ipdmeta (version 1.1)

ipd.meta.power: IPD meta-analysis covariate-treatment power estimate

Description

Estimates the individual-patient meta-analysis power of a Wald test of interaction in a mixed effects model for a continuous or binary outcome with only study-level summaries (no patient-level data).

Usage

ipd.meta.power(
y0,
y1,
var0,
var1,
x0,
x1,
s20,
s21,
n0,
n1,
interaction,
alpha=.05,
logOR=TRUE,
level=95)

Arguments

y0
vector of control group response
y1
vector of treatment group response
var0
vector of variances of patient-level responses for control group
var1
vector of variances of patient-level responses for treatment group
x0
vector of covariate means for control group
x1
vector of covariate means for control group
s20
vector of covariate sample variances for control group
s21
vector of covariate sample variances for treatment group
n0
vector of number of control subjects
n1
vector of number of treated subjects
interaction
scalar, supposed alternative interaction effect
alpha
scalar significance level of Wald test (two-sided)
logOR
logical, whether the effect size is logit odds ratio
level
0 to 100 level for confidence interval of variance/power estimate

Value

  • A list with the following named components:

    ll{ esimated.power The estmated IPD meta-analysis interactive effect power power.lower Lower bound for level CI power.upper Upper bound for level CI estimated.se Estimated standard error of IPD meta-analysis interaction effect se.lower Lower bound for level CI se.upper Upper bound for level CI sigma Estimate of residual variance sigma0 Estimate of intercept random effect variance from simple RE meta-analysis with DL estimator sigma1 Estimate of treatment random effect variance simple RE meta-analysis with DL estimator level 0 to 100 level of uncertainty intervals }

Details

The estimator applies to an IPD generalized linear mixed model with a logit or idenity link, corresponding to a binary or continuous outcome, respectively. The supposed IPD GLMM has an intervention, covariate and covariate-treatment interaction term. Random effects by study for baseline and treatment effect are assumed.

The study-level data for a continuous response has effect sizes that are the mean difference between groups; summary responses are the treatment arm means. For a binary outcome, the effect size is the log-odds ratio and the response for each treatment arm is the log odds of the mean proportion. When the effect size is the log-odds the residual variance sigma is always one as is the case for a GLM model with logit link and no overdispersion.

When logOR is FALSE it is assumed that the patient-level response is a continuous variable.

Examples

data(poynard)

#CREATE META SUMMARY OF LOG-OR EFFECT SIZE FOR 2-YR BLEEDING EVENTS

binary.mean <- function(x){
n <- length(x)
if(sum(x)==0){
 return(.5/(n+.5))
 }
else if(sum(x)==n){
 return(n/(n+.5))
 }
else{
return(mean(x))
 }
}

logit <- function(x){log(x/(1-x))}

meta <- data.frame(p0=sapply((poynard$bleed0/poynard$n0),binary.mean))

meta$p1 <- sapply((poynard$bleed1/poynard$n1),binary.mean)
meta$y0 <- logit(meta$p0)
meta$y1 <- logit(meta$p1)
meta$var0 <- 1/(meta$p0*(1-meta$p0))
meta$var1 <- 1/(meta$p1*(1-meta$p1))
meta$n0 <- poynard$n0
meta$n1 <- poynard$n1

#ALTERNATIVE HYPOTHESIS FOR AGE-TREATMENT EFFECT
#WITH 10 YEARS CHANGE TO OR TREATMENT EFFECT exp(beta*10)
#EFFECT MODIFIER CHANGES TREATMENT EFFECT BY 30%

beta = log(1.3)/10

ipd.power <- 

ipd.meta.power(
 int=beta,
 y0=meta$y0,y1=meta$y1,
 var0=meta$var0,var1=meta$var1,
 n0=meta$n0,n1=meta$n1,
 x0=poynard$age0,x1=poynard$age1,
 s20=poynard$age.s20,s21=poynard$age.s21
)

ipd.power