Example: Continuous data imputation through GLM

The example data, which was from an antidepressant clinical trial, can be found in DIA Missing Data webpage. Original data was from an antidepressant clinical trial with four treatments; two doses of an experimental medication, a positive control, and placebo. Hamilton 17-item rating scale for depression (HAMD17) was observed at baseline and its change scores at weeks 1, 2, 4, 6, and 8 ( Goldstein et al. 2004). To mask the real data Week-8 observations were removed. The example data is a sub-sample of the original data: two arms were created; the original placebo arm and a “drug arm” created by randomly selecting patients from the three non-placebo arms.

data(antidep)
head(antidep) %>% kbl(align = "c") %>% 
  kable_classic(full_width = F, html_font = "Cambria") %>%
  column_spec(1:2, width = "2cm") %>%
  add_header_above(c(" " = 1, " "=1, "Responses at the baseline, week 1, 2, 4, and 6" = 5))

Missing pattern is displayed in the following plot:

Figure 1. Missing pattern of antidepressant data

The planned statistical method to analyze this endpoint was mixed-effects model with last-observation-carry-forward (LOCF) as the imputation method. In this example, missing values are imputed with GLM models. This is implemented through family argument, say, family = gaussian() (its default link is identity). The same imputation setting is applied for imputing y2 and y4, i.e. argument models is set to be glm_gaussian_identity. We run the GLM imputation model with an adaptation of 10000 and 2000 iterations for 4 chains. Chains run in parallel, which is set through doFuture package:

registerDoFuture()
plan(multisession(workers = 4))

an.test = remiod(formula=y6 ~ tx + y0 + y1 + y2 + y4, data=antidep, family = gaussian(),
                 models = c(y2="glm_gaussian_identity",y4="glm_gaussian_identity"),
                 n.iter = 100000,  n.chains = 4, n.adapt = 10000, thin=100,
                 algorithm = "jags", trtvar = 'tx', method="MAR", mess=TRUE, warn=FALSE)

plan(sequential)

The following plots show trace plots and the estimated intervals as shaded areas under the posterior density curves for the parameters of treatment variable tx in imputation models:

• beta[2] is the coefficient of tx in imputation model y6 ~ tx + y0 + y1 + y2 + y4;
• alpha[2] is the coefficient of tx in imputation model y4 ~ tx + y0 + y1 + y2;
• alpha[7] is the coefficient of tx in imputation model y2 ~ tx + y0 + y1;

The specified set of parameters can be submitted through argument subset with keyword selected_vars (alternatively, keyword selected_parms can be used):

mcsub = get_subset(object = an.test$mc.mar, subset=c(selected_vars = list("tx")))

color_scheme_set("purple")
mcmc_trace(mcsub, facet_args = list(ncol = 1, strip.position = "left"))

Figure 2. Traceplot for coefficients of tx in imputation models

mcmc_areas(
  mcsub, 
  prob = 0.95, # 95% intervals
  prob_outer = 0.99, 
  point_est = "mean"
)

Figure3. Intervals under the estimated posterior density curves for coefficients of tx in imputation models

To obtain jump-to-reference analysis, we extract MI data with method="J2R", and pool analysis results with miAnalyze:

j2r = extract_MIdata(object=an.test, method="J2R", M=1000, minspace=4)
res.j2r = miAnalyze(formula = y6 ~ y0 + tx, data = j2r, family = gaussian())

data.frame(res.j2r$Est.pool) %>% select(-6) %>%
  mutate_if(is.numeric, format, digits=4,nsmall = 0) %>%
  kbl(align = "c") %>% 
  column_spec(1:6, width = "3cm") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Reference

Wang and Liu. 2022. “Remiod: Reference-Based Controlled Multiple Imputation of Longitudinal Binary and Ordinal Outcomes with Non-Ignorable Missingness.” arXiv 2203.02771.

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