In this case the differences are easy to see, but often it is hard to see small differences between curves when they are simply overlaid, especially on slopes or shoulders of peaks.

Part of the reason for this is is the overall shape shared by both curves is a kind of noise when you consider the difference to be the signal.

But more significant I think is how we visually assess differences in curves. Our vision or mind tends to see the difference in parallell lines or curves to be the minimum distance between them, formed by a line segment perpendicular to both curves, rather than a difference in the Y axis direction.

So in the case of the figure above, the difference looks relatively small, maybe 0.1 or less, both in the region around 6 months and the region around 10 months. The difference in the region near 6 months looks like it balances out the difference near 10 months since they are in opposite direction. I think this is exascerbated by the proximity of the markers near 6 months — the circles seem to pair with the triangles which are one time unit in advance.

But the true difference looks to be much closer to 0.2 in the region near 6 months while the true difference near 10 months looks to be closer to 0.05. Those estimates are quite different. I suspect a plot of the differences would make that obvious.

Finally, I am glad you included 1.0 and especially 0.0 in the Y-axis, but I think a line at Y=0.0 would help. Towards 12 months it looks like the Bayes factor has a small but significantly non-zero probability of early stopping, with the Thall-Wooten having roughly twice the probability. But a closer look reveals that the Bayes factor probability is actually very close to 0 in that region.