Time to Quality of Life score deterioration

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Description

A program that computes the time to deterioration in a quality of life score.

Usage

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TTD(X, score = "", MCID, ref.init = "baseline", order = 1, 
no_baseline = "censored", no_follow = "censored", death = NA, sensitivity = FALSE)

Arguments

X

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

score

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

MCID

the minimal clinically important difference

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 score is the reference score. If ref.init is "previous", the immediately preceding score is the reference score.

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 "censored" to indicate that patients with no baseline score are censored at baseline (Day 0). If no_baseline equals "event", these patients are deteriorated since baseline. If no_baseline equals "excluded", these patients are excluded from the analysis

no_follow

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

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 equal 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 simultaneously no baseline, no follow and death (SA3)

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 the following order:

  1. Patient identification number

  2. Variable identify the number of the quality of life assessment, i.e. the visit number

  3. Date of quality of life measures

  4. quality of life scores

  5. Other variables such as the date of death or the treatment arm.

The dataset must also be sorted by patient identification number and quality of life measurement time. Dates must be in Julian format (i.e. number of days since a reference time point).

All these definitions are extensively described in the referenced papers below.

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 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 equal to 1 if the patient is deteriorated and 0 if not. The second variable time equal to the time in months to deterioration since baseline date if the patient is deteriorated or the time to censoring.

As example, for a given score "qol" and one analyse performed (i.e. sensitivity is FALSE), then two variables are created called event.qol and time.qol.

Author(s)

Amelie Anota

Maintainer: Amelie Anota <aanota@chu-besancon.fr>

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.

Hamidou Z., et al. Time to deterioration in quality of life score as a modality of longitudinal analysis in patients with breast cancer. The Oncologist 2011, 16(10):1458-1468.

See Also

TUDD

Examples

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data(dataqol2)
# deterioration of 5 points at least as compared to the baseline score for
# the score "QoL" and the score "pain"
# order = 1 for "QoL" score because a deterioration is observed when the score decreases
# order = 2 for pain score bacause a deterioration is observed when the score increases
ttd1=TTD(dataqol2,score=c("QoL","pain"),order=1:2,MCID=5)
head(ttd1)

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