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Use the allowed ratio-of-expectations approximation where $G(u)>0$. The unconditional numerator and denominator from part (b) giveThe common censoring survivor function cancels in this approximation. By differentiating the logarithm, its value is the population integrated hazard:This is not generally the arithmetic mean of the $H_i(t)$; it is the cumulative hazard function of the population mixture from part (a). The cancellation requires a common independent censoring law and adequate observation of the time range.

The approximation establishes approximate unbiasedness, not exact finite-sample unbiasedness. For a direct counterexample, take one uncensored individual with exponential distribution of rate $\lambda$. The Nelson–Aalen estimator is then $1_{T\le t}$, whose expectation is $1-e^{-\lambda t}$, whereas $\overline{H}(t)=\lambda t$. Thus it is not an unbiased estimator in general.

More explicitly, for independent individuals with a common event hazard $h$, the exact expected estimator isThis exhibits finite-sample bias of Nelson–Aalen estimation and dependence on $G$ when empty risk sets are possible. In a heterogeneous population, replacing the random ratios by ratios of their means introduces an additional approximation. On adequately observed intervals with large risk sets, the population-mixture expression is the appropriate large-sample target.

The cumulative hazard function is defined through the conditional mean incrementEquivalently, $dH(t)=\mathbb{E}\{dN(t)\mid Y(t)=1\}$ and $H(t)={\int }_0^{t}dH(u)$.

Since $T_i$ has cumulative hazard function ${\varphi }_i{H}_0(t)$,Thus the cumulative-hazard time change $U_i=H_0(T_i)$ has an exponential distribution of rate ${\varphi }_i$.

The conditional mean frailty among survivors isBecause the cumulative hazard function is nondecreasing, this mean is nonincreasing: high-frailty individuals tend to experience the event earlier, leaving a progressively more robust surviving population.

At time $x_i$, the observed number of events is $v_i$ and the exposure to the common instantaneous hazard is the risk-set size $n-i+1$. The likelihood score for a hazard increment therefore equates observed and expected events:Thus the Nelson–Aalen estimator isIt estimates the cumulative hazard function by adding event count divided by current exposure at every observed event time.

Let $Y(t)$ be the at-risk process and $N(t)$ the counting process for observed events. Over a short interval, the multiplicative-intensity model giveswhere $h$ is the hazard function and $H$ the cumulative hazard function. Solving this relation for the infinitesimal hazard increment suggests $d\hat{H}(t)=dN(t)/Y(t)$. Summing over distinct event times gives the Nelson–Aalen estimatorwhere $d_j$ events occur among $r_j$ individuals at risk. Here there are no ties, so $d_j=1$.

A proportional hazards family has hazard functions related bywhere the hazard ratio $c(z)$ is positive and independent of time. Equivalently, its cumulative hazard functions satisfy $H(t\mid z)=c(z)H_0(t)$ and its survivor functions satisfy $S(t\mid z)=S_0(t{)}^{c(z)}$.

For an equally weighted mixture of individual survivor functions $F_i$, the population hazard function is $\overline{h}(t)={\sum }_i{F}_i(t)h_i(t)/{\sum }_i{F}_i(t)$. Thus the weights are the surviving proportions, and the cumulative hazard function is $-\log (n^{-1}{\sum }_i{e}^{-H_i(t)})$, not generally the mean individual integrated hazard.

A survival distribution is the probability distribution of an event or failure time, commonly summarized by its survivor function, hazard function or cumulative hazard function. It may contain atoms or a positive probability of an event never occurring; an ordinary density-based hazard therefore need not describe every survival distribution.