epi.sscomps: Sample size and power when comparing time to event

In epiR: Tools for the Analysis of Epidemiological Data

Sample size and power when comparing time to event

Description

Sample size and power when comparing time to event.

Usage

epi.sscomps(treat, control, n, power, r = 1, design = 1,
   sided.test = 2, nfractional = FALSE, conf.level = 0.95)

Arguments

treat	the expected value for the treatment group (see below).
control	the expected value for the control group (see below).
n	scalar, defining the total number of subjects in the study (i.e., the number in the treatment and control group).
power	scalar, the required study power.
r	scalar, the number in the treatment group divided by the number in the control group. This argument is ignored when method = "proportions".
design	scalar, the estimated design effect.
sided.test	use a one- or two-sided test? Use a two-sided test if you wish to evaluate whether or not the outcome hazard in the exposed (treatment) group is greater than or less than the outcome hazard in the unexposed (control) group. Use a one-sided test to evaluate whether or not the outcome hazard in the exposed (treatment) group is greater than the outcome hazard in the unexposed (control) group.
nfractional	logical, return fractional sample size.
conf.level	scalar, defining the level of confidence in the computed result.

Details

The argument treat is the proportion of treated subjects that will have not experienced the event of interest at the end of the study period and control is the proportion of control subjects that will have not experienced the event of interest at the end of the study period. See Therneau and Grambsch pp 61 - 65.

Value

A list containing one or more of the following:

n.crude	the crude estimated total number of events required for the specified level of confidence and power.
n.total	the total estimated number of events required for the specified level of confidence and power, respecting the requirement for r times as many events in the treatment group compared with the control group.
hazard	the minimum detectable hazard ratio >1 and the maximum detectable hazard ratio <1.
power	the power of the study given the number of events, the expected hazard ratio and level of confidence.

Note

The power of a study is its ability to demonstrate the presence of an association, given that an association actually exists.

See the documentation for epi.sscohortc for an example using the design facility implemented in this function.

References

Therneau TM, Grambsch PM (2000). Modelling Survival Data - Extending the Cox Model. Springer, London, pp. 61 - 65.

Woodward M (2014). Epidemiology Study Design and Data Analysis. Chapman & Hall/CRC, New York, pp. 295 - 329.

Examples

## EXAMPLE 1 (from Therneau and Grambsch 2000 p. 63):
## The 5-year survival probability of patients receiving a standard treatment 
## is 0.30 and we anticipate that a new treatment will increase it to 0.45. 
## Assume that a study will use a two-sided test at the 0.05 level with 0.90
## power to detect this difference. How many events are required?

epi.sscomps(treat = 0.45, control = 0.30, n = NA, power = 0.90, 
   r = 1, design = 1, sided.test = 2, nfractional = FALSE, conf.level = 0.95)

## A total of 250 events are required. Assuming one event per individual, 
## assign 125 individuals to the treatment group and 125 to the control group.


## EXAMPLE 2 (from Therneau and Grambsch 2000 p. 63):
## What is the minimum detectable hazard in a study involving 500 subjects where 
## the treatment to control ratio is 1:1, assuming a power of 0.90 and a
## 2-sided test at the 0.05 level?

epi.sscomps(treat = NA, control = NA, n = 500, power = 0.90, 
   r = 1, design = 1, sided.test = 2, nfractional = FALSE, conf.level = 0.95)

## Assuming treatment increases time to event (compared with controls), the 
## minimum detectable hazard of a study involving 500 subjects (250 in the 
## treatment group and 250 in the controls) is 1.33.

epiR documentation built on Sept. 30, 2024, 9:16 a.m.