Example
The example uses the cancer data set from the survival
package and compares survival, defined by the time variable
time (recoded to years) and the event variable
status (recoded to 1 = death, 0 = censored), by sex.
library(rifttable)
data(cancer, package = "survival")
cancer <- cancer |>
tibble::as_tibble() |>
dplyr::mutate(
# The exposure (here, 'sex') must be categorical (a factor)
sex = factor(
sex,
levels = 1:2,
labels = c("Male", "Female")
),
time = time / 365.25, # transform to years
status = status - 1
)
tibble::tribble(
~label, ~type,
"**Absolute estimates**", "",
"*Counts and sums*", "",
" Observations, *N*", "total",
" Events, *n*", "events",
" Events/observations", "events/total",
" Events/person-years", "events/time",
"*Follow-up*", "",
" Person-years", "time",
" Maximum follow-up, years", "maxfu",
" Median follow-up, years", "medfu",
" Median follow-up (IQR), years", "medfu (iqr)",
"*Rates*", "",
" Rate per 1000 person-years", "rate",
" Rate per 1000 person-years (95% CI)", "rate (ci)",
" Events/py (rate per 1000 py)", "events/time (rate)",
"*Risks*", "",
" 1-year survival", "surv",
" 1-year survival (95% CI)", "surv (ci)",
" 1-year risk/cumulative incidence", "cuminc",
" 1-year risk (95% CI)", "cuminc (ci)",
" Median survival, years", "medsurv",
" Median survival (95 CI), years", "medsurv (ci)",
"", "",
"**Comparative estimates**", "",
" 1-year survival difference", "survdiff",
" 1-year risk difference", "cumincdiff",
" 1-year survival ratio", "survratio",
" 1-year risk ratio", "cumincratio",
" Hazard ratio (95% CI)", "hr"
) |>
dplyr::mutate(
time = "time",
event = "status",
exposure = "sex",
arguments = list(list(timepoint = 1))
) |>
rifttable(
data = cancer,
overall = TRUE
) |>
rt_gt()| Summary | Overall | Male | Female |
|---|---|---|---|
| Absolute estimates | |||
| Counts and sums | |||
| Observations, N | 228 | 138 | 90 |
| Events, n | 165 | 112 | 53 |
| Events/observations | 165/228 | 112/138 | 53/90 |
| Events/person-years | 165/191 | 112/107 | 53/84 |
| Follow-up | |||
| Person-years | 191 | 107 | 84 |
| Maximum follow-up, years | 2.80 | 2.80 | 2.64 |
| Median follow-up, years | 1.61 | 2.30 | 1.45 |
| Median follow-up (IQR), years | 1.61 (0.82, 2.64) | 2.30 (1.11, 2.77) | 1.45 (0.76, 2.25) |
| Rates | |||
| Rate per 1000 person-years | 866.0 | 1046.6 | 634.6 |
| Rate per 1000 person-years (95% CI) | 866.0 (743.4, 1008.7) | 1046.6 (869.7, 1259.6) | 634.6 (484.8, 830.6) |
| Events/py (rate per 1000 py) | 165/191 (866.0) | 112/107 (1046.6) | 53/84 (634.6) |
| Risks | |||
| 1-year survival | 0.41 | 0.34 | 0.53 |
| 1-year survival (95% CI) | 0.41 (0.34, 0.49) | 0.34 (0.26, 0.43) | 0.53 (0.42, 0.66) |
| 1-year risk/cumulative incidence | 0.59 | 0.66 | 0.47 |
| 1-year risk (95% CI) | 0.59 (0.51, 0.66) | 0.66 (0.57, 0.74) | 0.47 (0.34, 0.58) |
| Median survival, years | 0.85 | 0.74 | 1.17 |
| Median survival (95 CI), years | 0.85 (0.78, 0.99) | 0.74 (0.58, 0.85) | 1.17 (0.95, 1.51) |
| Comparative estimates | |||
| 1-year survival difference | 0 (reference) | 0.19 (0.05, 0.34) | |
| 1-year risk difference | 0 (reference) | -0.19 (-0.33, -0.04) | |
| 1-year survival ratio | 1 (reference) | 1.57 (1.12, 2.19) | |
| 1-year risk ratio | 1 (reference) | 0.71 (0.55, 1.00) | |
| Hazard ratio (95% CI) | 1 (reference) | 0.59 (0.42, 0.82) |
Absolute estimates per exposure category
type |
Description | Options (arguments =) |
|---|---|---|
"cuminc" |
Cumulative incidence (“risk”) from the Kaplan-Meier estimator or, if
competing risks are present, its generalized form, the Aalen-Johansen
estimator. If no time point is provided, returns cumulative incidence at
end of follow-up. Change between display as proportion or percent using
the parameter risk_percent of
rifttable(). |
timepoint, id. Example:
list(timepoint = 2.5) to show 2.5-year risk. |
"cuminc (ci)" |
Cumulative incidence (“risk”), as above, with confidence intervals
(default: 95%; Greenwood standard errors with log transformation, the
default of the survival package/survival::survfit()). |
See "cuminc". |
"events" |
Event count. | |
"events/time" |
Events slash person-time. | |
"events/time (rate)" |
A combination: Events slash time followed by rate in parentheses. | |
"events/total" |
Events slash number of observations. | |
"rate" |
Event rate: event count divided by person-time, multiplied by the
rifttable() parameter factor. |
|
"rate (ci)" |
Event rate with confidence interval (default: 95%; Poisson-type large-sample interval). | |
"surv" |
Survival from the Kaplan-Meier estimator. Not estimated if competing
risks are present. If no time point is provided, returns survival at end
of follow-up. Change between display as proportion or percent using the
parameter risk_percent of rifttable(). |
See "cuminc". |
"surv (ci)" |
Survival from the Kaplan-Meier estimator, as above, with confidence
interval (default: 95%; Greenwood standard errors with log
transformation, the default of the survival
package/survival::survfit()). |
See "cuminc". |
"time" |
Person-time. | |
"maxfu" |
Maximum follow-up time. | |
"medfu" |
Median follow-up (“reverse Kaplan-Meier”), equals median survival for censoring. If competing risks are present, events other than the event of interest are also considered censoring to estimate the median follow-up for the event of interest. | |
"medfu (iqr)" |
Median and interquartile range for follow-up, as above. | |
"medsurv" |
Median survival. | |
"medsurv (ci)" |
Median survival with confidence interval (default: 95%). |
Comparative estimates with confidence intervals
type |
Description | Options (arguments =) |
|---|---|---|
"cumincdiff" |
Difference in cumulative incidence (risk difference) from
Kaplan-Meier estimator or, if competing risks are present, its
generalized form, the Aalen-Johansen estimator, with confidence interval
(default: 95%). Uses rifttable::survdiff_ci(). |
timepoint. Example: list(timepoint = 2.5)
to calculate difference in 2.5-year risk. |
"cumincratio" |
Ratio of cumulative incidence (risk ratio), with confidence interval
(default: 95%), similar to "cumincdiff". |
timepoint |
"hr" |
Hazard ratio from Cox proportional hazards regression, with confidence interval (default: 95%). If competing events are present, hazard ratios are cause-specific for the event of interest. |
list(robust = TRUE) for robust (sandwich) standard
errors. Use "+ cluster(id_variable)" in
confounders to obtain clustering specifically for Cox
models, or use the id argument of the main
rifttable() call. |
"survdiff" |
Difference in survival from Kaplan-Meier estimator, with confidence
interval (default: 95%). Not estimated if competing risks are present.
Uses rifttable::survdiff_ci(). |
See "cumincdiff". |
"survratio" |
Ratio of survival from Kaplan-Meier estimator, with confidence
interval (default: 95%), similar to "survdiff". |
See "cumincdiff". |
Competing events
With only one event type, the event variable only has
two levels: censoring, typically encoded as 0, and the
event, typically encoded as 1.
With competing events, the event variable will have
additional levels. The survival::Surv() function used by
rifttable assumes that the first-ordered level represents censoring and
the others are different non-censoring events. For example, if the event
variable is a factor, then "Censoring" needs to be the
first of the factor’s levels().
It is necessary to specify the event of interest in the
design if competing events are present. For example, if the
event variable is a factor variable
status_competing, with levels "Censored",
"Outcome of interest", and
"Other-cause death", then specify
event = "status_competing@Outcome of interest" in the table
design.
See the tables above for how competing events are handled. When no details are noted, the event of interest is recoded as the sole event and other events are considered censoring.
Weighted estimates
The name of a weights variable, for example inverse-probability
weights, can be provided via the column weights of the
design table. Weights, if present, are used by all
comparative estimators of survival as well as by
type = "cuminc" and type = "surv", and are
ignored otherwise.
Clustering and robust variance
An id variable identifying individuals in the
data must be provided when time and
time2 are used in the setting of competing events, or when
non-integer weights are present. If no id variable is
provided, each row is taken as one individual. Robust variance will then
automatically be calculated by estimators of survival/cumulative
incidence and Cox models.