suppressMessages({ suppressPackageStartupMessages({ library(YESCDS) library(tibble) library(dplyr) library(plotly) library(ggplot2) library(ggbeeswarm) library(survival) library(DT) }) })
In section C2 we learned how to interpret survival curves, which indicate the probability of surviving beyond a given period of time from diagnosis of disease.
In this section we will examine data from a study
published in 1979, that is conveniently available with R's survival
package.
The citation for the study is
J H Edmonson, T R Fleming, D G Decker, G D Malkasian, E O Jorgensen, J A Jefferies, M J Webb, L K Kvols, Cancer Treat Rep . 1979 Feb;63(2):241-7. Different chemotherapeutic sensitivities and host factors affecting prognosis in advanced ovarian carcinoma versus minimal residual disease
The abstract is provided at the end of this vignette.
The data for the ovarian cancer study has the following form:
library(survival) datatable(ovarian)
The variable description is
Format: futime: survival or censoring time fustat: censoring status age: in years resid.ds: residual disease present (1=no,2=yes) rx: treatment group ecog.ps: ECOG performance status (1 is better, see reference)
We will consider three aspects of interpretation of these data.
osurv = Surv(ovarian$futime, ovarian$fustat) ofit1 = survfit(osurv~ovarian$rx) plot(ofit1, lty=1:2) legend(0, .4, lty=1:2, legend=c("cyc 1g/m2", "cyc .5g/m2 + adria"))
survdiff(osurv~ovarian$rx)
We can produce a very compact, two parameter model for the survival distributions for patients with and without residual disease.
summary(survreg(osurv~I(ovarian$resid.ds-1), dist="exponential")) ofit2 = survfit(osurv~ovarian$resid.ds) plot(ofit2, lty=1:2) tim = 1:1200 pp_nores = 1-pexp(1:1200, 1/exp(7.9)) # round parameter value lines(tim, pp_nores, col="blue") pp_res = 1-pexp(1:1200, 1/exp(7.9-1.2)) lines(tim, pp_res, col="red")
D.4.1 Interpret confidence intervals for the one-year survival probabilities for the two treatments, ignoring the presence or absence of residual disease.
par(mfrow=c(1,2)) with(ovarian[ovarian$rx==1,], plot(survfit(Surv(futime,fustat)~1),conf.int=TRUE)) with(ovarian[ovarian$rx==2,], plot(survfit(Surv(futime,fustat)~1),conf.int=TRUE))
D.4.1
Abstract of 1979 paper:
Treatment of patients with advanced ovarian carcinoma (stages IIIB and IV) using either cyclophosphamide alone (1 g/m2) or cyclophosphamide (500 mg/m2) plus adriamycin (40 mg/m2) by iv injection every 3 weeks each produced partial regression in approximately one third of the patients. Survival curves and time-to-progression curves for the two regimens were nearly identical in these patients with advanced disease. These same regimens produced different results when used monthly in patients who had minimal residual disease (stages II and IIIA). In patients with minimal residual disease the therapeutic index of the combination regimen was superior to that of cyclophosphamide alone. Prognosis was better overall among patients with minimal residual disease than among patients with advanced disease. Within the minimal-disease group grossly complete excision of tumor prior to chemotherapy was associated with still better prognosis. Among patients with advanced disease, prognosis was significantly better for older patients despite their generally less favorable performance scores. Much of this prognostic superiority appeared to be related to menopausal status and presumably to the depletion of endogenous estrogens in the older patients.
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