Meta-analysis of the prognostic role of hormone receptors in endometrial cancer

This dataset comprises the results from 16 studies assessing the prognostic role of human epidermal growth factor receptor 2 (HER2) in endometrial cancer. These studies were previously identified in a systematic review by Zhang et al. to evaluate the overall risk of several hormone receptors for endometrial cancer survival.

Usage

data("Zhang")

Format

A data frame with 20 observations on the following 10 variables.

Study: a factor with 16 levels to indicate the study
PrimaryAuthor: a factor indicating the first author's last name
year: a numeric vector indicating the publication year
Country: a factor indicating the source country of the study data
Disease: a factor indicating the studied disease. Possible levels are EC (endometrial cancer), EEC (endometrioid endometrial cancer) and UPSC (uterine papillary serous carcinoma)
N: a numeric vector describing the total sample size of each study
HR: a numeric vector describing the estimated hazard ratio of each study
HR.025: a numeric vector describing the lower boundary of the 95% confidence interval of HR
HR.975: a numeric vector describing the upper boundary of the 95% confidence interval of HR
outcome: a factor indicating the studied outcome. Possible levels are OS (overall survival) and PFS (progression-free survival)

Details

Eligible studies were identified by searching the PubMed and EMBASE databases for publications from 1979 to May 2014. Data were collected from studies comparing overall survival or progression-free survival in patients with elevated levels of human epidermal growth factor receptor 2 with those in patients with lower levels.

Source

Zhang Y, Zhao D, Gong C, Zhang F, He J, Zhang W, et al. Prognostic role of hormone receptors in endometrial cancer: a systematic review and meta-analysis. World J Surg Oncol. 2015 Jun 25;13:208.

References

Riley RD, Jackson D, Salanti G, Burke DL, Price M, Kirkham J, et al. Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples. BMJ. 2017 13;358:j3932.

Examples

data(Zhang)

# Display the hazard ratios for overall survival in a forest plot
ds <- subset(Zhang, outcome=="OS")
with(ds, forest(theta = HR, theta.ci.lb = HR.025, theta.ci.ub = HR.975, 
                theta.slab = Study, xlab = "Hazard ratio of HER2 versus OS", refline = 1))