This dataset was used in Connors et al. (1996): The effectiveness of RHC in the initial care of critically ill patients. J American Medical Association 276:889-897. The dataset pertains to day 1 of hospitalization, i.e., the "treatment" variable swang1 is whether or not a patient received a RHC (also called the Swan-Ganz catheter) on the first day in which the patient qualified for the SUPPORT study (see above). The dataset is suitable for use in papers submitted in response to the call for papers on causal inference, by the journal Health Services and Outcomes Research Methodology. The original analysis by Connors et al. used binary logistic model to develop a propensity score that was then used for matching RHC patients with non-RHC patients. A sensitivity analysis was also done. The results provided some evidence that patients receiving RHC had decreased survival time, and the sensitivity analysis indicated that any unmeasured confounder would have to be somewhat strong to explain away the results. See Lin DY, Psaty BM, Kronmal RA (1998): Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics 54:948-963 for useful methods for sensitivity analysis, one of which was applied to the RHC results.
Here is a .zip containing the original SAS code used to do the published analyses, from Charles Thomas. A reverse chronological directory of the SAS code files is here.
The S-Plus dataset is easy to use if you have the Hmisc library in effect. If you don't, you need to define the following S-Plus functions.
ddmmmyy <- function(x) { y <- month.day.year(trunc(oldUnclass(x)), attr(x,"origin")) yr <- y$year m <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct", "Nov","Dec")[y$month] ifelse(yr<1900 | yr>=2000, paste(y$day,m,yr,sep=""), paste(y$day,m,yr-1900,sep="")) } "[.labelled"<- function(x, ...) { lab <- attr(x, "label") x <- NextMethod("[") attr(x, "label") <- lab if(!inherits(x,'labelled')) attr(x,'class') <- c("labelled", attr(x,'class')) x } as.data.frame.labelled <- function(x, ...) { y <- x cy <- attr(y,'class') cy <- if(length(cy)>1) cy[cy!='labelled'] else NULL if(length(cy)==0) cy <- NULL # handles wierd case e.g. class=rep('lab..',2) attr(y,'class') <- cy d <- data.class(y) methodname <- paste("as.data.frame", d, sep = '.') if(exists(methodname, mode = "function")) (get(methodname, mode = "function"))(x, ...) else { if(options()$check) warning(paste("no method for coercing",d,"to data.frame")) as.data.frame.AsIs(y, ...) } }If using S-Plus 5.x or 6.x, you don't need to define the last two functions. Instead you will need to run the data frame through a special function cleanup.import to remove old S-Plus classes.
August 11, 2000
Division of Biostatistics and Epidemiology
Department of Health Evaluation Sciences
University of Virginia Health System
This documents the SAS transport data file and the S-Plus data file for The Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT).
Table 1: SUPPORT Variable Description
Variable name |
Variable Definition |
Age |
Age |
Sex |
Sex |
Race |
Race |
Edu |
Years of education |
Income |
Income |
Ninsclas |
Medical insurance |
Cat1 |
Primary disease category |
Cat2 |
Secondary disease category |
Categories of admission diagnosis: |
|
Resp |
Respiratory Diagnosis |
Card |
Cardiovascular Diagnosis |
Neuro |
Neurological Diagnosis |
Gastr |
Gastrointestinal Diagnosis |
Renal |
Renal Diagnosis |
Meta |
Metabolic Diagnosis |
Hema |
Hematologic Diagnosis |
Seps |
Sepsis Diagnosis |
Trauma |
Trauma Diagnosis |
Ortho |
Orthopedic Diagnosis |
Adld3p |
ADL |
Das2d3pc |
DASI ( Duke Activity Status Index) |
Dnr1 |
DNR status on day1 |
Ca |
Cancer |
Surv2md1 |
Support model estimate of the prob. of surviving 2 months |
Aps1 |
APACHE score |
Scoma1 |
Glasgow Coma Score |
Wtkilo1 |
Weight |
Temp1 |
Temperature |
Meanbp1 |
Mean blood pressure |
Resp1 |
Respiratory rate |
Hrt1 |
Heart rate |
Pafi1 |
PaO2/FIO2 ratio |
Paco21 |
PaCo2 |
Ph1 |
PH |
Wblc1 |
WBC |
Hema1 |
Hematocrit |
Sod1 |
Sodium |
Pot1 |
Potassium |
Crea1 |
Creatinine |
Bili1 |
Bilirubin |
Alb1 |
Albumin |
Urin1 |
Urine output |
Categories of comorbidities illness: |
|
Cardiohx |
Acute MI, Peripheral Vascular Disease, Severe Cardiovascular Symptoms (NYHA-Class III), Very Severe Cardiovascular Symptoms (NYHA-Class IV) |
Chfhx |
Congestive Heart Failure |
Dementhx |
Dementia, Stroke or Cerebral Infarct, Parkinson’s Disease |
Psychhx |
Psychiatric History, Active Psychosis or Severe Depression |
Chrpulhx |
Chronic Pulmonary Disease, Severe Pulmonary Disease, Very Severe Pulmonary Disease |
Renalhx |
Chronic Renal Disease, Chronic Hemodialysis or Peritoneal Dialysis |
Liverhx |
Cirrhosis, Hepatic Failure |
Gibledhx |
Upper GI Bleeding |
Malighx |
Solid Tumor, Metastatic Disease, Chronic Leukemia/Myeloma, Acute Leukemia, Lymphoma |
Immunhx |
Immunosupperssion, Organ Transplant, HIV Positivity, Diabetes Mellitus Without End Organ Damage, Diabetes Mellitus With End Organ Damage, Connective Tissue Disease |
Transhx |
Transfer (> 24 Hours) from Another Hospital |
Amihx |
Definite Myocardial Infarction |
|
|
Swang1 |
Right Heart Catheterization (RHC) |
Sadmdte |
Study Admission Date |
Dthdte |
Date of Death |
Lstctdte |
Date of Last Contact |
Dschdte |
Hospital Discharge Date |
Death |
Death at any time up to 180 Days |
Ptid |
Patient ID |