pool_scalar_RR function

Rubin's Rules for scalar estimates

pool_scalar_RR Applies Rubin's pooling Rules for scalar estimates

pool_scalar_RR(
  est,
  se,
  logit_trans = FALSE,
  conf.level = 0.95,
  statistic = FALSE,
  dfcom = NULL,
  df_small = TRUE,
  approxim = "tdistr"
)

Arguments

• est: a numerical vector of parameter estimates.
• se: a numerical vector of standard error estimates.
• logit_trans: If TRUE logit transformation of parameter values is applied before pooling, if FALSE (default), pooling is done on the original parameter scale.
• conf.level: Confidence level of the confidence intervals.
• statistic: if TRUE the test statistic and confidence interval are provided, if FALSE (default) these are not shown.
• dfcom: The complete data analysis degrees of freedom.
• df_small: if TRUE (default) the (Barnard & Rubin) small sample correction for the degrees of freedom is applied, if FALSE the old number of degrees of freedom is calculated.
• approxim: if "tdistr" a t-distribution is used (default), if "zdistr" a z-distribution is used to derive a p-value according to the test statistic.

Returns

A list object from which the following objects are extracted:

• pool_est the pooled parameter value.
• pool_se the pooled standard error value.
• t quantile of the t-distribution (to calculate confidence intervals).
• r the relative increase in variance due to missing data.
• dfcom complete data degrees of freedom.
• v_adj adjusted degrees of freedom (according to Barnard and Rubin 1999)

Details

The t-value is the quantile value of the t-distribution that can be used to calculate confidence intervals according to $est_{pooled} +/- t_{1-\alpha/2} * se_{pooled}$. When statistic is TRUE the test statistic is calculated as $statistic = est{pooled}/se{pooled}$. The p-value is than derived using the t-distribution and adjusted degrees of freedom.

Examples

est <- c(0.4, 0.6, 0.8)
se <- c(0.02, 0.05, 0.03)
res <- pool_scalar_RR(est, se, dfcom=500)
res

Author(s)

Martijn Heymans, 2021