EFM

Description

The electronic fetal monitoring (EFM) and cesarean section (CS) dataset from Neutra, Greenland, and Friedman (1980) consists of observations on 14,484 women who delivered at Beth Israel Hospital, Boston from January 1970 to December 1975. The purpose of the study is to evaluate the impact of EFM on cesarean section (CS) rates. It is found by Neutra, Greenland, and Friedman (1980) that relevant confounding factors are: nulliparity (nullipar), arrest of labor progression (arrest), malpresentation (breech), and year of study (year). The dataset provided in the R package is from the supplementary materials of Richardson, Robins, and Wang (2017), who used this dataset to illustrate their proposed methods for modeling and estimating relative risk and risk difference.

Usage

EFM

Format

A data frame with 14484 rows and 6 variables:

cesarean

Outcome: 1 if delivery was via cesarean section; 0 otherwise

monitor

Treatment: 1 if electronic fetal monitoring (EFM) was used; 0 otherwise

arrest

Covariate: 1 = arrest of labor progression; 0 otherwise

breech

Covariate: 1 = malpresentation (breech); 0 otherwise

nullipar

Covariate: 1 = nulliparity; 0 otherwise

year

Year of study: 0,...,5 (actual values are 1970,...,1975)

Source

The dataset from Neutra, Greenland, and Friedman (1980) is available as part of supplementary materials of Richardson, Robins, and Wang (2017) on Journal of the American Statistical Association website at doi: 10.1080/01621459.2016.1192546.

References

Neutra, R.R., Greenland, S. and Friedman, E.A., 1980. Effect of fetal monitoring on cesarean section rates. Obstetrics and gynecology, 55(2), pp.175-180.

Richardson, T.S., Robins, J.M. and Wang, L., 2017. On modeling and estimation for the relative risk and risk difference. Journal of the American Statistical Association, 112(519), pp.1121-1130.

RHC

Description

The right heart catheterization (RHC) dataset is publicly available on the Vanderbilt Biostatistics website. RHC is a diagnostic procedure for directly measuring cardiac function in critically ill patients. The dependent variable is 1 if a patient survived after 30 days of admission, 0 if a patient died within 30 days. The treatment variable is 1 if RHC was applied within 24 hours of admission, and 0 otherwise. The sample size was n = 5735, and 2184 patients were treated with RHC. Connors et al. (1996) used a propensity score matching approach to study the efficacy of RHC, using data from the observational study called SUPPORT (Murphy and Cluff, 1990). Many authors used this dataset subsequently. The 72 covariates are constructed, following Hirano and Imbens (2001).

Usage

RHC

Format

A data frame with 5735 rows and 74 variables:

survival

Outcome: 1 if a patient survived after 30 days of admission, and 0 if a patient died within 30 days

RHC

Treatment: 1 if RHC was applied within 24 hours of admission, and 0 otherwise.

age

Age in years

edu

Years of education

cardiohx

Cardiovascular symptoms

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

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

Immunosuppression, organ transplant, HIV, Diabetes Mellitus, Connective Tissue Disease

transhx

transfer (> 24 hours) from another hospital

amihx

Definite myocardial infarction

das2d3pc

DASI - Duke Activity Status Index

surv2md1

Estimate of 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/FI02 ratio

paco21

PaCO2

ph1

PH

wblc1

WBC

hema1

Hematocrit

sod1

Sodium

pot1

Potassium

crea1

Creatinine

bili1

Bilirubin

alb1

Albumin

cat1_CHF

1 if the primary disease category is CHF, and 0 otherwise (Omitted category = ARF).

cat1_Cirrhosis

1 if the primary disease category is Cirrhosis, and 0 otherwise (Omitted category = ARF).

cat1_Colon_Cancer

1 if the primary disease category is Colon Cancer, and 0 otherwise (Omitted category = ARF).

cat1_Coma

1 if the primary disease category is Coma, and 0 otherwise (Omitted category = ARF).

cat1_COPD

1 if the primary disease category is COPD, and 0 otherwise (Omitted category = ARF).

cat1_Lung_Cancer

1 if the primary disease category is Lung Cancer, and 0 otherwise (Omitted category = ARF).

cat1_MOSF_Malignancy

1 if the primary disease category is MOSF w/Malignancy, and 0 otherwise (Omitted category = ARF).

cat1_MOSF_Sepsis

1 if the primary disease category is MOSF w/Sepsis, and 0 otherwise (Omitted category = ARF).

ca_Metastatic

1 if cancer is metastatic, and 0 otherwise (Omitted category = no cancer).

ca_Yes

1 if cancer is localized, and 0 otherwise (Omitted category = no cancer).

ninsclas_Medicaid

1 if medical insurance category is Medicaid, and 0 otherwise (Omitted category = Private).

ninsclas_Medicare

1 if medical insurance category is Medicare, and 0 otherwise (Omitted category = Private).

ninsclas_Medicare_and_Medicaid

1 if medical insurance category is Medicare & Medicaid, and 0 otherwise (Omitted category = Private).

ninsclas_No_insurance

1 if medical insurance category is No Insurance, and 0 otherwise (Omitted category = Private).

ninsclas_Private_and_Medicare

1 if medical insurance category is Private & Medicare, and 0 otherwise (Omitted category = Private).

race_black

1 if Black, and 0 otherwise (Omitted category = White).

race_other

1 if Other, and 0 otherwise (Omitted category = White).

income3

1 if Income >$50k, and 0 otherwise (Omitted category = under $11k).

income1

1 if Income $11–$25k, and 0 otherwise (Omitted category = under $11k).

income2

1 if Income $25–$50k, and 0 otherwise (Omitted category = under $11k).

resp_Yes

Respiratory diagnosis

card_Yes

Cardiovascular diagnosis

neuro_Yes

Neurological diagnosis

gastr_Yes

Gastrointestinal diagnosis

renal_Yes

Renal diagnosis

meta_Yes

Metabolic diagnosis

hema_Yes

Hematological diagnosis

seps_Yes

Sepsis diagnosis

trauma_Yes

Trauma diagnosis

ortho_Yes

Orthopedic diagnosis

dnr1_Yes

Do Not Resuscitate status on day 1

sex_Female

Female

cat2_Cirrhosis

1 if the secondary disease category is Cirrhosis, and 0 otherwise (Omitted category = NA).

cat2_Colon_Cancer

1 if secondary disease category is Colon Cancer, and 0 otherwise (Omitted category = NA).

cat2_Coma

1 if the secondary disease category is Coma, and 0 otherwise (Omitted category = NA).

cat2_Lung_Cancer

1 if the secondary disease category is Lung Cancer, and 0 otherwise (Omitted category = NA).

cat2_MOSF_Malignancy

1 if the secondary disease category is MOSF w/Malignancy, and 0 otherwise (Omitted category = NA).

cat2_MOSF_Sepsis

1 if the secondary disease category is MOSF w/Sepsis, and 0 otherwise (Omitted category = NA).

wt0

weight = 0 (missing)

Source

The dataset is publicly available on the Vanderbilt Biostatistics website at https://hbiostat.org/data/.

References

Connors, A.F., Speroff, T., Dawson, N.V., Thomas, C., Harrell, F.E., Wagner, D., Desbiens, N., Goldman, L., Wu, A.W., Califf, R.M. and Fulkerson, W.J., 1996. The effectiveness of right heart catheterization in the initial care of critically III patients. JAMA, 276(11), pp.889-897. doi: 10.1001/jama.1996.03540110043030

Hirano, K., Imbens, G.W. Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization, 2001. Health Services & Outcomes Research Methodology 2, pp.259–278. doi: 10.1023/A:1020371312283

D. J. Murphy, L. E. Cluff, SUPPORT: Study to understand prognoses and preferences for outcomes and risks of treatments—study design, 1990. Journal of Clinical Epidemiology, 43, pp. 1S–123S https://www.jclinepi.com/issue/S0895-4356(00)X0189-8 .

Bounding the average treatment effect (ATE)

Description

Bounds the average treatment effect (ATE) under the unconfoundedness assumption without the overlap condition.

Usage

atebounds(
  Y,
  D,
  X,
  rps,
  Q = 3L,
  studentize = TRUE,
  alpha = 0.05,
  x_discrete = FALSE,
  n_hc = NULL
)

Arguments

Value

An S3 object of type "ATbounds". The object has the following elements.

References

Sokbae Lee and Martin Weidner. Bounding Treatment Effects by Pooling Limited Information across Observations.

Examples

  Y <- RHC[,"survival"]
  D <- RHC[,"RHC"]
  X <- RHC[,c("age","edu")]
  rps <- rep(mean(D),length(D))
  results_ate <- atebounds(Y, D, X, rps, Q = 3)

Bounding the average treatment effect on the treated (ATT)

Description

Bounds the average treatment effect on the treated (ATT) under the unconfoundedness assumption without the overlap condition.

Usage

attbounds(
  Y,
  D,
  X,
  rps,
  Q = 3L,
  studentize = TRUE,
  alpha = 0.05,
  x_discrete = FALSE,
  n_hc = NULL
)

Arguments

Value

An S3 object of type "ATbounds". The object has the following elements.

References

Sokbae Lee and Martin Weidner. Bounding Treatment Effects by Pooling Limited Information across Observations.

Examples

  Y <- RHC[,"survival"]
  D <- RHC[,"RHC"]
  X <- RHC[,c("age","edu")]
  rps <- rep(mean(D),length(D))
  results_att <- attbounds(Y, D, X, rps, Q = 3)

Simulating observations from the data-generating process considered in Lee and Weidner (2021)

Description

Simulates observations from the data-generating process considered in Lee and Weidner (2021)

Usage

simulation_dgp(n, ps_spec = "overlap", x_discrete = FALSE)

Arguments

Value

An S3 object of type "ATbounds". The object has the following elements.

References

Sokbae Lee and Martin Weidner. Bounding Treatment Effects by Pooling Limited Information across Observations.

Examples

  data <- simulation_dgp(100, ps_spec = "overlap")
  y <- data$outcome
  d <- data$treat
  x <- data$covariate
  ate <- data$ate_oracle
  att <- data$att_oracle

Summary method for ATbounds objects

Description

Produce a summary for an ATbounds object.

Usage

## S3 method for class 'ATbounds'
summary(object, ...)

Arguments

Value

A summary is produced with bounds estimates and confidence intervals. In addition, it has the following elements.

References

Sokbae Lee and Martin Weidner. Bounding Treatment Effects by Pooling Limited Information across Observations.

Examples

  Y <- RHC[,"survival"]
  D <- RHC[,"RHC"]
  X <- RHC[,c("age","edu")]
  rps <- rep(mean(D),length(D))
  results_ate <- atebounds(Y, D, X, rps, Q = 3)
  summary(results_ate)