Estimate restricted mean times in favor of treatment
rmtfit.RdEstimate and make inference on the overall and component-wise restricted mean times in favor of treatment.
Usage
rmtfit(...)
# Default S3 method
rmtfit(id, time, status, trt, type = "multistate", ...)
# S3 method for class 'formula'
rmtfit(formula, data, ...)Arguments
- ...
Further arguments.
- id
A vector of id variable.
- time
A vector of follow-up times.
- status
For
type="multistate", k = entering into state \(k\) (\(K+1\) represents death) and 0 = censoring; Fortype="recurrent", 1 = recurrent event, 2 = death, and 0 = censoring;- trt
A vector of binary variable for treatment group.
- type
"multistate"= multistate data;"recurrent"= recurrent event data.- formula
A formula object. For multistate data, use
ms(id,time,status)~trt; for recurrent event data, userec(id,time,status)~trt.- data
A data frame, which contains the variables names in the formula.
Value
An object of class rmtfit. See rmtfit.object for details.
Examples
#######################
# Multistate outcome #
#######################
# load the colon cancer trial data
library(rmt)
head(colon_lev)
#> id time status rx sex age
#> 1 1 2.6502396 1 Lev+5FU 1 43
#> 2 1 4.1642710 2 Lev+5FU 1 43
#> 3 2 8.4517454 0 Lev+5FU 1 63
#> 4 3 1.4839151 1 Control 0 71
#> 5 3 2.6365503 2 Control 0 71
#> 6 4 0.6707734 1 Lev+5FU 0 66
# fit the data
obj=rmtfit(ms(id,time,status)~rx,data=colon_lev)
# print the event numbers by group
obj
#> Call:
#> rmtfit.formula(formula = ms(id, time, status) ~ rx, data = colon_lev)
#>
#> N State 1 Death Med follow-up time
#> Control 315 177 168 5.081451
#> Lev+5FU 304 119 123 5.749487
# summarize the inference results for tau=7.5 years
summary(obj,tau=7.5)
#> Call:
#> rmtfit.formula(formula = ms(id, time, status) ~ rx, data = colon_lev)
#>
#> Restricted mean winning time by tau = 7.5:
#> State 1 Survival Overall
#> Control 0.2659633 1.130967 1.396930
#> Lev+5FU 0.6120146 1.750924 2.362938
#>
#> Restricted mean time in favor of group "Lev+5FU" by time tau = 7.5:
#> Estimate Std.Err Z value Pr(>|z|)
#> State 1 0.346051 0.072066 4.8018 1.572e-06 ***
#> Survival 0.619957 0.213610 2.9023 0.003704 **
#> Overall 0.966008 0.252585 3.8245 0.000131 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
############################
# Recurrent event outcome #
############################
# load the HF-ACTION trial data
library(rmt)
head(hfaction)
#> patid time status trt_ab age60
#> 1 HFACT00001 0.60506502 1 0 1
#> 2 HFACT00001 1.04859685 0 0 1
#> 3 HFACT00002 0.06297057 1 0 1
#> 4 HFACT00002 0.35865845 1 0 1
#> 5 HFACT00002 0.39698836 1 0 1
#> 6 HFACT00002 3.83299110 0 0 1
# fit the data
obj=rmtfit(rec(patid,time,status)~trt_ab,data=hfaction)
# print the event numbers by group
obj
#> Call:
#> rmtfit.formula(formula = rec(patid, time, status) ~ trt_ab, data = hfaction)
#>
#> N Event 1 Event 2 Event 3 Event 4 Event 5 Event 6 Event 7 Event 8 Event 9
#> 0 221 170 117 86 56 33 23 15 13 13
#> 1 205 145 89 55 43 32 21 15 11 7
#> Event 10 Event 11 Event 12 Event 13 Event 14 Event 15 Event 16 Event 17
#> 0 11 7 6 6 5 3 2 2
#> 1 5 4 3 2 2 2 2 2
#> Event 18 Event 19 Event 20 Event 21 Event 22 Event 23 Event 24 Event 25
#> 0 2 1 0 0 0 0 0 0
#> 1 2 2 1 1 1 1 1 1
#> Event 26 Death Med follow-up time
#> 0 0 57 2.390144
#> 1 1 36 2.302533
# summarize the inference results for tau=3.5 years
summary(obj,tau=3.5,Kmax=4) # aggregating results for recurrent-event
#> Call:
#> rmtfit.formula(formula = rec(patid, time, status) ~ trt_ab, data = hfaction)
#>
#> Restricted mean winning time by tau = 3.5:
#> Event 1 Event 2 Event 3 Event 4 Event 5 Event 6 Event 7
#> 0 0.2461459 0.1797341 0.07400189 0.05705096 0.06778913 0.03229824 0.02901336
#> 1 0.2606647 0.2246581 0.18911776 0.07928876 0.05043218 0.04036515 0.01434557
#> Event 8 Event 9 Event 10 Event 11 Event 12 Event 13
#> 0 0.02467620 0.01351584 0.007900133 0.001056981 0.007932054 0.0006445581
#> 1 0.01169075 0.01232759 0.009301100 0.004563094 0.001627585 0.0024747931
#> Event 14 Event 15 Event 16 Event 17 Event 18 Event 19
#> 0 0.0007787642 0.0003834044 0.0003137311 0.0001010352 0.0001123888 0.0004848228
#> 1 0.0077805017 0.0019915287 0.0007116988 0.0003040931 0.0003580146 0.0001079202
#> Event 20 Event 21 Event 22 Event 23 Event 24 Event 25
#> 0 0.0001781611 0.0007311137 0.0003162343 0.0005731831 0.001192464 0.0002290646
#> 1 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.000000000 0.0000000000
#> Event 26 Survival Overall
#> 0 0.004352256 0.2967478 1.048254
#> 1 0.000000000 0.4958881 1.407999
#>
#> Restricted mean time in favor of group "1" by time tau = 3.5:
#> Estimate Std.Err Z value Pr(>|z|)
#> Event 1 0.014519 0.047535 0.3054 0.760034
#> Event 2 0.044924 0.045661 0.9839 0.325185
#> Event 3 0.115116 0.035992 3.1984 0.001382 **
#> Event 4+ -0.013954 0.049358 -0.2827 0.777403
#> Survival 0.199140 0.093300 2.1344 0.032810 *
#> Overall 0.359745 0.154062 2.3351 0.019540 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# frequency >=4.