Marc Jacobs MJacobs1985
MJacobs1985
/ gist:9d0836127e6be3804b9ed8dcb3610ec4
Created
November 10, 2022 13:50
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| summary(jointFit1) | |
| str(fitLME) | |
| par(mfrow = c(4,4)) | |
| traceplot(jointFit1) | |
| ggtraceplot(jointFit1, "alphas") | |
| ggdensityplot(jointFit1, "alphas") | |
| gelman_diag(jointFit1, "alphas") | |
| densplot(jointFit1, "alphas") | |
| cumuplot(jointFit1, "alphas") |
MJacobs1985
/ gist:143fdbf1aed5fe04c55fba6e3427157c
Created
November 10, 2022 13:42
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| fitLME <- lme(Severity ~ ns(time,3), | |
| random = ~ time | Patientnr, | |
| data=try2, | |
| control=lmeControl(maxIter = 10000), | |
| na.action=na.exclude) | |
| fitSURV<-coxph(Surv(SurvTimeWeek, Event) ~ 1, | |
| data = pancreas2, x = TRUE, | |
| na.action=na.exclude, | |
| cluster = Patientnr) | |
| jointFit1 <- jm(fitSURV, |
MJacobs1985
/ gist:fe1d89d573f6f4581c9d534daf49c1a8
Created
November 10, 2022 13:41
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| try2<-total%>%dplyr::filter(!Patientnr%in%8 & !Patientnr%in%20) | |
| try2$meds <- c(0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,0) | |
| try2$RT <- recode(try2$Total_dose_spec, "1:2=1; 3=2") | |
| table(try2$RT) | |
| class(try2$RT) | |
| fitLME <- lme(Severity ~ ns(time,3) + | |
| Gender + | |
| NRS_WorstPain_at_consultation + | |
| Age_at_consultation + | |
| Opioid_spec + |
MJacobs1985
/ gist:e62e10d2d7ed3619285967e1976e471b
Created
November 10, 2022 13:34
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| ## Linearity assumption | |
| resMart<-residuals(fitSURV, type="martingale") | |
| plot(pancreas2$NRS_WorstPain_at_consultation, resMart, main="Martingale residuals for Pain", ylab="Residuals", xlab="Ca4"); lines(loess.smooth(pancreas2$NRS_WorstPain_at_consultation, resMart), lwd=2, col="blue"); abline(h=0, col="red", lty=2, lwd=1.5) | |
| plot(pancreas2$Age_at_consultation, resMart, main="Martingale residuals for Age", ylab="Residuals", xlab="Ca4"); lines(loess.smooth(pancreas2$Age_at_consultation, resMart), lwd=2, col="blue"); abline(h=0, col="red", lty=2, lwd=1.5) | |
| ## Influential observations | |
| dfbetas<-residuals(fitSURV, type="dfbetas") | |
| par(mfrow=c(4,3)) | |
| plot(dfbetas[,1], type='h', main="dfBETAS for GenderMale", ylab="DfBETAS", lwd=2);abline(h=c(2/sqrt(dim(dfbetas)[1]),-(2/sqrt(dim(dfbetas)[1]))),lty=2, col="red") | |
| plot(dfbetas[,2], type='h', main="dfBETAS for NRS Worst Pain", ylab="DfBETAS", lwd=2);abline(h=c(2/sqrt(dim(dfbetas)[1]),-(2/sqrt(dim(dfbetas)[1]))),lty=2, col="red") | |
| plot(dfbetas[,3], type='h', main="dfBETAS fo |
MJacobs1985
/ gist:0942af3cdc7aa331e16f1a13c91cbfd4
Created
November 10, 2022 13:33
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| # Log-log plot for categorized predictor | |
| KM_Surv <-with(pancreas2, Surv(SurvTime,Event==1)) | |
| KM_by_Tumorsite <-npsurv(KM_Surv~Tumorsite_in_pancreas, data=pancreas2, type="kaplan-meier", conf.type="log-log") | |
| KM_by_DiseaseStage <-npsurv(KM_Surv~Disease_stage_at_consultation, data=pancreas2, type="kaplan-meier", conf.type="log-log") | |
| KM_by_Gender <-npsurv(KM_Surv~Gender, data=pancreas2, type="kaplan-meier", conf.type="log-log") | |
| KM_by_Non_Opioid <-npsurv(KM_Surv~Non_opioid_spec, data=pancreas2, type="kaplan-meier", conf.type="log-log") | |
| KM_by_Opioid <-npsurv(KM_Surv~Opioid_spec, data=pancreas2, type="kaplan-meier", conf.type="log-log") | |
| KM_by_Surgery <-npsurv(KM_Surv~Surgery_prior_to_consultation, data=pancreas2, type="kaplan-meier", conf.type="log-log") | |
| KM_by_TotalDose <-npsurv(KM_Surv~RT, data=pancreas2, type="kaplan-meier", conf.type="log-log") | |
| par(mfrow=c(4,2)) |
MJacobs1985
/ gist:c44dd5eadac8bf618f69619c0bcad7be
Created
November 10, 2022 13:32
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| PHassump <- cox.zph(fitSURV) | |
| PHassump | |
| par(mfrow=c(5,2)) | |
| plot(PHassump[1], xlab="Time", ylab="Residual", main="Schoenfeld residuals (scaled) for Gender"); abline(h=0, lty=2, lwd=1.5, col="red") | |
| plot(PHassump[2], xlab="Time", ylab="Residual", main="Schoenfeld residuals (scaled) for NRS"); abline(h=0, lty=2, lwd=1.5, col="red") | |
| plot(PHassump[3], xlab="Time", ylab="Residual", main="Schoenfeld residuals (scaled) for Disease Stage"); abline(h=0, lty=2, lwd=1.5, col="red") | |
| plot(PHassump[4], xlab="Time", ylab="Residual", main="Schoenfeld residuals (scaled) for Age"); abline(h=0, lty=2, lwd=1.5, col="red") | |
| plot(PHassump[5], xlab="Time", ylab="Residual", main="Schoenfeld residuals (scaled) for Tumorsite"); abline(h=0, lty=2, lwd=1.5, col="red") | |
| plot(PHassump[6], xlab="Time", ylab="Residual", main="Schoenfeld residuals (scaled) for Non-Opioid"); abline(h=0, lty=2, lwd=1.5, col="red") | |
| plot(PHassump[7], xlab="Time", ylab="Residual", main="Schoenfeld residuals (scaled) for Opioid"); abline(h=0, lty=2, lwd=1.5, col="red") |
MJacobs1985
/ gist:8a900f8ef9d6136ca09dfc297cd9168f
Last active
November 10, 2022 13:33
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| pancreas2<-pancreas%>%dplyr::filter(!Patientnr%in%8 & !Patientnr%in%20) | |
| pancreas2$meds <- c(0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,0) | |
| pancreas2$RT <- car::recode(pancreas2$Total_dose_spec, "1:2=1; 3=2") | |
| table(pancreas2$RT) | |
| class(pancreas2$RT) | |
| fitSURV<-coxph(Surv(SurvTimeWeek, Event) ~ | |
| Gender+ | |
| NRS_WorstPain_at_consultation+ | |
| Disease_stage_at_consultation+ | |
| Age_at_consultation+ |
MJacobs1985
/ gist:5cf517b9a6e270ed1210a020305b29d5
Created
November 10, 2022 13:25
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| vis_miss(total[,c(3,6,9,12,15,19,20,23,26,29,32,35,38)]) | |
| gg_miss_upset(total[,c(3,6,9,12,15,19,20,23,26,29,32,35,38)]) | |
| gg_miss_upset(total) | |
| n_var_miss(total[,c(3,6,9,12,15,19,20,23,26,29,32,35,38)]) | |
| gg_miss_var(total) | |
| gg_miss_var(total,show_pct = TRUE) | |
| gg_miss_var(total[,c(3,6,9,12,15,19,20,23,26,29,32,35,38,79)],show_pct = TRUE, facet=timef) | |
| gg_miss_case(total[,c(3,6,9,12,15,19,20,23,26,29,32,35,38,79)],show_pct = TRUE, facet=timef) | |
| gg_miss_case(total[,c(3,6,9,12,15,19,20,23,26,29,32,35,38,49)],show_pct = TRUE, facet=Disease_stage_at_consultation) | |
| gg_miss_fct(x=total[,c(3,6,9,12,15,19,20,23,26,29,32,35,38,79)], fct = timef) |
MJacobs1985
/ gist:83e79269c084d0e58228e08446d95ab6
Created
November 10, 2022 13:24
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| histogram(~ SurvTimeWeek | is.na(Physical_Functioning), data = total) | |
| histogram(~ Emotional_Functioning | is.na(Physical_Functioning), data = total) | |
| histogram(~ SurvTimeWeek | timef*is.na(Interference), data = total,type = "density", | |
| panel = function(x, ...) { | |
| panel.histogram(x, ...) | |
| panel.mathdensity(dmath = dnorm, col = "black", | |
| args = list(mean=mean(x),sd=sd(x))) | |
| } ) | |
| histogram(~ Severity | timef*is.na(Relief), data = total) | |
| histogram(~ Severity | timef*is.na(Pain), data = total) |
MJacobs1985
/ gist:8e6ca72e9f04fa274312f8f986c23260
Created
November 10, 2022 13:22
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| ggplot(total,aes(x = Relief,y = Severity)) + geom_miss_point() + facet_wrap(~timef) + theme_bw() # not sure this helps, a lof missings deletes already | |
| ggplot(total,aes(x = Age_at_consultation,y = Overall_QOL)) + geom_miss_point() + facet_wrap(~Disease_stage_at_consultation)+theme_bw() | |
| ggplot(total,aes(x = Severity,y = Overall_QOL)) + geom_miss_point() + theme_bw() |
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