Kaplan–Meier from 120 subjects, 59 of them censored
The step curve is the estimate, the smooth curve is the truth it is trying to recover. 59 of 120 subjects were still event-free when observation stopped; they are not dropped, and they are not counted as events — they leave the risk set at the time they were last seen.
When the data stops earlyslider: subjects, 5 positionswide9 views
What else it draws
The same object, drawn to answer the other questions the essays put to it.
At time 2 the truth is 0.497. Kaplan–Meier gives 0.532; dropping the censored subjects gives 0.180; treating the censoring time as the event time gives 0.392. Both naive readings understate survival, because the subjects they mishandle are the ones doing well.
Kaplan–Meier tracks the truth from 37% censoring to 78%. Dropping the censored subjects gets steadily worse, from 0.220 to 0.037 against a truth of 0.497.
The denominator Kaplan–Meier divides by at each event. It falls both when an event happens and when a subject is censored, which is exactly how a censored subject contributes: it was at risk until it left, and it is not counted afterwards.
How often each interval contains the true survival at every quarter from 0.25 to 5.5, over 4000 simulated studies of 40 subjects (rate 0.35, dropout 0.15, follow-up 6); beneath each time is the mean number still under observation. The plain interval covers 84.0% at t = 0.25, where the estimate is near one and its upper limit exceeds one in 55.1% of studies, rises towards 95% in the middle, and falls to 88.7% at t = 5.5, where 2.5 subjects remain on average and its lower limit is below zero in 46.2% of studies. Its worst reading is 84.0% at t = 0.25. The log-log interval reads 92.5% and 92.5% at the same two times. Each reading carries a Monte Carlo standard error of about 0.5 points.
World A gives each subject a frailty with mean one and variance 1, and multiplies both its event hazard (0.35) and its dropout hazard (0.5) by it, so the subjects likeliest to leave are the ones likeliest to fail. World B has independent event and dropout times whose hazards are world A's crude hazards. Kaplan–Meier over 1000 studies of 400 gives the same curve from both — 0.7750 and 0.7763 at t = 1; 0.6627 and 0.6641 at t = 2; 0.5924 and 0.5932 at t = 3; 0.5047 and 0.5042 at t = 5 — and that curve is world B's truth, 0.5052 at t = 5. World A's truth is 0.3636 there. The dashed lines are the two bounds that assume nothing, from every dropout failing on leaving (0.1905 at t = 5) to none ever failing (0.6667).
Two causes of an ending event with constant hazards 0.2 (the one of interest) and 0.3 (the competitor), random dropout at 0.1 and follow-up to 6. The lower line is the cumulative incidence, (0.2/0.5)(1 − e^(−0.5t)), the chance of actually having had this event by t; the dots on it are the Aalen–Johansen estimate over 2000 studies of 300, 0.3670 at t = 5 against 0.3672. The upper line is 1 − e^(−0.2t), and the dots on it are one minus Kaplan–Meier with the competing event treated as censoring: 0.6318 at t = 5 against 0.6321. The second is larger by a factor of 1.722 at t = 5, and it is not an error of estimation. It estimates, correctly, the risk in a population where the competing cause does not exist.
The hazard ratio a Cox model converges to, found as the root of its expected score by numerical integration, as the trial runs longer; dropout at 0.1 throughout. The proportional treatment reads 0.5 at every τ. The waning treatment reads 0.5000 at τ = 1, 0.6362 at τ = 3 and 0.7890 at τ = 8 — the same two arms, the same effect in the same first year, and a number that drifts towards one as later, effect-free events are added to the average. The dots are the mean of 400 Cox fits with 400 subjects an arm: 0.5014 at τ = 1, 0.7020 at τ = 2, 0.7635 at τ = 3, 0.8034 at τ = 5, 0.8208 at τ = 8. The crossing treatment reads 0.3429 at τ = 1, exactly 1 at τ = 3 by construction, and 1.0611 at τ = 8: beneficial, null or harmful according to when the trial stopped.
Treatment minus control, in closed form, with dropout irrelevant to the truth. The proportional treatment's difference grows to 0.4766 at τ = 3 and the waning treatment's to 0.2675. The crossing treatment's rises to 0.1776 at τ = 2, near where the two survival curves cross, and falls back to 0.1366 at τ = 3. The ticks along the bottom are the eleven horizons, from 0.5 to 3 in quarters, at which a trial below reads its differences.
Where it is used
10 essays draw this figure, each at the numbers its own argument is about, so the same picture answers 10 different questions.
- Simpson's reversal is a region, not a table Reversals that are not errors
- The data that stops early When the data stops early
- Sums of almost anything The distribution itself
- The curve that survives censoring When the data stops early
- The interval at the end of the curve When the data stops early
- A dropout the data cannot see When the data stops early
- Where the bootstrap lies Intervals, counted
- One minus Kaplan–Meier is not a risk When the data stops early
- The hazard ratio the follow-up chose When the data stops early
- A horizon chosen after looking When the data stops early