Easy Forest Plots in R

Forest plots are great ways to visualize individual group estimates as well as investigate heterogeneity of effect.  Most forest plot programs will display combined effect estimates and give you an indicator of whether there is evidence for heterogeneity among subgroups.  Fortunately, the R metafor package makes meta-analysis and plotting forest plots relatively easy.  Below is a sample R script that will combine beta and standard error estimates from an input file (input.txt) and create simple forest plots with an overall estimate as well as p-values for association and heterogeneity.

In general, the input.txt file should either

(1) Have the columns:
group - name for the study or group
beta - the log odds ratio for the effect of interest
se - the standard error for the log odds ratio estimate

or

(2) Have the columns:
group - name for the study or group
OR - odds ratio for the effect of interest
LCL - lower confidence interval for the odds ratio
UCL - upper confidence interval for the odds ratio

For the second case where you have an odds ratio and 95% confidence estimates, beta and se need to be estimated.  This is done by uncommenting lines 8 and 9 of the script.  Of note: due to rounding error the final forest plot may have 95% CI limits that are one digit off.

The R script that uses the metafor package as well as an example input.txt are below.

Calculate P-value for Linear Mixed Model in R

The lme4 R package is a powerful tool for fitting mixed models in R where you can specify fixed and random effects.  One oddity about the program is it returns t statistics, but no p-value.  To get the p-value takes a little extra coding.  Here is a quick example for fitting a linear mixed model in R (using lmer) and then the added code to calculate p-values from the t statistic.  Either p1 or p2 are acceptable p-values to use.

Where to Find 2014 Cancer Statistics and Estimates

With cancer accounting for approximately 1 in 4 US deaths it is important to know a good resource for cancer-related statistics.  Luck for us, the 2014 Cancer Facts and Figures has just been released by the American Cancer Society.  This is an excellent resource on the distribution of cancer by age, sex, race, type, and geographic location in the United States.  It includes cancer specific information on new cases, deaths, signs and symptoms, risk factors, prevention, early detection, treatment, and survival.  This is a wonderful, up-to-date resource for getting cancer-related estimates on prevalence, incidence, mortality, and survival in the US as well as look at trends and statistics from years past.  In addition, the website includes a wealth of other resources including tables, slides, and figures that are handy visual aids for presentations.