knitr - Adding pspline term to coxph model changes summary table in R -

i'm trying use knitr/xtable produce tables coxph() objects in report. when don't include pspline term in model works expected. in single chunk:

<<results = 'asis'>>= require(survival, quietly = t) require(xtable, quietly = t) data(cancer,  package = "survival") fit0 <- coxph(surv(time, status) ~ meal.cal + ph.ecog + age, cancer) # construct data frame tables - no spline fit0table <- data.frame(variable = c("calories consumed", "ecog performance score","age"), riskratio = summary(fit0)$conf.int[,1], lower = summary(fit0)$conf.int[,3],   upper = summary(fit0)$conf.int[,4], pval = summary(fit0)$coeff[,5]) # print latex table print(xtable(fit0table, digits = 3), include.rownames = f) @ 

but when include penalized spline term, structure of summary() object changes , $conf.int , $coeff slots no longer available.

> fit1 <- coxph(surv(time, status) ~ meal.cal + ph.ecog + pspline(age, 3), cancer) > str(summary(fit0)) list of 14 $ call        : language coxph(formula = surv(time, status) ~ meal.cal + ph.ecog + age, data = cancer) $ fail        : null  $ na.action   :class 'omit'  named int [1:48] 3 5 12 13 14 16 23 25 33 44 ...   .. ..- attr(*, "names")= chr [1:48] "3" "5" "12" "13" ... $ n           : int 180 $ loglik      : num [1:2] -574 -567 $ nevent      : num 133 $ coefficients: num [1:3, 1:5] 3.84e-05 4.00e-01 1.10e-02 1.00 1.49 ...   ..- attr(*, "dimnames")=list of 2   .. ..$ : chr [1:3] "meal.cal" "ph.ecog" "age"   .. ..$ : chr [1:5] "coef" "exp(coef)" "se(coef)" "z" ... $ conf.int    : num [1:3, 1:4] 1 1.491 1.011 1 0.671 ...   ..- attr(*, "dimnames")=list of 2   .. ..$ : chr [1:3] "meal.cal" "ph.ecog" "age" .. ..$ : chr [1:4] "exp(coef)" "exp(-coef)" "lower .95" "upper .95" $ logtest     : named num [1:3] 13.2142 3 0.0042 ..- attr(*, "names")= chr [1:3] "test" "df" "pvalue" $ sctest      : named num [1:3] 13.46468 3 0.00373 ..- attr(*, "names")= chr [1:3] "test" "df" "pvalue" $ rsq         : named num [1:2] 0.0708 0.9983 ..- attr(*, "names")= chr [1:2] "rsq" "maxrsq" $ waldtest    : named num [1:3] 13.28 3 0.00407   ..- attr(*, "names")= chr [1:3] "test" "df" "pvalue" $ used.robust : logi false $ concordance : named num [1:2] 0.6061 0.0291 ..- attr(*, "names")= chr [1:2] "concordance.concordant" "se.std(c-d)" - attr(*, "class")= chr "summary.coxph"   > str(summary(fit1)) call:  coxph(formula = surv(time, status) ~ meal.cal + ph.ecog + pspline(age,  3), data = cancer)  n= 180, number of events= 133  (48 observations deleted due missingness)                          coef     se(coef) se2      chisq df   p      meal.cal                3.65e-05 0.000228 0.000228 0.03  1.00 0.8700 ph.ecog                 3.98e-01 0.131938 0.131738 9.10  1.00 0.0026 pspline(age, 3), linear 1.07e-02 0.010694 0.010694 1.00  1.00 0.3200 pspline(age, 3), nonlin                            2.90  2.07 0.2500            exp(coef) exp(-coef) lower .95 upper .95 meal.cal       1.00     1.0000     1.000      1.00 ph.ecog        1.49     0.6717     1.150      1.93 ps(age)3       1.75     0.5717     0.473      6.47 ps(age)4       3.03     0.3302     0.365     25.14 ps(age)5       4.49     0.2228     0.395     50.96 ps(age)6       4.65     0.2150     0.405     53.43 ps(age)7       3.96     0.2526     0.363     43.12 ps(age)8       3.84     0.2604     0.360     41.01 ps(age)9       4.44     0.2250     0.413     47.84 ps(age)10      5.39     0.1855     0.486     59.82 ps(age)11      7.94     0.1260     0.599    105.23 ps(age)12     12.25     0.0816     0.537    279.91  iterations: 4 outer, 12 newton-raphson      theta= 0.836  degrees of freedom terms= 1.0 1.0 3.1  concordance= 0.616  (se = 0.029 ) rsquare= 0.092   (max possible= 0.998 ) likelihood ratio test= 17.5  on 5.06 df,   p=0.00389 wald test            = 15.9  on 5.06 df,   p=0.0073  null   > coefficients(fit1) # doesn't give p-values meal.cal      ph.ecog     ps(age)3     ps(age)4     ps(age)5     ps(age)6         ps(age)7     ps(age)8  3.647054e-05 3.980039e-01 5.590767e-01 1.108052e+00 1.501557e+00 1.537249e+00     1.375833e+00 1.345564e+00  ps(age)9    ps(age)10    ps(age)11    ps(age)12  1.491454e+00 1.684622e+00 2.071641e+00 2.505932e+00   > confint(fit1) # getting closer                   2.5 %       97.5 % meal.cal  -0.0004104346 0.0004833757 ph.ecog    0.1394097867 0.6565980826 ps(age)3  -0.7493022459 1.8674555679 ps(age)4  -1.0084545140 3.2245588375 ps(age)5  -0.9278798219 3.9309933396 ps(age)6  -0.9038092211 3.9783071434 ps(age)7  -1.0122388810 3.7639051908 ps(age)8  -1.0226368192 3.7137644105 ps(age)9  -0.8849251510 3.8678337954 ps(age)10 -0.7221442743 4.0913878825 ps(age)11 -0.5129062130 4.6561876883 ps(age)12 -0.6226068259 5.6344701023 

i not think there single number (or 2 or three) meaningful describe confidence interval penalized spline term fit suitable inclusion in table, , (edit: meant not) think long list of intervals produced confint meaningful. (there no confint.coxph.penal function.) when similar question (albeit 1 asking graphical display) posed 7 years ago on r-help, terry therneau posted code displaying thought meaningful, have modified fit names , display fit , ci 'age':

fit1 <- coxph(surv(time, status) ~ meal.cal + ph.ecog + pspline(age, 3), na.omit(cancer) ) temp <- predict(fit1, type='terms', se=true)  matplot(na.omit(cancer)$age, exp(cbind( temp$fit[, 3],                                    temp$fit[,3] - 2* temp$se.fit[,3],                                    temp$fit[,3] + 2* temp$se.fit[,3])),          log='y', xlab="age", ylab="estimated relative risk", col=c('red',"blue","blue") ) 

btw: there nothing returned summary(fit0) except invisible(), seeing str(summary(fit1)) output sent console cat calls followed lonely little null. if doubt veracty review code getanywhere(summary.coxph.penal).