Given a proportional hazards model for visit intensities, compute inverse-intensity weights.
iiw.RdFor a longitudinal dataset subject to irregular observation, use a Cox proportional hazards model for visit intensities to compute inverse intensity weights
Arguments
- phfit
coxph object for the visit process
- data
The dataset featuring longitudinal data subject to irregular observation for which inverse-intensity weights are desired
- id
character string indicating which column of the data identifies subjects
- time
character string indicating which column of the data contains the time at which the visit occurred
- first
logical variable. If TRUE, the first observation for each individual is assigned an intensity of 1. This is appropriate if the first visit is a baseline visit at which recruitment to the study occurred; in this case the baseline visit is observed with probability 1.
Value
A vector of inverse-intensity weights for each row of the dataset. The first observation for each subject is assumed to have an intensity of 1.
See also
Other iiw:
iiw.weights(),
iiwgee()
Examples
library(nlme)
#>
#> Attaching package: ‘nlme’
#> The following object is masked _by_ ‘.GlobalEnv’:
#>
#> Phenobarb
#> The following objects are masked from ‘package:MEMSS’:
#>
#> Alfalfa, Assay, BodyWeight, Cefamandole, Dialyzer, Earthquake,
#> Fatigue, Gasoline, Glucose, Glucose2, Gun, IGF, Machines,
#> MathAchieve, Meat, Milk, Muscle, Nitrendipene, Oats, Orthodont,
#> Ovary, Oxide, PBG, Phenobarb, Pixel, Quinidine, Rail, RatPupWeight,
#> Relaxin, Remifentanil, Soybean, Spruce, Tetracycline1,
#> Tetracycline2, Wafer, Wheat, Wheat2, ergoStool
#> The following object is masked from ‘package:lme4’:
#>
#> lmList
library(survival)
library(geepack)
library(data.table)
data(Phenobarb)
Phenobarb$event <- 1-as.numeric(is.na(Phenobarb$conc))
data <- Phenobarb
data <- data[data$event==1,]
data$id <- as.numeric(data$Subject)
data <- data[data$time<16*24,]
data <- lagfn(data, lagvars=c("time","conc"), id="Subject", time="time", lagfirst = NA)
head(data)
#> Subject Wt Apgar ApgarInd time dose conc event id time.lag conc.lag
#> 1 42 2.8 9 >= 5 14.0 NA 13.3 1 1 NA NA
#> 2 42 2.8 9 >= 5 95.5 NA 13.9 1 1 14.0 13.3
#> 3 28 3.2 9 >= 5 2.0 NA 16.9 1 2 NA NA
#> 4 30 1.8 8 >= 5 6.3 NA 17.9 1 3 NA NA
#> 5 30 1.8 8 >= 5 226.3 NA 16.5 1 3 6.3 17.9
#> 6 56 0.6 4 < 5 20.0 NA 18.8 1 4 NA NA
mph <- coxph(Surv(time.lag,time,event)~I(conc.lag>0 & conc.lag<=20) + I(conc.lag>20 & conc.lag<=30)
+ I(conc.lag>30)+ cluster(id),,data=data)
summary(mph)
#> Call:
#> coxph(formula = Surv(time.lag, time, event) ~ I(conc.lag > 0 &
#> conc.lag <= 20) + I(conc.lag > 20 & conc.lag <= 30) + I(conc.lag >
#> 30), data = data, cluster = id)
#>
#> n= 95, number of events= 95
#> (59 observations deleted due to missingness)
#>
#> coef exp(coef) se(coef) robust se z
#> I(conc.lag > 0 & conc.lag <= 20)TRUE 0.6546 1.9243 0.3566 0.2914 2.246
#> I(conc.lag > 20 & conc.lag <= 30)TRUE 0.3741 1.4537 0.3142 0.2621 1.427
#> I(conc.lag > 30)TRUE NA NA 0.0000 0.0000 NA
#> Pr(>|z|)
#> I(conc.lag > 0 & conc.lag <= 20)TRUE 0.0247 *
#> I(conc.lag > 20 & conc.lag <= 30)TRUE 0.1535
#> I(conc.lag > 30)TRUE NA
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> exp(coef) exp(-coef) lower .95 upper .95
#> I(conc.lag > 0 & conc.lag <= 20)TRUE 1.924 0.5197 1.0870 3.406
#> I(conc.lag > 20 & conc.lag <= 30)TRUE 1.454 0.6879 0.8697 2.430
#> I(conc.lag > 30)TRUE NA NA NA NA
#>
#> Concordance= 0.544 (se = 0.023 )
#> Likelihood ratio test= 3.53 on 2 df, p=0.2
#> Wald test = 5.19 on 2 df, p=0.07
#> Score (logrank) test = 3.49 on 2 df, p=0.2, Robust = 6.28 p=0.04
#>
#> (Note: the likelihood ratio and score tests assume independence of
#> observations within a cluster, the Wald and robust score tests do not).
data$weight <- iiw(mph,data,"id","time",TRUE)
head(data)
#> Subject Wt Apgar ApgarInd time dose conc event id time.lag conc.lag
#> 1 42 2.8 9 >= 5 14.0 NA 13.3 1 1 NA NA
#> 2 42 2.8 9 >= 5 95.5 NA 13.9 1 1 14.0 13.3
#> 3 28 3.2 9 >= 5 2.0 NA 16.9 1 2 NA NA
#> 4 30 1.8 8 >= 5 6.3 NA 17.9 1 3 NA NA
#> 5 30 1.8 8 >= 5 226.3 NA 16.5 1 3 6.3 17.9
#> 6 56 0.6 4 < 5 20.0 NA 18.8 1 4 NA NA
#> weight
#> 1 1.0000000
#> 2 0.5196749
#> 3 1.0000000
#> 4 1.0000000
#> 5 0.5196749
#> 6 1.0000000