## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(rankinma) ## ----echo = FALSE, out.width = "10%"------------------------------------------ knitr::include_graphics("rankinma_logo.png") ## ----eval = FALSE------------------------------------------------------------- # library(rankinma) ## ----setup, echo = FALSE, warning = FALSE, message = FALSE-------------------- library(rankinma) library(netmeta) ## ----eval = FALSE------------------------------------------------------------- # library(rankinma) # library(netmeta) # data(Senn2013) # nmaOutput <- netmeta(TE, # seTE, # treat1, # treat2, # studlab, # data = Senn2013, # sm = "SMD") ## ----eval = FALSE------------------------------------------------------------- # dataMetrics <- GetMetrics(nmaOutput, # outcome = "HbA1c.random", # prefer = "small", # metrics = "Probabilities", # model = "random", # simt = 1000) ## ----eval = FALSE------------------------------------------------------------- # dataRankinma <- SetMetrics(dataMetrics, # tx = tx, # outcome = outcome, # metrics.name = "Probabilities") ## ----eval = FALSE------------------------------------------------------------- # PlotLine(data = dataRankinma, # compo = TRUE) ## ----eval = TRUE, echo = FALSE, warning = FALSE, results = FALSE, message = FALSE, results = "hide", fig.cap = "**Figure 1**. Composite line chart for probabilities of treatments on each rank.", fig.height = 5, fig.width = 7, fig.align = "center", out.width = "90%"---- data(Senn2013) nmaOutput <- netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "SMD") dataMetrics <- GetMetrics(nmaOutput, outcome = "HbA1c.random", prefer = "small", metrics = "Probabilities", model = "random", simt = 1000) dataRankinma <- SetMetrics(dataMetrics, tx = tx, outcome = outcome, metrics.name = "Probabilities") PlotLine(data = dataRankinma, compo = TRUE) ## ----eval = FALSE------------------------------------------------------------- # PlotBar(data = dataRankinma, # accum = TRUE) ## ----eval = TRUE, echo = FALSE, warning = FALSE, results = FALSE, message = FALSE, results = "hide", fig.cap = "**Figure 2**. Accumulative bar chart for probabilities of treatments on each rank.", fig.height = 5, fig.width = 7, fig.align = "center", out.width = "90%"---- data(Senn2013) nmaOutput <- netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "SMD") dataMetrics <- GetMetrics(nmaOutput, outcome = "HbA1c.random", prefer = "small", metrics = "Probabilities", model = "random", simt = 1000) dataRankinma <- SetMetrics(dataMetrics, tx = tx, outcome = outcome, metrics.name = "Probabilities") PlotBar(data = dataRankinma, accum = TRUE) ## ----eval = FALSE------------------------------------------------------------- # library(rankinma) # library(netmeta) # data(Senn2013) # nmaOutput <- netmeta(TE, # seTE, # treat1, # treat2, # studlab, # data = Senn2013, # sm = "SMD") ## ----eval = FALSE------------------------------------------------------------- # nmaRandom <- GetMetrics(nmaOutput, # outcome = "HbA1c.random", # prefer = "small", # metrics = "SUCRA", # model = "random", # simt = 1000) # nmaCommon <- GetMetrics(nmaOutput, # outcome = "HbA1c.common", # prefer = "small", # metrics = "SUCRA", # model = "common", # simt = 1000) ## ----eval = FALSE------------------------------------------------------------- # nmaRandom <- GetMetrics(nmaOutput, # outcome = "HbA1c.random", # prefer = "small", # metrics = "P-score", # model = "random", # simt = 1000) # nmaCommon <- GetMetrics(nmaOutput, # outcome = "HbA1c.common", # prefer = "small", # metrics = "P-score", # model = "common", # simt = 1000) ## ----eval = FALSE------------------------------------------------------------- # nmaRandom <- GetMetrics(nmaOutput, # outcome = "HbA1c.random", # prefer = "small", # metrics = "P-best", # model = "random", # simt = 1000) # nmaCommon <- GetMetrics(nmaOutput, # outcome = "HbA1c.common", # prefer = "small", # metrics = "P-best", # model = "common", # simt = 1000) ## ----eval = FALSE------------------------------------------------------------- # dataMetrics <- rbind(nmaRandom, nmaCommon) ## ----eval = FALSE------------------------------------------------------------- # dataRankinma <- (dataMetrics, # tx = tx, # outcome = outcome, # metrics = SUCRA, # metrics.name = "SUCRA") ## ----eval = FALSE------------------------------------------------------------- # dataRankinma <- (dataMetrics, # tx = tx, # outcome = outcome, # metrics = P.score, # metrics.name = "P-score") ## ----eval = FALSE------------------------------------------------------------- # dataRankinma <- (dataMetrics, # tx = tx, # outcome = outcome, # metrics = P.best, # metrics.name = "P-best") ## ----eval = FALSE------------------------------------------------------------- # PlotBeads(data = dataRankinma) ## ----eval = TRUE, echo = FALSE, message = FALSE, results = "hide", fig.cap = "**Figure 3A**. Beading plot for SUCRA on two outcomes", fig.height = 6, fig.width = 8, fig.align = "center", out.width = "90%"---- data(Senn2013) nmaOutput <- netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "SMD") nmaRandom <- GetMetrics(nmaOutput, outcome = "HbA1c.random", prefer = "small", metrics = "SUCRA", model = "random", simt = 1000) nmaCommon <- GetMetrics(nmaOutput, outcome = "HbA1c.common", prefer = "small", metrics = "SUCRA", model = "common", simt = 1000) dataMetrics <- rbind(nmaRandom, nmaCommon) dataRankinma <- SetMetrics(dataMetrics, tx = tx, outcome = outcome, metrics = SUCRA, metrics.name = "SUCRA") PlotBeads(data = dataRankinma) ## ----eval = FALSE------------------------------------------------------------- # PlotBeads(data = dataRankinma, # scaleX = "Rank", # txtValue = "Effects") ## ----eval = TRUE, echo = FALSE, message = FALSE, results = "hide", fig.cap = "**Figure 3A**. Beading plot for SUCRA on two outcomes", fig.height = 6, fig.width = 8, fig.align = "center", out.width = "90%"---- data(Senn2013) nmaOutput <- netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "SMD") nmaRandom <- GetMetrics(nmaOutput, outcome = "HbA1c.random", prefer = "small", metrics = "P-score", model = "random", simt = 1000) nmaCommon <- GetMetrics(nmaOutput, outcome = "HbA1c.common", prefer = "small", metrics = "P-score", model = "common", simt = 1000) dataMetrics <- rbind(nmaRandom, nmaCommon) dataRankinma <- SetMetrics(dataMetrics, tx = tx, outcome = outcome, metrics = P.score, metrics.name = "P-score") PlotBeads(data = dataRankinma, scaleX = "Rank", txtValue = "Effects") ## ----eval = FALSE------------------------------------------------------------- # PlotBeads(data = dataRankinma, # lgcBlind = TRUE) ## ----eval = TRUE, echo = FALSE, message = FALSE, results = "hide", fig.cap = "**Figure 3C**. Colorblind friendly beading plot for P-score on two outcomes", fig.height = 6, fig.width = 8, fig.align = "center", out.width = "90%"---- data(Senn2013) nmaOutput <- netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "SMD") nmaRandom <- GetMetrics(nmaOutput, outcome = "HbA1c.random", prefer = "small", metrics = "P-best", model = "random", simt = 1000) nmaCommon <- GetMetrics(nmaOutput, outcome = "HbA1c.common", prefer = "small", metrics = "P-best", model = "common", simt = 1000) dataMetrics <- rbind(nmaRandom, nmaCommon) dataRankinma <- SetMetrics(dataMetrics, tx = tx, outcome = outcome, metrics = P.best, metrics.name = "P-best") PlotBeads(data = dataRankinma, lgcBlind = TRUE) ## ----eval = FALSE------------------------------------------------------------- # PlotSpie(data = dataRankinma) ## ----eval = TRUE, echo = FALSE, message = FALSE, results = "hide", fig.cap = "**Figure 3B**. Spie plot for P-score on two outcomes", fig.height = 6, fig.width = 8, fig.align = "center", out.width = "90%"---- data(Senn2013) nmaOutput <- netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "SMD") nmaRandom <- GetMetrics(nmaOutput, outcome = "HbA1c.random", prefer = "small", metrics = "P-score", model = "random", simt = 1000) nmaCommon <- GetMetrics(nmaOutput, outcome = "HbA1c.common", prefer = "small", metrics = "P-score", model = "common", simt = 1000) dataMetrics <- rbind(nmaRandom, nmaCommon) dataRankinma <- SetMetrics(dataMetrics, tx = tx, outcome = outcome, metrics = P.score, metrics.name = "P-score") PlotSpie(data = dataRankinma)