epiR (version 0.9-22)

epi.studysize: Estimate the sample size to compare means, proportions, and survival

Description

Computes the sample size, power, and minimum detectable difference for cohort studies (using count data), case control studies, when comparing means and survival.

Usage

epi.studysize(treat, control, n, sigma, power, r = 1, 
   conf.level = 0.95, sided.test = 2, method = "means")

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).

sigma

when method = "means" this is the expected standard deviation of the variable of interest for both treatment and control groups. When method = "case.control" this is the expected proportion of study subjects exposed to the risk f

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".

conf.level

scalar, defining the level of confidence in the computed result.

sided.test

use a one- or two-sided test? Use a two-sided test if you wish to evaluate whether or not the treatment group is better or worse than the control group. Use a one-sided test to evaluate whether or not the treatment group is better than the control group.

method

a character string indicating the method to be used. Options are means, proportions, survival, cohort.count, or case.control.

Value

• A list containing one of the following:
• n
  the total number of subjects required for the specified level of confidence and power.
• delta
  the minimum detectable difference given the specified level of confidence and power.
• lambda
  the minimum detectable risk ratio >1 and the maximum detectable risk ratio <1.< description="">
• power
  the power of the study given the specified number of study subjects and power.

Details

The methodologies adopted in this function follow closely the approach described in Chapter 8 of Woodward (2005). When method = "means" the argument treat defines the mean outcome for the treatment group, control defines the mean outcome for the control group, and sigma defines the standard deviation of the outcome, assumed to be the same across the treatment and control groups (see Woodward pp 397 - 403). When method = "proportions" the argument treat defines the proportion in the treatment group and control defines the proportion in the control group. The arguments sigma and r are ignored. When method = "survival" 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. The argument sigma is ignored (see Therneau and Grambsch pp 61 - 65). When method = "cohort.count" the argument treat defines the estimated incidence risk (cumulative incidence) of the event of interest in the treatment group and control defines the estimated incidence risk of the event of interest in the control group. The argument sigma is ignored (see Woodward pp 405 - 410). When method = "case.control" the argument treat defines the estimated incidence risk (cumulative incidence) of the event of interest in the treatment group and control defines the estimated incidence risk of the event of interest in the control group. The argument sigma is the expected proportion of study subjects exposed to the risk factor of interest (see Woodward pp 410 - 412). In case control studies sample size estimates are worked out on the basis of an expected odds (or risk) ratio. When method = "case.control" the estimated incidence risk estimates in the treat and control groups are used to define the expected risk ratio. See example 7 below, taken from Woodward p 412. For method = "proportions" it is assumed that one of the two proportions is known and we want to test the null hypothesis that the second proportion is equal to the first. In contrast, method = "cohort.count" relates to the two-sample problem where neither proportion is known (or assumed, at least). Thus, there is much more uncertainty in the method = "cohort.count" situation (compared with method = "proportions") and correspondingly a requirement for a much larger sample size. Generally, method = "cohort.count" is more useful in practice. method = "proportions" is used in special situations, such as when a politician claims that at least 90% of the population use seatbelts and we want to see if the data supports this claim.

References

Fleiss JL (1981). Statistical Methods for Rates and Proportions. Wiley, New York. Kelsey JL, Thompson WD, Evans AS (1986). Methods in Observational Epidemiology. Oxford University Press, London, pp. 254 - 284. Therneau TM, Grambsch PM (2000). Modelling Survival Data - Extending the Cox Model. Springer, London, pp. 61 - 65. Woodward M (2005). Epidemiology Study Design and Data Analysis. Chapman & Hall/CRC, New York, pp. 381 - 426.

Examples

## EXAMPLE 1 (from Woodward pp 399 - 400)
## Women taking oral contraceptives sometimes experience anaemia due to 
## impaired iron absorption. A study is planned to compare the use of iron
## tablets against a course of placebos. Oral contraceptive users are 
## randomly allocated to one of the two treatment groups and mean serum
## iron concentration compared after 6 months. Data from previous studies
## indicates that the standard deviation of the increase in iron
## concentration will be around 4 micrograms\% over a 6-month period.
## The average increase in serum iron concentration without supplements is
## also thought to be 4 micrograms\%. The investigators wish to be 90\% sure
## of detecting when the supplement doubles the serum iron concentration using
## a two-sided 5\% significance test. It is decided to allocate 4 times as many
## women to the treatment group so as to obtain a better idea of its effect. How
## many women should be enrolled in this study?

epi.studysize(treat = 8, control = 4, n = NA, sigma = 4, power = 0.90, 
   r = 4, conf.level = 0.95, sided.test = 2, method = "means")
   
## The estimated sample size is 66. We round this up to the nearest multiple
## of 5, to 70. We allocate 70/5 = 14 women to the placebo group and four
## times as many (56) to the iron treatment group.


## EXAMPLE 2 (from Woodward pp 403 - 404)
## A government initiative has decided to reduce the prevalence of male  
## smoking to, at most, 0.30. A sample survey is planned to test, at the 
## 0.05 level, the hypothesis that the proportion of smokers in the male 
## population is 0.30 against the one-sided alternative that it is greater.
## The survey should be able to find a prevalence of 0.32, when it is true,
## with 0.90 power. How many men need to be sampled?

epi.studysize(treat = 0.30, control = 0.32, n = NA, sigma = NA, power = 0.90, 
   r = 1, conf.level = 0.95, sided.test = 1, method = "proportions")
   
## ## A total of 4568 men should be sampled: 2284 in the treatment group and
## 2284 in the control group. 

## EXAMPLE 3 (from Therneau and Grambsch p 63)
## The 5-year survival probability of patients receiving a standard treatment 
## 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.studysize(treat = 0.45, control = 0.30, n = NA, sigma = NA, power = 0.90, 
   r = 1, conf.level = 0.95, sided.test = 2, method = "survival")

## 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 4 (from Therneau and Grambsch 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.studysize(treat = NA, control = NA, n = 500, sigma = NA, power = 0.90, 
   r = 1, conf.level = 0.95, sided.test = 2, method = "survival")

## 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.

## EXAMPLE 5 (from Woodward p 406)
## A cohort study of smoking and coronary heart disease (CHD) in middle aged men
## is planned. A sample of men will be selected at random from the population
## and will be asked to complete a questionnaire. The follow-up period will be
## 5 years. The investigators would like to be 0.90 sure of being able to 
## detect when the risk ratio of CHD is 1.4 for smokers, using a 0.05
## significance test. Previous evidence suggests that the death rate in 
## non-smokers is 413 per 100000 per year. Assuming equal numbers of smokers
## and non-smokers are sampled, how many should be sampled overall?

treat = 1.4 * (5 * 413)/100000
control = (5 * 413)/100000
epi.studysize(treat = treat, control = control, n = NA, sigma = NA, 
   power = 0.90, r = 1, conf.level = 0.95, sided.test = 1, method = "cohort.count")

## A total of 12130 men need to be sampled (6065 smokers and 6065 non-smokers).

## EXAMPLE 6 (from Woodward p 406)
## Say, for example, we are only able to enrol 5000 subjects into the study
## described above. What is the minimum and maximum detectable risk ratio?

control = (5 * 413)/100000
epi.studysize(treat = NA, control = control, n = 5000, sigma = NA, power = 0.90, 
   r = 1, conf.level = 0.95, sided.test = 1, method = "cohort.count")

## The minimum detectable risk ratio >1 is 1.65. The maximum detectable
## risk ratio <1 is 0.50.


## EXAMPLE 7 (from Woodward p 412)
## A case-control study of the relationship between smoking and CHD is 
## planned. A sample of men with newly diagnosed CHD will be compared for
## smoking status with a sample of controls. Assuming an equal number of 
## cases and controls, how many are needed to detect an approximate risk
## ratio of 2.0 with 0.90 power using a two-sided 0.05 test? Previous surveys
## indicate that 0.30 of the male population are smokers.

epi.studysize(treat = 2/100, control = 1/100, n = NA, sigma = 0.30, 
   power = 0.90, r = 1, conf.level = 0.95, sided.test = 2, 
   method = "case.control")

## A total of 376 men need to be sampled: 188 cases and 188 controls.

## EXAMPLE 8 (from Woodward p 414)
## Suppose we wish to determine the power to detect an approximate risk
## ratio of 2.0 using a two-sided 0.05 test when 188 cases and 940 controls
## are available (that is, the ratio of cases to controls is 1:5). Assume 
## a 0.30 prevalence of smoking in the male population.

n <- 188 + 940
epi.studysize(treat = 2/100, control = 1/100, n = n, sigma = 0.30, 
   power = NA, r = 0.2, conf.level = 0.95, sided.test = 2, 
   method = "case.control")

## The power of this study, with the given sample size allocation is 0.99.