Responder proportions from continuous arm summaries — responder

Estimates, for each study arm, the probability that a patient's change score crosses the minimal important difference (MID) threshold under a parametric model for the change scores, together with a delta-method (sampling) variance for that probability.

Usage

responder_proportions(
  change,
  sd,
  n,
  mid,
  direction = c("higher", "lower"),
  dist = c("normal", "lognormal", "t"),
  df = NULL
)

Arguments

change: Numeric vector of mean change scores.
sd: Numeric vector of standard deviations (> 0).
n: Numeric vector of sample sizes (>= 2).
mid: Single finite number: the minimal important difference threshold.
direction: "higher" (a larger change indicates response) or "lower".
dist: Change-score distribution: "normal" (default), "lognormal" or "t".
df: Degrees of freedom when dist = "t".

Value

A data frame with one row per input element and columns p (responder probability) and var_p (delta-method variance).

References

Thorlund K, Walter SD, Johnston BC, Furukawa TA, Guyatt GH (2011). Pooling health-related quality of life outcomes in meta-analysis: a tutorial and review of methods for enhancing interpretability. Research Synthesis Methods, 2(3), 188 to 203. doi:10.1002/jrsm.46

Anzures-Cabrera J, Sarpatwari A, Higgins JPT (2011). Expressing findings from meta-analyses of continuous outcomes in terms of risks. Statistics in Medicine, 30(25), 2867 to 2880. doi:10.1002/sim.4298

Examples

responder_proportions(
  change = c(0.96, 0.79, 1.02), sd = c(1.26, 1.28, 1.34),
  n = c(43, 139, 156), mid = 1
)
#>           p       var_p
#> 1 0.4873373 0.003699457
#> 2 0.4348410 0.001129700
#> 3 0.5059541 0.001020111