est45 {glmfitmiss} | R Documentation |
EST data – Eastern Cooperative Oncology Group clinical trials, EST 2282
Description
The dataset est45 is from the Eastern Cooperative Oncology Group clinical trials, specifically EST 2282 (Falkson, Cnaan, and Simson, 1990) and EST 1286 (Falkson et al., 1995) containing 45 observations. The dataset consists of 191 observations. It includes several covariates: Fetoprtn (alpha fetoprotein), Antigen (antihepatitis B antigen), Jaundice (a biochemical marker; coded as 1 if present, 0 otherwise), and Age (age in years). The response variable Y represents the number of cancerous liver cells present at the start of the clinical trial.
To assess the impact of these covariates on the likelihood of survival, a new variable called "survive" is created. "survive" is dichotomized based on Y: it is set to 1 if the number of cancerous liver cells is less than or equal to 8, and 0 otherwise.
Usage
est45
Format
A data frame with 45 rows and 9 variables:
- Y
Response variable
- Weeks
Time in weeks
- Fetoprtn
Alpha fetoprotein
- Antigen
Anti-hepatitis B antigen
- Jaundice
Jaundice indicator
- BMI
Body mass index
- Age
Age in years
- grp
Group identifier
- resp
Response variable dichotomized
Source
Generated for example purposes
References
Cytel Inc (2010). LogXact 9 User Manual: Discrete Regression Analysis. Cambridge, Massachusetts: Cytel Inc.
Falkson, G., Lipsitz, S., Borden, E., Simson, I., W., and Haller, D. (1995). A ECOG randomized phase II study of beta interferon and Menogoril. American Journal of Clinical Oncology 18, 287–292.
Maiti, T., Pradhan, V. (2009). Bias reduction and a solution of separation of logistic regression with missing covariates. Biometrics, 65, 1262-1269.
Pradhan, V., Nychka, D. and Bandyopadhyay, S. (2025). Beyond the Odds: Fitting Logistic Regression with Missing Data in Small Samples (submitted).
Examples
data(est45)
f_fit <- emforbeta(resp ~ Fetoprtn + Antigen + Jaundice + Age,
data = est45, family = binomial, method = "glm.fit")
summary(f_fit$mfit)
#Bias-reduced estimates due to Firth (1993)
f_fit <- emforbeta(resp ~ Fetoprtn + Antigen + Jaundice + Age,
data = est45, family = binomial, method = "brglmFit")
summary(f_fit$mfit)