ssizeCT: Sample Size Calculation in the Analysis of Survival Data for... In powerSurvEpi: Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies

Description

Sample size calculation for the Comparison of Survival Curves Between Two Groups under the Cox Proportional-Hazards Model for clinical trials. Some parameters will be estimated based on a pilot data set.

Usage

 1 ssizeCT(formula, dat, power, k, RR, alpha = 0.05) 

Arguments

 formula A formula object, e.g. Surv(time, status) ~ x, where time is a vector of survival/censoring time, status is a vector of censoring indicator, x is the group indicator, which is a factor object in R and takes only two possible values (C for control group and E for experimental group). See also the documentation of the function survfit in the library survival. dat a data frame representing the pilot data set and containing at least 3 columns: (1) survival/censoring time; (2) censoring indicator; (3) group indicator which is a factor object in R and takes only two possible values (C for control group and E for experimental group). power power to detect the magnitude of the hazard ratio as small as that specified by RR. k ratio of participants in group E (experimental group) compared to group C (control group). RR postulated hazard ratio. alpha type I error rate.

Details

This is an implementation of the sample size calculation method described in Section 14.12 (page 807) of Rosner (2006). The method was proposed by Freedman (1982).

The movitation of this function is that some times we do not have information about m or p_E and p_C available, but we have a pilot data set that can be used to estimate p_E and p_C hence m, where m=n_E p_E + n_C p_C is the expected total number of events over both groups, n_E and n_C are numbers of participants in group E (experimental group) and group C (control group), respectively. p_E is the probability of failure in group E (experimental group) over the maximum time period of the study (t years). p_C is the probability of failure in group C (control group) over the maximum time period of the study (t years).

Suppose we want to compare the survival curves between an experimental group (E) and a control group (C) in a clinical trial with a maximum follow-up of t years. The Cox proportional hazards regression model is assumed to have the form:

h(t|X_1)=h_0(t)\exp(β_1 X_1).

Let n_E be the number of participants in the E group and n_C be the number of participants in the C group. We wish to test the hypothesis H0: RR=1 versus H1: RR not equal to 1, where RR=\exp(β_1)=underlying hazard ratio for the E group versus the C group. Let RR be the postulated hazard ratio, α be the significance level. Assume that the test is a two-sided test. If the ratio of participants in group E compared to group C = n_E/n_C=k, then the number of participants needed in each group to achieve a power of 1-β is

n_E=\frac{m k}{k p_E + p_C}, n_C=\frac{m}{k p_E + p_C}

where

m=\frac{1}{k}≤ft(\frac{k RR + 1}{RR - 1}\right)^2≤ft( z_{1-α/2}+z_{1-β} \right)^2,

and z_{1-α/2} is the 100 (1-α/2) percentile of the standard normal distribution N(0, 1).

p_C and p_E can be calculated from the following formulaes:

p_C=∑_{i=1}^{t}D_i, p_E=∑_{i=1}^{t}E_i,

where D_i=λ_i A_i C_i, E_i=RRλ_i B_i C_i, A_i=∏_{j=0}^{i-1}(1-λ_j), B_i=∏_{j=0}^{i-1}(1-RRλ_j), C_i=∏_{j=0}^{i-1}(1-δ_j). And λ_i is the probability of failure at time i among participants in the control group, given that a participant has survived to time i-1 and is not censored at time i-1, i.e., the approximate hazard time i in the control group, i=1,...,t; RRlambda_i is the probability of failure at time i among participants in the experimental group, given that a participant has survived to time i-1 and is not censored at time i-1, i.e., the approximate hazard time i in the experimental group, i=1,...,t; delta is the prbability that a participant is censored at time i given that he was followed up to time i and has not failed, i=0, 1, ..., t, which is assumed the same in each group.

Value

 mat.lambda  a matrix with 9 columns and nTimes+1 rows, where nTimes is the number of observed time points for the control group in the data set. The 9 columns are (1) time - observed time point for the control group; (2) lambda; (3) RRlambda; (4) delta; (5) A; (6) B; (7) C; (8) D; (9) E. Please refer to the Details section for the definitions of elements of these quantities. See also Table 14.24 on page 809 of Rosner (2006). mat.event  a matrix with 5 columns and nTimes+1 rows, where nTimes is the number of observed time points for control group in the data set. The 5 columns are (1) time - observed time point for the control group; (2) nEvent.C - number of events in the control group at each time point; (3) nCensored.C - number of censorings in the control group at each time point; (4) nSurvive.C - number of alived in the control group at each time point; (5) nRisk.C - number of participants at risk in the control group at each time point. Please refer to Table 14.12 on page 787 of Rosner (2006). pC estimated probability of failure in group C (control group) over the maximum time period of the study (t years). pE estimated probability of failure in group E (experimental group) over the maximum time period of the study (t years). ssize a two-element vector. The first element is n_E and the second element is n_C.

Note

(1) The estimates of RRlambda_i=RR*λ_i. That is, RRlambda is not directly estimated based on data from the experimental group; (2) The sample size formula assumes that the central-limit theorem is valid and hence is appropriate for large samples. (3) n_E and n_C will be rounded up to integers.

References

Freedman, L.S. (1982). Tables of the number of patients required in clinical trials using the log-rank test. Statistics in Medicine. 1: 121-129

Rosner B. (2006). Fundamentals of Biostatistics. (6-th edition). Thomson Brooks/Cole.

ssizeCT.default
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  # Example 14.42 in Rosner B. Fundamentals of Biostatistics. # (6-th edition). (2006) page 809 library(survival) data(Oph) res <- ssizeCT(formula = Surv(times, status) ~ group, dat = Oph, power = 0.8, k = 1, RR = 0.7, alpha = 0.05) # Table 14.24 on page 809 of Rosner (2006) print(round(res$mat.lambda, 4)) # Table 14.12 on page 787 of Rosner (2006) print(round(res$mat.event, 4)) # the sample size print(res\$ssize)