washb_mh. washb 0.1

Usage

washb_mh(Y,tr,strat,contrast,measure="RR")

Arguments

Y: binary outcome variable (here: diar7d)
tr: binary treatment group variable, comparison group first
strat: stratification variable (here: block)
contrast: vector of length 2 that includes the tr groups to contrast (control(reference arm) and then intervention)
measure: measure of effect. RR = prev ratio, RD = prev difference

Value

res: Matrix of RD, se.RD, ci.lb, ci.ub, Z, p-value.

Description

Mantel-Haenszel Pooled estimates of the prevalence ratio (PR) or the prevalence difference (PD) using randomization block as the stratification variable.

Details

The function calls the M-H estimator for two different arms of the study. It relies on the rma.mh() function in the metafor package.

Estimate the Mantel-Haenszel prevalence ratio note: strata with no outcomes (i.e., missing PR) are dropped. This is consistent with a fixed-effects regression analysis, in which those strata would not contribute to the estimates. The arguments Y,tr,strat, below need to be from the same dataset.

Examples

#Prescreen function applied to the Bangladesh diarrheal disease outcome.
#The function will test a matrix of covariates and return those related to child diarrheal disease with
#a <0.2 p-value from a likelihood ratio test.

#Load  diarrhea data
library(washb)
data(washb_bd_diar)
data(washb_bd_enrol)

 # drop svydate and month because they are superceded in the child level diarrhea data
washb_bd_enrol$svydate <- NULL
washb_bd_enrol$month <- NULL

# merge the baseline dataset to the follow-up dataset
ad <- merge(washb_bd_enrol,washb_bd_diar,by=c("dataid","clusterid","block","tr"),all.x=F,all.y=T)

# subset to the relevant measurement
# Year 1 or Year 2
ad <- subset(ad,svy==1|svy==2)

#subset the diarrhea to children <36 mos at enrollment
### (exlude new births that are not target children)
ad <- subset(ad,sibnewbirth==0)
ad <- subset(ad,gt36mos==0)

# Exclude children with missing data
ad <- subset(ad,!is.na(ad$diar7d))

#Re-order the tr factor for convenience
ad$tr <- factor(ad$tr,levels=c("Control","Water","Sanitation","Handwashing","WSH","Nutrition","Nutrition + WSH"))

###Create vector of contrasts for each hypothesis to facilitate comparisons between arms.
#Hypothesis 1: Each intervention arm vs. Control
h1.contrasts <- list(
  c("Control","Water"),
  c("Control","Sanitation"),
  c("Control","Handwashing"),
  c("Control","WSH"),
  c("Control","Nutrition"),
  c("Control","Nutrition + WSH")
)

#Apply washb_mh to the water vs. control arm contrast.
washb_mh(Y=ad$diar7d,tr=ad$tr, contrast=c("Control","Water"), strat=ad$block,measure="RR")
       PR,      ci.lb      ci.ub      logPR   se.logPR          Z          p 
 0.7898050  0.5793340  1.0767399 -0.2359692  0.1581187 -1.4923542  0.1356063 


#Return the risk difference instead of the risk ration:
washb_mh(Y=ad$diar7d,tr=ad$tr, contrast=c("Control","Water"), strat=ad$block,measure="RD")
          RD        se.RD        ci.lb        ci.ub            Z            p 
-0.008315341  0.005348086 -0.018797397  0.002166714 -1.554825694  0.119987588 



#Use sapply command to efficiently apply the function to all the treatment arm contrasts

diff.h1 <- t(sapply(h1.contrasts,washb_mh,Y=ad$diar7d,tr=ad$tr,strat=ad$block,measure="RR"))
rownames(diff.h1) <- c("Water v C","Sanitation v C","Handwashing v C","WSH v C","Nutrition v C","Nutrition + WSH v C")
print(diff.h1)
                          PR,     ci.lb    ci.ub       logPR  se.logPR          Z           p
Water v C           0.7898050 0.5793340 1.076740 -0.23596915 0.1581187 -1.4923542 0.135606316
Sanitation v C      1.1676839 0.8962390 1.521342  0.15502223 0.1349874  1.1484202 0.250795139
Handwashing v C     1.2307787 0.9493770 1.595590  0.20764708 0.1324495  1.5677447 0.116940720
WSH v C             0.9375256 0.6999909 1.255665 -0.06451118 0.1490725 -0.4327504 0.665196128
Nutrition v C       1.0946230 0.8303822 1.442949  0.09040999 0.1409614  0.6413813 0.521274952
Nutrition + WSH v C 1.4996864 1.1705008 1.921450  0.40525600 0.1264433  3.2050413 0.001350431