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I have worked out the risk ratio of different doses of a drug relative to a placebo and want to find out if there is a dose-dependent increase in an outcome/effect I am looking at. I have the (for example) following risk ratios and Confidence intervals:

5 mg: 2.36 (1.56, 4.39) 10 mg: 3.53 (2.04, 5.68) 15 mg 2.22 (1.84, 3.07)

When describing/interpreting the results would it be appropriate to say from 5mg to 10mg a dose-dependent increase in the effect is seen, but the same is not seen from 10 mg to 15 mg?

Thanks!

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    $\begingroup$ Do you have more than three studies? I think you may be better to work on the log scale as well. $\endgroup$
    – mdewey
    Apr 10, 2016 at 12:46
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    $\begingroup$ For some doses I have 4 studies, some 3 and there are few doses that are only looked at by 1/2 studies.. $\endgroup$
    – Harose
    Apr 10, 2016 at 12:54
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    $\begingroup$ Has each study only examined one dosage (compared to placebo) or do you have studies that compared two or more dosages against placebo? $\endgroup$
    – Wolfgang
    Apr 10, 2016 at 13:06
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    $\begingroup$ No all of the studies I have included have examined 2/more dosages against placebo. Some studies have examined the same dosages so I have pooled the results together, as some dosages were only examined in e.g. 1 trial I only have data from 1 trial for that specific dosage. (I hope that is clear) $\endgroup$
    – Harose
    Apr 10, 2016 at 13:42
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    $\begingroup$ It is clear but I would question your approach here. You are ignoring the existence of the studies and the correlation between estimates from the same study. I think you need to investigate a multivariate approach or network meta-analysis. $\endgroup$
    – mdewey
    Apr 10, 2016 at 14:20

1 Answer 1

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I agree with @Wolfgang and @mdewey that you need to clarify better your goals, and that a multivariate/network approach is most precise.

In case you want to pursue this further, you can refer to the sample R code below, exploiting the netmeta package, and which hypothesizes a dataframe of 10 studies each with 4 arms (Rx 1 [eg placebo], Rx 2, Rx 3, and Rx 4), providing you pairwise as well as network estimates.

library(netmeta)

id <- c(1:10)
treatment1 <- 1 # eg placebo
treatment2 <- 2
treatment3 <- 3
treatment4 <- 4
events1 <- sample(25:100, 10, replace = T)
events2 <- sample(50:150, 10, replace = T)
events3 <- sample(75:200, 10, replace = T)
events4 <- sample(75:200, 10, replace = T)
patients1 <- sample(300:1000, 10, replace = T)
patients2 <- sample(300:1000, 10, replace = T)
patients3 <- sample(300:1000, 10, replace = T)
patients4 <- sample(300:1000, 10, replace = T)
mydata1 <- data.frame(cbind(id, treatment1, treatment2, treatment3, treatment4, events1, events2, 
                            events3, events4, patients1, patients2, patients3, patients4))
mydata1

pairwise1 <- pairwise(list(treatment1, treatment2, treatment3, treatment4), list(events1, 
                      events2, events3, events4), list(patients1, patients2, patients3,
                      patients4), studlab=id, sm = "RR", data = mydata1)
pairwise1

netmeta1 <- netmeta(TE, seTE, treat1, treat2, studlab, data=pairwise1, sm = "RR", comb.random = TRUE)
summary(netmeta1)

netgraph(netmeta1, points=TRUE, cex.points=3, cex=1.25)

forest(netmeta1, ref="1", xlim=c(0.1, 10))

netrank1 <- netrank(netmeta1, small.values="bad")
netrank1

decomp.design(netmeta1)
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  • $\begingroup$ If the answer is fine remember to flag it as correct... $\endgroup$ Apr 11, 2016 at 14:35

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