Help for package QoLMiss

Title:

Scales Score Calculation from Quality of Life Data

Type:

Package

Version:

0.1.0

Date:

2022-01-06

Description:

There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set.

LazyDataCompression:

ByteCompile:

Yes

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

Depends:

R (≥ 3.5.0)

Imports:

survival,utils,dplyr,missMethods

Maintainer:

Atanu Bhattacharjee <atanustat@gmail.com>

RoxygenNote:

7.1.2

NeedsCompilation:

Packaged:

2022-01-06 18:37:28 UTC; atanu

Author:

Atanu Bhattacharjee [aut, cre, ctb], Ankita Pal [aut, ctb]

Repository:

CRAN

Date/Publication:

2022-01-07 13:32:52 UTC

Breast cancer Quality of Life.

Description

A simulated data for Breast cancer Quality of Life.

Usage

brc_df

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
BR_Q31: Breast Cancer Quality of Q31 Question
BR_Q32: Breast Cancer Quality of Q32 Question
BR_Q33: Breast Cancer Quality of Q33 Question
BR_Q34: Breast Cancer Quality of Q34 Question
BR_Q35: Breast Cancer Quality of Q35 Question
BR_Q36: Breast Cancer Quality of Q36 Question
BR_Q37: Breast Cancer Quality of Q37 Question
BR_Q38: Breast Cancer Quality of Q38 Question
BR_Q39: Breast Cancer Quality of Q39 Question
BR_Q40: Breast Cancer Quality of Q40 Question
BR_Q41: Breast Cancer Quality of Q41 Question
BR_Q42: Breast Cancer Quality of Q42 Question
BR_Q43: Breast Cancer Quality of Q43 Question
BR_Q44: Breast Cancer Quality of Q44 Question
BR_Q45: Breast Cancer Quality of Q45 Question
BR_Q46: Breast Cancer Quality of Q46 Question
BR_Q47: Breast Cancer Quality of Q47 Question
BR_Q48: Breast Cancer Quality of Q48 Question
BR_Q49: Breast Cancer Quality of Q49 Question
BR_Q50: Breast Cancer Quality of Q50 Question
BR_Q51: Breast Cancer Quality of Q51 Question
BR_Q52: Cancer Quality of Q52 Question
BR_Q53: Breast Cancer Quality of Q53 Question

#' @source <https://github.com/apstat/QoLMiss-Package>

Breast cancer Quality of Life with missing values.

Description

A simulated data for Breast cancer Quality of Life.

Usage

brc_df_miss

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
BR_Q31: Breast Cancer Quality of Q31 Question
BR_Q32: Breast Cancer Quality of Q32 Question
BR_Q33: Breast Cancer Quality of Q33 Question
BR_Q34: Breast Cancer Quality of Q34 Question
BR_Q35: Breast Cancer Quality of Q35 Question
BR_Q36: Breast Cancer Quality of Q36 Question
BR_Q37: Breast Cancer Quality of Q37 Question
BR_Q38: Breast Cancer Quality of Q38 Question
BR_Q39: Breast Cancer Quality of Q39 Question
BR_Q40: Breast Cancer Quality of Q40 Question
BR_Q41: Breast Cancer Quality of Q41 Question
BR_Q42: Breast Cancer Quality of Q42 Question
BR_Q43: Breast Cancer Quality of Q43 Question
BR_Q44: Breast Cancer Quality of Q44 Question
BR_Q45: Breast Cancer Quality of Q45 Question
BR_Q46: Breast Cancer Quality of Q46 Question
BR_Q47: Breast Cancer Quality of Q47 Question
BR_Q48: Breast Cancer Quality of Q48 Question
BR_Q49: Breast Cancer Quality of Q49 Question
BR_Q50: Breast Cancer Quality of Q50 Question
BR_Q51: Breast Cancer Quality of Q51 Question
BR_Q52: Breast Cancer Quality of Q52 Question
BR_Q53: Breast Cancer Quality of Q53 Question

#' @source <https://github.com/apstat/QoLMiss-Package>

Calculates the domain-based scale scores using the data of QLQ-BR23

Description

Creates a dataset containing the domain-based scale scores using the data from QLQ-BR23

Usage

brc_qol(x)

Arguments

x

A data frame with ID, BR_Q31,BR_Q32,...,BR_Q53 columns along with other columns if data is available.

Details

brc_miss function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It extracts only the columns named 'BR_Q31','BR_Q32',...,'BR_Q53' and replaces the missing data with the minimum value of the particular question.

Using each of the 30 columns, the Raw Score is computed, and one column is obtained containing the Raw Score for each patient.

Further, using each of the Raw Scores, three domain-based Scale Scores are computed, they are, Global Scales Score, Functional Scales Score and Symptoms Scales Score.

Thus, the columns 'BR_Q31','BR_Q32',...,'BR_Q53' are replaced by the domain-based scale scores, which is obtained as the output.

brc_qol(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'BR_Q31' for data from question 31, 'BR_Q32' for data from question 32, and so on until 'BR_Q53' for data from question 53

3) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, BR_Q31,BR_Q32,...,BR_Q53 columns along with other columns if data is available.

rs - A matrix containing the Raw Score computed using all BR_Q31 to BR_Q53 data for each patient. The RS(a) function is used in this case.

fs - A matrix containing the Functional Scale Scores computed using all BR_Q31 to BR_Q53 data for each patient. The FS(a,b) function is used in this case.

ss - A matrix containing the Global Scale Scores computed using all BR_Q31 to BR_Q53 data for each patient. The SS(a,b) function is used in this case.

final_data - A data frame formed by replacing the columns 'BR_Q31','BR_Q32',...,'BR_Q53' by the domain-based scale scores.

Value

A data frame by replacing the columns 'BR_Q31','BR_Q32',...,'BR_Q53' by the domain-based scale scores.

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(brc_df)
brc_qol(brc_df)
data(brc_df_miss)
brc_qol(brc_df_miss)
##

Simulated data for cancer Quality of Life.

Description

A simulated data for cancer Quality of Life.

Usage

c30_df

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
Q1: Cancer Quality of Q1 Question
Q2: Cancer Quality of Q2 Question
Q3: Cancer Quality of Q3 Question
Q4: Cancer Quality of Q4 Question
Q5: Cancer Quality of Q5 Question
Q6: Cancer Quality of Q6 Question
Q7: Cancer Quality of Q7 Question
Q8: Cancer Quality of Q8 Question
Q9: Cancer Quality of Q9 Question
Q10: Cancer Quality of Q10 Question
Q11: Cancer Quality of Q11 Question
Q12: Cancer Quality of Q12 Question
Q13: Cancer Quality of Q13 Question
Q14: Cancer Quality of Q14 Question
Q15: Cancer Quality of Q15 Question
Q16: Cancer Quality of Q16 Question
Q17: Cancer Quality of Q17 Question
Q18: Cancer Quality of Q18 Question
Q19: Cancer Quality of Q19 Question
Q20: Cancer Quality of Q20 Question
Q21: Cancer Quality of Q21 Question
Q22: Cancer Quality of Q22 Question
Q23: Cancer Quality of Q23 Question
Q24: Cancer Quality of Q24 Question
Q25: Cancer Quality of Q25 Question
Q26: Cancer Quality of Q26 Question
Q27: Cancer Quality of Q27 Question
Q28: Cancer Quality of Q28 Question
Q29: Cancer Quality of Q29 Question
Q30: Cancer Quality of Q30 Question

@source <https://github.com/apstat/QoLMiss-Package>

Data for cancer Quality of Life with missing values.

Description

A simulated data for cancer Quality of Life.

Usage

c30_df_miss

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
Q1: Cancer Quality of Q1 Question
Q2: Cancer Quality of Q2 Question
Q3: Cancer Quality of Q3 Question
Q4: Cancer Quality of Q4 Question
Q5: Cancer Quality of Q5 Question
Q6: Cancer Quality of Q6 Question
Q7: Cancer Quality of Q7 Question
Q8: Cancer Quality of Q8 Question
Q9: Cancer Quality of Q9 Question
Q10: Cancer Quality of Q10 Question
Q11: Cancer Quality of Q11 Question
Q12: Cancer Quality of Q12 Question
Q13: Cancer Quality of Q13 Question
Q14: Cancer Quality of Q14 Question
Q15: Cancer Quality of Q15 Question
Q16: Cancer Quality of Q16 Question
Q17: Cancer Quality of Q17 Question
Q18: Cancer Quality of Q18 Question
Q19: Cancer Quality of Q19 Question
Q20: Cancer Quality of Q20 Question
Q21: Cancer Quality of Q21 Question
Q22: Cancer Quality of Q22 Question
Q23: Cancer Quality of Q23 Question
Q24: Cancer Quality of Q24 Question
Q25: Cancer Quality of Q25 Question
Q26: Cancer Quality of Q26 Question
Q27: Cancer Quality of Q27 Question
Q28: Cancer Quality of Q28 Question
Q29: Cancer Quality of Q29 Question
Q30: Cancer Quality of Q30 Question

@source <https://github.com/apstat/QoLMiss-Package>

Head and Neck cancer Quality of Life data.

Description

A simulated data for Head and Neck cancer Quality of Life.

Usage

hnc_df

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
HN_Q31: HNC Cancer Quality of Q31 Question
HN_Q32: HNC Cancer Quality of Q32 Question
HN_Q33: HNC Cancer Quality of Q33 Question
HN_Q34: HNC Cancer Quality of Q34 Question
HN_Q35: HNC Cancer Quality of Q35 Question
HN_Q36: HNC Cancer Quality of Q36 Question
HN_Q37: HNC Cancer Quality of Q37 Question
HN_Q38: HNC Cancer Quality of Q38 Question
HN_Q39: HNC Cancer Quality of Q39 Question
HN_Q40: HNC Cancer Quality of Q40 Question
HN_Q41: HNC Cancer Quality of Q41 Question
HN_Q42: HNC Cancer Quality of Q42 Question
HN_Q43: HNC Cancer Quality of Q43 Question
HN_Q44: HNC Cancer Quality of Q44 Question
HN_Q45: HNC Cancer Quality of Q45 Question
HN_Q46: HNC Cancer Quality of Q46 Question
HN_Q47: HNC Cancer Quality of Q47 Question
HN_Q48: HNC Cancer Quality of Q48 Question
HN_Q49: HNC Cancer Quality of Q49 Question
HN_Q50: HNC Cancer Quality of Q50 Question
HN_Q51: HNC Cancer Quality of Q51 Question
HN_Q52: HNC Cancer Quality of Q52 Question
HN_Q53: HNC Cancer Quality of Q53 Question
HN_Q54: HNC Cancer Quality of Q54 Question
HN_Q55: HNC Cancer Quality of Q55 Question
HN_Q56: HNC Cancer Quality of Q56 Question
HN_Q57: HNC Cancer Quality of Q57 Question
HN_Q58: HNC Cancer Quality of Q58 Question
HN_Q59: HNC Cancer Quality of Q59 Question
HN_Q60: HNC Cancer Quality of Q60 Question
HN_Q61: HNC Cancer Quality of Q61 Question
HN_Q62: HNC Cancer Quality of Q62 Question
HN_Q63: HNC Cancer Quality of Q63 Question
HN_Q64: HNC Cancer Quality of Q64 Question
HN_Q65: HNC Cancer Quality of Q65 Question

#' @source <https://github.com/apstat/QoLMiss-Package>

Head and Neck cancer data for cancer Quality of Life with missing values.

Description

A simulated data for Head and Neck cancer Quality of Life.

Usage

hnc_df_miss

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
HN_Q31: HNC Cancer Quality of Q31 Question
HN_Q32: HNC Cancer Quality of Q32 Question
HN_Q33: HNC Cancer Quality of Q33 Question
HN_Q34: HNC Cancer Quality of Q34 Question
HN_Q35: HNC Cancer Quality of Q35 Question
HN_Q36: HNC Cancer Quality of Q36 Question
HN_Q37: HNC Cancer Quality of Q37 Question
HN_Q38: HNC Cancer Quality of Q38 Question
HN_Q39: HNC Cancer Quality of Q39 Question
HN_Q40: HNC Cancer Quality of Q40 Question
HN_Q41: HNC Cancer Quality of Q41 Question
HN_Q42: HNC Cancer Quality of Q42 Question
HN_Q43: HNC Cancer Quality of Q43 Question
HN_Q44: HNC Cancer Quality of Q44 Question
HN_Q45: HNC Cancer Quality of Q45 Question
HN_Q46: HNC Cancer Quality of Q46 Question
HN_Q47: HNC Cancer Quality of Q47 Question
HN_Q48: HNC Cancer Quality of Q48 Question
HN_Q49: HNC Cancer Quality of Q49 Question
HN_Q50: HNC Cancer Quality of Q50 Question
HN_Q51: HNC Cancer Quality of Q51 Question
HN_Q52: HNC Cancer Quality of Q52 Question
HN_Q53: HNC Cancer Quality of Q53 Question
HN_Q54: HNC Cancer Quality of Q54 Question
HN_Q55: HNC Cancer Quality of Q55 Question
HN_Q56: HNC Cancer Quality of Q56 Question
HN_Q57: HNC Cancer Quality of Q57 Question
HN_Q58: HNC Cancer Quality of Q58 Question
HN_Q59: HNC Cancer Quality of Q59 Question
HN_Q60: HNC Cancer Quality of Q60 Question
HN_Q61: HNC Cancer Quality of Q61 Question
HN_Q62: HNC Cancer Quality of Q62 Question
HN_Q63: HNC Cancer Quality of Q63 Question
HN_Q64: HNC Cancer Quality of Q64 Question
HN_Q65: HNC Cancer Quality of Q65 Question

#' @source <https://github.com/apstat/QoLMiss-Package>

Calculates the domain-based scale scores using the data of QLQ-HN35

Description

Creates a dataset containing the domain-based scale scores using the data from QLQ-HN35

Usage

hnc_qol(x)

Arguments

x

A data frame with ID, HN_Q31,HN_Q32,...,HN_Q65 columns along with other columns if data is available.

Details

Calculates the domain-based scale scores using the data of QLQ-HN35

hn_miss function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It extracts only the columns named 'HN_Q31','HN_Q32',...,'HN_Q65' and replaces the missing data with the minimum value of the particular question.

Using each of the 30 columns, the Raw Score is computed, and one column is obtained containing the Raw Score for each patient.

Further, using each of the Raw Scores, three domain-based Scale Scores are computed, they are, Global Scales Score, Functional Scales Score and Symptoms Scales Score.

Thus, the columns 'HN_Q31','HN_Q32',...,'HN_Q65' are replaced by the domain-based scale scores, which is obtained as the output.

hnc_qol(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'HN_Q31' for data from question 31, 'HN_Q32' for data from question 32, and so on until 'HN_Q65' for data from question 65.

3) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, HN_Q31,HN_Q32,...,HN_Q65 columns along with other columns if data is available.

rs - A matrix containing the Raw Score computed using all HN_Q31 to HN_Q65 data for each patient. The RS(a) function is used in this case.

ss - A matrix containing the Global Scale Scores computed using all HN_Q31 to HN_Q65 data for each patient. The SS(a,b) function is used in this case.

final_data - A data frame formed by replacing the columns 'HN_Q31','HN_Q32',...,'HN_Q65' by the domain-based scale scores.

Value

A data frame by replacing the columns 'HN_Q31','HN_Q32',...,'HN_Q65' by the domain-based scale scores.

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(hnc_df)
hnc_qol(hnc_df)
data(hnc_df_miss)
hnc_qol(hnc_df_miss)
##

Simulated data for Lung cancer Quality of Life.

Description

A simulated data for Lung cancer Quality of Life.

Usage

lc_df

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
LC_Q31: Lung Cancer Quality of Q31 Question
LC_Q32: Lung Cancer Quality of Q32 Question
LC_Q33: Lung Cancer Quality of Q33 Question
LC_Q34: Lung Cancer Quality of Q34 Question
LC_Q35: Lung Cancer Quality of Q35 Question
LC_Q36: Lung Cancer Quality of Q36 Question
LC_Q37: Lung Cancer Quality of Q37 Question
LC_Q38: Lung Cancer Quality of Q38 Question
LC_Q39: Lung Cancer Quality of Q39 Question
LC_Q40: Lung Cancer Quality of Q40 Question
LC_Q41: Lung Cancer Quality of Q41 Question
LC_Q42: Lung Cancer Quality of Q42 Question

@source <https://github.com/apstat/QoLMiss-Package>

Lung cancer data for cancer Quality of Life with missing values.

Description

A simulated data for Lung cancer Quality of Life.

Usage

lc_df_miss

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
LC_Q31: Lung Cancer Quality of Q31 Question
LC_Q32: Lung Cancer Quality of Q32 Question
LC_Q33: Lung Cancer Quality of Q33 Question
LC_Q34: Lung Cancer Quality of Q34 Question
LC_Q35: Lung Cancer Quality of Q35 Question
LC_Q36: Lung Cancer Quality of Q36 Question
LC_Q37: Lung Cancer Quality of Q37 Question
LC_Q38: Lung Cancer Quality of Q38 Question
LC_Q39: Lung Cancer Quality of Q39 Question
LC_Q40: Lung Cancer Quality of Q40 Question
LC_Q41: Lung Cancer Quality of Q41 Question
LC_Q42: Lung Cancer Quality of Q42 Question

@source <https://github.com/apstat/QoLMiss-Package>

Calculates the domain-based scale scores using the data of QLQ-LC13.

Description

Creates a dataset containing the domain-based scale scores using the data from QLQ-LC13

Usage

lc_qol(x)

Arguments

x

A data frame with ID, LC_Q31,LC_Q32,...,LC_Q42 columns along with other columns if data is available.

Details

Calculates the domain-based scale scores using the data of QLQ-LC13

lc_miss function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It extracts only the columns named 'LC_Q31','LC_Q32',...,'LC_Q42' and replaces the missing data with the minimum value of the particular question.

Using each of the 30 columns, the Raw Score is computed, and one column is obtained containing the Raw Score for each patient.

Further, using each of the Raw Scores, three domain-based Scale Scores are computed, they are, Global Scales Score, Functional Scales Score and Symptoms Scales Score.

Thus, the columns 'LC_Q31','LC_Q32',...,'LC_Q42' are replaced by the domain-based scale scores, which is obtained as the output.

lc_qol(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'LC_Q31' for data from question 31, 'LC_Q32' for data from question 32, and so on until 'LC_Q42' for data from question 42.

3) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, LC_Q31,LC_Q32,...,LC_Q42 columns along with other columns if data is available.

rs - A matrix containing the Raw Score computed using all LC_Q31 to LC_Q42 data for each patient. The RS(a) function is used in this case.

ss - A matrix containing the Global Scale Scores computed using all LC_Q31 to LC_Q42 data for each patient. The SS(a,b) function is used in this case.

final_data - A data frame formed by replacing the columns 'LC_Q31','LC_Q32',...,'LC_Q42' by the domain-based scale scores.

Value

A data frame by replacing the columns 'LC_Q31','LC_Q32',...,'LC_Q42' by the domain-based scale scores.

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(lc_df)
lc_qol(lc_df)
data(lc_df_miss)
lc_qol(lc_df_miss)
##

Simulated data for Ovarian Cancer Quality of Life.

Description

A simulated data for Breast cancer Quality of Life.

Usage

ovc_df

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
OV_Q31: Breast Cancer Quality of Q31 Question
OV_Q32: Breast Cancer Quality of Q32 Question
OV_Q33: Breast Cancer Quality of Q33 Question
OV_Q34: Breast Cancer Quality of Q34 Question
OV_Q35: Breast Cancer Quality of Q35 Question
OV_Q36: Breast Cancer Quality of Q36 Question
OV_Q37: Breast Cancer Quality of Q37 Question
OV_Q38: Breast Cancer Quality of Q38 Question
OV_Q39: Breast Cancer Quality of Q39 Question
OV_Q40: Breast Cancer Quality of Q40 Question
OV_Q41: Breast Cancer Quality of Q41 Question
OV_Q42: Breast Cancer Quality of Q42 Question
OV_Q43: Breast Cancer Quality of Q43 Question
OV_Q44: Breast Cancer Quality of Q44 Question
OV_Q45: Breast Cancer Quality of Q45 Question
OV_Q46: Breast Cancer Quality of Q46 Question
OV_Q47: Breast Cancer Quality of Q47 Question
OV_Q48: Breast Cancer Quality of Q48 Question
OV_Q49: Breast Cancer Quality of Q49 Question
OV_Q50: Breast Cancer Quality of Q50 Question
OV_Q51: Breast Cancer Quality of Q51 Question
OV_Q52: Breast Cancer Quality of Q52 Question
OV_Q53: Breast Cancer Quality of Q53 Question
OV_Q54: Breast Cancer Quality of Q54 Question
OV_Q55: Breast Cancer Quality of Q55 Question
OV_Q56: Breast Cancer Quality of Q56 Question
OV_Q57: Breast Cancer Quality of Q57 Question
OV_Q58: Breast Cancer Quality of Q58 Question

@source <https://github.com/apstat/QoLMiss-Package>

Ovarian cancer Quality of Life data with missing values.

Description

A simulated data for ovarian cancer Quality of Life.

Usage

ovc_df_miss

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
OV_Q31: Ovarian Cancer Quality of Q31 Question
OV_Q32: Ovarian Cancer Quality of Q32 Question
OV_Q33: Ovarian Cancer Quality of Q33 Question
OV_Q34: Ovarian Cancer Quality of Q34 Question
OV_Q35: Ovarian Cancer Quality of Q35 Question
OV_Q36: Ovarian Cancer Quality of Q36 Question
OV_Q37: Ovarian Cancer Quality of Q37 Question
OV_Q38: Ovarian Cancer Quality of Q38 Question
OV_Q39: Ovarian Cancer Quality of Q39 Question
OV_Q40: Ovarian Cancer Quality of Q40 Question
OV_Q41: Ovarian Cancer Quality of Q41 Question
OV_Q42: Ovarian Cancer Quality of Q42 Question
OV_Q43: Ovarian Cancer Quality of Q43 Question
OV_Q44: Ovarian Cancer Quality of Q44 Question
OV_Q45: Ovarian Cancer Quality of Q45 Question
OV_Q46: Ovarian Cancer Quality of Q46 Question
OV_Q47: Ovarian Cancer Quality of Q47 Question
OV_Q48: Ovarian Cancer Quality of Q48 Question
OV_Q49: Ovarian Cancer Quality of Q49 Question
OV_Q50: Ovarian Cancer Quality of Q50 Question
OV_Q51: Ovarian Cancer Quality of Q51 Question
OV_Q52: Ovarian Cancer Quality of Q52 Question
OV_Q53: Ovarian Cancer Quality of Q53 Question
OV_Q54: Ovarian Cancer Quality of Q54 Question
OV_Q55: Ovarian Cancer Quality of Q55 Question
OV_Q56: Ovarian Cancer Quality of Q56 Question
OV_Q57: Ovarian Cancer Quality of Q57 Question
OV_Q58: Ovarian Cancer Quality of Q58 Question

@source <https://github.com/apstat/QoLMiss-Package>

Calculates the domain-based scale scores using the data of QLQ-OV28.

Description

Creates a dataset containing the domain-based scale scores using the data from QLQ-OV28

Usage

ovc_qol(x)

Arguments

x

A data frame with ID, OV_Q31,OV_Q32,...,OV_Q58 columns along with other columns if data is available.

Details

Calculates the domain-based scale scores using the data of QLQ-OV28

brc_miss function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It extracts only the columns named 'OV_Q31','OV_Q32',...,'OV_Q58' and replaces the missing data with the minimum value of the particular question.

Using each of the 30 columns, the Raw Score is computed, and one column is obtained containing the Raw Score for each patient.

Further, using each of the Raw Scores, three domain-based Scale Scores are computed, they are, Global Scales Score, Functional Scales Score and Symptoms Scales Score.

Thus, the columns 'OV_Q31','OV_Q32',...,'OV_Q58' are replaced by the domain-based scale scores, which is obtained as the output.

ovc_qol(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'OV_Q31' for data from question 31, 'OV_Q32' for data from question 32, and so on until 'OV_Q58' for data from question 58

3) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, OV_Q31,OV_Q32,...,OV_Q58 columns along with other columns if data is available.

rs - A matrix containing the Raw Score computed using all OV_Q31 to OV_Q58 data for each patient. The RS(a) function is used in this case.

ss - A matrix containing the Global Scale Scores computed using all OV_Q31 to OV_Q58 data for each patient. The SS(a,b) function is used in this case.

final_data - A data frame formed by replacing the columns 'OV_Q31','OV_Q32',...,'OV_Q58' by the domain-based scale scores.

Value

A data frame by replacing the columns 'OV_Q31','OV_Q32',...,'OV_Q58' by the domain-based scale scores.

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(ovc_df)
ovc_qol(ovc_df)
data(ovc_df_miss)
ovc_qol(ovc_df_miss)
##

Cancer Quality of Life data with missing values.

Description

A simulated data for cancer Quality of Life.

Usage

patient_miss

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
Q1: Cancer Quality of Q1 Question
Q2: Cancer Quality of Q2 Question
Q3: Cancer Quality of Q3 Question
Q4: Cancer Quality of Q4 Question
Q5: Cancer Quality of Q5 Question
Q6: Cancer Quality of Q6 Question
Q7: Cancer Quality of Q7 Question
Q8: Cancer Quality of Q8 Question
Q9: Cancer Quality of Q9 Question
Q10: Cancer Quality of Q10 Question
Q11: Cancer Quality of Q11 Question
Q12: Cancer Quality of Q12 Question
Q13: Cancer Quality of Q13 Question
Q14: Cancer Quality of Q14 Question
Q15: Cancer Quality of Q15 Question
Q16: Cancer Quality of Q16 Question
Q17: Cancer Quality of Q17 Question
Q18: Cancer Quality of Q19 Question
Q19: Cancer Quality of Q19 Question
Q20: Cancer Quality of Q20 Question
Q21: Cancer Quality of Q21 Question
Q22: Cancer Quality of Q22 Question
Q23: Cancer Quality of Q23 Question
Q24: Cancer Quality of Q24 Question
Q25: Cancer Quality of Q25 Question
Q26: Cancer Quality of Q26 Question
Q27: Cancer Quality of Q27 Question
Q28: Cancer Quality of Q28 Question
Q29: Cancer Quality of Q29 Question
Q30: Cancer Quality of Q30 Question

#' @source <https://github.com/apstat/QoLMiss-Package>

Calculates the domain-based scale scores using the data from Quality of Life questionnaire

Description

Creates a dataset containing the domain-based scale scores using the data from Quality of Life questionnaire

Usage

qol(x)

Arguments

x

A data frame with ID, Q1, Q2,..., Q30 columns along with other columns if data is available.

Details

Calculates the domain-based scale scores using the data from Quality of Life questionnaire

qol function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It extracts only the columns named 'Q1','Q2',...,'Q30' and replaces the missing data with the minimum value of the particular question.

Using each of the 30 columns, the Raw Score is computed, and one column is obtained containing the Raw Score for each patient.

Further, using each of the Raw Scores, three domain-based Scale Scores are computed, they are, Global Scales Score, Functional Scales Score and Symptoms Scales Score.

Thus, the columns 'Q1','Q2',...,'Q30' are replaced by the domain-based scale scores, which is obtained as the output.

qol(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'Q1' for data from question 1, 'Q2' for data from question 2, and so on until 'Q30' for data from question 30.

3) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, Q1, Q2,..., Q30 columns along with other columns if data is available.

rs - A matrix containing the Raw Score computed using all Q1 to Q30 data for each patient. The RS(a) function is used in this case.

fs - A matrix containing the Functional Scale Scores computed using all Q1 to Q30 data for each patient. The FS(a,b) function is used in this case.

ss_gs - A matrix containing the Global Scale Scores computed using all Q1 to Q30 data for each patient. The SS_GS(a,b) function is used in this case.

final_data - A data frame formed by replacing the columns 'Q1','Q2',...,'Q30' by the domain-based scale scores.

Value

A data frame by replacing the columns 'Q1','Q2',...,'Q30' by the domain-based scale scores.

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(c30_df)
qol(c30_df)
data(c30_df_miss)
qol(c30_df_miss)
##

Cancer Quality of Life data analysis with missing values.

Description

Creates a dataset containing the domain-based scale scores using the data from Quality of Life questionnaire

Usage

qol_miss(x)

Arguments

x

A data frame with ID, Q1, Q2,..., Q30 columns along with other columns if data is available.

Details

Calculates the domain-based scale scores using the data from Quality of Life questionnaire

miss_patient function inputs a dataset in which the information of some patients are completely missing. The information of these patients are omitted from the data and only the columns named 'Q1','Q2',...,'Q30' are extracted.

Using each of the 30 columns, the Raw Score is computed, and one column is obtained containing the Raw Score for each patient.

Further, using each of the Raw Scores, three domain-based Scale Scores are computed, they are, Global Scales Score, Functional Scales Score and Symptoms Scales Score.

Thus, the columns 'Q1','Q2',...,'Q30' are replaced by the domain-based scale scores, which is obtained as the output.

qol_miss(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'Q1' for data from question 1, 'Q2' for data from question 2, and so on until 'Q30' for data from question 30.

3) Only those data can be used which contains no information for some patients, that is, some rows contain only NA.

4) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, Q1, Q2,..., Q30 columns along with other columns if data is available.

rs - A matrix containing the Raw Score computed using all Q1 to Q30 data for each patient. The RS(a) function is used in this case.

fs - A matrix containing the Functional Scale Scores computed using all Q1 to Q30 data for each patient. The FS(a,b) function is used in this case.

ss_gs - A matrix containing the Global Scale Scores computed using all Q1 to Q30 data for each patient. The SS_GS(a,b) function is used in this case.

final_data - A data frame formed by replacing the columns 'Q1','Q2',...,'Q30' by the domain-based scale scores.

Value

A data frame by replacing the columns 'Q1','Q2',...,'Q30' by the domain-based scale scores.

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(patient_miss)
qol_miss(patient_miss)
##

Dataset contains survival outcomes and quality of life for breast cancer patients

Description

Creates a dataset containing the domain-based relative hazard ratio (95 the arm-wise data from QLQ-BR23

Usage

surv_br23(x)

Arguments

x

A data frame with ID, time, event, arm, BR_Q31,BR_Q32,...,BR_Q53 columns along with other columns if data is available.

Details

Calculates the domain-wise relative hazard ratio (95

surv_br23 function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It passes the data to the brc_qol() function, which in turn gives the domain-wise scale scores. These domain-wise scale scores are used for calculating the relative hazard ratio (95 the data arm-wise.

The surv_br23 function includes the brc_qol() function which will consider the arm-wise data and calculate the domain-wise scale scores. Hence, two set of domain-wise scale scores will be obtained, one for each arm.

Each of the domain-wise scales, 'BRBI','BRSEF','BRSEE','BRFU','BRST','BRBS','BRAS','BRHL', are considered as the covariates. Using these columns, Cox-Proportional model will be used for univariate analysis for each of the covariates. The hazard ratio (95

Thus, the output will contain three columns, Hazard Ratio(HR), Lower 95

surv_br23(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'BR_Q31' for data from question 31,'BR_Q32' for data from question 32, and so on until 'BR_Q53' for data from question 53.

3) Data must contain columns for 'time', 'event' and 'arm'.

4) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, time, event, arm, BR_Q31,BR_Q32,...,BR_Q53 columns along with other columns if data is available.

Value

A data frame containing the Hazard Ratio(HR), Lower 95

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(brc_df)
surv_br23(brc_df)
##

Dataset contains survival outcomes and quality of life for cancer patients

Description

Creates a dataset containing the domain-based relative hazard ratio (95 the arm-wise data from QLQ-C30

Usage

surv_c30(x)

Arguments

x

A data frame with ID, time, event, arm, Q1,Q2,...,Q30 columns along with other columns if data is available.

Details

Calculates the domain-wise relative hazard ratio (95

surv_c30 function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It passes the data to the qol() function, which in turn gives the domain-wise scale scores. These domain-wise scale scores are used for calculating the relative hazard ratio (95 the data arm-wise.

The surv_c30 function includes the qol() function which will consider the arm-wise data and calculate the domain-wise scale scores. Hence, two set of domain-wise scale scores will be obtained, one for each arm.

Each of the domain-wise scales, 'QL','PF','RF','EF','CF','SF','FA','NV','PA','DY','SL','AP','CO','DI','FI', are considered as the covariates. Using these columns, Cox-Proportional model will be used for univariate analysis for each of the covariates. The hazard ratio (95

Thus, the output will contain three columns, Hazard Ratio(HR), Lower 95

surv_c30(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'Q1' for data from question 1,'Q2' for data from question 2, and so on until 'Q30' for data from question 30.

3) Data must contain columns for 'time', 'event' and 'arm'.

4) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, time, event, arm, Q1,Q2,...,Q30 columns along with other columns if data is available.

Value

A data frame containing the Hazard Ratio(HR), Lower 95

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(c30_df)
surv_c30(c30_df)
##

Dataset contains survival outcomes and quality of life for cancer patients with missing observation

Description

Creates a dataset containing the domain-based relative hazard ratio (95 the arm-wise data from QLQ-C30

Usage

surv_c30_miss(x)

Arguments

x

A data frame with ID, time, event, arm, Q1,Q2,...,Q30 columns along with other columns if data is available.

Details

Calculates the domain-wise relative hazard ratio (95

surv_c30_miss function inputs a dataset where information of some patients are completely missing, that is, some rows contain only NA. It passes the data to the qol_miss() function, which in turn gives the domain-wise scale scores. These domain-wise scale scores are used for calculating the relative hazard ratio (95 the data arm-wise.

The surv_c30_miss function includes the qol_miss() function which will consider the arm-wise data and calculate the domain-wise scale scores. Hence, two set of domain-wise scale scores will be obtained, one for each arm.

Thus, the output will contain three columns, Hazard Ratio(HR), Lower 95

surv_c30_miss(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'Q1' for data from question 1,'Q2' for data from question 2, and so on until 'Q30' for data from question 30.

3) Only those data can be used which contains no information for some patients, that is, some rows contain only NA.

4) Data must contain columns for 'time', 'event' and 'arm'.

5) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, time, event, arm, Q1,Q2,...,Q30 columns along with other columns if data is available.

Value

A data frame containing the Hazard Ratio(HR), Lower 95

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(patient_miss)
surv_c30_miss(patient_miss)
##

Dataset contains survival outcomes and quality of life for head and neck cancer patients

Description

Creates a dataset containing the domain-based relative hazard ratio (95 the arm-wise data from QLQ-HN35

Usage

surv_hn35(x)

Arguments

x

A data frame with ID, time, event, arm, HN_Q31,HN_Q32,...,HN_Q65 columns along with other columns if data is available.

Details

Calculates the domain-wise relative hazard ratio (95

surv_hn35 function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It passes the data to the hnc_qol() function, which in turn gives the domain-wise scale scores. These domain-wise scale scores are used for calculating the relative hazard ratio (95 the data arm-wise.

The surv_hn35 function includes the hnc_qol() function which will consider the arm-wise data and calculate the domain-wise scale scores. Hence, two set of domain-wise scale scores will be obtained, one for each arm.

Each of the domain-wise scales are considered as the covariates. Using these columns, Cox-Proportional model will be used for univariate analysis for each of the covariates. The hazard ratio (95

Thus, the output will contain three columns, Hazard Ratio(HR), Lower 95

surv_hn35(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'HN_Q31' for data from question 31, HN_Q32' for data from question 32, and so on until 'HN_Q65' for data from question 65.

3) Data must contain columns for 'time', 'event' and 'arm'.

4) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, time, event, arm, HN_Q31,HN_Q32,...,HN_Q65 columns along with other columns if data is available.

Value

A data frame containing the Hazard Ratio(HR), Lower 95

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(hnc_df)
surv_hn35(hnc_df)
##

Dataset contains survival outcomes and quality of life for lung cancer patients

Description

Creates a dataset containing the domain-based relative hazard ratio (95 the arm-wise data from QLQ-LC13

Usage

surv_lc13(x)

Arguments

x

A data frame with ID, time, event, arm, LC_Q31,LC_Q32,...,LC_Q42 columns along with other columns if data is available.

Details

Calculates the domain-wise relative hazard ratio (95

surv_lc13 function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It passes the data to the lc_qol() function, which in turn gives the domain-wise scale scores. These domain-wise scale scores are used for calculating the relative hazard ratio (95 the data arm-wise.

The surv_lc13 function includes the lc_qol() function which will consider the arm-wise data and calculate the domain-wise scale scores. Hence, two set of domain-wise scale scores will be obtained, one for each arm.

Each of the domain-wise scales, 'LCDY','LCCO','LCHA','LCSM','LCDS','LCPN','LCHR','LCPC','LCPA','LCPO', are considered as the covariates. Using these columns, Cox-Proportional model will be used for univariate analysis for each of the covariates. The hazard ratio (95

Thus, the output will contain three columns, Hazard Ratio(HR), Lower 95

surv_lc13(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'LC_Q31' for data from question 31,'LC_Q32' for data from question 32, and so on until 'LC_Q42' for data from question 42.

3) Data must contain columns for 'time', 'event' and 'arm'.

4) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, time, event, arm, LC_Q31,LC_Q32,...,LC_Q42 columns along with other columns if data is available.

Value

A data frame containing the Hazard Ratio(HR), Lower 95

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(lc_df)
surv_lc13(lc_df)
##

Dataset contains survival outcomes and quality of life for ovarian cancer patients

Description

Creates a dataset containing the domain-based relative hazard ratio (95 the arm-wise data from QLQ-OV28

Usage

surv_ov28(x)

Arguments

x

A data frame with ID, time, event, arm, OV_Q31,OV_Q32,...,OV_Q58 columns along with other columns if data is available.

Details

Calculates the domain-wise relative hazard ratio (95

surv_ov28 function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It passes the data to the ovc_qol() function, which in turn gives the domain-wise scale scores. These domain-wise scale scores are used for calculating the relative hazard ratio (95 the data arm-wise.

The surv_ov28 function includes the ovc_qol() function which will consider the arm-wise data and calculate the domain-wise scale scores. Hence, two set of domain-wise scale scores will be obtained, one for each arm.

Each of the domain-wise scales, 'Abdominal_GI','Peripheral_Neuropathy','Hormonal','Body_Image', 'Attitude_to_Disease','Chemotherapy_side_effects','Other_Single_Items','Sexuality', are considered as the covariates. Using these columns, Cox-Proportional model will be used for univariate analysis for each of the covariates. The hazard ratio (95

Thus, the output will contain three columns, Hazard Ratio(HR), Lower 95

surv_ov28(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'OV_Q31' for data from question 31,'OV_Q32' for data from question 32, and so on until 'OV_Q58' for data from question 58.

3) Data must contain columns for 'time', 'event' and 'arm'.

4) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, time, event, arm, OV_Q31,OV_Q32,...,OV_Q58 columns along with other columns if data is available.

Value

A data frame containing the Hazard Ratio(HR), Lower 95

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(ovc_df)
surv_ov28(ovc_df)
##

Dataset contains survival outcomes and quality of life for thyroid cancer patients

Description

Creates a dataset containing the domain-based relative hazard ratio (95 the arm-wise data from QLQ-THY34

Usage

surv_thy34(x)

Arguments

x

A data frame with ID, time, event, arm, THY_Q31,THY_Q32,...,THY_Q64 columns along with other columns if data is available.

Details

Calculates the domain-wise relative hazard ratio (95

surv_thy34 function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It passes the data to the thyc_qol() function, which in turn gives the domain-wise scale scores. These domain-wise scale scores are used for calculating the relative hazard ratio (95 the data arm-wise.

The surv_thy34 function includes the thyc_qol() function which will consider the arm-wise data and calculate the domain-wise scale scores. Hence, two set of domain-wise scale scores will be obtained, one for each arm.

Each of the domain-wise scales are considered as the covariates. Using these columns, Cox-Proportional model will be used for univariate analysis for each of the covariates. The hazard ratio (95

Thus, the output will contain three columns, Hazard Ratio(HR), Lower 95

surv_thy34(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'THY_Q31' for data from question 31,'THY_Q32' for data from question 32, and so on until 'THY_Q64' for data from question 64.

3) Data must contain columns for 'time', 'event' and 'arm'.

4) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, time, event, arm, THY_Q31,THY_Q32,...,THY_Q64 columns along with other columns if data is available.

Value

A data frame containing the Hazard Ratio(HR), Lower 95

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(thyc_df)
surv_thy34(thyc_df)
##

Thyroid cancer Quality of Life.

Description

A simulated data for Thyroid cancer Quality of Life.

Usage

thyc_df

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
THY_Q31: Thyroid Cancer Quality of Q31 Question
THY_Q32: Thyroid Cancer Quality of Q32 Question
THY_Q33: Thyroid Cancer Quality of Q33 Question
THY_Q34: Thyroid Cancer Quality of Q34 Question
THY_Q35: Thyroid Cancer Quality of Q35 Question
THY_Q36: Thyroid Cancer Quality of Q36 Question
THY_Q37: Thyroid Cancer Quality of Q37 Question
THY_Q38: Thyroid Cancer Quality of Q38 Question
THY_Q39: Thyroid Cancer Quality of Q39 Question
THY_Q40: Thyroid Cancer Quality of Q40 Question
THY_Q41: Thyroid Cancer Quality of Q41 Question
THY_Q42: Thyroid Cancer Quality of Q42 Question
THY_Q43: Thyroid Cancer Quality of Q43 Question
THY_Q44: Thyroid Cancer Quality of Q44 Question
THY_Q45: Thyroid Cancer Quality of Q45 Question
THY_Q46: Thyroid Cancer Quality of Q46 Question
THY_Q47: Thyroid Cancer Quality of Q47 Question
THY_Q48: Thyroid Cancer Quality of Q48 Question
THY_Q49: Thyroid Cancer Quality of Q49 Question
THY_Q50: Thyroid Cancer Quality of Q50 Question
THY_Q51: Thyroid Cancer Quality of Q51 Question
THY_Q52: Thyroid Cancer Quality of Q52 Question
THY_Q53: Thyroid Cancer Quality of Q53 Question
THY_Q54: Thyroid Cancer Quality of Q54 Question
THY_Q55: Thyroid Cancer Quality of Q55 Question
THY_Q56: Thyroid Cancer Quality of Q56 Question
THY_Q57: Thyroid Cancer Quality of Q57 Question
THY_Q58: Thyroid Cancer Quality of Q58 Question
THY_Q59: Thyroid Cancer Quality of Q59 Question
THY_Q60: Thyroid Cancer Quality of Q60 Question
THY_Q61: Thyroid Cancer Quality of Q61 Question
THY_Q62: Thyroid Cancer Quality of Q62 Question
THY_Q63: Thyroid Cancer Quality of Q63 Question
THY_Q64: Thyroid Cancer Quality of Q64 Question

@source <https://github.com/apstat/QoLMiss-Package>

Thyroid cancer Quality of Life data with missing values.

Description

A simulated data for Thyroid cancer Quality of Life.

Usage

thyc_df_miss

Format

A data frame with 60 rows and 2 variables:

ID: Participant's identification
time: Time Variable
event: status as Variable
arm: Therapeutic Arm
THY_Q31: Thyroid Cancer Quality of Q31 Question
THY_Q32: Thyroid Cancer Quality of Q32 Question
THY_Q33: Thyroid Cancer Quality of Q33 Question
THY_Q34: Thyroid Cancer Quality of Q34 Question
THY_Q35: Thyroid Cancer Quality of Q35 Question
THY_Q36: Thyroid Cancer Quality of Q36 Question
THY_Q37: Thyroid Cancer Quality of Q37 Question
THY_Q38: Thyroid Cancer Quality of Q38 Question
THY_Q39: Thyroid Cancer Quality of Q39 Question
THY_Q40: Thyroid Cancer Quality of Q40 Question
THY_Q41: Thyroid Cancer Quality of Q41 Question
THY_Q42: Thyroid Cancer Quality of Q42 Question
THY_Q43: Thyroid Cancer Quality of Q43 Question
THY_Q44: Thyroid Cancer Quality of Q44 Question
THY_Q45: Thyroid Cancer Quality of Q45 Question
THY_Q46: Thyroid Cancer Quality of Q46 Question
THY_Q47: Thyroid Cancer Quality of Q47 Question
THY_Q48: Thyroid Cancer Quality of Q48 Question
THY_Q49: Thyroid Cancer Quality of Q49 Question
THY_Q50: Thyroid Cancer Quality of Q50 Question
THY_Q51: Thyroid Cancer Quality of Q51 Question
THY_Q52: Thyroid Cancer Quality of Q52 Question
THY_Q53: Thyroid Cancer Quality of Q53 Question
THY_Q54: Thyroid Cancer Quality of Q54 Question
THY_Q55: Thyroid Cancer Quality of Q55 Question
THY_Q56: Thyroid Cancer Quality of Q56 Question
THY_Q57: Thyroid Cancer Quality of Q57 Question
THY_Q58: Thyroid Cancer Quality of Q58 Question
THY_Q59: Thyroid Cancer Quality of Q59 Question
THY_Q60: Thyroid Cancer Quality of Q60 Question
THY_Q61: Thyroid Cancer Quality of Q61 Question
THY_Q62: Thyroid Cancer Quality of Q62 Question
THY_Q63: Thyroid Cancer Quality of Q63 Question
THY_Q64: Thyroid Cancer Quality of Q64 Question

@source <https://github.com/apstat/QoLMiss-Package>

Calculates the domain-based scale scores of Thyroid cancer using the data of QLQ-THY34

Description

Creates a dataset containing the domain-based scale scores using the data from QLQ-THY34

Usage

thyc_qol(x)

Arguments

x

A data frame with ID, THY_Q31,THY_Q32,...,THY_Q64 columns along with other columns if data is available.

Details

brc_miss function inputs either a dataset containing missing information, represented as, 9 or 99 or NA or a data not containing any missing information. It extracts only the columns named 'THY_Q31','THY_Q32',...,'THY_Q64' and replaces the missing data with the minimum value of the particular question.

Using each of the 30 columns, the Raw Score is computed, and one column is obtained containing the Raw Score for each patient.

Further, using each of the Raw Scores, three domain-based Scale Scores are computed, they are, Functional Scales Score and Symptoms Scales Score.

Thus, the columns 'THY_Q31','THY_Q32',...,'THY_Q64' are replaced by the domain-based scale scores, which is obtained as the output.

thyc_qol(x)

1) Subject ID column should be named as 'ID'.

2) Each question column should be named as 'THY_Q31' for data from question 31, 'THY_Q32' for data from question 32, and so on until 'THY_Q64' for data from question 64

3) Data may contain more variables, such as, Age, Gender, etc.

x - A data frame with ID, THY_Q31,THY_Q32,...,THY_Q64 columns along with other columns if data is available.

rs - A matrix containing the Raw Score computed using all THY_Q31 to THY_Q64 data for each patient. The RS(a) function is used in this case.

ss - A matrix containing the Global Scale Scores computed using all THY_Q31 to THY_Q64 data for each patient. The SS(a,b) function is used in this case.

final_data - A data frame formed by replacing the columns 'THY_Q31','THY_Q32',...,'THY_Q64' by the domain-based scale scores.

Value

A data frame by replacing the columns 'THY_Q31','THY_Q32',...,'THY_Q64' by the domain-based scale scores.

Author(s)

Atanu Bhattacharjee and Ankita Pal

References

QoLMiss: Package for Repeatedly measured Quality of Life of Cancer Patients Data

Examples

##
data(thyc_df)
thyc_qol(thyc_df)
data(thyc_df_miss)
thyc_qol(thyc_df_miss)
##

Breast cancer Quality of Life.

Description

Usage

Format

Breast cancer Quality of Life with missing values.

Description

Usage

Format

Calculates the domain-based scale scores using the data of QLQ-BR23

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Simulated data for cancer Quality of Life.

Description

Usage

Format

Data for cancer Quality of Life with missing values.

Description

Usage

Format

Head and Neck cancer Quality of Life data.

Description

Usage

Format

Head and Neck cancer data for cancer Quality of Life with missing values.

Description

Usage

Format

Calculates the domain-based scale scores using the data of QLQ-HN35

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Simulated data for Lung cancer Quality of Life.

Description

Usage

Format

Lung cancer data for cancer Quality of Life with missing values.

Description

Usage

Format

Calculates the domain-based scale scores using the data of QLQ-LC13.

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Simulated data for Ovarian Cancer Quality of Life.

Description

Usage

Format

Ovarian cancer Quality of Life data with missing values.

Description

Usage

Format

Calculates the domain-based scale scores using the data of QLQ-OV28.

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples