]
1. Educación básica (primari
2. Educación media (bachille
3. Educación preescolar
4. Educación superior (unive
5. Educación técnica
6. Posgrado (especializacion
7. Sin educación formal
73(36.5%)
42(21.0%)
5(2.5%)
27(13.5%)
37(18.5%)
12(6.0%)
4(2.0%)
 200 (100.0%) 0 (0.0%)
71 SIT_MARITAL [character]
1. Casado(a)
2. Divorciado(a)
3. Separado(a)
4. Soltero(a)
5. Unión libre
6. Viudo(a)
76(38.0%)
1(0.5%)
20(10.0%)
46(23.0%)
23(11.5%)
34(17.0%)
 200 (100.0%) 0 (0.0%)
72 OCUPACION [character]
1. Ama de Casa
2. Pensionado
3. Hogar
4. Vendedor
5. Auxiliar Varios
6. Cesante
7. Personal de Servidumbre
8. Administrador de Empresas
9. Contadores
10. Empleados de Oficina
[ 11 others ]
59(29.5%)
56(28.0%)
32(16.0%)
9(4.5%)
5(2.5%)
5(2.5%)
5(2.5%)
4(2.0%)
4(2.0%)
4(2.0%)
17(8.5%)
 200 (100.0%) 0 (0.0%)
73 FRACCION_OCUPACIONES [numeric]
Mean (sd) : 60.5 (30.6)
min ≤ med ≤ max:
1 ≤ 56.5 ≤ 100
IQR (CV) : 55.2 (0.5)
 31 distinct values 200 (100.0%) 0 (0.0%)
74 SATISFACCION_ATENCION_SALUD [character]
1. Extremadamente satisfecho
2. Moderadamente satisfecho
3. Muy satisfecho
4. Poco satisfecho
99(49.5%)
11(5.5%)
85(42.5%)
5(2.5%)
 200 (100.0%) 0 (0.0%)
75 PERCEPCION_RELIGION [numeric]
Mean (sd) : 8.1 (2.3)
min ≤ med ≤ max:
0 ≤ 9 ≤ 10
IQR (CV) : 3 (0.3)
0:4(2.0%)
1:1(0.5%)
3:4(2.0%)
4:1(0.5%)
5:26(13.0%)
6:9(4.5%)
7:12(6.0%)
8:40(20.0%)
9:17(8.5%)
10:86(43.0%)
 200 (100.0%) 0 (0.0%)
76 PERCEPCION_ESPIRITUAL [numeric]
Mean (sd) : 8.5 (1.9)
min ≤ med ≤ max:
0 ≤ 9 ≤ 10
IQR (CV) : 3 (0.2)
0:2(1.0%)
5:17(8.5%)
6:8(4.0%)
7:25(12.5%)
8:34(17.0%)
9:17(8.5%)
10:97(48.5%)
 200 (100.0%) 0 (0.0%)
77 PERCEPCION_SOPORTE_SOCIAL [numeric]
Mean (sd) : 8.6 (2)
min ≤ med ≤ max:
1 ≤ 10 ≤ 10
IQR (CV) : 2 (0.2)
1:1(0.5%)
2:3(1.5%)
3:3(1.5%)
4:2(1.0%)
5:13(6.5%)
6:7(3.5%)
7:11(5.5%)
8:29(14.5%)
9:26(13.0%)
10:105(52.5%)
 200 (100.0%) 0 (0.0%)
78 COMPLETE_SOCIODEMOGRAFICO [character] 1. Complete
200(100.0%)
 200 (100.0%) 0 (0.0%)
79 TIEMPO_SUPERVIVENCIA [numeric]
Mean (sd) : 8.2 (4.8)
min ≤ med ≤ max:
1 ≤ 7 ≤ 26
IQR (CV) : 6 (0.6)
 23 distinct values 200 (100.0%) 0 (0.0%)
80 EDAD_DX [numeric]
Mean (sd) : 56.9 (10.1)
min ≤ med ≤ max:
31 ≤ 57.5 ≤ 90
IQR (CV) : 14.2 (0.2)
 43 distinct values 200 (100.0%) 0 (0.0%)
81 LATERIALDAD_MAMA_DERECHO [character]
1. Checked
2. Unchecked
108(54.0%)
92(46.0%)
 200 (100.0%) 0 (0.0%)
82 LATERIALDAD_MAMA_IZQUIERDO [character]
1. Checked
2. Unchecked
90(45.0%)
110(55.0%)
 200 (100.0%) 0 (0.0%)
83 LATERIALDAD_MAMA_BILATERAL [character]
1. Checked
2. Unchecked
3(1.5%)
197(98.5%)
 200 (100.0%) 0 (0.0%)
84 ESTADIFICACION_MAMA_NCCN [character]
1. 0
2. IA
3. IB
4. IIA
5. IIB
6. IIIA
7. IIIB
8. IIIC
9. IV
10. No reporta
3(1.5%)
52(26.0%)
3(1.5%)
54(27.0%)
38(19.0%)
26(13.0%)
9(4.5%)
5(2.5%)
2(1.0%)
8(4.0%)
 200 (100.0%) 0 (0.0%)
85 TIPO_HISTOLOGICO_CIE_10 [character]
1. Adenoideo quístico
2. Ductal
3. Lobulillar
4. Medular
5. Mucinoso
6. No reporta
7. Otro
8. Papilar
9. Tubular
1(0.5%)
155(77.5%)
11(5.5%)
2(1.0%)
3(1.5%)
12(6.0%)
7(3.5%)
8(4.0%)
1(0.5%)
 200 (100.0%) 0 (0.0%)
86 CLASIFICACION_INMUNOHISTOQUIMICA_RE_POSITIVO [character]
1. Checked
2. Unchecked
161(80.5%)
39(19.5%)
 200 (100.0%) 0 (0.0%)
87 CLASIFICACION_INMUNOHISTOQUIMICA_RP_POSITIVO [character]
1. Checked
2. Unchecked
136(68.0%)
64(32.0%)
 200 (100.0%) 0 (0.0%)
88 CLASIFICACION_INMUNOHISTOQUIMICA_TRIPLE_NEGATIVO [character]
1. Checked
2. Unchecked
17(8.5%)
183(91.5%)
 200 (100.0%) 0 (0.0%)
89 CLASIFICACION_INMUNOHISTOQUIMICA_HER2_POSITIVO [character]
1. Checked
2. Unchecked
31(15.5%)
169(84.5%)
 200 (100.0%) 0 (0.0%)
90 CLASIFICACION_INMUNOHISTOQUIMICA_KI_67_POSITIVO [character]
1. Checked
2. Unchecked
54(27.0%)
146(73.0%)
 200 (100.0%) 0 (0.0%)
91 CLASIFICACION_INMUNOHISTOQUIMICA_OTRO [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
92 CLASIFICACION_INMUNOHISTOQUIMICA_NO_REPORTA [character]
1. Checked
2. Unchecked
9(4.5%)
191(95.5%)
 200 (100.0%) 0 (0.0%)
93 TRAT_QUIMIOTERAPIA_NEOADYUVANTE [character]
1. Checked
2. Unchecked
69(34.5%)
131(65.5%)
 200 (100.0%) 0 (0.0%)
94 TRAT_ANTIHER2 [character]
1. Checked
2. Unchecked
29(14.5%)
171(85.5%)
 200 (100.0%) 0 (0.0%)
95 TRAT_RADIOTERAPIA_NEOADYUVANTE [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
96 TRAT_CIRUGIA_RADICAL [character]
1. Checked
2. Unchecked
16(8.0%)
184(92.0%)
 200 (100.0%) 0 (0.0%)
97 TRAT_CIRUGIA_RADICAL_MODIFICADA [character]
1. Checked
2. Unchecked
64(32.0%)
136(68.0%)
 200 (100.0%) 0 (0.0%)
98 TRAT_CIRUGIA_CONSERVADORA [character]
1. Checked
2. Unchecked
127(63.5%)
73(36.5%)
 200 (100.0%) 0 (0.0%)
99 TRAT_VACIAMIENTO_GANGLIONAR [character]
1. Checked
2. Unchecked
145(72.5%)
55(27.5%)
 200 (100.0%) 0 (0.0%)
100 TRAT_TRATAMIENTO_HORMONAL [character]
1. Checked
2. Unchecked
152(76.0%)
48(24.0%)
 200 (100.0%) 0 (0.0%)
101 TRAT_QUIMIOTERAPIA_ADYUVANTE [character]
1. Checked
2. Unchecked
98(49.0%)
102(51.0%)
 200 (100.0%) 0 (0.0%)
102 TRAT_RADIOTERAPIA_ADYUVANTE [character]
1. Checked
2. Unchecked
165(82.5%)
35(17.5%)
 200 (100.0%) 0 (0.0%)
103 TRAT_CIRUGIA_RECONSTRUCTIVA [character]
1. Checked
2. Unchecked
35(17.5%)
165(82.5%)
 200 (100.0%) 0 (0.0%)
104 TRAT_OTRO [character]
1. Checked
2. Unchecked
1(0.5%)
199(99.5%)
 200 (100.0%) 0 (0.0%)
105 TRAT_NO_REPORTA [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
106 TIPO_T_SISTEMICO_NEOADYUVANTE_QUIMIOTERAPIA [character]
1. Checked
2. Unchecked
64(32.0%)
136(68.0%)
 200 (100.0%) 0 (0.0%)
107 TIPO_T_SISTEMICO_NEOADYUVANTE_ANTIHER2 [character]
1. Checked
2. Unchecked
11(5.5%)
189(94.5%)
 200 (100.0%) 0 (0.0%)
108 TIPO_T_SISTEMICO_NEOADYUVANTE_T.BLANCO [character]
1. Checked
2. Unchecked
2(1.0%)
198(99.0%)
 200 (100.0%) 0 (0.0%)
109 TIPO_T_SISTEMICO_NEOADYUVANTE_INMUNOTERAPIA [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
110 TIPO_T_SISTEMICO_NEOADYUVANTE_HORMONOTERAPIA [character]
1. Checked
2. Unchecked
3(1.5%)
197(98.5%)
 200 (100.0%) 0 (0.0%)
111 TIPO_T_SISTEMICO_NEOADYUVANTE_NO_REPORTA [character]
1. Checked
2. Unchecked
1(0.5%)
199(99.5%)
 200 (100.0%) 0 (0.0%)
112 TIPO_T_SISTEMICO_ADYUVANTE_QUIMIOTERAPIA [character]
1. Checked
2. Unchecked
86(43.0%)
114(57.0%)
 200 (100.0%) 0 (0.0%)
113 TIPO_T_SISTEMICO_ADYUVANTE_ANTIHER2 [character]
1. Checked
2. Unchecked
18(9.0%)
182(91.0%)
 200 (100.0%) 0 (0.0%)
114 TIPO_T_SISTEMICO_ADYUVANTE_T.BLANCO [character]
1. Checked
2. Unchecked
1(0.5%)
199(99.5%)
 200 (100.0%) 0 (0.0%)
115 TIPO_T_SISTEMICO_ADYUVANTE_INMUNOTERAPIA [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
116 TIPO_T_SISTEMICO_ADYUVANTE_HORMONOTERAPIA [character]
1. Checked
2. Unchecked
40(20.0%)
160(80.0%)
 200 (100.0%) 0 (0.0%)
117 TIPO_T_SISTEMICO_ADYUVANTE_NO_REPORTA [character]
1. Checked
2. Unchecked
3(1.5%)
197(98.5%)
 200 (100.0%) 0 (0.0%)
118 TIPO_RADIOTERAPIA_NEOADYUVANTE_RADIACION_TODO_SENO_CONVENCIONAL [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
119 TIPO_RADIOTERAPIA_NEOADYUVANTE_RADIACION_TODO_SENO_HIPOFRACCIONADA [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
120 TIPO_RADIOTERAPIA_NEOADYUVANTE_RADIOT_IORT [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
121 TIPO_RADIOTERAPIA_NEOADYUVANTE_RADIOT_3D_CRT [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
122 TIPO_RADIOTERAPIA_NEOADYUVANTE_RADIOT_IMRT [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
123 TIPO_RADIOTERAPIA_NEOADYUVANTE_BRAQUITERAPIA [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
124 TIPO_RADIOTERAPIA_NEOADYUVANTE_RADIACION_GANGLIOS [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
125 TIPO_RADIOTERAPIA_NEOADYUVANTE_OTRO [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
126 TIPO_RADIOTERAPIA_NEOADYUVANTE_NO_REPORTA [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
127 TIPO_RADIOTERAPIA_ADYUVANTE_RADIACION_TODO_SENO_CONVENCIONAL [character]
1. Checked
2. Unchecked
4(2.0%)
196(98.0%)
 200 (100.0%) 0 (0.0%)
128 TIPO_RADIOTERAPIA_ADYUVANTE_RADIACION_TODO_SENO_HIPOFRACCIONADA [character]
1. Checked
2. Unchecked
2(1.0%)
198(99.0%)
 200 (100.0%) 0 (0.0%)
129 TIPO_RADIOTERAPIA_ADYUVANTE_RADIOT_IORT [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
130 TIPO_RADIOTERAPIA_ADYUVANTE_RADIOT_3D_CRT [character]
1. Checked
2. Unchecked
6(3.0%)
194(97.0%)
 200 (100.0%) 0 (0.0%)
131 TIPO_RADIOTERAPIA_ADYUVANTE_RADIOT_IMRT [character]
1. Checked
2. Unchecked
123(61.5%)
77(38.5%)
 200 (100.0%) 0 (0.0%)
132 TIPO_RADIOTERAPIA_ADYUVANTE_BRAQUITERAPIA [character]
1. Checked
2. Unchecked
1(0.5%)
199(99.5%)
 200 (100.0%) 0 (0.0%)
133 TIPO_RADIOTERAPIA_ADYUVANTE_RADIACION_GANGLIOS [character]
1. Checked
2. Unchecked
10(5.0%)
190(95.0%)
 200 (100.0%) 0 (0.0%)
134 TIPO_RADIOTERAPIA_ADYUVANTE_OTRO [character]
1. Checked
2. Unchecked
1(0.5%)
199(99.5%)
 200 (100.0%) 0 (0.0%)
135 TIPO_RADIOTERAPIA_ADYUVANTE_NO_REPORTA [character]
1. Checked
2. Unchecked
29(14.5%)
171(85.5%)
 200 (100.0%) 0 (0.0%)
136 TRAT_HORMONAL_COMPLETADO [character]
1. Inhibidor de aromatasa ex
2. Inhibidor de aromatasa po
3. Otro
4. Tamoxifeno 2 años y switc
5. Tamoxifeno 2 años y switc
6. Tamoxifeno extendido (7-1
7. Tamoxifeno por 5 años
23(14.6%)
33(21.0%)
11(7.0%)
20(12.7%)
15(9.6%)
20(12.7%)
35(22.3%)
 157 (78.5%) 43 (21.5%)
137 TRAT_HORMONAL_ACTUAL [character]
1. Anastrazol
2. Exemestane
3. Letrozol
4. No esta recibiendo actual
5. Otro
6. Tamoxifeno
15(9.6%)
1(0.6%)
22(14.0%)
104(66.2%)
1(0.6%)
14(8.9%)
 157 (78.5%) 43 (21.5%)
138 RECAIDA_CANCER_MAMA [character]
1. No
2. Si
196(98.0%)
4(2.0%)
 200 (100.0%) 0 (0.0%)
139 CATEGORIA_RIESGO_HC [character]
1. Alto riesgo
2. Bajo riesgo
3. Intermedio riesgo
4. No reporta
75(37.5%)
58(29.0%)
50(25.0%)
17(8.5%)
 200 (100.0%) 0 (0.0%)
140 TRAT_RECAIDA_QUIMIOTERAPIA [character]
1. Checked
2. Unchecked
1(0.5%)
199(99.5%)
 200 (100.0%) 0 (0.0%)
141 TRAT_RECAIDA_ANTI_HER2 [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
142 TRAT_RECAIDA_T._BLANCO [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
143 TRAT_RECAIDA_INMUNOTERAPIA [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
144 TRAT_RECAIDA_HORMONOTERAPIA [character]
1. Checked
2. Unchecked
3(1.5%)
197(98.5%)
 200 (100.0%) 0 (0.0%)
145 TRAT_RECAIDA_RADIOTERAPIA [character]
1. Checked
2. Unchecked
1(0.5%)
199(99.5%)
 200 (100.0%) 0 (0.0%)
146 TRAT_RECAIDA_NO_REPORTA [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
147 ATENCION_UA_CUIDADO_PALIATIVO [character]
1. Checked
2. Unchecked
25(12.5%)
175(87.5%)
 200 (100.0%) 0 (0.0%)
148 ATENCION_UA_PROGRAMA_LINFEDEMA [character]
1. Checked
2. Unchecked
2(1.0%)
198(99.0%)
 200 (100.0%) 0 (0.0%)
149 ATENCION_UA_PROGRAMA_FATIGA_REH_ONCOLOGICA [character]
1. Checked
2. Unchecked
3(1.5%)
197(98.5%)
 200 (100.0%) 0 (0.0%)
150 ATENCION_UA_PSICOONCOLOGIA [character]
1. Checked
2. Unchecked
7(3.5%)
193(96.5%)
 200 (100.0%) 0 (0.0%)
151 ATENCION_UA_SEXOLOGIA_GINECOLOGIA_FUNCIONAL [character]
1. Checked
2. Unchecked
2(1.0%)
198(99.0%)
 200 (100.0%) 0 (0.0%)
152 ATENCION_UA_NEUROLOGIA [character]
1. Checked
2. Unchecked
5(2.5%)
195(97.5%)
 200 (100.0%) 0 (0.0%)
153 ATENCION_UA_TRABAJO_SOCIAL [character]
1. Checked
2. Unchecked
2(1.0%)
198(99.0%)
 200 (100.0%) 0 (0.0%)
154 ATENCION_UA_NUTRICION [character]
1. Checked
2. Unchecked
5(2.5%)
195(97.5%)
 200 (100.0%) 0 (0.0%)
155 ATENCION_UA_MEDICINA_OCUPACIONAL [character]
1. Checked
2. Unchecked
1(0.5%)
199(99.5%)
 200 (100.0%) 0 (0.0%)
156 ATENCION_UA_MEDICINA_CIRUGIA_PLASTICA_RECONSTRUCTIVA [character]
1. Checked
2. Unchecked
11(5.5%)
189(94.5%)
 200 (100.0%) 0 (0.0%)
157 ATENCION_UA_MEDICINA_FISIATRIA_MEDICINA_FISICA [character]
1. Checked
2. Unchecked
12(6.0%)
188(94.0%)
 200 (100.0%) 0 (0.0%)
158 ATENCION_UA_OTRO [character]
1. Checked
2. Unchecked
4(2.0%)
196(98.0%)
 200 (100.0%) 0 (0.0%)
159 ATENCION_UA_NINGUNO [character]
1. Checked
2. Unchecked
154(77.0%)
46(23.0%)
 200 (100.0%) 0 (0.0%)
160 SEGUIMIENTO_SUPERVIVENCIA_UA_MEDICINA FAMILIAR [character]
1. Checked
2. Unchecked
170(85.0%)
30(15.0%)
 200 (100.0%) 0 (0.0%)
161 SEGUIMIENTO_SUPERVIVENCIA_UA_ONCOLOGIA_CLINICA [character]
1. Checked
2. Unchecked
64(32.0%)
136(68.0%)
 200 (100.0%) 0 (0.0%)
162 SEGUIMIENTO_SUPERVIVENCIA_UA_CIRUGIA_MAMA [character]
1. Checked
2. Unchecked
72(36.0%)
128(64.0%)
 200 (100.0%) 0 (0.0%)
163 SEGUIMIENTO_SUPERVIVENCIA_UA_OTRO [character]
1. Checked
2. Unchecked
1(0.5%)
199(99.5%)
 200 (100.0%) 0 (0.0%)
164 SEGUIMIENTO_SUPERVIVENCIA_UA_NINGUNO [character] 1. Unchecked
200(100.0%)
 200 (100.0%) 0 (0.0%)
165 ANT_INFARTO_MIOCARDIO [character]
1. No
2. Si
199(99.5%)
1(0.5%)
 200 (100.0%) 0 (0.0%)
166 ANT_FALLA_CARDIACA [character]
1. No
2. Si
191(96.0%)
8(4.0%)
 199 (99.5%) 1 (0.5%)
167 ANT_FALLA_ENF_VASCULAR_PERIFERICA [character]
1. No
2. Si
177(88.9%)
22(11.1%)
 199 (99.5%) 1 (0.5%)
168 ANT_FALLA_ENF_CEREBROVASCULAR [character]
1. No
2. Si
193(97.0%)
6(3.0%)
 199 (99.5%) 1 (0.5%)
169 ANT_FALLA_ENF_DEMENCIA [character]
1. No
2. Si
199(99.5%)
1(0.5%)
 200 (100.0%) 0 (0.0%)
170 ANT_FALLA_ENF_PULMONAR_OBSTRUCTIVA_CRONICA [character]
1. No
2. Si
194(97.0%)
6(3.0%)
 200 (100.0%) 0 (0.0%)
171 ANT_FALLA_ENF_TEJIDO_CONECTIVO [character]
1. No
2. Si
192(96.0%)
8(4.0%)
 200 (100.0%) 0 (0.0%)
172 ANT_FALLA_ULCERA_PEPTICA [character]
1. No
2. Si
188(94.5%)
11(5.5%)
 199 (99.5%) 1 (0.5%)
173 ANT_FALLA_ENF_HEPATICA [character]
1. Enfermedad hepática leve
2. Enfermedad hepática moder
3. Sin antecedente de enferm
7(3.5%)
1(0.5%)
190(96.0%)
 198 (99.0%) 2 (1.0%)
174 ANT_FALLA_DIABETES_MELLITUS [character]
1. Antecedente de Diabetes m
2. Diabetes mellitus sin co
3. Sin antecedente de diabet
2(1.0%)
12(6.1%)
183(92.9%)
 197 (98.5%) 3 (1.5%)
175 ANT_FALLA_HEMIPLEJIA [character]
1. No
2. Si
195(98.0%)
4(2.0%)
 199 (99.5%) 1 (0.5%)
176 ANT_ENF_RENAL_CRONICA [character]
1. Enfermedad renal crónica
2. Enfermedad renal crónica
3. Sin antecedente de enferm
5(2.5%)
3(1.5%)
190(96.0%)
 198 (99.0%) 2 (1.0%)
177 ANT_VIH [character] 1. No
198(100.0%)
 198 (99.0%) 2 (1.0%)
178 ANT_SIDA [character] 1. No
194(100.0%)
 194 (97.0%) 6 (3.0%)
179 INDICE_CHARLSON [numeric]
Mean (sd) : 4.6 (1.4)
min ≤ med ≤ max:
2 ≤ 4 ≤ 9
IQR (CV) : 1 (0.3)
2:10(5.0%)
3:31(15.5%)
4:61(30.5%)
5:55(27.5%)
6:25(12.5%)
7:14(7.0%)
8:2(1.0%)
9:2(1.0%)
 200 (100.0%) 0 (0.0%)
180 ANTECEDENTES_COMPLETE [character] 1. Complete
200(100.0%)
 200 (100.0%) 0 (0.0%)

Generated by summarytools 1.1.4 (R version 4.4.2)
2025-10-14

Analisis descriptivo

# Estrato 

library(dplyr)
DATA_SET <- DATA_SET %>% mutate(Estrato_cat = cut(ESTRATO, 
                                          breaks = c (0,2, 4, 6), 
                                          right = T, 
                                          labels = c("Bajo", "Medio", "Alto")))
# EDUCACION

DATA_SET$EDUCACION_CAT <- ifelse(DATA_SET$EDUCACION %in% c("Educación preescolar", "Educación básica (primaria)", "Sin educación formal"), "Primaria o menos", ifelse(DATA_SET$EDUCACION %in% c("Educación media (bachiller)"), "Basica/secundaria", ifelse(DATA_SET$EDUCACION %in% c("Educación superior (universitaria)", "Educación técnica", "Posgrado (especializacion, maestria, doctorado)"), "Mas que secundaria", DATA_SET$EDUCACION)))

DATA_SET$EDUCACION_CAT <- factor(DATA_SET$EDUCACION_CAT, levels = c("Primaria o menos", "Basica/secundaria", "Mas que secundaria") ) #REORDENANDO LAS CATEGORIAS


#Situacion marital 
DATA_SET$SIT_MARITAL_CAT <- ifelse(DATA_SET$SIT_MARITAL %in% c("Casado(a)", "Unión libre"), "Casada/Union Libre", 
                            ifelse(DATA_SET$SIT_MARITAL %in% c("Separado(a)", "Divorciado(a)", "Viudo(a)"), "Separada/Viuda", 
                            ifelse(DATA_SET$SIT_MARITAL %in% c("Soltero(a)"), "Soltera", DATA_SET$SIT_MARITAL)))

DATA_SET$SIT_MARITAL_CAT <- factor(DATA_SET$SIT_MARITAL_CAT, levels = c("Soltera", "Casada/Union Libre", "Separada/Viuda") ) #REORDENANDO LAS CATEGORIAS

#OCUPACION 

DATA_SET$OCUPACIONL_CAT <- ifelse(DATA_SET$OCUPACION %in% c("Ama de Casa", "Hogar"), "Ama de casa/hogar", 
                            ifelse(DATA_SET$OCUPACION %in% c("Abogado", "Administrador de Empresas", "Auxiliar Varios", "Auxiliares de Enfermeria", "Cocineros", "Contadores", "Empleados de Oficina", "Estilistas", "Ingenieros", "Oficiales de Cubierta", "Personal de Servidumbre", "Personal Directivo de la Administraci󮠐blica", "Profesores y Educadores", "Sastres y Modistos", "Secretaria", "Socioantrop󬯧o", "Vendedor"), "empleado/ocupado", 
                            DATA_SET$OCUPACION))

DATA_SET$OCUPACIONL_CAT<- factor(DATA_SET$OCUPACIONL_CAT, levels = c("Ama de casa/hogar", "empleado/ocupado", "Pensionado", "Cesante") ) #REORDENANDO LAS CATEGORIAS

#Satisfaccion salud

DATA_SET$SATISFACCION_CAT <- ifelse(DATA_SET$SATISFACCION_ATENCION_SALUD %in% c("Poco satisfecho", "Moderadamente satisfecho"), "Poco satisfechas", 
                             ifelse(DATA_SET$SATISFACCION_ATENCION_SALUD %in% c("Muy satisfecho", "Extremadamente satisfecho"), "Muy satisfechas", DATA_SET$SATISFACCION_ATENCION_SALUD))

#ESTADIO 

DATA_SET$ESTADIO <- ifelse(DATA_SET$ESTADIFICACION_MAMA_NCCN %in% c("0", "IA", "IB", "IIA"), "Temprano", 
                    ifelse(DATA_SET$ESTADIFICACION_MAMA_NCCN %in% c("IIB", "IIIA", "IIIB", "IIIC"), "Localmente avanzado", 
                    ifelse(DATA_SET$ESTADIFICACION_MAMA_NCCN %in% c("IV"), "Metastasico", DATA_SET$ESTADIFICACION_MAMA_NCCN)))


DATA_SET$ESTADIO<- factor(DATA_SET$ESTADIO, levels = c("Temprano", "Localmente avanzado", "Metastasico", "No reporta") ) #REORDENANDO LAS CATEGORIAS

#CLASIFICACION_INMUNOHISTOQUIMICA

DATA_SET <- DATA_SET %>% 
  mutate(LUMINAL_A = ifelse((CLASIFICACION_INMUNOHISTOQUIMICA_RE_POSITIVO == "Checked" | CLASIFICACION_INMUNOHISTOQUIMICA_RP_POSITIVO == "Checked" ) & (CLASIFICACION_INMUNOHISTOQUIMICA_KI_67_POSITIVO == "Unchecked" & CLASIFICACION_INMUNOHISTOQUIMICA_HER2_POSITIVO  == "Unchecked"), "Si", "No"))


DATA_SET <- DATA_SET %>% 
  mutate(LUMINAL_B = ifelse((CLASIFICACION_INMUNOHISTOQUIMICA_RE_POSITIVO == "Checked" | CLASIFICACION_INMUNOHISTOQUIMICA_RP_POSITIVO == "Checked" ) & (CLASIFICACION_INMUNOHISTOQUIMICA_KI_67_POSITIVO == "Checked" & CLASIFICACION_INMUNOHISTOQUIMICA_HER2_POSITIVO  == "Unchecked"), "Si", "No"))

DATA_SET <- DATA_SET %>% 
  mutate(HER2_POSITIVO = ifelse(CLASIFICACION_INMUNOHISTOQUIMICA_HER2_POSITIVO == "Checked", "Si", "No"))

DATA_SET <- DATA_SET %>% 
  mutate(TRIPLE_NEGATIVO = ifelse(CLASIFICACION_INMUNOHISTOQUIMICA_RE_POSITIVO == "Unchecked" & CLASIFICACION_INMUNOHISTOQUIMICA_RP_POSITIVO == "Unchecked" &  CLASIFICACION_INMUNOHISTOQUIMICA_HER2_POSITIVO  == "Unchecked", "Si", "No"))


#CLASIFICACION TRATAMIENTOS

DATA_SET <- DATA_SET %>% 
  mutate(TRAT_SISTEMICO = ifelse(TRAT_QUIMIOTERAPIA_NEOADYUVANTE == "Checked" |  TRAT_QUIMIOTERAPIA_ADYUVANTE == "Checked" | TIPO_T_SISTEMICO_NEOADYUVANTE_QUIMIOTERAPIA == "Checked" | TIPO_T_SISTEMICO_NEOADYUVANTE_T.BLANCO == "Checked" | TIPO_T_SISTEMICO_NEOADYUVANTE_INMUNOTERAPIA == "Checked" | TIPO_T_SISTEMICO_ADYUVANTE_QUIMIOTERAPIA == "Checked" | TIPO_T_SISTEMICO_ADYUVANTE_T.BLANCO == "Checked", "Si", "No"))


DATA_SET <- DATA_SET %>% 
  mutate(TRAT_RADIOTERAPIA = ifelse(TRAT_RADIOTERAPIA_NEOADYUVANTE == "Checked" |TRAT_RADIOTERAPIA_ADYUVANTE == "Checked" | TIPO_RADIOTERAPIA_ADYUVANTE_RADIACION_TODO_SENO_CONVENCIONAL == "Checked" | TIPO_RADIOTERAPIA_ADYUVANTE_RADIACION_TODO_SENO_HIPOFRACCIONADA == "Checked" | TIPO_RADIOTERAPIA_ADYUVANTE_RADIOT_3D_CRT == "Checked" | TIPO_RADIOTERAPIA_ADYUVANTE_RADIOT_IMRT == "Checked" | TIPO_RADIOTERAPIA_ADYUVANTE_BRAQUITERAPIA == "Checked" | TIPO_RADIOTERAPIA_ADYUVANTE_RADIACION_GANGLIOS == "Checked" | TIPO_RADIOTERAPIA_ADYUVANTE_OTRO == "Checked", "Si", "No"))

DATA_SET <- DATA_SET %>% 
  mutate(TRAT_CIRUGIA_RADICAL = ifelse(TRAT_CIRUGIA_RADICAL == "Checked" | TRAT_CIRUGIA_RADICAL_MODIFICADA == "Checked" & TRAT_CIRUGIA_CONSERVADORA == "Unchecked", "Si", "No"))

DATA_SET <- DATA_SET %>% 
  mutate(
    TRAT_HORMONOTERAPIA = ifelse(
      TRAT_TRATAMIENTO_HORMONAL == "Checked" |
      !is.na(TRAT_HORMONAL_COMPLETADO) |
      !is.na(TRAT_HORMONAL_ACTUAL),
      "Si",
      "No"
    )
  )

# Cuidado de soporte se prueban dos maneras: Recibio o no atencion y por recepcion de cuidado de soporte oncologico primario

DATA_SET <- DATA_SET %>% 
  mutate(Cuidado_soporte_UA = ifelse(ATENCION_UA_NINGUNO == "Unchecked", "Si", "No"))

#Indice charlson 

DATA_SET <- DATA_SET %>% 
  mutate(charlson_cat = cut(INDICE_CHARLSON,
                            breaks = c (-Inf, 2, 4, Inf),
                            right = T, 
                            labels = c ("1-2", "3-4", "=>5")
                            ))


DATA_SET <- DATA_SET %>% 
  mutate(edad_cat = cut(EDAD_DX,
                            breaks = c (-Inf, 50, Inf),
                            right = F, 
                            labels = c ("<50", "=>50")
                            ))

DATA_SET <- DATA_SET %>% 
  mutate(Tiemposup_cat = cut(TIEMPO_SUPERVIVENCIA,
                            breaks = c (-Inf, 5, Inf),
                            right = T, 
                            labels = c ("<=5", ">5")
                            ))

Cruce de variables

Analisis descriptivo de las variables

#dput(names(DATA_SET))


myVars1 <- c("EDAD","Estrato_cat","EDUCACION_CAT", "SIT_MARITAL_CAT", "OCUPACIONL_CAT", "FRACCION_OCUPACIONES" , "SATISFACCION_CAT", "TIEMPO_SUPERVIVENCIA", "Tiemposup_cat",  "EDAD_DX", "edad_cat", "ESTADIO", "LUMINAL_A", "LUMINAL_B", "HER2_POSITIVO", "TRIPLE_NEGATIVO", "TRAT_SISTEMICO", "TRAT_ANTIHER2", "TRAT_RADIOTERAPIA", "TRAT_CIRUGIA_RADICAL", "TRAT_CIRUGIA_CONSERVADORA", "TRAT_HORMONOTERAPIA", "RECAIDA_CANCER_MAMA", "charlson_cat", "Cuidado_soporte_UA", "Cuidado_soporte_primario") 
catVars1 <- c("Estrato_cat","EDUCACION_CAT", "SIT_MARITAL_CAT", "OCUPACIONL_CAT",  "SATISFACCION_CAT","Tiemposup_cat", "edad_cat", "ESTADIO", "LUMINAL_A", "LUMINAL_B", "HER2_POSITIVO", "TRIPLE_NEGATIVO", "TRAT_SISTEMICO", "TRAT_ANTIHER2", "TRAT_RADIOTERAPIA", "TRAT_CIRUGIA_RADICAL", "TRAT_CIRUGIA_CONSERVADORA", 
"TRAT_HORMONOTERAPIA", "RECAIDA_CANCER_MAMA", "charlson_cat", "Cuidado_soporte_UA")  

library(tableone)
tab1 <- CreateTableOne(vars = myVars1, factorVars= catVars1, data = DATA_SET, includeNA = T, test = T,addOverall = T)

table1 <- as.data.frame(print(tab1, showAllLevels= TRUE, printToggle = FALSE, noSpaces = TRUE))
rownames(table1) <- gsub("\\.{3,}", "", rownames(table1))  # Quita puntos suspensivos "..."
rownames(table1) <- gsub("\\.{1,}", "_", rownames(table1))  # Quita puntos suspensivos "..."
rownames(table1) <- gsub("\\_{1,}", " ", rownames(table1))  # Quita puntos suspensivos "..."


library(knitr)
library(kableExtra)

kable(table1, format = "html", caption = "Variable sociodemograficas") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F,position = "center") %>%
  column_spec(1, bold = T, color = "white", background = "black") %>%
  column_spec(2, border_left = T, color = "white", background = "grey")
Variable sociodemograficas
level Overall
n 200
EDAD mean SD 65.12 (9.54)
Estrato cat Bajo 82 (41.0)
X Medio 111 (55.5)
X 1 Alto 7 (3.5)
EDUCACION CAT Primaria o menos 82 (41.0)
X 2 Basica/secundaria 42 (21.0)
X 3 Mas que secundaria 76 (38.0)
SIT MARITAL CAT Soltera 46 (23.0)
X 4 Casada/Union Libre 99 (49.5)
X 5 Separada/Viuda 55 (27.5)
OCUPACIONL CAT Ama de casa/hogar 91 (45.5)
X 6 empleado/ocupado 48 (24.0)
X 7 Pensionado 56 (28.0)
X 8 Cesante 5 (2.5)
FRACCION OCUPACIONES mean SD 60.47 (30.57)
SATISFACCION CAT Muy satisfechas 184 (92.0)
X 9 Poco satisfechas 16 (8.0)
TIEMPO SUPERVIVENCIA mean SD 8.20 (4.84)
Tiemposup cat <=5 56 (28.0)
X 10 >5 144 (72.0)
EDAD DX mean SD 56.92 (10.15)
edad cat <50 49 (24.5)
X 11 =>50 151 (75.5)
ESTADIO Temprano 112 (56.0)
X 12 Localmente avanzado 78 (39.0)
X 13 Metastasico 2 (1.0)
X 14 No reporta 8 (4.0)
LUMINAL A No 89 (44.5)
X 15 Si 111 (55.5)
LUMINAL B No 168 (84.0)
X 16 Si 32 (16.0)
HER2 POSITIVO No 169 (84.5)
X 17 Si 31 (15.5)
TRIPLE NEGATIVO No 174 (87.0)
X 18 Si 26 (13.0)
TRAT SISTEMICO No 56 (28.0)
X 19 Si 144 (72.0)
TRAT ANTIHER2 Checked 29 (14.5)
X 20 Unchecked 171 (85.5)
TRAT RADIOTERAPIA No 35 (17.5)
X 21 Si 165 (82.5)
TRAT CIRUGIA RADICAL No 127 (63.5)
X 22 Si 73 (36.5)
TRAT CIRUGIA CONSERVADORA Checked 127 (63.5)
X 23 Unchecked 73 (36.5)
TRAT HORMONOTERAPIA No 43 (21.5)
X 24 Si 157 (78.5)
RECAIDA CANCER MAMA No 196 (98.0)
X 25 Si 4 (2.0)
charlson cat 1-2 10 (5.0)
X 26 3-4 92 (46.0)
X 27 =>5 98 (49.0)
Cuidado soporte UA No 154 (77.0)
X 28 Si 46 (23.0)

Cruce variables

Cruzar ocupadas - ocupadas con fraccion de ocupaciones

DATA_CR <- DATA_SET %>% select(OCUPACIONL_CAT, FRACCION_OCUPACIONES)
print(stby(data = DATA_CR, INDICES = DATA_CR$OCUPACIONL_CAT, 
     FUN = descr, stats = "common", transpose = TRUE), method = 'render')

Descriptive Statistics

by OCUPACIONL_CAT

Data Frame: DATA_CR
N: 200
Mean Std.Dev Min Median Max N.Valid N Pct.Valid
Ama de casa/hogar 72.93 25.76 10.00 80.00 100.00 91 91 100.00
empleado/ocupado 39.83 26.09 5.00 30.00 100.00 48 48 100.00
Pensionado 59.20 31.87 1.00 56.50 100.00 56 56 100.00
Cesante 45.80 25.75 4.00 50.00 75.00 5 5 100.00

Generated by summarytools 1.1.4 (R version 4.4.2)
2025-10-14

# %>%
 # stview() ver en markdown

Revisando diferencias

Se realiza una prueba de normalidad por cada categoría.

#install.packages("broom")
library(broom)
library(tidyr)
library(purrr)

tabla_shapiro <- DATA_CR %>%
  group_by(OCUPACIONL_CAT) %>%
  summarise(
    shapiro = list(shapiro.test(FRACCION_OCUPACIONES)),
    .groups = "drop"
  ) %>%
  mutate(shapiro = map(shapiro, tidy)) %>%
  unnest(shapiro)


library(gt)
tabla_shapiro %>%
  gt() %>%
  tab_header(
    title = md("**Prueba de normalidad Shapiro-Wilk por grupo**"),
    subtitle = "Resultados agrupados"
  ) %>%
  tab_style(
    style = cell_text(align = "center", weight = "bold", color = "darkblue"),
    locations = cells_column_labels(everything())
  ) %>%
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_body(columns = everything())
  ) %>%
  fmt_number(
    columns = where(is.numeric),
    decimals = 4
  ) %>%
  opt_table_outline() %>%
  opt_row_striping()
Prueba de normalidad Shapiro-Wilk por grupo
Resultados agrupados
OCUPACIONL_CAT statistic p.value method
Ama de casa/hogar 0.8764 0.0000 Shapiro-Wilk normality test
empleado/ocupado 0.8972 0.0005 Shapiro-Wilk normality test
Pensionado 0.9183 0.0010 Shapiro-Wilk normality test
Cesante 0.8394 0.1634 Shapiro-Wilk normality test

Se encontró una distribución no normal, se usa la prueba Kruskall wallis

kruskal_res <- kruskal.test(
  x = DATA_CR$FRACCION_OCUPACIONES,
  g = DATA_CR$OCUPACIONL_CAT
)

# convertir a tibble
kruskal_table <- tidy(kruskal_res)


kruskal_table %>%
  gt() %>%
  tab_header(
    title = md("**Prueba de Kruskal-Wallis**"),
    subtitle = md("Comparación de medianas entre grupos")
  ) %>%
  fmt_number(
    columns = everything(),
    decimals = 4
  ) %>%
  tab_style(
    style = list(
      cell_text(weight = "bold", color = "darkblue", align = "center")
    ),
    locations = cells_column_labels(everything())
  ) %>%
  opt_table_outline() %>% 
  tab_options(
    table.border.top.color    = "black",
    table.border.bottom.color = "black",
    table.font.size = 14
  )
Prueba de Kruskal-Wallis
Comparación de medianas entre grupos
statistic p.value parameter method
37.9675 0.0000 3.0000 Kruskal-Wallis rank sum test

Hay diferencias en al menos en la distribución de una variable (hay al menos una mediana diferente). Se revisa la comparación entre pares (no parametrico)

library(FSA)

# Correr la prueba de Dunn
dunn_res <- dunnTest(FRACCION_OCUPACIONES ~ OCUPACIONL_CAT, 
                     data = DATA_CR, 
                     method = "bonferroni")

# Extraer la tabla de resultados
tabla_dunn <- dunn_res$res

# Pasar a gt para embellecer
tabla_dunn %>%
  gt() %>%
  tab_header(
    title = "Prueba de Dunn post-hoc",
    subtitle = "Ajuste por Bonferroni"
  ) %>%
  cols_label(
    Comparison = "Comparación",
    Z          = "Estadístico Z",
    P.unadj    = "p-valor sin ajuste",
    P.adj      = "p-valor ajustado"
  ) %>%
  fmt_number(
    columns = c(Z, P.unadj, P.adj),
    decimals = 4
  ) %>%
  tab_style(
    style = list(
      cell_text(weight = "bold", align = "center")
    ),
    locations = cells_column_labels(everything())
  )
Prueba de Dunn post-hoc
Ajuste por Bonferroni
Comparación Estadístico Z p-valor sin ajuste p-valor ajustado
Ama de casa/hogar - Cesante 1.9816 0.0475 0.2851
Ama de casa/hogar - empleado/ocupado 6.0429 0.0000 0.0000
Cesante - empleado/ocupado 0.3570 0.7211 1.0000
Ama de casa/hogar - Pensionado 2.5181 0.0118 0.0708
Cesante - Pensionado −1.0338 0.3012 1.0000
empleado/ocupado - Pensionado −3.3060 0.0009 0.0057

Hay diferencias entre todas las modalidades de ocupación y la fracción de ocupaciones.

Revisando intersecciones cuidado de soporte

library(limma)
library(UpSetR)
BASE_UPSET <- DATA_SET %>% select(ATENCION_UA_CUIDADO_PALIATIVO, ATENCION_UA_PSICOONCOLOGIA, ATENCION_UA_NUTRICION, ATENCION_UA_PROGRAMA_LINFEDEMA, ATENCION_UA_PROGRAMA_FATIGA_REH_ONCOLOGICA, ATENCION_UA_SEXOLOGIA_GINECOLOGIA_FUNCIONAL, ATENCION_UA_NEUROLOGIA, ATENCION_UA_TRABAJO_SOCIAL, ATENCION_UA_MEDICINA_OCUPACIONAL, ATENCION_UA_MEDICINA_CIRUGIA_PLASTICA_RECONSTRUCTIVA, ATENCION_UA_MEDICINA_FISIATRIA_MEDICINA_FISICA)

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_CUIDADO_PALIATIVO"] <- "Cuidado paliativo"

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_PSICOONCOLOGIA"] <- "Psicooncologia"

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_NUTRICION"] <- "Nutricion"

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_PROGRAMA_LINFEDEMA"] <- "Linfedema"

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_PROGRAMA_FATIGA_REH_ONCOLOGICA"] <- "Programa Fatiga"

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_SEXOLOGIA_GINECOLOGIA_FUNCIONAL"] <- "Sexologia Gineco"

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_NEUROLOGIA"] <- "Neurologia"

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_TRABAJO_SOCIAL"] <- "Trabajo social"

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_MEDICINA_OCUPACIONAL"] <- "Medicina Ocupacional"

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_MEDICINA_CIRUGIA_PLASTICA_RECONSTRUCTIVA"] <- "Cirugia plastica"

colnames(BASE_UPSET)[colnames(BASE_UPSET) =="ATENCION_UA_MEDICINA_FISIATRIA_MEDICINA_FISICA"] <- "Medicina fisica"


BASE_UPSET <- BASE_UPSET %>%
  mutate(across(c(1:11), ~ recode(., "Checked" = 1, "Unchecked" = 0)))

BASE_UPSET<- as.data.frame(BASE_UPSET)

upset(BASE_UPSET, order.by = "freq", nsets = 11, nintersects = 20,
      matrix.color = "#2F4F4F",  main.bar.color = "darkblue", sets.bar.color = "#5F9EA0", mainbar.y.label = "Intersecciones Cuidado soporte",  sets.x.label = "Atencion especialidad", point.size = 2, line.size = 0.5, number.angles = 0, group.by = "degree", shade.alpha = 1, matrix.dot.alpha = 1, scale.intersections = "identity", scale.sets = "identity", text.scale = 1, set_size.show = T)

Perfiles latentes

  • Se ajusta la base de datos (variables continuas)
  • Se busca identificar potenciales pacientes “descuidados”
  • Se imputan los datos (vecino más cercano)
  • Se estiman los perfiles con todos los datos y sin todos los datos
#Ajuste base datos

library(dplyr)
DATA_PROFILES <- DATA_SET %>% mutate(across(c(14:41, 44:66), ~ recode(., "Un poco" = 1, "Para nada" = 2, "Mucho"  = 3, "Bastante" = 4))) # no se incluyeron las columnas 44 y 45 debido que son items que van de 1 a 7

# se invirtieron los items 44, 45 y 46. 
DATA_PROFILES<- DATA_PROFILES %>%
  mutate(across(57:59, ~ recode(., `1` = 4,`2` = 3, `3` = 2, `4` = 1)))

Revisando posibles pacientes “descuidados”

#install.packages("careless")
library(careless)
library(psych)


DATA_PROFILES_careless <- DATA_PROFILES %>%
  select(1, 14:43) %>%  # Solo columna 1 (ID) + columnas 14:66 (variables QLQ)
  mutate(string = longstring(select(., -1))) %>%      # Calcular longstring excluyendo ID
  mutate(md = outlier(select(., -1), plot = FALSE))   # Calcular MD excluyendo ID

library(knitr)
library(kableExtra)

# Crear tabla de frecuencias
tabla_freq <- freq(DATA_PROFILES_careless$string, report.nas = TRUE)
tabla_df <- as.data.frame(tabla_freq)


# Mostrar tabla con bordes
kable(tabla_df, caption = "Frecuencia de la variable", align = "c") %>%
  kable_styling(
    bootstrap_options = c("striped", "bordered", "hover", "condensed"),
    full_width = FALSE,
    position = "center"
  )
Frecuencia de la variable
Freq % Valid % Valid Cum. % Total % Total Cum.
2 1 0.5 0.5 0.5 0.5
3 7 3.5 4.0 3.5 4.0
4 12 6.0 10.0 6.0 10.0
5 28 14.0 24.0 14.0 24.0
6 16 8.0 32.0 8.0 32.0
7 28 14.0 46.0 14.0 46.0
8 17 8.5 54.5 8.5 54.5
9 29 14.5 69.0 14.5 69.0
10 20 10.0 79.0 10.0 79.0
11 3 1.5 80.5 1.5 80.5
12 6 3.0 83.5 3.0 83.5
14 3 1.5 85.0 1.5 85.0
15 2 1.0 86.0 1.0 86.0
16 2 1.0 87.0 1.0 87.0
17 4 2.0 89.0 2.0 89.0
18 4 2.0 91.0 2.0 91.0
19 6 3.0 94.0 3.0 94.0
20 2 1.0 95.0 1.0 95.0
21 2 1.0 96.0 1.0 96.0
22 1 0.5 96.5 0.5 96.5
24 2 1.0 97.5 1.0 97.5
27 1 0.5 98.0 0.5 98.0
28 4 2.0 100.0 2.0 100.0
<NA> 0 NA NA 0.0 100.0
Total 200 100.0 100.0 100.0 100.0

Se identifican los ID

indices_problematicos <- which(DATA_PROFILES_careless$string >= 20)
print(paste("Números de fila con longstring >= 20:", paste(indices_problematicos, collapse = ", ")))
## [1] "Números de fila con longstring >= 20: 16, 25, 65, 82, 95, 104, 105, 107, 120, 123, 160, 199"
tabla_df2<- DATA_PROFILES_careless[c(16, 25, 65, 82, 95, 104, 105, 107, 120, 123, 160, 199),]


kable(tabla_df2, caption = "Casos con patron ´descuidado´", align = "c") %>%
  kable_styling(
    bootstrap_options = c("striped", "bordered", "hover", "condensed"),
    full_width = FALSE,
    position = "center"
  )
Casos con patron ´descuidado´
ID qlq_c30_1 qlq_c30_2 qlq_c30_3 qlq_c30_4 qlq_c30_5 qlq_c30_6 qlq_c30_7 qlq_c30_8 qlq_c30_9 qlq_c30_10 qlq_c30_11 qlq_c30_12 qlq_c30_13 qlq_c30_14 qlq_c30_15 qlq_c30_16 qlq_c30_17 qlq_c30_18 qlq_c30_19 qlq_c30_20 qlq_c30_21 qlq_c30_22 qlq_c30_23 qlq_c30_24 qlq_c30_25 qlq_c30_26 qlq_c30_27 qlq_c30_28 qlq_c30_29 qlq_c30_30 string md
16 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 28 13.067318
25 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 28 13.067318
65 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 6 6 22 8.212105
82 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 2 2 2 7 7 20 8.298888
95 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 6 7 21 8.607638
104 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 28 13.067318
105 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 28 13.067318
107 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 7 7 24 9.251880
120 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 7 7 21 8.054821
123 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 6 6 24 9.773573
160 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 6 6 20 6.927155
199 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 6 7 27 27.569145

Se mantuvieron los ID, puesto que tienen sentido clínico

#se limpio la base de datos eliminando esos 5 datos. 
#DATA_PROFILES_clean <- DATA_PROFILES %>%
# filter(!ID %in% c(16, 25, 104, 105, 199))

Generacion de totales

########################## BASE #############################


DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(PF = qlq_c30_1 + qlq_c30_2 + qlq_c30_3 + qlq_c30_4 + qlq_c30_5) #Physical functioning 1

DATA_PROFILES <- DATA_PROFILES%>% 
  mutate(RF = qlq_c30_6 + qlq_c30_7) # Role functioning 2 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(CF = qlq_c30_20 + qlq_c30_25) #Cognitive functioning 3 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(EF = qlq_c30_21 + qlq_c30_22 + qlq_c30_23 + qlq_c30_24) #Emotional functioning 4 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(SF = qlq_c30_26 + qlq_c30_27)#Social functioning 5

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(GLH = qlq_c30_29 + qlq_c30_30) #Global health status / QoL 6 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(FA = qlq_c30_10 + qlq_c30_12 + qlq_c30_18) #Fatigue 7 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(NV = qlq_c30_14 + qlq_c30_15) #Nausea and vomiting 8 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(PA = qlq_c30_9 + qlq_c30_19) #Pain 9 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(DY = qlq_c30_8) #Dyspnoea 10 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(SL = qlq_c30_11) #Insomnia 11 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(AP = qlq_c30_13) #Appetite loss 12 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(CO = qlq_c30_16) #Constipation 13 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(DI = qlq_c30_17) #Diarrhoea 14

#################### Escalas suplementarias ############################

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(FI = qlq_c30_28) #Financial difficulties 15 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRST = qlq_c30_31 + qlq_c30_32 + qlq_c30_33 + qlq_c30_34 + qlq_c30_36 + qlq_c30_37 + qlq_c30_38) #Systemic therapy side effects 16 

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRHL = qlq_c30_35) #Upset by hair loss 17

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRBI = qlq_c30_39 + qlq_c30_40 + qlq_c30_41 + qlq_c30_42) #Body image 18

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRFU = qlq_c30_43) #Future perspective 19

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRSEF = qlq_c30_44 + qlq_c30_45) #Sexual functionning 20

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRSEE = qlq_c30_46) #Sexual enjoyment 21

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRAS = qlq_c30_47 + qlq_c30_48 + qlq_c30_49) #Arm symptoms 22

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRBS = qlq_c30_50 + qlq_c30_51 + qlq_c30_52 + qlq_c30_53) #Breast symptoms 23

######## Mediana escala suplementarias ################

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(FI_cat = cut(FI,
                      breaks = c (-Inf, 2, Inf),
                      right = T, 
                      labels = c ("<=2", ">2")
                      ))

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRST_cat = cut(BRST,
                      breaks = c (-Inf, 14, Inf),
                      right = T, 
                      labels = c ("<=14", ">=14")
                      ))


DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRHL_cat = cut(BRHL,
                      breaks = c (-Inf, 2, Inf),
                      right = T, 
                      labels = c ("<=2", ">2")
                      ))


DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRFU_cat = cut(BRFU,
                      breaks = c (-Inf, 2, Inf),
                      right = T, 
                      labels = c ("<=2", ">2")
                      ))

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRBI_cat = cut(BRBI,
                      breaks = c (-Inf, 8, Inf),
                      right = T, 
                      labels = c ("<=8", ">8")
                      ))

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRSEF_cat = cut(BRSEF,
                      breaks = c (-Inf, 6, Inf),
                      right = T, 
                      labels = c ("<=6", ">6")
                      ))

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRSEE_cat = cut(BRSEE,
                      breaks = c (-Inf, 3, Inf),
                      right = T, 
                      labels = c ("<=3", ">3")
                      ))

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRAS_cat = cut(BRAS,
                      breaks = c (-Inf, 6, Inf),
                      right = T, 
                      labels = c ("<=6", ">6")
                      ))

DATA_PROFILES <- DATA_PROFILES %>% 
  mutate(BRBS_cat = cut(BRBS,
                      breaks = c (-Inf, 8, Inf),
                      right = T, 
                      labels = c ("<=8", ">8")
                      ))

Perfiles

#latent profile analysis 


colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_1"] <- "QL1"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_2"] <- "QL2"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_3"] <- "QL3"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_4"] <- "QL4"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_5"] <- "QL5"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_6"] <- "QL6"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_7"] <- "QL7"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_8"] <- "QL8"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_9"] <- "QL9"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_10"] <- "QL10"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_11"] <- "QL11"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_12"] <- "QL12"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_13"] <- "QL13"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_14"] <- "QL14"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_15"] <- "QL15"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_16"] <- "QL16"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_17"] <- "QL17"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_18"] <- "QL18"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_19"] <- "QL19"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_20"] <- "QL20"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_21"] <- "QL21"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_22"] <- "QL22"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_23"] <- "QL23"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_24"] <- "QL24"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_25"] <- "QL25"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_26"] <- "QL26"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_27"] <- "QL27"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_28"] <- "QL28"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_29"] <- "QL29"

colnames(DATA_PROFILES)[colnames(DATA_PROFILES) =="qlq_c30_30"] <- "QL30"

library(mclust)
DATA_PROFILES_ANALYSIS_TOTAL <- DATA_PROFILES %>% 
  select(ID, PF, RF, CF, EF, SF, GLH, FA, NV, PA, DY, SL, AP, CO, DI )

DATA_PROFILES_ITEM <- DATA_PROFILES %>% 
  select(ID, QL1, QL2, QL3, QL4, QL5, QL6, QL7, QL20, QL25, QL21, QL22, QL23, QL24, QL26, QL27, QL10, QL12, QL18, QL14, QL15, QL9, QL19, QL8, QL11, QL13, QL16, QL17, QL29, QL30)


mc1 <-mclustBIC(DATA_PROFILES_ITEM[,-1]) #create the model items
mc2 <-mclustBIC(DATA_PROFILES_ANALYSIS_TOTAL[,-1]) #create the model totals

set.seed(2025)
summary(mc1) # 2 - 4 GRUPOS CON item
## Best BIC values:
##              VEI,3      VEI,2     EEI,2
## BIC      -8932.165 -9907.6195 -11702.78
## BIC diff     0.000  -975.4549  -2770.62
summary(mc2) # 7 - 9 GRUPOS CON totales
## Best BIC values:
##             VEI,9      VEI,8      VEI,7
## BIC      -6226.15 -6454.1912 -6492.3054
## BIC diff     0.00  -228.0413  -266.1555

Cuando se analizan items se podrian encontrar entre 2 a 4 perfiles

Cuando se analizan totales se podrian encontrar entre 7 a 9 perfiles

Hay menos grupos cuando se analizan items que los totales

#install.packages("factoextra")
library(factoextra) #plotting the best model
mc1.1 <-Mclust(DATA_PROFILES_ITEM[,-1])
fviz_mclust_bic(mc1.1)

Se encontrarian una mejor solucion en 3 perfiles

Se utiliza el modelo de analisis por items

set.seed(2025)
model1 <- Mclust(DATA_PROFILES_ITEM[,-1], modelNames = "VEI", G = 3)
summary(model1)
## ---------------------------------------------------- 
## Gaussian finite mixture model fitted by EM algorithm 
## ---------------------------------------------------- 
## 
## Mclust VEI (diagonal, equal shape) model with 3 components: 
## 
##  log-likelihood   n  df       BIC       ICL
##       -4148.183 200 120 -8932.165 -8933.862
## 
## Clustering table:
##   1   2   3 
##  67  30 103

Graficando los perfiles

Se obtienen primero las probabilidades de pertencia

library(tibble)
library(tidyr)

means <- data.frame(model1$parameters$mean) %>%
  rownames_to_column() %>%
  rename(DATA_PROFILES_imp = rowname) %>%
  pivot_longer(cols = c(X1, X2, X3), names_to = "Profile", values_to = "Mean") %>%
  mutate(
    Mean = round(Mean, 2),
    # Estandarización Z (normalización usando media y desviación estándar)
    Mean = (Mean - mean(Mean)) / sd(Mean)
  )


p <- means %>%
  mutate(Profile = recode(Profile, 
                          X1 = "P1 (67)",
                          X2 = "P2 (30)", 
                          X3 = "P3 (103)")) %>%
  ggplot(aes(DATA_PROFILES_imp, Mean, group = Profile, color = Profile)) +
  geom_point(size = 2.25) +
  geom_line(size = 1.25) +
  scale_x_discrete(limits = c("QL1", "QL2", "QL3", "QL4", "QL5", "QL6", "QL7", "QL20", "QL25", 
"QL21", "QL22", "QL23", "QL24", "QL26", "QL27", "QL10", "QL12", "QL18", "QL14", "QL15", "QL9", "QL19", "QL8", "QL11", "QL13", "QL16", "QL17", "QL29", "QL30" )) +
    scale_color_manual(values = c("P1 (67)" = "blue2", "P2 (30)" = "green4", "P3 (103)" = "darkorange2"))+
  labs(x = "Items calidad vida", y = "Media estandarizada", title = "Perfiles Calidad de vida") +
  theme_bw(base_size = 14) +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, size = 10, face = "bold"),   # Letras eje X más grandes y en negrita
    axis.text.y = element_text(size = 14, face = "bold"),                         # Letras eje Y más grandes y en negrita
    axis.title.x = element_text(size = 16, face = "bold"),                       # Aumentar tamaño de la etiqueta del eje X
    axis.title.y = element_text(size = 16, face = "bold"),                       # Aumentar tamaño de la etiqueta del eje Y
    legend.position = "right", 
    legend.text = element_text(size = 10, face = "bold"),                        # Aumentar el tamaño de la leyenda y ponerla en negrita
    legend.title = element_text(size = 16, face = "bold")                       # Aumentar tamaño del título de la leyenda
  )
p

Se encontraron solamente 3 perfiles

Se procede a caracterizarlos

#Primero se conoce la clasificaicón por perfil 

# Extraer las clasificaciones del modelo
clases <- model1$classification

# Agregar las clases al data frame
DATA_PROFILES <- DATA_PROFILES %>%
  mutate(Perfil = clases)
myVars2 <- c("EDAD","Estrato_cat","EDUCACION_CAT", "SIT_MARITAL_CAT", "OCUPACIONL_CAT", "FRACCION_OCUPACIONES" , "SATISFACCION_CAT", "TIEMPO_SUPERVIVENCIA","Tiemposup_cat", "EDAD_DX", "edad_cat", "ESTADIO", "LUMINAL_A", "LUMINAL_B", "HER2_POSITIVO", "TRIPLE_NEGATIVO", "TRAT_SISTEMICO", "TRAT_ANTIHER2", "TRAT_RADIOTERAPIA", "TRAT_CIRUGIA_RADICAL", "TRAT_CIRUGIA_CONSERVADORA", "TRAT_HORMONOTERAPIA", "RECAIDA_CANCER_MAMA", "charlson_cat", "Cuidado_soporte_UA", "Cuidado_soporte_primario", "PF", "RF", "CF", "EF", "SF", "GLH", "FA", "NV", "PA", "DY", "SL", "AP", "CO", "DI", "FI","FI_cat", "BRST","BRST_cat", "BRHL", "BRHL_cat", "BRBI", "BRBI_cat", "BRFU","BRFU_cat", "BRSEF","BRSEF_cat", "BRSEE","BRSEE_cat", "BRAS", "BRAS_cat","BRBS", "BRBS_cat") 


catVars2 <- c("Estrato_cat","EDUCACION_CAT", "SIT_MARITAL_CAT", "OCUPACIONL_CAT",  "SATISFACCION_CAT","Tiemposup_cat", "edad_cat", "ESTADIO", "LUMINAL_A", "LUMINAL_B", "HER2_POSITIVO", "TRIPLE_NEGATIVO", "TRAT_SISTEMICO", "TRAT_ANTIHER2", "TRAT_RADIOTERAPIA", "TRAT_CIRUGIA_RADICAL", "TRAT_CIRUGIA_CONSERVADORA", "TRAT_HORMONOTERAPIA", "RECAIDA_CANCER_MAMA", "charlson_cat", "Cuidado_soporte_UA", "Cuidado_soporte_primario","FI_cat", "BRST_cat", "BRHL_cat", "BRBI_cat", "BRFU_cat", "BRSEF_cat", "BRSEE_cat", "BRAS_cat","BRBS_cat")  


tab2 <- CreateTableOne(vars = myVars2, factorVars= catVars2, data = DATA_PROFILES, strata ="Perfil", includeNA = T, test = T,addOverall = T)

table2 <- as.data.frame(print(tab2, showAllLevels= TRUE, printToggle = FALSE, noSpaces = TRUE))
rownames(table2) <- gsub("\\.{3,}", "", rownames(table2))  # Quita puntos suspensivos "..."
rownames(table2) <- gsub("\\.{1,}", "_", rownames(table2))  # Quita puntos suspensivos "..."
rownames(table2) <- gsub("\\_{1,}", " ", rownames(table2))  # Quita puntos suspensivos "..."

kable(table2, format = "html", caption = "Variable sociodemograficas") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F,position = "center") %>%
  column_spec(1, bold = T, color = "white", background = "black") %>%
  column_spec(2, border_left = T, color = "white", background = "grey")
Variable sociodemograficas
level Overall 1 2 3 p test
n 200 67 30 103
EDAD mean SD 65.12 (9.54) 65.75 (9.68) 66.53 (10.32) 64.31 (9.22) 0.432
Estrato cat Bajo 82 (41.0) 27 (40.3) 16 (53.3) 39 (37.9) 0.524
X Medio 111 (55.5) 37 (55.2) 14 (46.7) 60 (58.3)
X 1 Alto 7 (3.5) 3 (4.5) 0 (0.0) 4 (3.9)
EDUCACION CAT Primaria o menos 82 (41.0) 26 (38.8) 17 (56.7) 39 (37.9) 0.171
X 2 Basica/secundaria 42 (21.0) 11 (16.4) 4 (13.3) 27 (26.2)
X 3 Mas que secundaria 76 (38.0) 30 (44.8) 9 (30.0) 37 (35.9)
SIT MARITAL CAT Soltera 46 (23.0) 10 (14.9) 8 (26.7) 28 (27.2) 0.171
X 4 Casada/Union Libre 99 (49.5) 36 (53.7) 11 (36.7) 52 (50.5)
X 5 Separada/Viuda 55 (27.5) 21 (31.3) 11 (36.7) 23 (22.3)
OCUPACIONL CAT Ama de casa/hogar 91 (45.5) 26 (38.8) 16 (53.3) 49 (47.6) 0.430
X 6 empleado/ocupado 48 (24.0) 19 (28.4) 6 (20.0) 23 (22.3)
X 7 Pensionado 56 (28.0) 20 (29.9) 6 (20.0) 30 (29.1)
X 8 Cesante 5 (2.5) 2 (3.0) 2 (6.7) 1 (1.0)
FRACCION OCUPACIONES mean SD 60.47 (30.57) 59.34 (29.93) 54.27 (28.79) 63.00 (31.46) 0.364
SATISFACCION CAT Muy satisfechas 184 (92.0) 62 (92.5) 27 (90.0) 95 (92.2) 0.906
X 9 Poco satisfechas 16 (8.0) 5 (7.5) 3 (10.0) 8 (7.8)
TIEMPO SUPERVIVENCIA mean SD 8.20 (4.84) 8.55 (5.60) 7.87 (3.77) 8.08 (4.61) 0.757
Tiemposup cat <=5 56 (28.0) 21 (31.3) 9 (30.0) 26 (25.2) 0.664
X 10 >5 144 (72.0) 46 (68.7) 21 (70.0) 77 (74.8)
EDAD DX mean SD 56.92 (10.15) 57.19 (11.28) 58.67 (9.17) 56.23 (9.66) 0.496
edad cat <50 49 (24.5) 17 (25.4) 5 (16.7) 27 (26.2) 0.553
X 11 =>50 151 (75.5) 50 (74.6) 25 (83.3) 76 (73.8)
ESTADIO Temprano 112 (56.0) 37 (55.2) 25 (83.3) 50 (48.5) 0.025
X 12 Localmente avanzado 78 (39.0) 26 (38.8) 4 (13.3) 48 (46.6)
X 13 Metastasico 2 (1.0) 0 (0.0) 0 (0.0) 2 (1.9)
X 14 No reporta 8 (4.0) 4 (6.0) 1 (3.3) 3 (2.9)
LUMINAL A No 89 (44.5) 25 (37.3) 14 (46.7) 50 (48.5) 0.343
X 15 Si 111 (55.5) 42 (62.7) 16 (53.3) 53 (51.5)
LUMINAL B No 168 (84.0) 58 (86.6) 25 (83.3) 85 (82.5) 0.777
X 16 Si 32 (16.0) 9 (13.4) 5 (16.7) 18 (17.5)
HER2 POSITIVO No 169 (84.5) 59 (88.1) 24 (80.0) 86 (83.5) 0.551
X 17 Si 31 (15.5) 8 (11.9) 6 (20.0) 17 (16.5)
TRIPLE NEGATIVO No 174 (87.0) 59 (88.1) 27 (90.0) 88 (85.4) 0.768
X 18 Si 26 (13.0) 8 (11.9) 3 (10.0) 15 (14.6)
TRAT SISTEMICO No 56 (28.0) 21 (31.3) 12 (40.0) 23 (22.3) 0.125
X 19 Si 144 (72.0) 46 (68.7) 18 (60.0) 80 (77.7)
TRAT ANTIHER2 Checked 29 (14.5) 9 (13.4) 6 (20.0) 14 (13.6) 0.650
X 20 Unchecked 171 (85.5) 58 (86.6) 24 (80.0) 89 (86.4)
TRAT RADIOTERAPIA No 35 (17.5) 12 (17.9) 6 (20.0) 17 (16.5) 0.901
X 21 Si 165 (82.5) 55 (82.1) 24 (80.0) 86 (83.5)
TRAT CIRUGIA RADICAL No 127 (63.5) 43 (64.2) 22 (73.3) 62 (60.2) 0.417
X 22 Si 73 (36.5) 24 (35.8) 8 (26.7) 41 (39.8)
TRAT CIRUGIA CONSERVADORA Checked 127 (63.5) 42 (62.7) 23 (76.7) 62 (60.2) 0.253
X 23 Unchecked 73 (36.5) 25 (37.3) 7 (23.3) 41 (39.8)
TRAT HORMONOTERAPIA No 43 (21.5) 12 (17.9) 7 (23.3) 24 (23.3) 0.681
X 24 Si 157 (78.5) 55 (82.1) 23 (76.7) 79 (76.7)
RECAIDA CANCER MAMA No 196 (98.0) 65 (97.0) 29 (96.7) 102 (99.0) 0.560
X 25 Si 4 (2.0) 2 (3.0) 1 (3.3) 1 (1.0)
charlson cat 1-2 10 (5.0) 4 (6.0) 1 (3.3) 5 (4.9) 0.314
X 26 3-4 92 (46.0) 31 (46.3) 9 (30.0) 52 (50.5)
X 27 =>5 98 (49.0) 32 (47.8) 20 (66.7) 46 (44.7)
Cuidado soporte UA No 154 (77.0) 60 (89.6) 18 (60.0) 76 (73.8) 0.003
X 28 Si 46 (23.0) 7 (10.4) 12 (40.0) 27 (26.2)
PF mean SD 10.18 (1.71) 9.78 (0.83) 11.80 (2.54) 9.97 (1.59) <0.001
RF mean SD 4.03 (0.87) 3.97 (0.17) 4.57 (1.65) 3.90 (0.76) 0.001
CF mean SD 3.90 (1.31) 3.66 (0.64) 4.53 (1.70) 3.87 (1.46) 0.009
EF mean SD 7.92 (2.16) 7.40 (1.18) 8.87 (2.66) 7.99 (2.39) 0.007
SF mean SD 3.96 (0.90) 4.00 (0.00) 4.57 (2.06) 3.77 (0.49) <0.001
GLH mean SD 11.31 (2.05) 12.63 (1.27) 9.23 (2.31) 11.06 (1.78) <0.001
FA mean SD 5.70 (1.64) 5.58 (0.74) 7.60 (2.49) 5.22 (1.35) <0.001
NV mean SD 3.90 (0.45) 4.00 (0.00) 3.83 (0.99) 3.86 (0.34) 0.105
PA mean SD 4.03 (1.67) 3.57 (0.87) 4.83 (2.17) 4.10 (1.81) 0.002
DY mean SD 1.92 (0.57) 1.90 (0.31) 2.00 (0.91) 1.91 (0.58) 0.696
SL mean SD 1.97 (0.94) 1.82 (0.63) 2.47 (1.22) 1.91 (0.97) 0.005
AP mean SD 2.02 (0.50) 2.00 (0.00) 2.40 (0.93) 1.92 (0.44) <0.001
CO mean SD 1.93 (0.65) 1.82 (0.39) 1.93 (0.83) 1.99 (0.72) 0.251
DI mean SD 1.99 (0.43) 2.00 (0.00) 2.20 (0.92) 1.92 (0.30) 0.006
FI mean SD 2.00 (0.61) 1.99 (0.33) 2.20 (1.06) 1.96 (0.56) 0.156
FI cat <=2 185 (92.5) 66 (98.5) 22 (73.3) 97 (94.2) <0.001
X 29 >2 15 (7.5) 1 (1.5) 8 (26.7) 6 (5.8)
BRST mean SD 14.37 (2.50) 13.39 (1.49) 16.47 (2.64) 14.40 (2.62) <0.001
BRST cat <=14 119 (59.5) 51 (76.1) 6 (20.0) 62 (60.2) <0.001
X 30 >=14 81 (40.5) 16 (23.9) 24 (80.0) 41 (39.8)
BRHL mean SD 2.14 (0.74) 1.94 (0.25) 2.21 (0.83) 2.25 (0.89) 0.029
BRHL cat <=2 157 (78.5) 62 (92.5) 21 (70.0) 74 (71.8) 0.001
X 31 >2 30 (15.0) 0 (0.0) 7 (23.3) 23 (22.3)
X 32 NA 13 (6.5) 5 (7.5) 2 (6.7) 6 (5.8)
BRBI mean SD 7.75 (1.29) 7.76 (0.61) 7.57 (2.28) 7.80 (1.23) 0.692
BRBI cat <=8 179 (89.5) 65 (97.0) 22 (73.3) 92 (89.3) 0.002
X 33 >8 21 (10.5) 2 (3.0) 8 (26.7) 11 (10.7)
BRFU mean SD 2.22 (1.14) 1.85 (0.82) 2.70 (1.24) 2.32 (1.23) 0.001
BRFU cat <=2 139 (69.5) 61 (91.0) 12 (40.0) 66 (64.1) <0.001
X 34 >2 61 (30.5) 6 (9.0) 18 (60.0) 37 (35.9)
BRSEF mean SD 6.19 (1.13) 6.31 (1.12) 6.07 (0.94) 6.15 (1.18) 0.519
BRSEF cat <=6 149 (74.5) 49 (73.1) 26 (86.7) 74 (71.8) 0.248
X 35 >6 51 (25.5) 18 (26.9) 4 (13.3) 29 (28.2)
BRSEE mean SD 2.89 (0.83) 2.82 (0.95) 2.92 (0.65) 2.94 (0.79) 0.677
BRSEE cat <=3 149 (74.5) 49 (73.1) 22 (73.3) 78 (75.7) 0.371
X 36 >3 29 (14.5) 11 (16.4) 2 (6.7) 16 (15.5)
X 37 NA 22 (11.0) 7 (10.4) 6 (20.0) 9 (8.7)
BRAS mean SD 5.88 (1.64) 5.34 (1.16) 6.90 (1.81) 5.94 (1.72) <0.001
BRAS cat <=6 155 (77.5) 62 (92.5) 15 (50.0) 78 (75.7) <0.001
X 38 >6 45 (22.5) 5 (7.5) 15 (50.0) 25 (24.3)
BRBS mean SD 7.68 (1.63) 7.52 (0.82) 7.97 (2.34) 7.69 (1.78) 0.464
BRBS cat <=8 174 (87.0) 65 (97.0) 25 (83.3) 84 (81.6) 0.011
X 39 >8 26 (13.0) 2 (3.0) 5 (16.7) 19 (18.4)

Nota:

Escalas complementarias

Se seleccionan las variables para el modelo multivariado si el p valor fue igual o menor a 0.2 EDUCACION CAT, ESTADIO, TRAT SISTEMICO, Cuidado soporte UA, “BRST”, “BRHL”, “BRFU”, “BRAS”. No se incluyen los ítems con los que se calcularon los perfiles

Modelo de regresión

Frecuencias por perfil

library(sjmisc)
library(sjPlot)
library(nnet)
library(wakefield)

frq(DATA_PROFILES, Perfil)
## Perfil <numeric> 
## # total N=200 valid N=200 mean=2.18 sd=0.91
## 
## Value |   N | Raw % | Valid % | Cum. %
## --------------------------------------
##     1 |  67 | 33.50 |   33.50 |  33.50
##     2 |  30 | 15.00 |   15.00 |  48.50
##     3 | 103 | 51.50 |   51.50 | 100.00
##  <NA> |   0 |  0.00 |    <NA> |   <NA>
library(MASS)
library(ordinal)

DATA_PROFILES$Perfil2 <- factor(DATA_PROFILES$Perfil, levels = c(2, 3, 1), labels = c("Baja", "Media", "Alta"))

modelo1 <- clm(Perfil2 ~ EDUCACION_CAT + SIT_MARITAL_CAT+ ESTADIO + TRAT_SISTEMICO + Cuidado_soporte_UA + FI_cat + BRST_cat + BRHL_cat + BRBI_cat + BRFU_cat + BRAS_cat + BRBS_cat, data = DATA_PROFILES)
summary(modelo1)
## formula: 
## Perfil2 ~ EDUCACION_CAT + SIT_MARITAL_CAT + ESTADIO + TRAT_SISTEMICO + Cuidado_soporte_UA + FI_cat + BRST_cat + BRHL_cat + BRBI_cat + BRFU_cat + BRAS_cat + BRBS_cat
## data:    DATA_PROFILES
## 
##  link  threshold nobs logLik  AIC    niter max.grad cond.H 
##  logit flexible  187  -139.03 314.06 6(0)  1.41e-09 2.3e+02
## 
## Coefficients:
##                                   Estimate Std. Error z value Pr(>|z|)    
## EDUCACION_CATBasica/secundaria     0.10179    0.42215   0.241 0.809452    
## EDUCACION_CATMas que secundaria    0.70042    0.37315   1.877 0.060510 .  
## SIT_MARITAL_CATCasada/Union Libre  0.35929    0.41867   0.858 0.390805    
## SIT_MARITAL_CATSeparada/Viuda      0.19267    0.46529   0.414 0.678805    
## ESTADIOLocalmente avanzado         0.31134    0.35280   0.882 0.377517    
## ESTADIOMetastasico                -1.32641    1.52246  -0.871 0.383628    
## ESTADIONo reporta                  0.06442    0.87960   0.073 0.941619    
## TRAT_SISTEMICOSi                   0.04185    0.37283   0.112 0.910634    
## Cuidado_soporte_UASi              -0.85482    0.41335  -2.068 0.038640 *  
## FI_cat>2                          -1.54832    0.67502  -2.294 0.021805 *  
## BRST_cat>=14                      -1.07830    0.36245  -2.975 0.002929 ** 
## BRHL_cat>2                        -0.85013    0.45671  -1.861 0.062684 .  
## BRBI_cat>8                        -0.90831    0.57698  -1.574 0.115427    
## BRFU_cat>2                        -1.15085    0.38239  -3.010 0.002616 ** 
## BRAS_cat>6                        -1.44871    0.42135  -3.438 0.000585 ***
## BRBS_cat>8                        -0.54755    0.49647  -1.103 0.270080    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Threshold coefficients:
##            Estimate Std. Error z value
## Baja|Media -3.66731    0.64781  -5.661
## Media|Alta  0.07983    0.53614   0.149
## (13 observations deleted due to missingness)
modelo2 <- clm(Perfil2 ~ Cuidado_soporte_UA + FI_cat + BRST_cat + BRHL_cat + BRFU_cat + BRAS_cat, data = DATA_PROFILES)
summary(modelo2)
## formula: 
## Perfil2 ~ Cuidado_soporte_UA + FI_cat + BRST_cat + BRHL_cat + BRFU_cat + BRAS_cat
## data:    DATA_PROFILES
## 
##  link  threshold nobs logLik  AIC    niter max.grad cond.H 
##  logit flexible  187  -144.02 304.05 6(0)  3.39e-10 1.9e+01
## 
## Coefficients:
##                      Estimate Std. Error z value Pr(>|z|)    
## Cuidado_soporte_UASi  -0.7850     0.3832  -2.049 0.040496 *  
## FI_cat>2              -1.6624     0.6226  -2.670 0.007584 ** 
## BRST_cat>=14          -1.2151     0.3392  -3.582 0.000341 ***
## BRHL_cat>2            -0.8847     0.4365  -2.027 0.042699 *  
## BRFU_cat>2            -1.2325     0.3729  -3.305 0.000951 ***
## BRAS_cat>6            -1.3693     0.4021  -3.405 0.000661 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Threshold coefficients:
##            Estimate Std. Error z value
## Baja|Media  -4.1354     0.4400  -9.399
## Media|Alta  -0.5340     0.2267  -2.355
## (13 observations deleted due to missingness)

Como primer criterio de retención de variables es la significancia estadística, como segundo criterio se realizó una prueba de supuestos de proporcionalidad de los ODDS, variables que no respeten el supuesto fueron excluídas.

nominal_test(modelo2)
## Tests of nominal effects
## 
## formula: Perfil2 ~ Cuidado_soporte_UA + FI_cat + BRST_cat + BRHL_cat + BRFU_cat + BRAS_cat
##                    Df  logLik    AIC     LRT  Pr(>Chi)    
## <none>                -144.02 304.05                      
## Cuidado_soporte_UA  1 -143.85 305.71  0.3423 0.5584894    
## FI_cat              1 -143.86 305.73  0.3197 0.5717739    
## BRST_cat            1 -142.65 303.31  2.7401 0.0978605 .  
## BRHL_cat            1 -138.28 294.56 11.4907 0.0006995 ***
## BRFU_cat            1 -143.96 305.92  0.1272 0.7213717    
## BRAS_cat            1 -144.02 306.05  0.0000 0.9960410    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Se realiza prueba de verosimilitud, fue necesario realizarlo solamente con los datos completos

vars_sub <- c("Perfil2", "Cuidado_soporte_UA", "FI_cat", "BRST_cat", "BRHL_cat", "BRFU_cat", "BRAS_cat")

DATA_SUB <- DATA_PROFILES[complete.cases(DATA_PROFILES[, vars_sub]), ]

modelo1.1 <- clm(Perfil2 ~ EDUCACION_CAT + SIT_MARITAL_CAT+ ESTADIO + TRAT_SISTEMICO + Cuidado_soporte_UA + FI_cat + BRST_cat + BRHL_cat + BRBI_cat + BRFU_cat + BRAS_cat + BRBS_cat, data = DATA_SUB)
modelo2.1 <- clm(Perfil2 ~ Cuidado_soporte_UA + FI_cat + BRST_cat + BRHL_cat + BRFU_cat + BRAS_cat, data = DATA_SUB)

library(lmtest)
lrtest(modelo1.1, modelo2.1)
## Likelihood ratio test
## 
## Model 1: Perfil2 ~ EDUCACION_CAT + SIT_MARITAL_CAT + ESTADIO + TRAT_SISTEMICO + 
##     Cuidado_soporte_UA + FI_cat + BRST_cat + BRHL_cat + BRBI_cat + 
##     BRFU_cat + BRAS_cat + BRBS_cat
## Model 2: Perfil2 ~ Cuidado_soporte_UA + FI_cat + BRST_cat + BRHL_cat + 
##     BRFU_cat + BRAS_cat
##   #Df  LogLik  Df  Chisq Pr(>Chisq)
## 1  18 -139.03                      
## 2   8 -144.02 -10 9.9912     0.4413

Con la prueba de verosimilitud se tiene adecuado resultado.

Se estima el OR

Modelo reduido manualmente

library(gtsummary)
tbl_regression(modelo2, exponentiate = TRUE, add_estimate_to_reference_rows=TRUE)  %>% add_global_p()
Characteristic OR 95% CI p-value
Cuidado_soporte_UA

0.040
    No 1.00 —
    Si 0.46 0.21, 0.96
FI_cat

0.008
    <=2 1.00 —
    >2 0.19 0.05, 0.63
BRST_cat

<0.001
    <=14 1.00 —
    >=14 0.30 0.15, 0.57
BRHL_cat

0.043
    <=2 1.00 —
    >2 0.41 0.17, 0.96
BRFU_cat

<0.001
    <=2 1.00 —
    >2 0.29 0.14, 0.60
BRAS_cat

<0.001
    <=6 1.00 —
    >6 0.25 0.11, 0.55
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Síntesis global de los resultados del modelo de regresión logística ordinal

Variable Categorías / Referencia OR IC 95% p-value Interpretación resumida
Cuidado_soporte_UA Sí vs. No (ref) 0.46 0.21 – 0.96 0.040 Los pacientes con cuidado de soporte en el último año tienen menor chance de reportar mejor calidad de vida.
FI_cat >2 vs. ≤2 (ref) 0.19 0.05 – 0.63 0.008 Mayores dificultades económicas se asocian con una peor calidad de vida.
BRST_cat ≥14 vs. ≤14 (ref) 0.30 0.15 – 0.57 <0.001 A mayor carga de síntomas de efectos secundarios de tratamiento reduce el chance de tener mejor calidad de vida.
BRHL_cat >2 vs. ≤2 (ref) 0.41 0.17 – 0.96 0.043 Mayor angustia por la pérdida del cabello se relacionan con peor calidad de vida.
BRFU_cat >2 vs. ≤2 (ref) 0.29 0.14 – 0.60 <0.001 “Mayor perspectiva futura” reduce el chances de tener mejor calidad de vida.
BRAS_cat >6 vs. ≤6 (ref) 0.25 0.11 – 0.55 <0.001 Mayor síntomas de brazo se asocia con menor calidad de vida.

Interpretación general:
Todas las variables presentan OR < 1, lo que indica que las variables se asocian con menores chances de estar en categorías más altas de calidad de vida (media o alta).