dat.nissen2007: Studies on the Effect of Rosiglitazone on the Risk of...
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dat.nissen2007 R Documentation
Studies on the Effect of Rosiglitazone on the Risk of Myocardial Infarction and Death from Cardiovascular Causes
Description
Results from 42 trials examining the effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes.
Usage
dat.nissen2007
Format
The data frame contains the following columns:
study character study identifier
type factor type of trial (as in Table 1)
phase factor study phase
population character description of the study population
start, end character study period (year-month)
treatment character treatment group medication
control character control group medication
weeks numeric follow-up duration (weeks)
treat.total numeric total number of patients in the treatment group
treat.infarction numeric number of patients with myocardial infarction in the treatment group
treat.death numeric number of deaths in the treatment group
cont.total numeric total number of patients in the control group
cont.infarction numeric number of patients with myocardial infarction in the control group
cont.death numeric number of deaths in the control group
Details
Nissen and Wolski (2007) performed a systematic literature review aiming for randomized controlled trials (RCTs) investigating the effects of Rosiglitazone (Avandia) in comparison to a control treatment, and with a follow-up duration of at least 24 weeks. 42 studies were included. A meta-analysis was performed to quantify the treatment effect on the risks of myocardial infarction and cardiovascular death in terms of the associated odds ratio (OR).
The data set features a number of “zero” trials (no event observed in one of the treatment groups) as well as “double-zero” trials (no event in either treatment group), which poses a challenge for some analysis methods. The original analysis was a common-effect analysis based on the Peto method (see the rma.peto help and the example code below). The data set as well as its original analysis have subsequently been discussed by other investigators (e.g., Diamond et al., 2007; Ruecker & Schumacher, 2008; Friedrich et al., 2009; Tian et al., 2009; Nissen & Wolski, 2010). Jackson et al. (2018) later surveyed a range of (random-effects) models that may be applicable in this context; see also the examples below.
See also dat.tian2009 for the same dataset, but with 6 additional trials where no event was observed in either group for both outcomes.
Concepts
medicine, cardiology, odds ratios, Peto's method, generalized linear models
Author(s)
Christian Röver, christian.roever@med.uni-goettingen.de
Source
Nissen, S. E., & Wolski, K. (2007). Effect of Rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. New England Journal of Medicine, 356(24), 2457-2471. https://doi.org/10.1056/NEJMoa072761
References
Diamond, G. A., Bax, L., & Kaul, S. (2007). Uncertain effects of Rosiglitazone on the risk for myocardial infarction and cardiovascular death. Annals of Internal Medicine, 147(8), 578–581. https://doi.org/10.7326/0003-4819-147-8-200710160-00182
Friedrich, J. O., Beyene, J., & Adhikari, N. K. J. (2009). Rosiglitazone: Can meta-analysis accurately estimate excess cardiovascular risk given the available data? Re-analysis of randomized trials using various methodologic approaches. BMC Research Notes, 2, 5. https://doi.org/10.1186/1756-0500-2-5
Jackson, D., Law, M., Stijnen, T., Viechtbauer, W., & White, I. R. (2018). A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio. Statistics in Medicine, 37(7), 1059–1085. https://doi.org/10.1002/sim.7588
Nissen, S. E., & Wolski, K. (2010). Rosiglitazone revisited: An updated meta-analysis of risk for myocardial infarction and cardiovascular mortality. Archives of Internal Medicine, 170(14), 1191–1201. https://doi.org/10.1001/archinternmed.2010.207
Rücker, G., & Schumacher, M. (2008). Simpson’s paradox visualized: The example of the Rosiglitazone meta-analysis. BMC Medical Research Methodology, 8, 34. https://doi.org/10.1186/1471-2288-8-34
Tian, L., Cai, T., Pfeffer, M. A., Piankov, N., Cremieux, P.-Y., & Wei, L. J. (2009). Exact and efficient inference procedure for meta-analysis and its application to the analysis of independent 2 x 2 tables with all available data but without artificial continuity correction. Biostatistics, 10(2), 275–281. https://doi.org/10.1093/biostatistics/kxn034
See Also
dat.tian2009
Examples
dat.nissen2007
## Not run:
library(metafor)
############################################################
# reproduce original "Peto" analyses
# infarction
ma01 <- rma.peto(ai=treat.infarction, ci=cont.infarction,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma01
# mortality
ma02 <- rma.peto(ai=treat.death, ci=cont.death,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma02
############################################################
# reproduce "Fixed, IV (CC)" analyses
# from Diamond/Bax/Kaul (2007), Table 1
# infarction
ma03 <- rma.uni(measure="OR", method="FE",
drop00=TRUE, # (exclude "double-zeroes")
ai=treat.infarction, ci=cont.infarction,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma03
# mortality
ma04 <- rma.uni(measure="OR", method="FE",
drop00=TRUE, # (exclude "double-zeroes")
ai=treat.death, ci=cont.death,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma04
############################################################
# estimate ORs based on a binomial GLMM
# (with *fixed* study effects)
# ("model 4" in Jackson et al., 2018)
# infarction
ma05 <- rma.glmm(measure="OR", model="UM.FS",
ai=treat.infarction, ci=cont.infarction,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma05
# mortality
ma06 <- rma.glmm(measure="OR", model="UM.FS",
ai=treat.death, ci=cont.death,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma06
############################################################
# estimate ORs based on binomial GLMM
# (with *random* study effects)
# ("model 5" in Jackson et al., 2018)
# infarction
ma07 <- rma.glmm(measure="OR", model="UM.RS", nAGQ=1,
ai=treat.infarction, ci=cont.infarction,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma07
# mortality
ma08 <- rma.glmm(measure="OR", model="UM.RS", nAGQ=1,
ai=treat.death, ci=cont.death,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma08
############################################################
# estimate ORs based on hypergeometric model
# (model 7 (approx.) in Jackson et al., 2018)
# infarction
ma09 <- rma.glmm(measure="OR", model="CM.AL",
ai=treat.infarction, ci=cont.infarction,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma09
# mortality
ma10 <- rma.glmm(measure="OR", model="CM.AL",
ai=treat.death, ci=cont.death,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma10
############################################################
# tabulate estimates and CIs
# log-OR infarction
logOR.inf <- rbind("Peto" =c("OR"=ma01$b, "lower"=ma01$ci.lb, "upper"=ma01$ci.ub),
"IV-CC" =c("OR"=ma03$b, "lower"=ma03$ci.lb, "upper"=ma03$ci.ub),
"M4-UM.FS"=c("OR"=ma05$b, "lower"=ma05$ci.lb, "upper"=ma05$ci.ub),
"M5-UM.RS"=c("OR"=ma07$b, "lower"=ma07$ci.lb, "upper"=ma07$ci.ub),
"M7-CM.AL"=c("OR"=ma09$b, "lower"=ma09$ci.lb, "upper"=ma09$ci.ub))
# log-OR mortality
logOR.mort <- rbind("Peto" =c("OR"=ma02$b, "lower"=ma02$ci.lb, "upper"=ma02$ci.ub),
"IV-CC" =c("OR"=ma04$b, "lower"=ma04$ci.lb, "upper"=ma04$ci.ub),
"M4-UM.FS"=c("OR"=ma06$b, "lower"=ma06$ci.lb, "upper"=ma06$ci.ub),
"M5-UM.RS"=c("OR"=ma08$b, "lower"=ma08$ci.lb, "upper"=ma08$ci.ub),
"M7-CM.AL"=c("OR"=ma10$b, "lower"=ma10$ci.lb, "upper"=ma10$ci.ub))
# show ORs (infarction)
round(exp(logOR.inf), 2)
# show ORs (mortality)
round(exp(logOR.mort), 2)
## End(Not run)
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| dat.nissen2007 | R Documentation |
Studies on the Effect of Rosiglitazone on the Risk of Myocardial Infarction and Death from Cardiovascular Causes
Description
Results from 42 trials examining the effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes.
Usage
dat.nissen2007
Format
The data frame contains the following columns:
| study | character | study identifier |
| type | factor | type of trial (as in Table 1) |
| phase | factor | study phase |
| population | character | description of the study population |
| start, end | character | study period (year-month) |
| treatment | character | treatment group medication |
| control | character | control group medication |
| weeks | numeric | follow-up duration (weeks) |
| treat.total | numeric | total number of patients in the treatment group |
| treat.infarction | numeric | number of patients with myocardial infarction in the treatment group |
| treat.death | numeric | number of deaths in the treatment group |
| cont.total | numeric | total number of patients in the control group |
| cont.infarction | numeric | number of patients with myocardial infarction in the control group |
| cont.death | numeric | number of deaths in the control group |
Details
Nissen and Wolski (2007) performed a systematic literature review aiming for randomized controlled trials (RCTs) investigating the effects of Rosiglitazone (Avandia) in comparison to a control treatment, and with a follow-up duration of at least 24 weeks. 42 studies were included. A meta-analysis was performed to quantify the treatment effect on the risks of myocardial infarction and cardiovascular death in terms of the associated odds ratio (OR).
The data set features a number of “zero” trials (no event observed in one of the treatment groups) as well as “double-zero” trials (no event in either treatment group), which poses a challenge for some analysis methods. The original analysis was a common-effect analysis based on the Peto method (see the rma.peto help and the example code below). The data set as well as its original analysis have subsequently been discussed by other investigators (e.g., Diamond et al., 2007; Ruecker & Schumacher, 2008; Friedrich et al., 2009; Tian et al., 2009; Nissen & Wolski, 2010). Jackson et al. (2018) later surveyed a range of (random-effects) models that may be applicable in this context; see also the examples below.
See also dat.tian2009 for the same dataset, but with 6 additional trials where no event was observed in either group for both outcomes.
Concepts
medicine, cardiology, odds ratios, Peto's method, generalized linear models
Author(s)
Christian Röver, christian.roever@med.uni-goettingen.de
Source
Nissen, S. E., & Wolski, K. (2007). Effect of Rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. New England Journal of Medicine, 356(24), 2457-2471. https://doi.org/10.1056/NEJMoa072761
References
Diamond, G. A., Bax, L., & Kaul, S. (2007). Uncertain effects of Rosiglitazone on the risk for myocardial infarction and cardiovascular death. Annals of Internal Medicine, 147(8), 578–581. https://doi.org/10.7326/0003-4819-147-8-200710160-00182
Friedrich, J. O., Beyene, J., & Adhikari, N. K. J. (2009). Rosiglitazone: Can meta-analysis accurately estimate excess cardiovascular risk given the available data? Re-analysis of randomized trials using various methodologic approaches. BMC Research Notes, 2, 5. https://doi.org/10.1186/1756-0500-2-5
Jackson, D., Law, M., Stijnen, T., Viechtbauer, W., & White, I. R. (2018). A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio. Statistics in Medicine, 37(7), 1059–1085. https://doi.org/10.1002/sim.7588
Nissen, S. E., & Wolski, K. (2010). Rosiglitazone revisited: An updated meta-analysis of risk for myocardial infarction and cardiovascular mortality. Archives of Internal Medicine, 170(14), 1191–1201. https://doi.org/10.1001/archinternmed.2010.207
Rücker, G., & Schumacher, M. (2008). Simpson’s paradox visualized: The example of the Rosiglitazone meta-analysis. BMC Medical Research Methodology, 8, 34. https://doi.org/10.1186/1471-2288-8-34
Tian, L., Cai, T., Pfeffer, M. A., Piankov, N., Cremieux, P.-Y., & Wei, L. J. (2009). Exact and efficient inference procedure for meta-analysis and its application to the analysis of independent 2 x 2 tables with all available data but without artificial continuity correction. Biostatistics, 10(2), 275–281. https://doi.org/10.1093/biostatistics/kxn034
See Also
dat.tian2009
Examples
dat.nissen2007
## Not run:
library(metafor)
############################################################
# reproduce original "Peto" analyses
# infarction
ma01 <- rma.peto(ai=treat.infarction, ci=cont.infarction,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma01
# mortality
ma02 <- rma.peto(ai=treat.death, ci=cont.death,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma02
############################################################
# reproduce "Fixed, IV (CC)" analyses
# from Diamond/Bax/Kaul (2007), Table 1
# infarction
ma03 <- rma.uni(measure="OR", method="FE",
drop00=TRUE, # (exclude "double-zeroes")
ai=treat.infarction, ci=cont.infarction,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma03
# mortality
ma04 <- rma.uni(measure="OR", method="FE",
drop00=TRUE, # (exclude "double-zeroes")
ai=treat.death, ci=cont.death,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma04
############################################################
# estimate ORs based on a binomial GLMM
# (with *fixed* study effects)
# ("model 4" in Jackson et al., 2018)
# infarction
ma05 <- rma.glmm(measure="OR", model="UM.FS",
ai=treat.infarction, ci=cont.infarction,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma05
# mortality
ma06 <- rma.glmm(measure="OR", model="UM.FS",
ai=treat.death, ci=cont.death,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma06
############################################################
# estimate ORs based on binomial GLMM
# (with *random* study effects)
# ("model 5" in Jackson et al., 2018)
# infarction
ma07 <- rma.glmm(measure="OR", model="UM.RS", nAGQ=1,
ai=treat.infarction, ci=cont.infarction,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma07
# mortality
ma08 <- rma.glmm(measure="OR", model="UM.RS", nAGQ=1,
ai=treat.death, ci=cont.death,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma08
############################################################
# estimate ORs based on hypergeometric model
# (model 7 (approx.) in Jackson et al., 2018)
# infarction
ma09 <- rma.glmm(measure="OR", model="CM.AL",
ai=treat.infarction, ci=cont.infarction,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma09
# mortality
ma10 <- rma.glmm(measure="OR", model="CM.AL",
ai=treat.death, ci=cont.death,
n1i=treat.total, n2i=cont.total,
slab=study, data=dat.nissen2007)
ma10
############################################################
# tabulate estimates and CIs
# log-OR infarction
logOR.inf <- rbind("Peto" =c("OR"=ma01$b, "lower"=ma01$ci.lb, "upper"=ma01$ci.ub),
"IV-CC" =c("OR"=ma03$b, "lower"=ma03$ci.lb, "upper"=ma03$ci.ub),
"M4-UM.FS"=c("OR"=ma05$b, "lower"=ma05$ci.lb, "upper"=ma05$ci.ub),
"M5-UM.RS"=c("OR"=ma07$b, "lower"=ma07$ci.lb, "upper"=ma07$ci.ub),
"M7-CM.AL"=c("OR"=ma09$b, "lower"=ma09$ci.lb, "upper"=ma09$ci.ub))
# log-OR mortality
logOR.mort <- rbind("Peto" =c("OR"=ma02$b, "lower"=ma02$ci.lb, "upper"=ma02$ci.ub),
"IV-CC" =c("OR"=ma04$b, "lower"=ma04$ci.lb, "upper"=ma04$ci.ub),
"M4-UM.FS"=c("OR"=ma06$b, "lower"=ma06$ci.lb, "upper"=ma06$ci.ub),
"M5-UM.RS"=c("OR"=ma08$b, "lower"=ma08$ci.lb, "upper"=ma08$ci.ub),
"M7-CM.AL"=c("OR"=ma10$b, "lower"=ma10$ci.lb, "upper"=ma10$ci.ub))
# show ORs (infarction)
round(exp(logOR.inf), 2)
# show ORs (mortality)
round(exp(logOR.mort), 2)
## End(Not run)
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