QoLR (version 1.0.1)

TUDD: Time until definitive deterioration in a quality of life score

Description

A program that computes the time until definitive deterioration in quality of score.

Usage

TUDD(X, score = "", MCID, ref.init = "baseline", ref.def = "def1", order = 1,
 no_baseline = "censure", no_follow = "censure", death = NA, sensitivity = FALSE)

Arguments

X

input data matrix or data frame with a quality of life score. Missing values are inserted as NA.

score

vector with the name of the quality of life scores of interest

MCID

a vector equals to the minimal clinically important difference (MCID). Several MCID can be specified

ref.init

the reference score to qualify the deterioration. By default, ref.init is "baseline", i.e. the reference score is the baseline score. If ref.init is "best", the best previous quality of life

ref.def

the deterioration is definitive 1: if there is no clinically significant improvement as compared to the reference score ("def1"); 2: if the deterioration is also observed at all times following the deterioration ("def2"); 3: or t

order

a vector equals to 1 if the deterioration corresponds to a decrease of the score, 2 otherwise

no_baseline

By default, no_baseline equals to "censure" to indicate that patients with no baseline score are censured at baseline (Day 0). If no_baseline equals "event", these patients are deteriorated since baselin

no_follow

By default, no_follow equals to "censure" to indicate that patients with no follow-up score are censured just after baseline (Day 1). If no_follow equals to "event", these patients are deteriorated just

death

missing if patients who died without experienced a deterioration are censored at the time of the last quality of life assessment, equals to the name of the death date in the dataframe X otherwise

sensitivity

Boolean equals to FALSE by default. If sensitivity is TRUE, then all sensitivity analyses are performed, integrating patients with no baseline or with no follow up as event (SA1), death as event (SA2) and simultaneou

Value

• The result is a dataframe with the id variable of the dataframe X and the results of the time to deterioration analyses performed. For each score and each time to deterioration analysis, two variables are created called event and time with the value of the MCID and the name of the corresponding score as a suffix. Moreover, if sensitivity is TRUE, a suffix is added to each result of this function reflecting the sensitivity analysis corresponding (SA1, SA2 or SA3). The first variable event is a dummy vector equals to 1 if the patient is deteriorated and 0 if not. The second variable time equals to the time in months to deterioration since baseline if the patient is deteriorated or the time to censure. As example, for a given score "qol", MCID = 5 and one analyse performed (i.e. sensitivity is FALSE), then two variables are created called event.5.qol and time.5.qol.

Details

To apply this function, the dataset must respect a general structure. The dataset X must be in long format with the following variables in this order:

1. Patient's identification number

Variable identify the quality of life assessment, i.e. the visit number Date of quality of life measure quality of life scores Other variables as the date of death or the treatment arm.

References

Anota A., et al. Time to Health-related Quality of Life score deterioration as a modality of longitudinal analysis for health-related quality of life studies in oncology: do we need RECIST for quality of life to achieve standardization? Qual Life Res. 2013 Nov 26. Bonnetain F., et al. Time until definitive deterioration as a means of longitudinal analysis for treatment trials in patients with metastatic pancreatic adenocarcinoma. Eur J Cancer 2010, 46(5): 2753-2762.

See Also

TTD

Examples

data(dataqol)
# Estimation of the scores using the 'scoring.QLQC30' function:
score_dataqol=scoring.QLQC30(dataqol,id="Id",time="time")
# Merge of "score_dataqol" dataframe with the important 
# variables of the dataqol dataframe as follows:
info=dataqol[,c("Id","time","date","death","Arm")]
dataqol_final=merge(score_dataqol,info,by=c("Id","time"))

# Then we reorganized the obtained "dataqol_final" dataset in order that
# the "date" variable appeared in the third position:
dataqol_final=dataqol_final[,c(1:2,18,3:17,19:20)]

# Finally, in order to apply the "TTD" function, the dataset has to be sorted by
# patient's identification number "Id" and HRQoL measures ("time"):
dataqol_final=dataqol_final[order(dataqol_final$time),]
dataqol_final=dataqol_final[order(dataqol_final$Id),]


# Time to definitive deterioration of 5 points at least of the "QL" score
# as compared to the best previous score with no further improvement of more 
# than 5 points : 
ttd=TUDD(dataqol_final,score=c("QL"),ref.init="best",order=1,MCID=5)
head(ttd)