Test Datasets for Time Interval Function

A collection of test datasets for the timeinterval function, which calculates time intervals between dates for survival analysis and person-time calculations.

Usage

timeinterval_test

timeinterval_ymd

timeinterval_dmy

timeinterval_mdy

timeinterval_ymdhms

timeinterval_landmark

timeinterval_quality

timeinterval_extreme

timeinterval_small

timeinterval_large

timeinterval_trial

timeinterval_shortterm

timeinterval_longterm

timeinterval_sameday

timeinterval_negative

timeinterval_allmissing

timeinterval_mixedformat

Format

`timeinterval_test`

Main test dataset with diagnosis and follow-up dates (100 patients):

patient_id: Character. Patient identifier
diagnosis_date: Character. Start date (YYYY-MM-DD format)
followup_date: Character. End date (YYYY-MM-DD format)
age: Numeric. Patient age
sex: Character. Male/Female
treatment: Character. Treatment type
stage: Character. Disease stage (I-IV)
event_occurred: Integer. Event indicator (0/1)

`timeinterval_ymd`

Standard YMD format (50 observations):

id: Character. Identifier
start_date: Character. Start date (YYYY-MM-DD)
end_date: Character. End date (YYYY-MM-DD)

`timeinterval_dmy`

European DMY format (50 observations):

id: Character. Identifier
start_date: Character. Start date (DD-MM-YYYY)
end_date: Character. End date (DD-MM-YYYY)

`timeinterval_mdy`

US MDY format (50 observations):

id: Character. Identifier
start_date: Character. Start date (MM-DD-YYYY)
end_date: Character. End date (MM-DD-YYYY)

`timeinterval_ymdhms`

DateTime format with hours/minutes/seconds (50 observations):

id: Character. Identifier
start_datetime: Character. Start datetime
end_datetime: Character. End datetime

`timeinterval_landmark`

Data for landmark analysis (80 patients):

patient_id: Character. Patient identifier
enrollment_date: Character. Study enrollment date
last_contact: Character. Last follow-up contact
treatment_arm: Character. Treatment group
biomarker_positive: Character. Biomarker status

`timeinterval_quality`

Dataset with quality issues (60 observations, ~10% missing, some negative intervals)

`timeinterval_extreme`

Dataset with extreme values (40 observations: very short, normal, very long durations)

`timeinterval_small`

Minimal dataset (5 observations)

`timeinterval_large`

Large dataset for performance testing (500 observations)

`timeinterval_trial`

Realistic clinical trial simulation (150 patients):

patient_id: Character. Trial patient ID
enrollment_date: Character. Enrollment date
followup_date: Character. Last follow-up
treatment_arm: Character. Trial arm
site: Character. Study site
age: Numeric. Patient age
ecog_ps: Integer. ECOG performance status
progression: Integer. Disease progression (0/1)
death: Integer. Death event (0/1)

Other Datasets

timeinterval_shortterm: Hospital stays (1-14 days)
timeinterval_longterm: Long-term study (5-15 years)
timeinterval_sameday: Zero-interval (same start and end)
timeinterval_negative: Negative intervals (end before start)
timeinterval_allmissing: All missing dates
timeinterval_mixedformat: Inconsistent date formats

An object of class tbl_df (inherits from tbl, data.frame) with 50 rows and 3 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 80 rows and 5 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 60 rows and 3 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 40 rows and 3 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 5 rows and 3 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 500 rows and 4 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 150 rows and 9 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 30 rows and 3 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 40 rows and 3 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 10 rows and 3 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 15 rows and 3 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 10 rows and 3 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 20 rows and 3 columns.

Source

Generated using data-raw/timeinterval_test_data.R (seed = 42)

Supported Date Formats

auto: Automatic detection
ymd: YYYY-MM-DD (ISO 8601)
dmy: DD-MM-YYYY (European)
mdy: MM-DD-YYYY (US)
ymdhms: YYYY-MM-DD HH:MM:SS

Time Units

days: Day-level precision
weeks: 7-day periods
months: 30.44-day months (standardized) or calendar months
years: 365.25-day years (standardized) or calendar years

Examples

# Load main test dataset
data(timeinterval_test)
head(timeinterval_test)
#> # A tibble: 6 × 8
#>   patient_id diagnosis_date followup_date   age sex    treatment    stage
#>   <chr>      <chr>          <chr>         <dbl> <chr>  <chr>        <chr>
#> 1 PT001      2020-02-18     2022-11-12       59 Male   Radiation    IV   
#> 2 PT002      2020-11-16     2021-10-22       58 Male   Chemotherapy IV   
#> 3 PT003      2020-06-01     2022-12-29       49 Female Radiation    I    
#> 4 PT004      2020-03-14     2021-07-30       61 Male   Chemotherapy I    
#> 5 PT005      2020-08-15     2022-12-03       44 Male   Radiation    III  
#> 6 PT006      2020-05-25     2020-08-04       61 Female Combined     III  
#> # ℹ 1 more variable: event_occurred <int>

# Basic time interval calculation
if (FALSE) { # \dontrun{
timeinterval(
  data = timeinterval_test,
  dx_date = "diagnosis_date",
  fu_date = "followup_date",
  time_format = "ymd",
  output_unit = "months"
)
} # }

# Landmark analysis
if (FALSE) { # \dontrun{
data(timeinterval_landmark)
timeinterval(
  data = timeinterval_landmark,
  dx_date = "enrollment_date",
  fu_date = "last_contact",
  use_landmark = TRUE,
  landmark_time = 6,
  output_unit = "months"
)
} # }

Usage

Format

timeinterval_test

timeinterval_ymd

timeinterval_dmy

timeinterval_mdy

timeinterval_ymdhms

timeinterval_landmark

timeinterval_quality

timeinterval_extreme

timeinterval_small

timeinterval_large

timeinterval_trial