Ketamine_analysis_revised
Stephen Murata
10/9/2021
Descriptive summary of source datafile
Descriptive summary stats by variable (long format)
options(width=190)
compareGroups::descrTable(~. , data = Biok_vert_df, hide.no = '0')##
## --------Summary descriptives table ---------
##
## _____________________________________________________________
## [ALL] N
## N=218
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex: 218
## male 82 (37.6%)
## female 136 (62.4%)
## age 44.3 (12.9) 218
## BMI 28.6 (5.58) 218
## race: 218
## asian 3 (1.38%)
## black 4 (1.83%)
## white 211 (96.8%)
## patientno 1016 (1365) 218
## Site_Location: 218
## Johns Hopkins 22 (10.1%)
## Univeristy of Michigan 69 (31.7%)
## Mayo Clinic 114 (52.3%)
## Pine Rest 13 (5.96%)
## infusionno: 218
## BL 73 (33.5%)
## 1st 72 (33.0%)
## 3rd 73 (33.5%)
## Blood_Draw_Event: 218
## Acute Infusion #1 Baseline 100 49 (22.5%)
## Acute Infusion #1 Baseline 40 24 (11.0%)
## Acute Infusion #1 Stop 100 48 (22.0%)
## Acute Infusion #1 Stop 40 24 (11.0%)
## Acute Infusion #3 Stop 100 28 (12.8%)
## Acute Infusion #3 Stop 40 45 (20.6%)
## Batch_Number 3.51 (1.71) 218
## BSS_Score 4.57 (6.82) 213
## MADRS_Score 17.0 (10.6) 217
## Remission: 215
## No remission 77 (35.8%)
## Remitter 138 (64.2%)
## TRP_nM 25449 (5942) 218
## five_HT_nM 205 (284) 218
## KYN_nM 917 (244) 218
## three_HK_nM 16.2 (7.44) 218
## KYNA_nM 19.5 (7.68) 218
## PIC_nM 19.2 (13.5) 218
## Quin_nM 146 (43.0) 218
## AA_nM 5.95 (3.12) 218
## KYN_TRP_ratio 0.04 (0.01) 218
## KYN_SER_ratio 33.2 (72.1) 218
## QUIN_PIC_ratio 9.14 (4.39) 218
## QUIN_KYNA_ratio 8.30 (3.40) 218
## threeHK_KYN_ratio 0.02 (0.01) 218
## threeHK_KYNA_ratio 0.91 (0.44) 218
## IL1B_pg_mL 0.28 (0.47) 42
## IL1B_pg_mL_LLOD 0.34 (0.19) 218
## IL2_pg_mL 1.27 (6.00) 152
## IL2_pg_mL_LLOD 0.92 (5.03) 218
## IL4_pg_mL 0.09 (0.03) 217
## IL4_pg_mL_LLOD 0.09 (0.03) 218
## IL6_pg_mL 1.04 (0.58) 218
## IL8_pg_mL 4.36 (1.66) 218
## IL10_pg_mL 0.64 (1.95) 218
## IL12p70_pg_mL 21.8 (186) 217
## IL12p70_pg_mL_LLOD 21.7 (185) 218
## IL13_pg_mL 5.07 (11.5) 215
## IL13_pg_mL_LLOD 5.01 (11.4) 218
## TNFa_pg_mL 1.44 (0.46) 218
## IFNy_pg_mL 6.97 (5.50) 218
## CRP_ng_mL 2835 (4774) 218
## NIC_nM 41.4 (340) 218
## NIC_nM_LLOD 41.4 (340) 218
## NTA_nM 306 (153) 218
## SAA_ng_mL 2870 (2145) 218
## VCAM_1_ng_mL 309 (67.9) 218
## ICAM_1_ng_mL 308 (82.3) 218
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯Missingness counts per variable
M<- sapply(Biok_vert_df, function(x) sum(is.na(x))); M[M>0]## BSS_Score MADRS_Score Remission IL1B_pg_mL IL2_pg_mL IL4_pg_mL IL12p70_pg_mL IL13_pg_mL
## 5 1 3 176 66 1 1 3Note: IL1B_pg_mL has really high missingness count.
Descriptive summary stats by infusion number (long format)
createTable(compareGroups(infusionno ~ ., data = Biok_vert_df, method = NA), hide.no = '0', show.p.mul = T)##
## --------Summary descriptives table by 'infusionno'---------
##
## ______________________________________________________________________________________________________________________________________________
## BL 1st 3rd p.overall p.BL vs 1st p.BL vs 3rd p.1st vs 3rd
## N=73 N=72 N=73
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex: 0.909 1.000 1.000 1.000
## male 26 (35.6%) 28 (38.9%) 28 (38.4%)
## female 47 (64.4%) 44 (61.1%) 45 (61.6%)
## age 46.0 [33.0;55.0] 47.0 [34.5;55.0] 47.0 [35.0;55.0] 0.977 0.916 0.916 0.916
## BMI 28.1 [24.9;32.3] 28.1 [24.7;31.6] 28.1 [24.9;31.7] 0.992 0.969 0.969 0.969
## race: 1.000 1.000 1.000 1.000
## asian 1 (1.37%) 1 (1.39%) 1 (1.37%)
## black 2 (2.74%) 1 (1.39%) 1 (1.37%)
## white 70 (95.9%) 70 (97.2%) 71 (97.3%)
## patientno 125 [97.0;3006] 126 [97.8;3005] 125 [97.0;3005] 0.993 0.956 0.956 0.956
## Site_Location: 1.000 1.000 1.000 1.000
## Johns Hopkins 7 (9.59%) 7 (9.72%) 8 (11.0%)
## Univeristy of Michigan 23 (31.5%) 23 (31.9%) 23 (31.5%)
## Mayo Clinic 38 (52.1%) 38 (52.8%) 38 (52.1%)
## Pine Rest 5 (6.85%) 4 (5.56%) 4 (5.48%)
## Blood_Draw_Event: <0.001 <0.001 <0.001 <0.001
## Acute Infusion #1 Baseline 100 49 (67.1%) 0 (0.00%) 0 (0.00%)
## Acute Infusion #1 Baseline 40 24 (32.9%) 0 (0.00%) 0 (0.00%)
## Acute Infusion #1 Stop 100 0 (0.00%) 48 (66.7%) 0 (0.00%)
## Acute Infusion #1 Stop 40 0 (0.00%) 24 (33.3%) 0 (0.00%)
## Acute Infusion #3 Stop 100 0 (0.00%) 0 (0.00%) 28 (38.4%)
## Acute Infusion #3 Stop 40 0 (0.00%) 0 (0.00%) 45 (61.6%)
## Batch_Number 4.00 [2.00;5.00] 3.50 [2.00;5.00] 3.00 [2.00;5.00] 0.978 0.981 0.981 0.981
## BSS_Score 7.00 [1.00;13.0] 0.00 [0.00;2.00] 0.00 [0.00;1.00] <0.001 <0.001 <0.001 0.242
## MADRS_Score 27.0 [24.0;32.0] 13.5 [8.00;19.0] 6.00 [4.00;12.2] <0.001 <0.001 <0.001 <0.001
## Remission: 0.992 1.000 1.000 1.000
## No remission 26 (36.1%) 25 (35.2%) 26 (36.1%)
## Remitter 46 (63.9%) 46 (64.8%) 46 (63.9%)
## TRP_nM 27561 (5740) 24360 (5841) 24412 (5747) 0.001 0.003 0.003 0.998
## five_HT_nM 75.7 [31.9;296] 77.5 [28.9;256] 73.0 [21.0;292] 0.542 0.693 0.693 0.716
## KYN_nM 936 [826;1149] 861 [743;999] 861 [729;997] 0.027 0.031 0.031 0.972
## three_HK_nM 14.9 [12.6;19.4] 13.9 [12.1;17.4] 14.0 [11.8;17.8] 0.199 0.196 0.196 0.968
## KYNA_nM 19.7 [14.3;24.2] 18.3 [14.2;22.8] 17.1 [13.7;23.0] 0.485 0.626 0.626 0.719
## PIC_nM 17.4 [12.8;24.4] 15.6 [12.4;21.4] 17.1 [13.4;22.6] 0.365 0.453 0.491 0.491
## Quin_nM 141 [112;175] 134 [106;174] 141 [113;172] 0.874 0.902 0.902 0.902
## AA_nM 5.29 [4.31;6.90] 5.38 [4.09;7.01] 5.05 [4.09;6.74] 0.552 0.934 0.592 0.592
## KYN_TRP_ratio 0.04 [0.03;0.04] 0.04 [0.03;0.04] 0.04 [0.03;0.04] 0.634 0.785 0.785 0.785
## KYN_SER_ratio 12.1 [2.85;28.9] 12.4 [3.95;33.2] 13.9 [3.43;39.4] 0.771 0.770 0.770 0.770
## QUIN_PIC_ratio 7.60 [5.75;10.6] 7.86 [6.52;10.9] 8.64 [6.01;11.6] 0.547 0.567 0.567 0.978
## QUIN_KYNA_ratio 7.42 [5.62;9.33] 7.63 [5.86;9.10] 7.81 [6.05;10.5] 0.591 0.716 0.716 0.716
## threeHK_KYN_ratio 0.02 [0.01;0.02] 0.02 [0.01;0.02] 0.02 [0.01;0.02] 0.924 0.856 0.856 0.856
## threeHK_KYNA_ratio 0.80 [0.64;1.09] 0.78 [0.64;1.01] 0.80 [0.62;1.06] 0.897 0.853 0.853 0.853
## IL1B_pg_mL 0.11 [0.08;0.24] 0.17 [0.13;0.27] 0.16 [0.10;0.27] 0.489 0.523 0.523 0.856
## IL1B_pg_mL_LLOD 0.32 [0.32;0.32] 0.32 [0.32;0.32] 0.32 [0.32;0.32] 0.871 0.989 0.989 0.989
## IL2_pg_mL 0.45 [0.30;0.63] 0.38 [0.26;0.52] 0.38 [0.29;0.56] 0.421 0.521 0.521 0.521
## IL2_pg_mL_LLOD 0.26 [0.10;0.49] 0.28 [0.10;0.42] 0.31 [0.10;0.48] 0.540 0.815 0.602 0.602
## IL4_pg_mL 0.08 [0.07;0.10] 0.09 [0.07;0.10] 0.09 [0.07;0.10] 0.898 0.821 0.821 0.821
## IL4_pg_mL_LLOD 0.08 [0.07;0.10] 0.09 [0.07;0.10] 0.09 [0.07;0.10] 0.891 0.899 0.899 0.899
## IL6_pg_mL 0.83 [0.69;1.19] 0.93 [0.66;1.32] 0.85 [0.64;1.11] 0.423 0.520 0.520 0.520
## IL8_pg_mL 4.48 [3.47;5.24] 4.41 [3.42;5.32] 3.86 [3.04;4.57] 0.040 0.812 0.057 0.057
## IL10_pg_mL 0.34 [0.27;0.43] 0.36 [0.29;0.45] 0.38 [0.28;0.44] 0.724 0.814 0.814 0.893
## IL12p70_pg_mL 0.37 [0.30;0.49] 0.40 [0.30;0.49] 0.40 [0.30;0.48] 0.893 0.818 0.818 0.818
## IL12p70_pg_mL_LLOD 0.37 [0.30;0.49] 0.39 [0.30;0.49] 0.40 [0.30;0.48] 0.880 0.934 0.934 0.934
## IL13_pg_mL 3.56 [3.15;4.12] 3.64 [3.16;4.21] 3.63 [3.22;4.05] 0.906 0.888 0.888 0.888
## IL13_pg_mL_LLOD 3.55 [3.13;4.10] 3.64 [3.15;4.19] 3.63 [3.22;4.05] 0.909 0.881 0.881 0.881
## TNFa_pg_mL 1.39 [1.21;1.63] 1.31 [1.17;1.58] 1.34 [1.17;1.58] 0.672 0.791 0.791 0.797
## IFNy_pg_mL 5.26 [4.14;7.52] 5.17 [4.06;7.07] 5.49 [4.27;7.54] 0.549 0.657 0.657 0.657
## CRP_ng_mL 1246 [538;3014] 1094 [460;2863] 1289 [424;2822] 0.902 0.968 0.968 0.968
## NIC_nM 0.56 [0.32;0.98] 0.51 [0.35;1.34] 0.51 [0.34;0.98] 0.876 0.874 0.874 0.874
## NIC_nM_LLOD 0.56 [0.32;0.98] 0.51 [0.35;1.34] 0.51 [0.34;0.98] 0.876 0.874 0.874 0.874
## NTA_nM 297 [213;429] 280 [194;391] 285 [194;330] 0.243 0.415 0.314 0.517
## SAA_ng_mL 2191 [1207;4154] 2265 [1246;3934] 2131 [1353;4145] 0.988 0.987 0.987 0.987
## VCAM_1_ng_mL 313 (66.6) 309 (66.1) 305 (71.5) 0.776 0.949 0.758 0.916
## ICAM_1_ng_mL 295 [258;358] 285 [256;364] 284 [252;344] 0.717 0.675 0.675 0.675
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯Note: If this is unreadable I can send an excel. Dont pay too much attention to p-values at this stage, but KYN and IL8 may distinguish infusionno
Descriptive summary of variables (wide format)
Outcome inspection: MADRS_Score variable
outcome_parent<-lm(MADRS_Score~sex+age+BMI, data=Biok_vert_df)
outcome_infusion<-lm(MADRS_Score_3rd~sex+age+BMI+MADRS_Score_BL, data=Biok_wide)
hist(Biok_vert_df$MADRS_Score, main="MADRS_Score (parent distribution)", xlab="MADRS total Score")
autoplot(outcome_parent, which=c(2,6))
hist(Biok_wide$MADRS_Score_3rd, main="MADRS_Score (infusionno #3 distribution)", xlab="MADRS total score")
autoplot(outcome_infusion, which=c(2,6))
Will hold off on transforming outcome until modelling stage
Inspection of residuals and coefficients of raw and transformed biomarkers
| Biomarker | VIF | Beta_coeff | T_test | Std_error | p_value | R_coeff | |
|---|---|---|---|---|---|---|---|
| AA_nM_1st | AA_nM_1st | 1.27 | 1.81 | 1.05 | 1.73 | 0.30 | 0.11 |
| AA_nM_3rd | AA_nM_3rd | 1.20 | 1.73 | 0.96 | 1.80 | 0.34 | 0.10 |
| AA_nM_BL | AA_nM_BL | 1.21 | 2.24 | 1.29 | 1.74 | 0.20 | 0.15 |
| CRP_ng_mL_1st | CRP_ng_mL_1st | 1.20 | 1.37 | 0.78 | 1.76 | 0.44 | 0.09 |
| CRP_ng_mL_3rd | CRP_ng_mL_3rd | 1.27 | 1.79 | 0.99 | 1.80 | 0.33 | 0.09 |
| CRP_ng_mL_BL | CRP_ng_mL_BL | 1.19 | 1.79 | 1.00 | 1.79 | 0.32 | 0.10 |
| five_HT_nM_1st | five_HT_nM_1st | 1.09 | 1.11 | 0.64 | 1.72 | 0.52 | 0.13 |
| five_HT_nM_3rd | five_HT_nM_3rd | 1.17 | 1.38 | 0.80 | 1.73 | 0.43 | 0.17 |
| five_HT_nM_BL | five_HT_nM_BL | 1.08 | 1.50 | 0.87 | 1.73 | 0.39 | 0.16 |
| ICAM_1_ng_mL_1st | ICAM_1_ng_mL_1st | 1.05 | 1.67 | 0.97 | 1.72 | 0.34 | 0.10 |
| ICAM_1_ng_mL_3rd | ICAM_1_ng_mL_3rd | 1.05 | 2.22 | 1.25 | 1.77 | 0.21 | 0.11 |
| ICAM_1_ng_mL_BL | ICAM_1_ng_mL_BL | 1.07 | 2.30 | 1.27 | 1.80 | 0.21 | 0.10 |
| IFNy_pg_mL_1st | IFNy_pg_mL_1st | 1.06 | 1.49 | 0.86 | 1.73 | 0.39 | 0.10 |
| IFNy_pg_mL_3rd | IFNy_pg_mL_3rd | 1.06 | 1.97 | 1.10 | 1.79 | 0.27 | 0.09 |
| IFNy_pg_mL_BL | IFNy_pg_mL_BL | 1.08 | 1.90 | 1.06 | 1.78 | 0.29 | 0.10 |
| IL10_pg_mL_1st | IL10_pg_mL_1st | 1.02 | 1.81 | 1.05 | 1.73 | 0.30 | 0.11 |
| IL10_pg_mL_3rd | IL10_pg_mL_3rd | 1.02 | 2.24 | 1.26 | 1.78 | 0.21 | 0.11 |
| IL10_pg_mL_BL | IL10_pg_mL_BL | 1.02 | 2.30 | 1.29 | 1.78 | 0.20 | 0.12 |
| IL12p70_pg_mL_1st | IL12p70_pg_mL_1st | 1.12 | 1.32 | 0.73 | 1.80 | 0.47 | 0.09 |
| IL12p70_pg_mL_3rd | IL12p70_pg_mL_3rd | 1.11 | 2.14 | 1.18 | 1.81 | 0.24 | 0.09 |
| IL12p70_pg_mL_BL | IL12p70_pg_mL_BL | 1.11 | 2.14 | 1.18 | 1.81 | 0.24 | 0.09 |
| IL13_pg_mL_1st | IL13_pg_mL_1st | 1.13 | 1.50 | 0.83 | 1.80 | 0.41 | 0.10 |
| IL13_pg_mL_3rd | IL13_pg_mL_3rd | 1.12 | 2.12 | 1.17 | 1.81 | 0.25 | 0.09 |
| IL13_pg_mL_BL | IL13_pg_mL_BL | 1.13 | 1.91 | 1.04 | 1.84 | 0.30 | 0.08 |
| IL1B_pg_mL_1st | IL1B_pg_mL_1st | 1.39 | -1.79 | -0.47 | 3.83 | 0.66 | 0.16 |
| IL1B_pg_mL_3rd | IL1B_pg_mL_3rd | 1.64 | -0.79 | -0.16 | 4.91 | 0.88 | 0.26 |
| IL1B_pg_mL_BL | IL1B_pg_mL_BL | 1.29 | -3.36 | -1.26 | 2.66 | 0.23 | 0.39 |
| IL2_pg_mL_1st | IL2_pg_mL_1st | 1.06 | -1.09 | -0.62 | 1.76 | 0.54 | 0.17 |
| IL2_pg_mL_3rd | IL2_pg_mL_3rd | 1.07 | -0.13 | -0.07 | 1.83 | 0.94 | 0.12 |
| IL2_pg_mL_BL | IL2_pg_mL_BL | 1.08 | -1.35 | -0.68 | 1.98 | 0.50 | 0.19 |
| IL4_pg_mL_1st | IL4_pg_mL_1st | 1.10 | 1.12 | 0.63 | 1.78 | 0.53 | 0.09 |
| IL4_pg_mL_3rd | IL4_pg_mL_3rd | 1.07 | 1.98 | 1.10 | 1.80 | 0.27 | 0.09 |
| IL4_pg_mL_BL | IL4_pg_mL_BL | 1.14 | 1.90 | 1.06 | 1.80 | 0.29 | 0.09 |
| IL6_pg_mL_1st | IL6_pg_mL_1st | 1.05 | 1.48 | 0.85 | 1.74 | 0.40 | 0.09 |
| IL6_pg_mL_3rd | IL6_pg_mL_3rd | 1.42 | 1.85 | 1.01 | 1.83 | 0.32 | 0.09 |
| IL6_pg_mL_BL | IL6_pg_mL_BL | 1.31 | 2.25 | 1.22 | 1.84 | 0.23 | 0.09 |
| IL8_pg_mL_1st | IL8_pg_mL_1st | 1.14 | 1.34 | 0.77 | 1.74 | 0.44 | 0.10 |
| IL8_pg_mL_3rd | IL8_pg_mL_3rd | 1.04 | 2.06 | 1.15 | 1.79 | 0.25 | 0.09 |
| IL8_pg_mL_BL | IL8_pg_mL_BL | 1.08 | 2.06 | 1.13 | 1.81 | 0.26 | 0.09 |
| KYN_nM_1st | KYN_nM_1st | 1.33 | 2.00 | 1.12 | 1.77 | 0.27 | 0.10 |
| KYN_nM_3rd | KYN_nM_3rd | 1.29 | 3.02 | 1.64 | 1.84 | 0.11 | 0.13 |
| KYN_nM_BL | KYN_nM_BL | 1.27 | 2.55 | 1.41 | 1.81 | 0.16 | 0.11 |
| KYN_SER_ratio_1st | KYN_SER_ratio_1st | 1.06 | 1.56 | 0.91 | 1.72 | 0.37 | 0.10 |
| KYN_SER_ratio_3rd | KYN_SER_ratio_3rd | 1.03 | 2.07 | 1.15 | 1.79 | 0.25 | 0.09 |
| KYN_SER_ratio_BL | KYN_SER_ratio_BL | 1.11 | 2.21 | 1.24 | 1.78 | 0.22 | 0.11 |
| KYN_TRP_ratio_1st | KYN_TRP_ratio_1st | 1.35 | 1.56 | 0.89 | 1.74 | 0.37 | 0.08 |
| KYN_TRP_ratio_3rd | KYN_TRP_ratio_3rd | 1.43 | 1.96 | 1.08 | 1.81 | 0.28 | 0.09 |
| KYN_TRP_ratio_BL | KYN_TRP_ratio_BL | 1.31 | 1.99 | 1.11 | 1.79 | 0.27 | 0.09 |
| KYNA_nM_1st | KYNA_nM_1st | 1.49 | 2.14 | 1.14 | 1.88 | 0.26 | 0.09 |
| KYNA_nM_3rd | KYNA_nM_3rd | 1.11 | 2.04 | 1.10 | 1.85 | 0.27 | 0.09 |
| KYNA_nM_BL | KYNA_nM_BL | 1.20 | 1.93 | 1.04 | 1.86 | 0.30 | 0.09 |
| NIC_nM_1st | NIC_nM_1st | 1.02 | 1.40 | 0.80 | 1.75 | 0.42 | 0.09 |
| NIC_nM_3rd | NIC_nM_3rd | 1.02 | 1.87 | 1.04 | 1.80 | 0.30 | 0.09 |
| NIC_nM_BL | NIC_nM_BL | 1.02 | 1.88 | 1.04 | 1.80 | 0.30 | 0.09 |
| NTA_nM_1st | NTA_nM_1st | 1.08 | 1.51 | 0.87 | 1.75 | 0.39 | 0.08 |
| NTA_nM_3rd | NTA_nM_3rd | 1.15 | 2.10 | 1.15 | 1.82 | 0.25 | 0.09 |
| NTA_nM_BL | NTA_nM_BL | 1.07 | 2.17 | 1.20 | 1.81 | 0.23 | 0.09 |
| PIC_nM_1st | PIC_nM_1st | 1.06 | 1.65 | 0.93 | 1.77 | 0.35 | 0.08 |
| PIC_nM_3rd | PIC_nM_3rd | 1.09 | 2.07 | 1.14 | 1.82 | 0.26 | 0.09 |
| PIC_nM_BL | PIC_nM_BL | 1.04 | 2.00 | 1.12 | 1.79 | 0.27 | 0.09 |
| QUIN_KYNA_ratio_1st | QUIN_KYNA_ratio_1st | 1.08 | 1.81 | 1.05 | 1.72 | 0.30 | 0.12 |
| QUIN_KYNA_ratio_3rd | QUIN_KYNA_ratio_3rd | 1.04 | 2.08 | 1.17 | 1.79 | 0.25 | 0.10 |
| QUIN_KYNA_ratio_BL | QUIN_KYNA_ratio_BL | 1.13 | 2.14 | 1.19 | 1.81 | 0.24 | 0.09 |
| Quin_nM_1st | Quin_nM_1st | 1.32 | 1.22 | 0.67 | 1.83 | 0.51 | 0.09 |
| Quin_nM_3rd | Quin_nM_3rd | 1.32 | 1.41 | 0.74 | 1.91 | 0.46 | 0.10 |
| Quin_nM_BL | Quin_nM_BL | 1.22 | 1.37 | 0.74 | 1.86 | 0.46 | 0.10 |
| QUIN_PIC_ratio_1st | QUIN_PIC_ratio_1st | 1.08 | 1.81 | 1.05 | 1.72 | 0.30 | 0.11 |
| QUIN_PIC_ratio_3rd | QUIN_PIC_ratio_3rd | 1.15 | 2.16 | 1.20 | 1.80 | 0.23 | 0.10 |
| QUIN_PIC_ratio_BL | QUIN_PIC_ratio_BL | 1.13 | 2.18 | 1.22 | 1.79 | 0.23 | 0.10 |
| SAA_ng_mL_1st | SAA_ng_mL_1st | 1.27 | 1.44 | 0.80 | 1.79 | 0.42 | 0.08 |
| SAA_ng_mL_3rd | SAA_ng_mL_3rd | 1.50 | 1.78 | 0.98 | 1.83 | 0.33 | 0.09 |
| SAA_ng_mL_BL | SAA_ng_mL_BL | 1.27 | 1.79 | 0.97 | 1.83 | 0.33 | 0.09 |
| three_HK_nM_1st | three_HK_nM_1st | 1.09 | 1.52 | 0.87 | 1.75 | 0.39 | 0.08 |
| three_HK_nM_3rd | three_HK_nM_3rd | 1.09 | 2.13 | 1.17 | 1.81 | 0.24 | 0.09 |
| three_HK_nM_BL | three_HK_nM_BL | 1.08 | 1.99 | 1.11 | 1.79 | 0.27 | 0.09 |
| threeHK_KYN_ratio_1st | threeHK_KYN_ratio_1st | 1.05 | 1.56 | 0.90 | 1.73 | 0.37 | 0.09 |
| threeHK_KYN_ratio_3rd | threeHK_KYN_ratio_3rd | 1.02 | 2.04 | 1.15 | 1.78 | 0.26 | 0.10 |
| threeHK_KYN_ratio_BL | threeHK_KYN_ratio_BL | 1.07 | 2.20 | 1.22 | 1.80 | 0.23 | 0.10 |
| threeHK_KYNA_ratio_1st | threeHK_KYNA_ratio_1st | 1.08 | 1.85 | 1.05 | 1.76 | 0.30 | 0.10 |
| threeHK_KYNA_ratio_3rd | threeHK_KYNA_ratio_3rd | 1.07 | 1.85 | 1.02 | 1.81 | 0.31 | 0.09 |
| threeHK_KYNA_ratio_BL | threeHK_KYNA_ratio_BL | 1.14 | 1.52 | 0.82 | 1.85 | 0.42 | 0.10 |
| TNFa_pg_mL_1st | TNFa_pg_mL_1st | 1.15 | 1.58 | 0.89 | 1.76 | 0.38 | 0.08 |
| TNFa_pg_mL_3rd | TNFa_pg_mL_3rd | 1.11 | 2.00 | 1.10 | 1.82 | 0.27 | 0.09 |
| TNFa_pg_mL_BL | TNFa_pg_mL_BL | 1.14 | 2.09 | 1.14 | 1.84 | 0.26 | 0.09 |
| TRP_nM_1st | TRP_nM_1st | 1.10 | 2.31 | 1.31 | 1.77 | 0.20 | 0.12 |
| TRP_nM_3rd | TRP_nM_3rd | 1.27 | 4.16 | 2.23 | 1.86 | 0.03 | 0.19 |
| TRP_nM_BL | TRP_nM_BL | 1.10 | 3.04 | 1.70 | 1.79 | 0.09 | 0.15 |
| VCAM_1_ng_mL_1st | VCAM_1_ng_mL_1st | 1.09 | 1.66 | 0.94 | 1.76 | 0.35 | 0.09 |
| VCAM_1_ng_mL_3rd | VCAM_1_ng_mL_3rd | 1.09 | 2.11 | 1.15 | 1.83 | 0.25 | 0.09 |
| VCAM_1_ng_mL_BL | VCAM_1_ng_mL_BL | 1.10 | 1.87 | 1.02 | 1.83 | 0.31 | 0.09 |
Only TRP_3rd was significant. TRP_BL and KYN_3rd trended to significance.
Visual inspection of biomarker residuals, using models tabulated above
Note: In this html document, multiple QQ plots were excluded for simplicity. I can provide separate file. Also race was left out of the model since there were only 1 asian and 2 blacks
Consolidating transformed variables into final dataframe
Leverage plots (residuals by cooks & hat values)
for (x in final_bx_names) {
LM1 <- lm(substitute(MADRS_Score_3rd~sex+age+BMI+MADRS_Score_BL+i, list(i=as.name(x))), data=final_df)
car::influencePlot(LM1, id.method="identify", main=(x), sub= "Influence plot - note: circle size is proportional to Cook’s distance")
}
Univariate analysis by treatment remission
final_df$Remission_new<-ifelse(final_df$MADRS_Score_3rd<9, "Remitter", "Non-Remitter")
createTable(compareGroups(Remission_new ~ ., data = final_df, method = NA), hide.no = '0', show.p.mul = T)##
## --------Summary descriptives table by 'Remission_new'---------
##
## ___________________________________________________________________________
## Non-Remitter Remitter p.overall
## N=31 N=41
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## AA_nM_3rd_inverse 0.19 [0.16;0.23] 0.21 [0.14;0.26] 0.932
## AA_nM_BL 5.25 [4.23;6.44] 5.47 [4.39;7.28] 0.196
## BSS_Score_BL 8.50 [2.50;13.0] 4.50 [0.75;11.2] 0.147
## CRP_ng_mL_BL 1675 [433;7521] 931 [503;2197] 0.114
## CRP_ng_mL_BL_inverse 0.00 [0.00;0.00] 0.00 [0.00;0.00] 0.114
## five_HT_nM_BL 144 [47.7;415] 69.9 [28.7;190] 0.097
## ICAM_1_ng_mL_BL 319 [272;398] 282 [254;321] 0.036
## IFNy_pg_mL_BL 4.95 [4.02;7.46] 5.38 [4.19;7.53] 0.678
## IL10_pg_mL_BL 0.34 [0.29;0.42] 0.33 [0.26;0.45] 0.618
## IL10_pg_mL_BL_sqrt 0.59 [0.54;0.65] 0.57 [0.51;0.67] 0.618
## IL12p70_pg_mL_BL 0.41 [0.34;0.55] 0.35 [0.26;0.46] 0.058
## IL12p70_pg_mL_LLOD_BL 0.41 [0.34;0.55] 0.35 [0.26;0.46] 0.058
## IL13_pg_mL_BL 3.48 [3.16;4.02] 3.70 [3.19;4.21] 0.529
## IL13_pg_mL_LLOD_BL 3.48 [3.16;4.02] 3.66 [3.13;4.20] 0.661
## IL1B_pg_mL_1st_log -1.80 (0.87) -1.82 (1.18) 0.975
## IL1B_pg_mL_BL 0.10 [0.09;0.18] 0.12 [0.08;0.25] 0.955
## IL1B_pg_mL_BL_log -2.18 (1.90) -2.17 (1.09) 0.996
## IL1B_pg_mL_LLOD_BL: 0.677
## 0.323239972 29 (96.7%) 39 (97.5%)
## 0.412595117 0 (0.00%) 1 (2.50%)
## 1.605879646 1 (3.33%) 0 (0.00%)
## IL2_pg_mL_1st_log -0.94 [-1.40;-0.67] -0.99 [-1.30;-0.70] 0.773
## IL2_pg_mL_BL 0.49 [0.29;0.57] 0.38 [0.30;0.72] 0.785
## IL2_pg_mL_LLOD_BL 0.27 [0.10;0.51] 0.26 [0.10;0.50] 0.971
## IL4_pg_mL_BL 0.09 [0.07;0.10] 0.08 [0.07;0.10] 0.610
## IL4_pg_mL_LLOD_BL 0.09 [0.07;0.10] 0.08 [0.07;0.10] 0.610
## IL6_pg_mL_BL 0.93 [0.63;1.37] 0.82 [0.71;1.09] 0.887
## IL8_pg_mL_BL 3.95 [2.99;4.95] 4.67 [4.07;5.99] 0.040
## KYN_nM_BL 1013 (226) 956 (258) 0.327
## KYN_SER_ratio_1st_inverse 0.11 [0.04;0.42] 0.07 [0.03;0.18] 0.254
## KYN_SER_ratio_3rd_log 2.14 (1.76) 2.74 (1.47) 0.128
## KYN_SER_ratio_BL 8.40 [2.15;24.5] 15.3 [4.56;30.3] 0.231
## KYN_TRP_ratio_BL 0.04 [0.03;0.04] 0.04 [0.03;0.04] 0.924
## KYNA_nM_BL 19.6 [14.4;24.0] 19.3 [15.1;24.3] 0.849
## MADRS_Score_BL 29.0 (5.70) 26.9 (5.69) 0.117
## NIC_nM_1st_inverse 2.39 [1.59;2.82] 1.52 [0.54;2.68] 0.116
## NIC_nM_BL 0.55 [0.31;0.79] 0.53 [0.33;1.14] 0.722
## NIC_nM_LLOD_BL 0.55 [0.31;0.79] 0.53 [0.33;1.14] 0.722
## NTA_nM_BL 300 [215;389] 298 [208;480] 0.731
## PIC_nM_1st_inverse 0.06 [0.05;0.08] 0.06 [0.04;0.08] 0.880
## PIC_nM_BL 17.9 [13.1;23.8] 16.9 [12.2;24.5] 0.722
## QUIN_KYNA_ratio_BL 7.29 [5.48;8.98] 7.43 [5.76;9.52] 0.868
## Quin_nM_BL 142 [109;174] 137 [115;174] 0.962
## QUIN_PIC_ratio_BL 7.56 [6.15;10.3] 7.62 [5.66;11.6] 0.934
## SAA_ng_mL_BL 2965 [1789;4670] 1739 [1158;3052] 0.055
## three_HK_nM_BL 15.4 [13.7;19.2] 14.4 [12.5;19.9] 0.469
## threeHK_KYN_ratio_BL 0.02 [0.01;0.02] 0.02 [0.01;0.02] 0.427
## threeHK_KYNA_ratio_BL 0.80 [0.62;1.00] 0.77 [0.64;1.14] 0.962
## TNFa_pg_mL_BL 1.33 [1.16;1.62] 1.40 [1.23;1.66] 0.740
## TRP_nM_BL 28829 (5861) 26846 (5658) 0.160
## VCAM_1_ng_mL_BL 315 (66.2) 313 (63.3) 0.891
## sex: 0.951
## male 11 (35.5%) 16 (39.0%)
## female 20 (64.5%) 25 (61.0%)
## age 45.0 [28.0;55.0] 49.0 [40.0;55.0] 0.372
## BMI 29.1 (6.38) 28.5 (4.99) 0.671
## MADRS_Score_3rd 15.0 [11.0;20.5] 4.00 [2.00;6.00] <0.001
## MADRS_Score_1st 17.6 (7.66) 11.8 (6.18) 0.001
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯significant variables: IL8_BL, ICAM1_BL, IL12_BL, 5HT_BL
Pearson correlation matrix of biomarkers and psychometrics at baseline
bx_BL_KP<-final_df %>% dplyr::select(all_of(final_bx_names)) %>% select(contains("BL"), -contains("ratio"), -contains("LLOD")) %>% na.omit() %>% stats::cor()
p.mat<-ggcorrplot::cor_pmat(bx_BL_KP)
ggcorrplot::ggcorrplot(bx_BL_KP,
hc.order = TRUE,
type = "lower",
outline.color = "black",
lab = TRUE,
lab_size = 3,
p.mat = p.mat, sig.level=0.05, insig="blank",
digits=1,
tl.srt=50,
tl.cex=10,
title="Correlation matrix: Bio-K biomarkers & symptoms (baseline, excluding LLOD and ratios)")
Model 1: post-treatment MADRS by baseline TRP, adjusted by demo & interactions
reg_cohend <- function (glm_output) {
beta <- summary(glm_output)$coefficients[,1]
SD <- sqrt(dim(glm_output$model)[1])*summary(glm_output)$coefficients[,2]
cohend <- round(beta/SD,3)
return(cohend)
}
Model_1<-lm(MADRS_Score_3rd~sex+age+BMI*TRP_nM_BL+MADRS_Score_BL+TRP_nM_BL, data=final_df)
summary(Model_1)##
## Call:
## lm(formula = MADRS_Score_3rd ~ sex + age + BMI * TRP_nM_BL +
## MADRS_Score_BL + TRP_nM_BL, data = final_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.635 -5.406 -0.262 5.061 14.403
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.384e+01 2.297e+01 -2.780 0.00717 **
## sexfemale 2.286e+00 1.765e+00 1.295 0.20003
## age 6.981e-02 6.605e-02 1.057 0.29453
## BMI 1.742e+00 7.456e-01 2.337 0.02263 *
## TRP_nM_BL 2.125e-03 7.896e-04 2.691 0.00910 **
## MADRS_Score_BL 3.056e-01 1.425e-01 2.144 0.03589 *
## BMI:TRP_nM_BL -6.247e-05 2.703e-05 -2.311 0.02412 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.621 on 63 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.2182, Adjusted R-squared: 0.1438
## F-statistic: 2.931 on 6 and 63 DF, p-value: 0.01382reg_cohend(Model_1)## (Intercept) sexfemale age BMI TRP_nM_BL MADRS_Score_BL BMI:TRP_nM_BL
## -0.332 0.155 0.126 0.279 0.322 0.256 -0.276plot(Model_1, which=c(2,5))
Model 2: post-treatment MADRS by baseline 5HT, adjusted by demo & interactions
Model_1<-lm(MADRS_Score_3rd~sex+age*five_HT_nM_BL+BMI+MADRS_Score_BL+five_HT_nM_BL, data=final_df)
summary(Model_1)##
## Call:
## lm(formula = MADRS_Score_3rd ~ sex + age * five_HT_nM_BL + BMI +
## MADRS_Score_BL + five_HT_nM_BL, data = final_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.648 -4.521 -1.997 3.678 17.344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.227e+01 7.001e+00 -1.752 0.08459 .
## sexfemale 1.316e+00 1.695e+00 0.777 0.44034
## age 1.485e-01 8.060e-02 1.843 0.07006 .
## five_HT_nM_BL 2.184e-02 8.203e-03 2.662 0.00984 **
## BMI 1.072e-01 1.501e-01 0.714 0.47787
## MADRS_Score_BL 3.359e-01 1.437e-01 2.338 0.02259 *
## age:five_HT_nM_BL -4.243e-04 2.136e-04 -1.987 0.05133 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.654 on 63 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.2103, Adjusted R-squared: 0.1351
## F-statistic: 2.796 on 6 and 63 DF, p-value: 0.01782reg_cohend(Model_1)## (Intercept) sexfemale age five_HT_nM_BL BMI MADRS_Score_BL age:five_HT_nM_BL
## -0.209 0.093 0.220 0.318 0.085 0.279 -0.237plot(Model_1, which=c(2,5))
Model 3: post-treatment MADRS by baseline IL12, adjusted by demo & interactions
Model_1<-lm(MADRS_Score_3rd~sex+age+BMI*IL12p70_pg_mL_BL+MADRS_Score_BL+IL12p70_pg_mL_BL, data=final_df)
summary(Model_1)##
## Call:
## lm(formula = MADRS_Score_3rd ~ sex + age + BMI * IL12p70_pg_mL_BL +
## MADRS_Score_BL + IL12p70_pg_mL_BL, data = final_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.957 -5.119 -1.438 3.786 16.292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -21.41364 8.80718 -2.431 0.01790 *
## sexfemale 1.91541 1.71538 1.117 0.26840
## age 0.06183 0.06777 0.912 0.36512
## BMI 0.46758 0.21709 2.154 0.03508 *
## IL12p70_pg_mL_BL 31.85231 10.77564 2.956 0.00438 **
## MADRS_Score_BL 0.35919 0.14520 2.474 0.01608 *
## BMI:IL12p70_pg_mL_BL -0.90483 0.30613 -2.956 0.00438 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.693 on 63 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.2012, Adjusted R-squared: 0.1251
## F-statistic: 2.644 on 6 and 63 DF, p-value: 0.02373reg_cohend(Model_1)## (Intercept) sexfemale age BMI IL12p70_pg_mL_BL MADRS_Score_BL BMI:IL12p70_pg_mL_BL
## -0.291 0.133 0.109 0.257 0.353 0.296 -0.353plot(Model_1, which=c(2,5))
