IM

IM

Factor	Detalis	Total
N=		211
Survival	μ ±DS	23.67 ±22.6
	M (min:max)	17 (0:114)
Status	0	81 (38.4%)
	1	123 (58.3%)
	2	7 (3.3%)
Treatment	Curative	83 (39.7%)
	Transplant	8 (3.8%)
	Loco-regional	46 (22.0%)
	Systemic	39 (18.7%)
	No treatment	33 (15.8%)
Diagnosis	NASH	55 (26.1%)
	ASH	79 (37.4%)
	VIRAL	77 (36.5%)
μ ±DS = Mean (standard deviation); M (min:max) = Median (min:max);

0.1 Treatment x DG

Factor	Detalis	Total	NASH	ASH	VIRAL	Statistics
Diagnosis		211	55 (26.1%)	79 (37.4%)	77 (36.5%)
Survival	μ ±DS	23.67 ±22.6	21.93 ±23.9	23.50 ±23.6	25.20 ±20.7	Kruskal-Wallis: p=0.368
	M (min:max)	17 (0:114)	14 (0:110)	15 (0:114)	20.5 (0:93)
Status	0	81 (38.4%)	23 (41.8%)	32 (40.5%)	26 (33.8%)	V=0.08 (p=0.661)
	1	123 (58.3%)	29 (52.7%)	45 (57.0%)	49 (63.6%)
	2	7 (3.3%)	3 (5.5%)	2 (2.5%)	2 (2.6%)
Treatment	Curative	83 (39.7%)	25 (45.5%)	27 (34.6%)	31 (40.8%)	V=0.14 (p=0.439)
	Transplant	8 (3.8%)	0	3 (3.8%)	5 (6.6%)
	Loco-regional	46 (22.0%)	12 (21.8%)	16 (20.5%)	18 (23.7%)
	Systemic	39 (18.7%)	12 (21.8%)	16 (20.5%)	11 (14.5%)
	No treatment	33 (15.8%)	6 (10.9%)	16 (20.5%)	11 (14.5%)
μ ±DS = Mean (standard deviation); M (min:max) = Median (min:max); MW = Mann-WhitneyTest; OR/RR = odds-ratio / risc-ratio [95% CI] and p value from Fisher test); V = Cramer V (p value from Chi² test);

Call: survfit(formula = as.formula("Surv(time = `Survival`, event = `Dead`) ~ " %+% 
    by))

                Treatment=Curative 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     65       4    0.944  0.0270        0.893        0.999
   36     26      20    0.608  0.0633        0.496        0.746
   60     10       7    0.388  0.0794        0.260        0.580

                Treatment=Transplant 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      7       2     0.75   0.153        0.503            1
   36      2       2     0.45   0.188        0.198            1
   60      2       0     0.45   0.188        0.198            1

                Treatment=Loco-regional 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     38       2   0.9540  0.0318       0.8937        1.000
   36     10      23   0.3158  0.0781       0.1945        0.513
   60      3       7   0.0947  0.0514       0.0327        0.274

                Treatment=Systemic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     12      20    0.417  0.0873       0.2766        0.628
   36      5       2    0.334  0.0875       0.1994        0.558
   60      2       3    0.133  0.0810       0.0406        0.439

                Treatment=No treatment 
        time       n.risk      n.event     survival      std.err 
     12.0000       3.0000      21.0000       0.1698       0.0767 
lower 95% CI upper 95% CI 
      0.0701       0.4114 

	records	n.max	n.start	events	*rmean	*se(rmean)	median	0.95LCL	0.95UCL
Treatment=Curative	80	80	80	35	48.99	3.78	50	29	82
Treatment=Transplant	8	8	8	4	46.38	12.54	23	23	NA
Treatment=Loco-regional	45	45	45	33	33.37	3.21	29	24	38
Treatment=Systemic	38	38	38	26	23.10	4.46	10	7	52
Treatment=No treatment	28	28	28	24	6.79	1.74	3	2	8

    Pairwise comparisons using Log-Rank test 

data:  db and Treatment 

              Curative             Transplant Loco-regional 
Transplant    0.943                -          -             
Loco-regional 0.007                0.395      -             
Systemic      0.000001150703       0.069      0.036         
No treatment  < 0.0000000000000002 0.001      0.000000000003
              Systemic      
Transplant    -             
Loco-regional -             
Systemic      -             
No treatment  0.000818611500

P value adjustment method: BH 

    Pairwise comparisons using Log-Rank test 

data:  db2 and Treatment 

              Curative             Loco-regional  Systemic      
Loco-regional 0.005                -              -             
Systemic      0.000000690422       0.025          -             
No treatment  < 0.0000000000000002 0.000000000002 0.000491166900

P value adjustment method: BH 

0.1.1 DG == “ASH”

Call: survfit(formula = as.formula("Surv(time = `Survival`, event = `Dead`) ~ " %+% 
    by))

                Treatment=Curative 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     21       2    0.913  0.0588        0.805        1.000
   36      6       7    0.523  0.1176        0.336        0.812
   60      3       1    0.418  0.1327        0.225        0.779

                Treatment=Transplant 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       0        1       0            1            1
   36      2       0        1       0            1            1
   60      2       0        1       0            1            1

                Treatment=Loco-regional 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     13       0    1.000   0.000       1.0000        1.000
   36      4       7    0.417   0.142       0.2133        0.814
   60      1       3    0.104   0.097       0.0168        0.646

                Treatment=Systemic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       7    0.521   0.132       0.3166        0.857
   36      5       1    0.434   0.136       0.2351        0.801
   60      2       3    0.174   0.110       0.0504        0.598

                Treatment=No treatment 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

	records	n.max	n.start	events	*rmean	*se(rmean)	median	0.95LCL	0.95UCL
Treatment=Curative	27	27	27	11	46.09	6.539	39.0	28	NA
Treatment=Transplant	3	3	3	0	76.00	0.000	NA	NA	NA
Treatment=Loco-regional	16	16	16	10	38.48	4.823	33.0	27	NA
Treatment=Systemic	16	16	16	12	28.49	6.742	13.0	7	NA
Treatment=No treatment	13	13	13	11	3.33	0.584	2.5	2	NA

    Pairwise comparisons using Log-Rank test 

data:  db and Treatment 

              Curative      Transplant Loco-regional Systemic     
Transplant    0.20          -          -             -            
Loco-regional 0.53          0.07       -             -            
Systemic      0.05          0.04       0.48          -            
No treatment  0.00000000005 0.01       0.00000011027 0.00008204834

P value adjustment method: BH 

    Pairwise comparisons using Log-Rank test 

data:  db2 and Treatment 

              Curative      Loco-regional Systemic     
Loco-regional 0.53          -             -            
Systemic      0.04          0.52          -            
No treatment  0.00000000003 0.00000006616 0.00004922900

P value adjustment method: BH 

0.1.2 DG == “NASH”

Call: survfit(formula = as.formula("Surv(time = `Survival`, event = `Dead`) ~ " %+% 
    by))

                Treatment=Curative 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     18       1    0.955  0.0444        0.871        1.000
   36      9       6    0.607  0.1174        0.415        0.887
   60      4       4    0.308  0.1227        0.141        0.673

                Treatment=Loco-regional 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       2    0.818   0.116       0.6192        1.000
   36      1       5    0.184   0.153       0.0359        0.944

                Treatment=Systemic 
        time       n.risk      n.event     survival      std.err 
      12.000        1.000        6.000        0.333        0.175 
lower 95% CI upper 95% CI 
       0.119        0.932 

                Treatment=No treatment 
        time       n.risk      n.event     survival      std.err 
      12.000        2.000        2.000        0.625        0.213 
lower 95% CI upper 95% CI 
       0.320        1.000 

	records	n.max	n.start	events	*rmean	*se(rmean)	median	0.95LCL	0.95UCL
Treatment=Curative	25	25	25	11	29.7	2.32	50.0	23	NA
Treatment=Loco-regional	11	11	11	8	21.2	3.54	17.0	14	NA
Treatment=Systemic	12	12	12	6	17.3	5.04	10.5	8	NA
Treatment=No treatment	6	6	6	4	12.8	4.09	16.0	3	NA

    Pairwise comparisons using Log-Rank test 

data:  db and Treatment 

              Curative Loco-regional Systemic
Loco-regional 0.02     -             -       
Systemic      0.01     0.35          -       
No treatment  0.01     0.35          0.88    

P value adjustment method: BH 

    Pairwise comparisons using Log-Rank test 

data:  db2 and Treatment 

              Curative Loco-regional Systemic
Loco-regional 0.02     -             -       
Systemic      0.01     0.35          -       
No treatment  0.01     0.35          0.88    

P value adjustment method: BH 

0.1.3 DG == “VIRAL”

Call: survfit(formula = as.formula("Surv(time = `Survival`, event = `Dead`) ~ " %+% 
    by))

                Treatment=Curative 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     21       2    0.913  0.0588        0.805        1.000
   36      6       7    0.523  0.1176        0.336        0.812
   60      3       1    0.418  0.1327        0.225        0.779

                Treatment=Transplant 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       0        1       0            1            1
   36      2       0        1       0            1            1
   60      2       0        1       0            1            1

                Treatment=Loco-regional 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     13       0    1.000   0.000       1.0000        1.000
   36      4       7    0.417   0.142       0.2133        0.814
   60      1       3    0.104   0.097       0.0168        0.646

                Treatment=Systemic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       7    0.521   0.132       0.3166        0.857
   36      5       1    0.434   0.136       0.2351        0.801
   60      2       3    0.174   0.110       0.0504        0.598

                Treatment=No treatment 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

	records	n.max	n.start	events	*rmean	*se(rmean)	median	0.95LCL	0.95UCL
Treatment=Curative	27	27	27	11	46.09	6.539	39.0	28	NA
Treatment=Transplant	3	3	3	0	76.00	0.000	NA	NA	NA
Treatment=Loco-regional	16	16	16	10	38.48	4.823	33.0	27	NA
Treatment=Systemic	16	16	16	12	28.49	6.742	13.0	7	NA
Treatment=No treatment	13	13	13	11	3.33	0.584	2.5	2	NA

    Pairwise comparisons using Log-Rank test 

data:  db and Treatment 

              Curative      Transplant Loco-regional Systemic     
Transplant    0.20          -          -             -            
Loco-regional 0.53          0.07       -             -            
Systemic      0.05          0.04       0.48          -            
No treatment  0.00000000005 0.01       0.00000011027 0.00008204834

P value adjustment method: BH 

    Pairwise comparisons using Log-Rank test 

data:  db2 and Treatment 

              Curative      Loco-regional Systemic     
Loco-regional 0.53          -             -            
Systemic      0.04          0.52          -            
No treatment  0.00000000003 0.00000006616 0.00004922900

P value adjustment method: BH 

0.2 DG x Treatment

Factor	Detalis	Total	Curative	Transplant	Loco-regional	Systemic	No treatment	Statistics
Treatment		209	83 (39.7%)	8 (3.8%)	46 (22.0%)	39 (18.7%)	33 (15.8%)
Survival	μ ±DS	23.67 ±22.6	31.23 ±24.5	33.12 ±28.9	27.98 ±19.2	14.68 ±17.8	5.46 ±7.35	Kruskal-Wallis: p<0.001
	M (min:max)	17 (0:114)	24.5 (1:114)	23 (5:83)	24 (4:93)	8 (1:66)	2.5 (0:32)
Status	0	80 (38.3%)	44 (53.0%)	4 (50.0%)	11 (23.9%)	13 (33.3%)	8 (24.2%)	V=0.21 (p=0.018)
	1	122 (58.4%)	35 (42.2%)	4 (50.0%)	33 (71.7%)	26 (66.7%)	24 (72.7%)
	2	7 (3.3%)	4 (4.8%)	0	2 (4.3%)	0	1 (3.0%)
Diagnosis	NASH	55 (26.3%)	25 (30.1%)	0	12 (26.1%)	12 (30.8%)	6 (18.2%)	V=0.14 (p=0.439)
	ASH	78 (37.3%)	27 (32.5%)	3 (37.5%)	16 (34.8%)	16 (41.0%)	16 (48.5%)
	VIRAL	76 (36.4%)	31 (37.3%)	5 (62.5%)	18 (39.1%)	11 (28.2%)	11 (33.3%)
μ ±DS = Mean (standard deviation); M (min:max) = Median (min:max); MW = Mann-WhitneyTest; OR/RR = odds-ratio / risc-ratio [95% CI] and p value from Fisher test); V = Cramer V (p value from Chi² test);

Call: survfit(formula = as.formula("Surv(time = `Survival`, event = `Dead`) ~ " %+% 
    by))

                DG=NASH 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     29      11    0.772  0.0609       0.6617        0.901
   36     10      13    0.388  0.0826       0.2560        0.589
   60      4       5    0.175  0.0744       0.0759        0.403

                DG=ASH 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     45      20    0.708  0.0555        0.607        0.825
   36     17      15    0.412  0.0674        0.299        0.568
   60      8       7    0.224  0.0643        0.128        0.393

                DG=VIRAL 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     51      18    0.741  0.0526        0.644        0.851
   36     16      22    0.367  0.0632        0.262        0.514
   60      5       5    0.205  0.0664        0.109        0.387

	records	n.max	n.start	events	*rmean	*se(rmean)	median	0.95LCL	0.95UCL
DG=NASH	54	54	54	29	38.7	6.43	23	17	50
DG=ASH	75	75	75	44	41.0	5.32	28	24	40
DG=VIRAL	70	70	70	49	33.9	3.92	23	19	46

    Pairwise comparisons using Log-Rank test 

data:  db and DG 

      NASH ASH
ASH   0.9  -  
VIRAL 0.9  0.9

P value adjustment method: BH 

[1] " Exclude: Transplant >>> "

    Pairwise comparisons using Log-Rank test 

data:  db2 and DG 

      NASH ASH
ASH   1    -  
VIRAL 1    1  

P value adjustment method: BH 

0.2.1 Treatment == “Curative”

Call: survfit(formula = as.formula("Surv(time = `Survival`, event = `Dead`) ~ " %+% 
    by))

                DG=NASH 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     18       1    0.955  0.0444        0.871        1.000
   36      9       6    0.607  0.1174        0.415        0.887
   60      4       4    0.308  0.1227        0.141        0.673

                DG=ASH 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     21       2    0.913  0.0588        0.805        1.000
   36      6       7    0.523  0.1176        0.336        0.812
   60      3       1    0.418  0.1327        0.225        0.779

                DG=VIRAL 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     26       1    0.963  0.0363        0.894        1.000
   36     11       7    0.680  0.0941        0.518        0.891
   60      3       2    0.496  0.1314        0.295        0.833

	records	n.max	n.start	events	*rmean	*se(rmean)	median	0.95LCL	0.95UCL
DG=NASH	25	25	25	11	56.3	9.72	50	23	NA
DG=ASH	27	27	27	11	60.3	10.95	39	28	NA
DG=VIRAL	28	28	28	13	50.4	5.50	53	48	NA

    Pairwise comparisons using Log-Rank test 

data:  db and DG 

      NASH ASH
ASH   1    -  
VIRAL 1    1  

P value adjustment method: BH 

0.2.2 Treatment == “Transplant”

Call: survfit(formula = as.formula("Surv(time = `Survival`, event = `Dead`) ~ " %+% 
    by))

                DG=ASH 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       0        1       0            1            1
   36      2       0        1       0            1            1
   60      2       0        1       0            1            1

                DG=VIRAL 
        time       n.risk      n.event     survival      std.err 
      12.000        4.000        2.000        0.600        0.219 
lower 95% CI upper 95% CI 
       0.293        1.000 

	records	n.max	n.start	events	*rmean	*se(rmean)	median	0.95LCL	0.95UCL
DG=ASH	3	3	3	0	53.0	0.00	NA	NA	NA
DG=VIRAL	5	5	5	4	17.2	3.33	23	12	NA

    Pairwise comparisons using Log-Rank test 

data:  db and DG 

      ASH 
VIRAL 0.03

P value adjustment method: BH 

0.2.3 Treatment == “Loco-regional”

Call: survfit(formula = as.formula("Surv(time = `Survival`, event = `Dead`) ~ " %+% 
    by))

                DG=NASH 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       2    0.818   0.116       0.6192        1.000
   36      1       5    0.184   0.153       0.0359        0.944

                DG=ASH 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     13       0    1.000   0.000       1.0000        1.000
   36      4       7    0.417   0.142       0.2133        0.814
   60      1       3    0.104   0.097       0.0168        0.646

                DG=VIRAL 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     17       0    1.000  0.0000       1.0000        1.000
   36      5      11    0.317  0.1167       0.1539        0.652
   60      2       3    0.127  0.0836       0.0347        0.462

	records	n.max	n.start	events	*rmean	*se(rmean)	median	0.95LCL	0.95UCL
DG=NASH	11	11	11	8	22.2	4.08	17	14	NA
DG=ASH	16	16	16	10	38.5	4.82	33	27	NA
DG=VIRAL	18	18	18	15	34.4	4.73	29	22	59

    Pairwise comparisons using Log-Rank test 

data:  db and DG 

      NASH ASH 
ASH   0.05 -   
VIRAL 0.11 0.57

P value adjustment method: BH 

0.2.4 Treatment == “Systemic”

Call: survfit(formula = as.formula("Surv(time = `Survival`, event = `Dead`) ~ " %+% 
    by))

                DG=NASH 
        time       n.risk      n.event     survival      std.err 
      12.000        1.000        6.000        0.333        0.175 
lower 95% CI upper 95% CI 
       0.119        0.932 

                DG=ASH 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       7    0.521   0.132       0.3166        0.857
   36      5       1    0.434   0.136       0.2351        0.801
   60      2       3    0.174   0.110       0.0504        0.598

                DG=VIRAL 
        time       n.risk      n.event     survival      std.err 
      12.000        3.000        7.000        0.300        0.145 
lower 95% CI upper 95% CI 
       0.116        0.773 

	records	n.max	n.start	events	*rmean	*se(rmean)	median	0.95LCL	0.95UCL
DG=NASH	12	12	12	6	16.3	4.53	10.5	8	NA
DG=ASH	16	16	16	12	19.1	3.57	13.0	7	NA
DG=VIRAL	10	10	10	8	10.7	3.85	4.5	3	NA

    Pairwise comparisons using Log-Rank test 

data:  db and DG 

      NASH ASH
ASH   0.6  -  
VIRAL 0.4  0.3

P value adjustment method: BH 

1 References

1. R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.