---
title: "Proportional hazards regression with tabular data"
author: "Göran Broström"
package: eha
date: "`r Sys.Date()`"
slug: eha
link-citations: yes
output:
bookdown::html_document2:
toc: yes
toc_depth: 2
pkgdown:
as_is: true
bibliography: mybib.bib
vignette: >
%\VignetteIndexEntry{Proportional hazards regression with tabular data}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
\usepackage[utf8]{inputenc}
---
```{r setup, include = FALSE}
library(eha)
options(digits = 4, fig.width=8)
```
Cox regression is not very suitable in the analysis of huge data sets with a lot
of events (e.g., deaths). For instance, consider analyzing the mortality of
the Swedish population aged 60--110 during the years 1968-2019, where we can
count to more than four million deaths.
The obvious way to handle that situation is by tabulation and applying a
*piecewise constant hazard* function, because it is a well-known fact that any
continuous function can arbitrary well be approximated by a step function,
simply by taking small enough steps.
# Tabular data
The data sets `swepop` and `swedeaths` in `eha` contain age and sex
specific yearly information on population size and number of deaths,
respectively. They both cover the full *Swedish population* the years 1968--2019.
The first few rows of each:
```{r swe}
head(swepop)
head(swedeaths)
```
The funny rownames and the column `id` are created by the function `reshape`,
which was used to transform the original tables, given in *wide format*, to
*long format*. In the original data, downloaded from
[Statistics Sweden](https://www.scb.se), the population size refers to the last
day, December 31, of the given year, but here it refers to an average of that
value and the corresponding one the previous year. In that way we get an estimate
of the number of *person years*, which allows us to consider number of
*occurrences* and *exposure time* in each cell of the data. This information will
allow us to fit *proportional hazards* survival models. So we start by joining
the two data sets and remove irrelevant stuff:
```{r cleantab}
dat <- swepop[, c("age", "sex", "year", "pop")]
dat$deaths <- swedeaths$deaths
rownames(dat) <- 1:NROW(dat) # Simplify rownames.
head(dat)
tail(dat)
```
We note that the age column ends with `age == 100`, which in fact means
`age >= 100`. There are in total `r sum(dat$deaths)` observed deaths during the years
1968--2019, or `r as.integer(round(sum(dat$deaths) / (2019 - 1967)))` deaths per
year on average. There are 101 age groups, two sexes, and 52 years, in all 10504
cells (rows in the data frame).
## Poission regression
Assuming a piecewise constant hazards model on the 101 age groups, we can fit a
proportional hazards model by *Poisson regression*, utilizing the fact that two
likelihood functions in fact are identical. In **R**, we use `glm`.
```{r glm}
fit.glm <- glm(deaths ~ offset(log(pop)) + I(year - 2000) + sex +
factor(age), data = dat, family = poisson)
summary(fit.glm)$coefficients[2:3, ]
```
The 101 coefficients corresponding to the intercept and the age factor can be
used to estimate the *hazard function*: The intercept, `r fit.glm$coefficients[1]`,
is the log of the hazard in the age interval 0-1, and the rest are differences to
that value, so we can reconstruct the baseline hazard by
```{r glmbase}
lhaz <- coefficients(fit.glm)[-(2:3)]
n <- length(lhaz)
lhaz[-1] <- lhaz[-1] + lhaz[1]
haz <- exp(lhaz)
```
and plot the result, see Figure \@ref(fig:glmbasefig).
```{r glmbasefig, fig.cap = "Age-specific mortality, Sweden 1968-2019. Poisson regression."}
oldpar <- par(las = 1, lwd = 1.5, mfrow = c(1, 2))
plot(0:(n-1), haz, type = "s", main = "log(hazards)",
xlab = "Age", ylab = "", log = "y")
plot(0:(n-1), haz, type = "s", main = "hazards",
xlab = "Age", ylab = "Deaths / Year")
```
```{r restore1, echo = FALSE}
par(oldpar) # Restore the default
```
## The tpchreg function
While it straightforward to use glm and Poisson regression to fit the model, it
takes some efforts to get it right. That is the reason for the creation of
the function `tpchreg` ("Tabular Piecewise Constant Hazards REGression"), and
with it, the "Poisson analysis" is performed by
```{r tpchreg1}
fit <- tpchreg(oe(deaths, pop) ~ I(year - 2000) + sex,
time = age, last = 101, data = dat)
```
Note:
* The function `oe` ("occurrence/exposure") takes two arguments, the first
is the number of events (deaths in our example), and the second is exposure time,
or person years.
* The argument `time` is the defining time intervals variable. It can be either character, like
c("0-1", "1-2", ..., "100-101") or numeric (as here). If numeric, the value refers
to the start of the corresponding interval, and the next start is the end of the
previous interval. This leaves the last interval's endpoint undefined, and if not given
by the `last` argument (see below), it is chosen so that the length of the last interval is
one.
* The argument `last` closes the last interval, if is not already closed, see
above. The exact value of last is only important for plotting and for the calculation
of the *restricted mean survival time*, (RMST) see the summary result below.
```{r showf}
summary(fit)
```
The restricted mean survival time is defined as the integral of the survivor
function over the given time interval. Note that if the lower limit of the interval
is larger than zero, it gives the *conditional* restricted mean survival time, given
survival to the lower endpoint.
Graphs of the hazards and the log(hazards) functions are shown in Figure
\@ref(fig:tpplot).
```{r tpplot, fig.cap = "Age-specific mortality, Sweden 1968-2019. 'tpch' regression. Baseline refers to women and the year 2000."}
oldpar <- par(mfrow = c(1, 2), las = 1, lwd = 1.5)
plot(fit, fn = "haz", log = "y", main = "log(hazards)",
xlab = "Age")
plot(fit, fn = "haz", log = "", main = "hazards",
xlab = "Age", ylab = "Deaths / Year")
```
Same results as with `glm` and Poisson regression, but a lot simpler.
# Tabulating standard survival data
Sometimes you have a large data file in classical, individual form, suitable for
*Cox regression* with `coxreg`, but the mere size makes it impractical, or even
impossible. Then help is close by tabulating and assuming a piecewise constant
hazard function, returning to the method in the previous section, that is, using
`tpchreg`.
The helper function is `toTpch`, and we illustrate its use on the `oldmort` data
frame:
```{r tpTpchex}
head(oldmort[, c("enter", "exit", "event", "sex", "civ", "birthdate")])
oldmort$birthyear <- floor(oldmort$birthdate) - 1800
om <- toTpch(Surv(enter, exit, event) ~ sex + civ + birthyear,
cuts = seq(60, 100, by = 2), data = oldmort)
head(om)
```
Note two things:
* The creation of a new variable, `birthyear`. The original `birthdate` is
given with precision days and contains `r length(unique(oldmort$birthdate))`
unique values, which will contribute to creating a very large table, so the
transformation gives *birth year* with `r length(unique(oldmort$birthyear))`
unique values. Further, the new variable is given a
*reference value* of 1800 by subtraction, necessary so that the baseline does
not coincide with the birth of Christ. Will foremost affect plotting of the
estimated survivor function. However, regression parameter estimates are
unaffected, as long as *no interaction effect* including `birthyear` is present.
* The length of the time pieces is set to two years, in order to avoid empty
intervals in the upper age range. This choice has only a marginal effect on the
final results of the analyses. You can try it out yourself. Note that it is not
necessary to use equidistant cut points, it is chosen here only for convenience.
Now we can run `tpchreg` as before
```{r rbef}
fit3 <- tpchreg(oe(event, exposure) ~ sex + civ +
birthyear, time = age, data = om)
summary(fit3)
```
And the hazards graphs are shown in Figure \@ref(fig:plotom).
```{r plotom, fig.cap = "Old age mortality, Skellefteå 1860-1880."}
oldpar <- par(mfrow = c(1, 2), las = 1, lwd = 1.5)
plot(fit3, fn = "haz", log = "y", main = "log(hazards)",
xlab = "Age", ylab = "log(Deaths / Year)", col = "blue")
plot(fit3, fn = "haz", log = "", main = "hazards",
xlab = "Age", ylab = "Deaths / Year", col = "blue")
```
The plots of the *survivor* and *cumulative hazards* functions are "smoother",
see Figure \@ref(fig:plotsurcum).
```{r plotsurcum, fig.cap = "Old age mortality, Skellefteå 1860-1880. Cumulative hazards and survivor functions."}
oldpar <- par(mfrow = c(1, 2), las = 1, lwd = 1.5)
plot(fit3, fn = "cum", log = "y", main = "Cum. hazards",
xlab = "Age", col = "blue")
plot(fit3, fn = "sur", log = "", main = "Survivor function",
xlab = "Age", col = "blue")
par(oldpar)
```
A comparison with Cox regression on the original data.
```{r coxorig}
fit4 <- coxreg(Surv(enter, exit, event) ~ sex + civ + I(birthdate - 1800),
data = oldmort)
summary(fit4)
```
And the graphs, see Figure \@ref(fig:coxgraphs).
```{r coxgraphs, fig.cap = "Old age mortality, Skellefteå 1860-1880. Cox regression with original data."}
oldpar <- par(mfrow = c(1, 2), lwd = 1.5, las = 1)
plot(fit4, main = "Cumulative hazards", xlab = "Age",
col = "blue")
plot(fit4, main = "Survivor function", xlab = "Age",
fn = "surv", col = "blue")
```
```{r echo = FALSE}
par(oldpar)
```
# References