= Recurrent postoperative event analysis
<Recurrent postoperative event analysis> studies several qualifying events occurring in the same patient after <surgery>, rather than retaining only a first event or the highest severity grade. It requires an event definition that distinguishes a new episode from another observation of an ongoing episode. The original <Clavien-Dindo classification> validation deliberately registered the most severe complication for patients with several; a recurrent-event question requires more of the event history.<Classification of Surgical Complications: A New Proposal With Evaluation in a Cohort of 6336 Patients and Results of a Survey by Daniel Dindo and others>[Methods, Validation of the Modified Classification in a Cohort of 6336 Patients]{ref}
Let $N_i(t)$ count qualifying events in patient $i$ by postoperative time $t$. This is a <counting process>. With complete observation of $n$ independent patients through $t$, the <mean cumulative postoperative event count> has the simple estimator
$$
\widehat\mu(t)=\frac1n\sum_{i=1}^nN_i(t).
$$
It describes average accumulated burden. It is different from the probability $\Pr(N_i(t)\ge1)$ of any event, and neither quantity specifies the severity of the events counted.
Within-patient dependence matters for uncertainty. At a fixed time, write $K_i=N_i(t)$ and let $s_K^2$ be the usual sample variance across patients. The patient-based standard error of the mean is $s_K/\sqrt n$ under independent-patient sampling. An original four-patient illustration with counts $(0,0,0,4)$ gives mean $1$, sample variance $4$ and estimated standard error $1$. Treating the total count as a Poisson count would instead give $\sqrt4/4=0.5$. This small example demonstrates different model assumptions; it does not validate either model or a normal confidence interval for four patients.
A proper record includes patient identity, episode times, resolution rules, severity and observation periods. Resampling patients with all their events intact can preserve within-patient dependence; resampling individual events as though they came from unrelated people cannot. An event intensity model may additionally condition on previous events, evolving health and exposure time, rather than assuming a constant rate.
Incomplete follow-up and death change the question. A rate among people currently alive and observed differs from total expected burden per original operated patient. Death prevents future postoperative events, whereas loss to follow-up hides whether events occurred. These require explicit observation and <competing risks> assumptions instead of an automatic independent-censoring treatment. The scientific aim is to measure repeated burden without confusing events, patients and time at risk.
Back to article page
