You may get a reply here, but this post belongs on a statistics list
like stats.stackexchange.com, not r-help, which is concerned about R
programming issues rather than statistical methodology.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Mon, Oct 31, 2016 at 5:54 AM, Luke Gaylor <luke.gay...@live.com.au> wrote:
> Friends,
>
>
> Matched pairs studies are well documented, but what happens if we were to
> alter the manner in which events were paired to one another. Say we change
> the order of our data and pair without replacement, so that event 1 may pair
> with event 23 in one instance, but also event 36 or 102 in other instances.
>
> Matched pair study can then be used to determine the effectiveness of, for
> example a treatment program, on a given outcome. The outcome may be
> continuous or variable. Therefore, a conditional logistic regression model
> can output an odds ratio (and confidence intervals for the treatment
> coefficient). Or we may use a paired t-test to assess if the difference in
> means between the pairs differ.
>
> I am more favored to the logistic regression and output of the odds ratio,
> however it changes for each permutation. As too does its confidence interval.
> So my question is how can we report on a string of given odds ratios - how
> can be arrive at a statistical sound conclusion? Or accounting for the study
> design, where we achieve different combinations of matched pairs, how can be
> comment on the effectiveness of a given treatment program?
>
> Below I have attached a code, which simulates a 100-fold permutation.
>
>
> Any advice would be greatly appreciated,
> Luke
>
>
> Set.seed(123)
> # prepare the data for the simulation
> #1.
> library(Matching)
> library(survival)
> library(dplyr)
> #2.
> require(doParallel)
> cl<-makeCluster(2)
> registerDoParallel(cl)
> #3.
> clusterEvalQ(cl,library(Matching))
> clusterEvalQ(cl,library(survival))
> clusterEvalQ(cl,library(dplyr))
>
> # number of permutations
> m <- 100
>
>
> Result = foreach(i=1:m,.combine=cbind) %do%{
>
> # taking from the example from the Matching package
> #attach the data
> data(lalonde)
>
> # we want to assess if treatment shows an influence on a given outcome of
> interest
> # lets create our hypothetical example
> lalonde$success <- with(lalonde, ifelse(re78 > 8125, 1, 0))
>
> # adjust the order of the data
> lalonde <- lalonde[sample(1:nrow(lalonde)), ]
> head(lalonde$age)
>
>
> # Define the covariates we want to match on
> Xmat = cbind(lalonde$age, lalonde$educ, lalonde$married, lalonde$nodegr)
>
> # define crude matching
> mgen1 <- Match(Y=NULL, Tr=lalonde$treat,
> X=Xmat,
> exact=c(0,1,1,1),
> replace=FALSE, ties=F)
>
> summary(mgen1)
>
> # obtain initial pair-combinations
> matched <- rbind(lalonde[mgen1$index.treated,],
> lalonde[mgen1$index.control,])
>
> # generate a dummy variable for our each pair of people
> matched$Pair_ID <- rep(1:length(mgen1$index.treated),2)
>
> # crude filtering
> # set hard limits of age
> # must be within +-2 years
> Matched2 <- matched %>%
> group_by(Pair_ID) %>%
> filter(abs( age[treat == 1] - age[treat == 0]) <= 2)
>
> # summary table
> table(Matched2$treat, Matched2$success)
>
> # so now we have a cohort of similar events
> # each pair contains one treat and one non-treat event
>
>
> #######################################################
> # #
> # H E R E W E A R E T R Y I N G T O #
> # A S S E S S T H E E F F E C T I V E N E S S #
> # O F T R E A T M E N T T O A C H I E V E #
> # s U C C E S S #
> # #
> #######################################################
>
>
> # One could implement a parametric difference of means test
> # t-test - (as the outcome of interest is binary)
> # to assess if differences between pairs change
>
> diff_means<- t.test(Matched2$success[Matched2$treat==1],
> Matched2$success[Matched2$treat==0],
> paired = T, alternative = "two.sided", conf.level = 0.95)
> diff_means$p.value
>
> # output the series of p-values for each permutation
>
> # ===========================================
> # Or
> # ===========================================
>
> # obtain an estimated effectiveness value from a conditional logistic
> regression
> # namely, the Odds Ratio coefficient for 'treat'
> model_1 <- clogit(success ~ treat + strata(Pair_ID), matched)
> summary(model_1)
>
> OR_M1 <- exp(model_1$coefficients[1])
>
> # we can save the confidence intervals for the 'treat' effectiveness
> CI_U1 <- exp(confint(model_1))[1,2]
> CI_L1 <- exp(confint(model_1))[1,1]
>
> # then we could output any of the following
> #Result <- diff_means$p.value
> #Result <- OR_M1
> #Result <- rbind(OR_M1, CI_U1, CI_L1)
>
> Result <- OR_M1
>
> }
>
> summary(Result[1,])
>
>
>
>
> [[alternative HTML version deleted]]
>
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