Function to get odds ratios or rate ratios

Define a function (see bottom for result)

..glm.ratio <- function(GLM.RESULT, DIGITS = 2, P.DIGITS = 3, CONF.LEVEL = 0.95) {

    if (GLM.RESULT$family$family == "binomial") {
        LABEL <- "OR"
    } else if (GLM.RESULT$family$family == "poisson") {
        LABEL <- "RR"
    } else {
        stop("Not logistic or Poisson model")
    }

    ## Extract coefficients and confidence interval
    COEF      <- stats::coef(GLM.RESULT)
    CONFINT   <- suppressMessages(stats::confint(GLM.RESULT, level = CONF.LEVEL))
    TABLE     <- cbind(coef = COEF, CONFINT)

    ## Turn them into OR
    TABLE.EXP <- round(exp(TABLE), DIGITS)

    colnames(TABLE.EXP)[1] <- LABEL

    ## Extract p-value
    PVAL <- round(summary(GLM.RESULT)$coef[,4], P.DIGITS)

    TABLE.EXP <- cbind(TABLE.EXP, "P" = PVAL)    
    TABLE.EXP
}

Load dataset

## Fix cases 10 and 39 to make them identical to Dr. Orav's dataset
lbw <- read.table("http://www.umass.edu/statdata/statdata/data/lowbwt.dat", head = T, skip = 4)
lbw[c(10,39),"BWT"] <- c(2655,3035)

## Change variable names to lower cases
names(lbw) <- tolower(names(lbw))

Recode data

## Recoding
lbw <- within(lbw, {

    ## Relabel race
    race.cat <- factor(race, levels = 1:3, labels = c("White","Black","Other"))

    ## Categorize ftv (frequency of visit)
    ftv.cat  <- cut(ftv, breaks = c(-Inf, 0, 2, Inf), labels = c("None","Normal","Many"))
    ftv.cat  <- relevel(ftv.cat, ref = "Normal")

    ## Dichotomize ptl
    preterm  <- factor(ptl >= 1, levels = c(FALSE,TRUE), labels = c("0","1+"))

    ## Categorize smoke ht ui
    smoke    <- factor(smoke, levels = 0:1, labels = c("No","Yes"))
    ht       <- factor(ht, levels = 0:1, labels = c("No","Yes"))
    ui       <- factor(ui, levels = 0:1, labels = c("No","Yes"))
})

Get odds ratios and Wald p-values

logit.full <- glm(low ~ age + lwt + smoke + ht + ui + ftv.cat + race.cat + preterm, data = lbw, family = binomial)

..glm.ratio(logit.full)

                OR 2.5 % 97.5 %     P
(Intercept)   1.56  0.13  19.43 0.724
age           0.97  0.89   1.04 0.355
lwt           0.99  0.97   1.00 0.032
smokeYes      2.16  0.94   5.03 0.070
htYes         6.09  1.57  26.74 0.011
uiYes         2.07  0.82   5.14 0.118
ftv.catNone   1.29  0.59   2.87 0.518
ftv.catMany   2.23  0.53   9.11 0.262
race.catBlack 3.29  1.15   9.61 0.027
race.catOther 2.12  0.87   5.32 0.101
preterm1+     3.50  1.41   8.96 0.007

Using epicalc package

library(epicalc)
logistic.display(logit.full)

Logistic regression predicting low 

                     crude OR(95%CI)    adj. OR(95%CI)     P(Wald's test) P(LR-test)
age (cont. var.)     0.95 (0.89,1.01)   0.97 (0.9,1.04)    0.355          0.351     

lwt (cont. var.)     0.99 (0.97,1)      0.99 (0.97,1)      0.032          0.023     

smoke: Yes vs No     2.02 (1.08,3.78)   2.16 (0.94,4.95)   0.07           0.068     

ht: Yes vs No        3.37 (1.02,11.09)  6.09 (1.51,24.52)  0.011          0.009     

ui: Yes vs No        2.58 (1.14,5.83)   2.07 (0.83,5.13)   0.118          0.122     

ftv.cat: ref.=Normal                                                      0.514     
   None              1.84 (0.95,3.59)   1.29 (0.59,2.83)   0.518                    
   Many              2.34 (0.66,8.28)   2.23 (0.55,9.09)   0.262                    

race.cat: ref.=White                                                      0.055     
   Black             2.33 (0.94,5.77)   3.29 (1.15,9.42)   0.027                    
   Other             1.89 (0.96,3.74)   2.12 (0.86,5.23)   0.101                    

preterm: 1+ vs 0     4.32 (1.92,9.73)   3.5 (1.4,8.75)     0.007          0.007     

Log-likelihood = -97.7515
No. of observations = 189
AIC value = 217.5031