simdata: Generating survival data
In jackknifeKME: Jackknife Estimates of Kaplan-Meier Estimators or Integrals

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/simdata.R

Generate survival data by keeping the second last largest subject as censored.

1	simdata(n, lambda)

`n`	the sample size.
`lambda`	value of the parameter `lambda` for Uniform distribution. Different values of `lambda` are analytically computed to obtain specific censoring percentages. `lambda` takes values 7.53, 4.81, 3.48, 2.64, 2.04, 1.58, 1.20, 0.87, 0.55 for corresponding censoring percentages 10, 20, 30, 40, 50, 60, 70, 80, 90.

Data are generated always keeping the second last largest subject as censored i.e. delta_(n-1)=0. The survival times and the censoring times are generated using log-normal(1.1, 1) and Uniform(lambda, 2xlambda) distribution respectively. This type of data is required to compute the actual and modified jackknife estimates of Kaplan-Meier estimators and their bias. This data is used in Khan and Shaw (2015).

`Y`	survival times censored or uncensored i.e. min(t, c)
`delta`	status
`Cper`	censoring percentage. Different censoring percentages are obtained for different values of `lambda` of censoring time distribution

Hasinur Rahaman Khan and Ewart Shaw

Khan and Shaw. (2015). Robust bias estimation for Kaplan-Meier Survival Estimator with Jackknifing. Journal of Statistical Theory and Practice, (published online; DOI:10.1080/15598608.2015.1062833). Also available in http://arxiv.org/abs/1312.4058.

jackknifeKME

#For Cper = 30%.
data<-simdata(n = 100,lambda = 3.48)
data 

#For Cper = 50%.
data2<-simdata(n = 100,lambda = 2.04) 
data2

#For Cper = 80%.
data3<-simdata(n = 100,lambda = 0.87) 
data3

Loading required package: imputeYn
Loading required package: quadprog
Loading required package: emplik
Loading required package: mvtnorm
Loading required package: survival
Loading required package: boot

Attaching package: 'boot'

The following object is masked from 'package:survival':

    aml


Attaching package: 'imputeYn'

The following object is masked from 'package:utils':

    data

$Y
  [1] 0.1310687 0.3503525 0.3846138 0.4183898 0.6316080 0.6743958 0.7341944
  [8] 0.7841998 0.8452440 0.8475564 0.9399467 0.9523598 1.0127401 1.1218087
 [15] 1.1558235 1.2184863 1.4126113 1.4142924 1.5219588 1.5724355 1.5782813
 [22] 1.6001224 1.6286559 1.7039539 1.9554714 1.9634628 1.9684326 1.9913779
 [29] 2.0904109 2.0956094 2.1230183 2.1726566 2.1852028 2.2115474 2.2408818
 [36] 2.2925232 2.3171124 2.4164537 2.4846747 2.6304435 2.8594330 2.9004197
 [43] 2.9591050 3.0135411 3.0996854 3.1348640 3.1418828 3.1852818 3.3625272
 [50] 3.3764088 3.7115323 3.7447903 3.7551162 3.7573427 3.7694217 3.7817080
 [57] 3.8263676 3.8685477 3.9017154 3.9134820 3.9322705 3.9539530 3.9575277
 [64] 4.0782337 4.1803278 4.1904263 4.2536779 4.2671646 4.2990077 4.4630618
 [71] 4.4665394 4.5393514 4.5483293 4.5991463 4.6019118 4.6138439 4.6466044
 [78] 4.7081217 4.7700657 4.7803802 4.8557580 4.9947010 5.0446335 5.0697597
 [85] 5.0847761 5.0848445 5.3464065 5.5228550 5.5368145 5.5761707 5.7839948
 [92] 5.9190662 6.2991238 6.4782062 6.5182173 6.5542816 6.5947777 6.6069596
 [99] 6.7101234 6.7425035

$delta
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1 1 1 1 0 1 0 0 0 1
 [75] 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1

$Pper
[1] 30

$Y
  [1] 0.2888003 0.2926266 0.3992288 0.4317922 0.4511685 0.5497073 0.5922252
  [8] 0.6117899 0.6987175 0.7095020 0.7498501 0.7646640 0.8474252 0.8924495
 [15] 0.9075773 1.0227729 1.0665712 1.0688141 1.1589103 1.1870626 1.2220480
 [22] 1.2294002 1.3552869 1.4130034 1.4363902 1.5329364 1.6406970 1.6660248
 [29] 1.9293195 1.9436284 1.9496723 2.0975068 2.1014344 2.1025307 2.1191424
 [36] 2.1364170 2.1381831 2.1397510 2.1719830 2.2081399 2.2181853 2.2187462
 [43] 2.2228370 2.2326320 2.2673721 2.2852296 2.2907973 2.3025053 2.3107560
 [50] 2.3221917 2.3376231 2.3484986 2.3913466 2.4270323 2.4318121 2.4418039
 [57] 2.4683939 2.4698397 2.4933130 2.5415946 2.5529371 2.6234643 2.6489710
 [64] 2.6790124 2.6871627 2.8094034 2.8160655 2.8583637 2.8589307 2.9289250
 [71] 2.9613821 2.9668683 3.0195426 3.0259364 3.0621787 3.1585356 3.1842607
 [78] 3.2071828 3.2939404 3.3771454 3.4185007 3.4194062 3.4635439 3.4709128
 [85] 3.5096882 3.5114240 3.5263157 3.5293063 3.5496694 3.5897239 3.5973014
 [92] 3.6952368 3.7390244 3.7600211 3.7625140 3.7646429 3.7648570 3.8545740
 [99] 3.8838455 3.9111896

$delta
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0
 [38] 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0
 [75] 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1

$Pper
[1] 50

$Y
  [1] 0.4701476 0.4853281 0.4929366 0.5708104 0.5906210 0.5984072 0.6483835
  [8] 0.6761869 0.7755325 0.8617393 0.8724242 0.8724755 0.8799132 0.8864263
 [15] 0.8964356 0.8975030 0.9080658 0.9146441 0.9293475 0.9327409 0.9404624
 [22] 0.9499364 0.9557119 0.9583160 0.9609667 0.9617423 0.9620461 0.9757796
 [29] 0.9823633 0.9836911 0.9879409 1.0094131 1.0103446 1.0129452 1.0171599
 [36] 1.0344274 1.0346576 1.0456539 1.0497663 1.0536858 1.0600371 1.0656067
 [43] 1.0733635 1.0828946 1.1058188 1.1070269 1.1100920 1.1313138 1.1349066
 [50] 1.1511601 1.1616966 1.1707718 1.1746123 1.1852639 1.1974203 1.2024270
 [57] 1.2112512 1.2186352 1.2259006 1.2620548 1.2814336 1.2847908 1.2863617
 [64] 1.2945797 1.3016849 1.3056340 1.3145442 1.3146902 1.3381005 1.3779224
 [71] 1.3833496 1.3959425 1.3961832 1.4376248 1.4706685 1.4745220 1.4967459
 [78] 1.5000750 1.5346702 1.5619464 1.5638018 1.5657090 1.5746954 1.5871956
 [85] 1.5954877 1.6021382 1.6030789 1.6031574 1.6062965 1.6197115 1.6254056
 [92] 1.6259963 1.6520060 1.6535787 1.6542722 1.6552424 1.6784704 1.6959850
 [99] 1.7097815 1.7125852

$delta
  [1] 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

$Pper
[1] 80