make_table2.Rdmake_table2 and its associated methods are provided to create standard
table 2s based on the supplied results from appropriate estimation routines.
A typical table 2 consist of one row for each treatment group in each results
object. The table contains the number of subjects, total amount of observed
person-time during follow-up, number of events, estimates of interest
(possibly at a specified time point, when applicable), and confidence
interval for the specified coverage level. When there are multiple treatment
groups for a given results object then a typical table 2 also adds a column
providing the specified effect measure and associated confidence interval
comparing a given treatment to the reference group for each row with the
exception of the reference group.
Note that the person-time, events, and rates are computed using all of the
follow-up time and not with respect to e.g. risk_time or count_time (as
appropriate).
make_table2(obj, ...)
# S3 method for cumrisk
make_table2(
...,
effect_measure_type = "RD",
rate_round = 2,
pt_round = 0,
pt_scale = 1,
risk_round = 1,
risk_time = NULL,
alpha = 0.05,
rate_alpha = 0.05,
ref = 1,
boot_method = "normal",
caption = "",
table_footer = "",
calc_rate_ci = FALSE,
scale_pt_display = TRUE
)
# S3 method for cumcount
make_table2(
...,
effect_measure_type = "CD",
count_round = 1,
count_time = NULL,
alpha = 0.05,
ref = 1,
boot_method = "normal",
caption = "",
table_footer = ""
)
# S3 method for hr
make_table2(..., hr_round = 2, alpha = 0.05, caption = "", table_footer = "")An object with an associated method for creating table 2s.
For the make_table2 generic function, arguments to be passed on
to the appropriate method. For a given method, any remaining inputs to
... should be objects with an associated method for creating table 2s
supplied as either as seperate arguments or as a list.
A string identifying the type of effect measure to be computed from among the following options for a given method.
cumrisk: "RD" (risk difference), "RR" (risk ratio), "logRR"
(logarithm of the risk ratio), or "AR" (attributable risk)
cumcount: "CD" (count difference) or "CR" (count ratio)
Note that when a given input results object was created using bootstrap
estimation and boot_method is "log-normal", then only effect measures
with support on the positive real line can be used (i.e. "RR" or "CR").
See the Cumulative risk effect measure types and Cumulative count effect
measure types sections for the definitions of the various effect measures.
The number of significant digits used for rounding the rate
The number of significant digits used for rounding person-time
A scaling factor for the person time (default pt_scale = 1).
The number of significant digits used for rounding the risk
The time at which the cumulative risk estimates should be returned
(the default value of NULL returns all times)
The desired nominal significance level of the confidence intervals
The desired nominal significance level of the confidence intervals for the rate
The category to use as the reference for effect measure calculations.
The specific bootstrap approach used to compute confidence
intervals (default method = "normal" for a normal approximation on the risk
scale, other choices include "log-normal" for normal approximation on the
log scale). When a given input results object was created without using
bootstrap estimation then the value of boot_method is ignored. Note that
when a given input results object was created using bootstrap estimation
and boot_method is "log-normal", then only effect measures specified
via effect_measure_type with support on the positive real line can be
used.
Text that will be added above the top left of the table, ie a title to the table
Additional footer information accepts html formatting
Logical that allows user to output confidence intervals for rates in table 2. Defaults to FALSE.
Logical that allows user to turn off pt_scale for person-time but not for rate (default = TRUE)
The number of significant digits used for rounding the count
The desired time at which to extract the the cumulative
count and count difference. If NULL, uses the median time in the cumcount
object.
The number of decimal places used for rounding the hazard ratio. Default is 2.
An object of class c("datatables", "htmlwidget") used to display
the results.
make_table2(cumrisk): Creates a formatted table 2 from supplied cumrisk objects
make_table2(cumcount): Creates a formatted table 2 from supplied cumcount
objects
make_table2(hr): Creates a formatted table 2 from supplied hr objects
Mean: \(\mathrm{E}[Y^{1}] - \mathrm{E}[Y^{0}]\)
Variance with IPW: \( \mathrm{Var}[Y^{1}] + \mathrm{Var}[Y^{0}] + \frac{2}{n} \mathrm{E}[Y^{1}] \, \mathrm{E}[Y^{0}] \)
Variance otherwise: \(\mathrm{Var}[Y^{1}] + \mathrm{Var}[Y^{0}]\)
Mean: \(\frac{\mathrm{E}[Y^{1}]}{\mathrm{E}[Y^{0}]}\)
Variance with IPW: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{0}])^{2}} + \frac{\mathrm{Var}[Y^{0}]\,(\mathrm{E}[Y^{1}])^2} {(\mathrm{E}[Y^{0}])^{4}} + \frac{2\,(\mathrm{E}[Y^{1}])^2}{n\,(\mathrm{E}[Y^{0}])^2} \)
Variance otherwise: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{0}])^{2}} + \frac{\mathrm{Var}[Y^{0}]\,(\mathrm{E}[Y^{1}])^2} {(\mathrm{E}[Y^{0}])^{4}} \)
Mean: \(\mathrm{log}\frac{\mathrm{E}[Y^{1}]}{\mathrm{E}[Y^{0}]}\)
Variance with IPW: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{1}])^{2}} + \frac{\mathrm{Var}[Y^{0}]}{(\mathrm{E}[Y^{0}])^{2}} + \frac{2}{n} \)
Variance otherwise: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{1}])^{2}} + \frac{\mathrm{Var}[Y^{0}]}{(\mathrm{E}[Y^{0}])^{2}} \)
Mean: \(\frac{\mathrm{E}[Y^{1}] - \mathrm{E}[Y^{0}]}{\mathrm{E}[Y^{1}]}\)
Variance with IPW: \( \frac{\mathrm{Var}[Y^{0}]}{(\mathrm{E}[Y^{1}])^{2}} + \frac{\mathrm{Var}[Y^{1}]\,(\mathrm{E}[Y^{0}])^{2}} {(\mathrm{E}[Y^{1}])^{4}} + \frac{2\,(\mathrm{E}[Y^{0}])^{2}}{n\,(\mathrm{E}[Y^{1}])^{2}} \)
Variance otherwise: \( \frac{\mathrm{Var}[Y^{0}]}{(\mathrm{E}[Y^{1}])^{2}} + \frac{\mathrm{Var}[Y^{1}]\,(\mathrm{E}[Y^{0}])^{2}} {(\mathrm{E}[Y^{1}])^{4}} \)
Mean: \(\mathrm{E}[Y^{1}] - \mathrm{E}[Y^{0}]\)
Variance with IPW: \( \mathrm{Var}[Y^{1}] + \mathrm{Var}[Y^{0}] + \frac{2}{n} \mathrm{E}[Y^{1}] \, \mathrm{E}[Y^{0}] \)
Variance otherwise: \(\mathrm{Var}[Y^{1}] + \mathrm{Var}[Y^{0}]\)
Mean: \(\frac{\mathrm{E}[Y^{1}]}{\mathrm{E}[Y^{0}]}\)
Variance with IPW: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{0}])^{2}} + \frac{\mathrm{Var}[Y^{0}]\,(\mathrm{E}[Y^{1}])^2} {(\mathrm{E}[Y^{0}])^{4}} + \frac{2\,(\mathrm{E}[Y^{1}])^2}{n\,(\mathrm{E}[Y^{0}])^2} \)
Variance otherwise: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{0}])^{2}} + \frac{\mathrm{Var}[Y^{0}]\,(\mathrm{E}[Y^{1}])^2} {(\mathrm{E}[Y^{0}])^{4}} \)