Test Datasets for Survival Continuous Variable Function
Source:R/data_survivalcont_docs.R
survivalcont_test_datasets.RdTest Datasets for Survival Continuous Variable Function
Usage
survivalcont_test
survivalcont_ki67
survivalcont_psa
survivalcont_hemoglobin
survivalcont_tumorsize
survivalcont_age
survivalcont_compete
survivalcont_dates
survivalcont_landmark
survivalcont_multicut
survivalcont_expression
survivalcont_small
survivalcont_nocutoff
survivalcont_extreme
survivalcont_missing
survivalcont_constant
survivalcont_largeFormat
survivalcont_test
Main test dataset (150 observations):
- patient_id
Patient identifier
- time_months
Survival time in months
- outcome
Outcome status (Alive/Dead)
- biomarker
Continuous biomarker (mean=100, SD=25)
- age
Patient age
- sex
Patient sex
survivalcont_ki67
Ki67 proliferation index data (140 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- ki67_percent
Ki67 percentage (0-100)
- tumor_grade
Tumor grade
- stage
Disease stage
survivalcont_psa
PSA level data (130 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- psa_level
PSA level (ng/mL)
- gleason_score
Gleason score (6-10)
- treatment
Treatment type
survivalcont_hemoglobin
Hemoglobin level data (120 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- hemoglobin_gL
Hemoglobin level (g/L)
- performance_status
ECOG performance status
survivalcont_tumorsize
Tumor size data (135 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- tumor_size_cm
Tumor size in cm
- lymph_nodes
Lymph node status
- histology
Histological type
survivalcont_age
Age as continuous predictor (145 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- age_years
Patient age in years
- comorbidity_index
Comorbidity index
survivalcont_compete
Competing risks data (110 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome (Alive_NED/Alive_Disease/Dead_Disease/Dead_Other)
- biomarker_score
Continuous biomarker score
- risk_category
Risk category
survivalcont_dates
Date-based data (100 observations):
- patient_id
Patient identifier
- diagnosis_date
Diagnosis date (YYYY-MM-DD)
- followup_date
Follow-up date (YYYY-MM-DD)
- outcome
Outcome status
- continuous_marker
Continuous marker
survivalcont_landmark
Landmark analysis data (125 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- early_response_score
Early response score
- baseline_score
Baseline score
survivalcont_multicut
Multiple cutoff optimization data (150 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- risk_score
Risk score with clear stratification
survivalcont_expression
Gene expression data (115 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- gene_expression
Gene expression level
- mutation_status
Mutation status
survivalcont_small
Small dataset (20 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- continuous_var
Continuous variable
survivalcont_nocutoff
No clear cutoff data (80 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- random_marker
Random marker (no survival correlation)
survivalcont_extreme
Extreme values data (70 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- extreme_values
Values with outliers
survivalcont_missing
Data with missing values (90 observations):
- patient_id
Patient identifier
- time_months
Survival time (with NAs)
- outcome
Outcome status (with NAs)
- biomarker
Biomarker (with NAs)
- covariate
Categorical covariate
survivalcont_constant
Constant variable data (50 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- constant_marker
Constant marker (no variation)
survivalcont_large
Large dataset for performance testing (500 observations):
- patient_id
Patient identifier
- time_months
Survival time
- outcome
Outcome status
- biomarker
Continuous biomarker
- age
Patient age
- stage
Disease stage
- grade
Tumor grade
- sex
Patient sex
- site
Study site
An object of class tbl_df (inherits from tbl, data.frame) with 140 rows and 6 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 130 rows and 6 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 120 rows and 5 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 135 rows and 6 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 145 rows and 5 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 110 rows and 5 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 100 rows and 5 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 125 rows and 5 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 150 rows and 4 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 115 rows and 5 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 20 rows and 4 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 80 rows and 4 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 70 rows and 4 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 90 rows and 5 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 50 rows and 4 columns.
An object of class tbl_df (inherits from tbl, data.frame) with 500 rows and 9 columns.
Examples
data(survivalcont_test)
if (FALSE) { # \dontrun{
survivalcont(
data = survivalcont_test,
elapsedtime = "time_months",
outcome = "outcome",
outcomeLevel = "Dead",
contexpl = "biomarker",
findcut = TRUE,
sc = TRUE
)
} # }