| Title: | One-Sample Log-Rank Test |
| Version: | 0.9.2 |
| Imports: | magrittr, dplyr, ggplot2, survival, survminer, rlang |
| Description: | The log-rank test is performed to assess the survival outcomes between two group. When there is no proper control group or obtaining such data is cumbersome, one sample log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003)<doi:10.1093/jnci/djt227> and variation of the test for small sample sizes which is detailed in FD Liddell (1984)<doi:10.1136/jech.38.1.85> paper. Visualization function in the package generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.2 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2024-02-02 15:50:36 UTC; dkangeyan |
| Author: | Divy Kangeyan [aut, cre], Jin Xie [aut] |
| Maintainer: | Divy Kangeyan <dkangeyan@kitepharma.com> |
| Depends: | R (≥ 3.5.0) |
| Repository: | CRAN |
| Date/Publication: | 2024-02-03 12:30:15 UTC |
Population data (1999-2020)
Description
This data set contains mortality rate for various demographic groups by age. all column has overall mortality rate for all groups. Race groups are indicated by the following notation: w - white, b - black, ai - american indian and as - asian. Female and Males are indicated by an additional suffix _f and _m.
Usage
dataPop_1999_2020Format
A dataframe with 16 columns and 151 rows.
Source
CDC Wonder Database. Data Colleceted from 1999 - 2020
References
https://wonder.cdc.gov/
Population data (2018_2021)
Description
This data set contains mortality rate for various demographic groups by age. all column has overall mortality rate for all groups. Race groups are indicated by the following notation: w - white, b - black, ai - american indian and as - asian, nh - native hawaiian. Female and Males are indicated by an additional suffix _f and _m.
Usage
dataPop_2018_2021Format
A dataframe with 19 columns and 151 rows.
Source
CDC Wonder Database. Data Colleceted from 2018_2021
References
https://wonder.cdc.gov/
Population data (2018_2021) by race, sex, ethnicity
Description
This data set contains mortality rate for various demographic groups by age. all column has overall mortality rate for all groups. Race groups are indicated by the following notation: w - white, b - black, ai - american indian and as - asian, nh - native hawaiian. Female and Males are indicated by an additional suffix _f and _m.
Usage
dataPop_2018_2021Format
A dataframe with 16 columns and 151 rows.
Source
CDC Wonder Database. Data Colleceted from 2018_2021
References
https://wonder.cdc.gov/
Survival data
Description
This data set is obtained from Finkelstein et al. paper that contains the following five columns: age, time, event status, sex and race.
Usage
dataSurvFormat
A dataframe with 5 columns and 33 rows.
Source
Finkelstein et al. (2003)
References
Finkelstein, D. M., Muzikansky, A., & Schoenfeld, D. A. (2003). Comparing survival of a sample to that of a standard population. Journal of the National Cancer Institute, 95(19), 1434-1439.
Survival data
Description
This data set is subset of data obtained from Finkelstein et al. paper that contains the following five columns: age, time, event status, sex and race. In order to apply the exact test 12 patients were randomly selected out of 33 patients.
Usage
dataSurvFormat
A dataframe with 5 columns and 12 rows.
Source
Finkelstein et al. (2003)
References
Finkelstein, D. M., Muzikansky, A., & Schoenfeld, D. A. (2003). Comparing survival of a sample to that of a standard population. Journal of the National Cancer Institute, 95(19), 1434-1439.
Find Matched Cumulative Survival Probability
Description
Find Matched Cumulative Survival Probability
Usage
findMatchedCumuSurvProb(time, ageDiag, sex, race, dataPop, maxFollowUp = NULL)
Arguments
time |
follow up length |
ageDiag |
age at diagnosis |
sex |
sex |
race |
race |
dataPop |
Population level mortality data |
maxFollowUp |
maximum follow-up, if max follow-up not provided then the time would be considered until death or censoring |
Value
matched survival probability
Examples
# load data
data(dataSurv_small)
data(dataPop_2018_2021)
# Extract info for the first subject
time_vec <- dataSurv_small$time[1]
age_vec <- dataSurv_small$age[1]
sex_vec <- dataSurv_small$sex[1]
race_vec <- dataSurv_small$race[1]
# Generate cumulative survival probability
findMatchedCumuSurvProb(time = time_vec, ageDiag = age_vec, sex = sex_vec,
race = race_vec, dataPop = dataPop_2018_2021)
#If maximum followup is determined to be 20 years
findMatchedCumuSurvProb(time = time_vec, ageDiag = age_vec, sex = sex_vec,
race = race_vec, dataPop = dataPop_2018_2021, maxFollowUp = 20)
Calculate One-Sample Log-Rank Test
Description
Calculate One-Sample Log-Rank Test
Usage
oneSampleLogRankTest(dataSurv, dataPop, type = c("exact", "approximate"))
Arguments
dataSurv |
Survival data |
dataPop |
Population data |
type |
Type of test |
Value
p-value for one-sample log-rank test
Examples
# load data
data(dataSurv_small)
data(dataPop_2018_2021)
# Since the dataset is small run an exact test
oneSampleLogRankTest(dataSurv_small, dataPop_2018_2021, type = "exact")
Plot Kaplan-Meier Curve against Population
Description
Plot Kaplan-Meier Curve against Population
Usage
plotKM(dataSurv, dataPop, type = c("exact", "approximate"))
Arguments
dataSurv |
Survival data |
dataPop |
Population data |
type |
Type of test to conduct in order to display p-value |
Value
ggplot object
Examples
# load data
data(dataSurv_small)
data(dataPop_2018_2021)
plotKM(dataSurv_small, dataPop_2018_2021, type = "exact")
Simultaed Clinical data
Description
This data is simulated data from clinical trial data that contains five columns: race, sex, age, event status and time in years.
Usage
simulated_clinical_dataFormat
A dataframe with 5 columns and 500 rows.
Source
Simulated
References
None