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Note: This vignette requires version 0.8.0 of marginaleffects, or the development version from Github.

This vignette replicates some of the analyses in this excellent blog post by Frank Harrell: Avoiding One-Number Summaries of Treatment Effects for RCTs with Binary Outcomes. Here, we show how one-number summaries and the entire distribution unit-level contrasts can be easily computed with comparisons().

Dr. Harrell discusses summaries from logistic regression models in the blog post above. He focuses on a context in which one is interested in comparing two groups, such as in randomized controlled trials. He highlights potential pitfalls of presenting “one-number summaries”, e.g., odds ratio and mean proportion difference. Finally, he recommends focusing on the entire distribution of proportion difference between groups.

For clarification, we use the following terms interchangeably in the context of logistic regression where the covariate of interest is categorical:

• Contrast
• Proportion difference
• Risk difference
• Absolute risk reduction

## Data

We focus on subset data from the GUSTO-I study, where patients were randomly assigned to accelerated tissue plasminogen activator (tPA) or streptokinase (SK).

Load libraries, data and fit a covariate-adjusted logistic regression model.

library(marginaleffects)
library(modelsummary)
library(rms)

load(url(
"https://github.com/vincentarelbundock/modelarchive/raw/main/data-raw/gusto.rda"
))

gusto <- subset(gusto, tx %in% c("tPA", "SK"))
gusto$tx <- factor(gusto$tx, levels = c("tPA", "SK"))

mod <- glm(
day30 ~ tx + rcs(age, 4) + Killip + pmin(sysbp, 120) + lsp(pulse, 50) +
pmi + miloc + sex, family = "binomial",
data = gusto)

### One-Number Summaries

As usual, we can produce a one-number summary of the relationship of interest by exponentiating the coefficients, which yields an Odds Ratio (OR):

modelsummary(mod, exponentiate = TRUE, coef_omit = "^(?!txSK)") 
Model 1
txSK 1.230
(0.065)
Num.Obs. 30510
AIC 12428.6
BIC 12553.5
Log.Lik. −6199.317
F 173.216
RMSE 0.24

Unlike ORs, adjusted risk differences vary from individual to individual based on the values of the control variables. The comparisons() function can compute adjusted risk differences for every individual. Here, we display only the first 6 of them:

comparisons(
mod,
variables = "tx") |>
head()
#>   rowid     type term contrast   comparison    std.error statistic    p.value
#> 1     1 response   tx SK - tPA 0.0010741928 0.0004966749  2.162768 0.03055900
#> 2     2 response   tx SK - tPA 0.0008573104 0.0003799743  2.256233 0.02405605
#> 3     3 response   tx SK - tPA 0.0017797796 0.0007784409  2.286339 0.02223446
#> 4     4 response   tx SK - tPA 0.0011367499 0.0004999032  2.273940 0.02296960
#> 5     5 response   tx SK - tPA 0.0013655083 0.0005934013  2.301155 0.02138288
#> 6     6 response   tx SK - tPA 0.0024015964 0.0010127226  2.371426 0.01771961
#>       conf.low   conf.high   predicted predicted_hi predicted_lo day30  tx
#> 1 0.0001007278 0.002047658 0.005769605  0.005769605  0.004695412     0  SK
#> 2 0.0001125746 0.001602046 0.003742994  0.004600304  0.003742994     0 tPA
#> 3 0.0002540634 0.003305496 0.009589391  0.009589391  0.007809612     0  SK
#> 4 0.0001569575 0.002116542 0.004970544  0.006107294  0.004970544     0 tPA
#> 5 0.0002024631 0.002528553 0.007343757  0.007343757  0.005978249     0  SK
#> 6 0.0004166965 0.004386496 0.012975875  0.012975875  0.010574279     0  SK
#>   Killip pmi    miloc    sex    age pulse sysbp
#> 1      I  no Anterior   male 19.027    60   130
#> 2      I  no Inferior   male 20.781    75   124
#> 3      I  no Anterior   male 20.969    85   135
#> 4      I  no Inferior   male 20.984    90   129
#> 5      I  no Anterior   male 21.449    70   157
#> 6      I  no Anterior female 22.523    84   135

Population-averaged (aka “marginal”) adjusted risk difference (see this vignette) can be obtained using the summary() function:

comparisons(
mod,
variables = "tx") |>
summary()
#>   Term Contrast  Effect Std. Error z value   Pr(>|z|)    2.5 % 97.5 %
#> 1   tx SK - tPA 0.01108   0.002766   4.005 6.1955e-05 0.005658 0.0165
#>
#> Model type:  glm
#> Prediction type:  response

The comparisons() function above computed the predicted probability of mortality (day30==1) for each observed row of the data in two counterfactual cases: when tx is “SK”, and when tx is “tPA”. Then, it computed the differences between these two sets of predictions. Finally, it computed the population-average of risk differences.

Instead of risk differences, we could compute population-averaged (marginal) adjusted risk ratios:

comparisons(
mod,
variables = "tx",
transform_pre = "lnratioavg",
transform_post = exp) |>
summary()
#>   Term                 Contrast Effect   Pr(>|z|) 2.5 % 97.5 %
#> 1   tx ln(mean(SK) / mean(tPA))  1.177 9.8075e-05 1.085  1.278
#>
#> Model type:  glm
#> Prediction type:  response
#> Post-transformation:  transform_post

Population-averaged (marginal) odds ratios:

comparisons(
mod,
variables = "tx",
transform_pre = "lnoravg",
transform_post = exp) |>
summary()
#>   Term                 Contrast Effect   Pr(>|z|) 2.5 % 97.5 %
#> 1   tx ln(odds(SK) / odds(tPA))  1.192 9.4701e-05 1.091  1.301
#>
#> Model type:  glm
#> Prediction type:  response
#> Post-transformation:  transform_post

### Unit-level Summaries

Instead of estimating one-number summaries, we can focus on unit-level proportion differences using comparisons(). This function applies the fitted logistic regression model to predict outcome probabilities for each patient, i.e., unit-level.

cmp <- comparisons(mod, variables = "tx")

head(cmp)
#>   rowid     type term contrast   comparison    std.error statistic    p.value
#> 1     1 response   tx SK - tPA 0.0010741928 0.0004966749  2.162768 0.03055900
#> 2     2 response   tx SK - tPA 0.0008573104 0.0003799743  2.256233 0.02405605
#> 3     3 response   tx SK - tPA 0.0017797796 0.0007784409  2.286339 0.02223446
#> 4     4 response   tx SK - tPA 0.0011367499 0.0004999032  2.273940 0.02296960
#> 5     5 response   tx SK - tPA 0.0013655083 0.0005934013  2.301155 0.02138288
#> 6     6 response   tx SK - tPA 0.0024015964 0.0010127226  2.371426 0.01771961
#>       conf.low   conf.high   predicted predicted_hi predicted_lo day30  tx
#> 1 0.0001007278 0.002047658 0.005769605  0.005769605  0.004695412     0  SK
#> 2 0.0001125746 0.001602046 0.003742994  0.004600304  0.003742994     0 tPA
#> 3 0.0002540634 0.003305496 0.009589391  0.009589391  0.007809612     0  SK
#> 4 0.0001569575 0.002116542 0.004970544  0.006107294  0.004970544     0 tPA
#> 5 0.0002024631 0.002528553 0.007343757  0.007343757  0.005978249     0  SK
#> 6 0.0004166965 0.004386496 0.012975875  0.012975875  0.010574279     0  SK
#>   Killip pmi    miloc    sex    age pulse sysbp
#> 1      I  no Anterior   male 19.027    60   130
#> 2      I  no Inferior   male 20.781    75   124
#> 3      I  no Anterior   male 20.969    85   135
#> 4      I  no Inferior   male 20.984    90   129
#> 5      I  no Anterior   male 21.449    70   157
#> 6      I  no Anterior female 22.523    84   135

Show the predicted probability for individual patients under both treatment alternatives.

plot(x = cmp$predicted_hi, y = cmp$predicted_lo,
main = "Risk of Mortality",
xlab = "SK",
ylab = "tPA")

abline(0, 1)

Lastly, present the entire distribution of unit-level proportion differences and its mean and median.

hist(cmp$comparison, breaks = 100, main = "Distribution of unit-level contrasts", xlab = "SK - tPA") abline(v = mean(cmp$comparison), col = "red")
abline(v = median(cmp$comparison), col = "blue") ### Appendix comparisons() performed the following calculations under the hood: d <- gusto d$tx = "SK"
predicted_hi <- predict(mod, newdata = d, type = "response")

d\$tx = "tPA"
predicted_lo <- predict(mod, newdata = d, type = "response")

comparison <- predicted_hi - predicted_lo

The original dataset contains 30510 patients, thus comparisons() generates an output with same amount of rows.

nrow(gusto)
#> [1] 30510
nrow(cmp)
#> [1] 30510