In a typical pretest-posttest RCT, subjects are randomized to two treatments, and response is measured at baseline, prior to intervention with the randomized treatment (pretest), and at prespecified follow-up time (posttest). Interest focuses on the effect of treatments on the change between mean baseline and follow-up response. Missing posttest response for some subjects is routine, and disregarding missing cases can lead to invalid inference.
prepost.test(baseline, post, treatment, conf.level = 0.95, delta = "estimate")
| baseline | A vector of quantitative baseline measurements |
|---|---|
| post | A vector of quantitative post-test measurements with same length as baseline. May contain missing values |
| treatment | A vector of 0s and 1s corresponding to treatment indicator. 1 = treated, Same length as baseline |
| conf.level | confidence level of the interval |
| delta | A numeric between 0 and 1 OR the string "estimate" (the default). The proportion of observation treated. |
Marie Davidian, Anastasios A. Tsiatis and Selene Leon (2005). "Semiparametric Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data". Statistical Science 20, 261-301.
chisq.test
# From Altman expo = c(rep(1,9),rep(0,7)) bp1w = c(137,120,141,137,140,144,134,123,142,139,134,136,151,147,137,149) bp_base = c(147,129,158,164,134,155,151,141,153,133,129,152,161,154,141,156) diff = bp1w-bp_base prepost.test(bp_base, bp1w, expo)#> #> Semiparametric Estimation of Treatment Effect in a Pretest-Posttest #> Study with Missing Data #> #> data: #> z = -2.8448, p-value = 0.004443 #> 95 percent confidence interval: #> -12.120605 -2.232171 #> sample estimates: #> estimated treatment effect #> -7.176388 #>