epiR (version 2.0.68)

epi.sscomps: 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)

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.

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.

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.