In Survival Analysis, a main goal is how to model a random variable which is nonnegative and typically continuous and represents the waiting time until some events. A common method for collection of survival-type data consists in deciding on an observation window over which n individuals are followed. Naturally, some events may take longer to occur than the window length; also, some individuals can be lost from the sample due to different reasons (such as changing hospitals in clinical studies). In those cases, instead of an event time a censoring time is observed. In that manner, at the end of the observation window, the researcher ends up with a sample of triplets where represents either the true duration or the censoring time, is the indicator that the observed time is uncensored, and is a vector of individual covariates. Statistical models for that type of data are the main goal of the branch of statistics called Survival Analysis, and the relevant literature is by now enormous. Some clasical textbook-long treatises were Kalbfleish and Prentice (1980) [1] , Fleming and Harrington (1991) [2] , Andersen et al. (1993) [3] , Aalen et al. (2008) and [4] , among others.

A particular type of survival models with great appeal among practitioners focuses on the so-called hazard function, which intuitively measures the instantaneous risk of the occurrence of the event at any given moment in time. While the most widespread model for the hazard function was the semiparametric Multiplicative Hazards Model due to Cox (1972) [5] , a popular alternative for datasets without proportionality of hazards was the Additive Hazards Model (AHM) presented by Aalen (1980) [6] . With time-fixed covariates, the latter proposes that, where is a vector of p nonnegative parameters. An estimation method for and the nonparametric baseline function for this model were first described in a seminal article by Lin and Ying (1994). They proposed an estimating equation for the Euclidean parameter which was independent of and which had the additional benefit of yielding an estimate in closed form, in addition to being consistent and asymptotically normal. It’s drawback, however, lies in the sensitivity to outliers.

Within the Cox model, the potential harmful effects of outliers were commented by Kalbfleisch and Prentice (1980, ch. 5) [1] and Bednarski (1989) [7] . Robust alternatives were first introduced by Sasieni (1993a, 1993b) [8] [9] by essentially modifying the Cox’s partial likelihood score function introducing weight functions. Along the same line, important work had been developed by Bednarski (1993) [10] , who proposed estimators that were consistent and efficient not only at the model but also on small contaminated neighbourhoods. His estimators had the advantage of being Fréchét differentiable for a wide class of weight functions.

As for the Additive Hazards Model, the proposal of robust alternatives has received much less atention in the literature. In Álvarez and Ferrario (2013) [11] , we introduced a family of estimators for the Euclidean parameter in the AHM by introducing weights in Lin and Yings’ estimating equation. The weight functions are carefully chosen so that the estimators remain Fisher-consistent and asymptotically normal, but they ad- ditionally represent a gain in robustness at the price of a modest loss in efficiency. The proposed family of estimators exhibits a gain in robustness against the classical LY (Lin and Yings) estimator because they have a bounded influence function. That type of robustness is qualitative in nature, and it means intuitively that an estimation method is able to tolerate a very small proportion of extreme values. To see this, recall that the Influence Function is heuristically the population version of the so called Sensitivity Curve.

where is the estimator from the pure (noncontaminated) sample and is the estimator in a contaminated sample where one random observation has been replaced by the triplet. i.e. the population contamination model for the triplet is of the form

where H is the noncontaminated distribution that belongs to the additive hazards family and Q represents a point mass at its argument. For the practitioner, estimators with bounded influence functions are of interest when (s) he seeks a guard against a very small proportion of outliers.

Appart from the fact that the contamination scheme above is very specific, a further drawback of the estimators presented in Álvarez and Ferrario (2013) [11] is that they have a zero breakdown point. Heuristically this means that just a small proportion of contamination, strategically located, is sufficient to drive the estima- tors nonsensical. Different notions and measures of robustness and their implications are developed in many classical books, such as Maronna, Martin and Yohai (2006) [12] , Huber and Ronchetti (2009) [13] and Hampel et al. (1986) [14] .

In this article we propose a new family of robust estimators for the additive hazards model in a manner similar to Bednarski (1993) [10] . This is, we start from Lin and Yings’ estimating equation and modify it by introducing appropriate weight functions that retain consistency and assymptotic normality while improving robustness, in the sense that the resulting estimating functionals are Fréchét differentiable about small neighborhoods of the true model. That type of differentiability entails three important consequences: 1) that the proposed family of estimators has bounded influence functions; 2) that they have a strictly positive (nonzero) breakdown point; and 3) that consistency and asymptotic normality hold about small neighbourhoods of the true model for generic contamination schemes, i.e. our family of estimators resists not only the outlier-type contamination presented in (2) but also small deviations in the structure of the model itself. For instance, one could contaminate with model a which does not have additive hazards, or with a model in which T and are dependent, even conditional on Z. That makes the proposed family of estimators attractive from the practitioner point of view, as a backup against model misspecification.

The advantage of the estimators we present in this paper over previous proposals arises whenever a dataset contains outliers. When a sample is contaminated by unusual observations, the classical estimator (Ling and Yings) rapidly becomes nonsensical (in that its value drifts away towards zero or infinity). The estimators in Álvarez and Ferrario [11] on the other hand, while they resist contamination by large times or large values of the covariates, they exhibit no advantage against more involved types of contamination. Here we develop a family of estimators that resist arbitrary contamination schemes. This paper is organized as follows, in section we introduce the estimating method and we construct explicitely the Additive Hazards Family of distribution functions for survival data. In subsection we prove that our estimators are Fréchét differentiable. That entails asymptotic normality not only at true distributions in the additive hazards family, but also under contiguous alternatives. In order to assess the performance of the proposed method in small samples, section contains a small simulation study which serves two purposes: 1) it illustrates the improvement of our proposed estimators from the robustness point of view against the classical counterparts; and 2) it exhibits a non-zero breakdown point which is apparently fairly high. A simulation approach to the breakdown point is important because it is not feasible to compute it analytically. That is in part beacause the calculations involve are formidable, as they involve identifying the worst possible contaminating distribution. But more importantly, it is because the breakdown point depends on the joint distribution of the triplet, wich is only specified semi- parametrically in the Additive Hazards Model, i.e. even if available, the closed-form expression for the theoretical breakdown point would depend on unknown quantities. We have written computing code for the method proposed in this article in the form or R-scripts, which is available from the authors upon request. All the proofs are presented as succintly as possible in the Appendix; further calculations can be found in Julieta Ferrario’s PhD dissertation [15] .

Let be the counting process which records the occurrence of the event for individual i, this is

, so that it jumps from zero to unity at the random time; and let be the

so-called at risk process defined by, which denotes that by time t neither the terminal event nor censoring has occurred for individual i, so that (s) he is still at risk. For the Additive Hazards Model, Lin and Ying (1994) [16] proposed the estimating equation

where for a column vector v, we denote the matrix, and we define the process

. This yields an estimator in closed form

Using classical Counting Process theory, Lin and Ying prove that their is consistent and asymptotically normal, and they provide a formula for the estimation of the asymptotic variance.

In order to propose a Fréchét differentiable alternative to the classical (Lin and Ying’s) estimator we need to express the estimator as a functional of the joint empirical distribution function and we need to make explicit the structure of the Additive Hazard Family of distributions. We pursue this as follows.

In this section, we evaluate the performance of our proposed family of estimators via simulations. Specifically, we carry out three simulation experiments choosing for simplicity a single covariate and a true additive hazard function, with and, in order to avoid censoring. In all cases, in the estimating Equation (8), we opt for, where M is the lower percentile of the z’s.

In the first simulation we study the behavior or our estimator, denoted “RD” (Robust Differentiable) for increasing sample sizes. We take, 200, 500, 1000 and 10,000. In the function, we select, 95 and 99. Table 1 compares our estimators with the classical ones, denoted “LY” (Lin and Yings’). We perform 100 replicates and average out the results. We observe in all cases a very good performance of the of the robust estimators at very small price in increased standard error, which decreases as m or n increase.

In the second simulation, we do a comparison among the classical estimator (LY), the bounded-influence- function (BIF) estimators proposed in Álvarez and Ferrario (2013) [11] , and the ones proposed in this paper (RD). This is done under outlier-type contamination, where an increasing percentage of the sample was replaced by a large covariate value equal to 10. We take 100 replicates for a sample size of. In Table 2, we show the

results of this experiment. For the BIF estimators we take the weight function where

and. With that weight function, 90% of the observations were unmodified if Z had an exponential distribution (e.g. Álvarez and Ferrario 2013 [11] ). For the RD estimators we opt for. We observe that while both the Classical and the BIF estimators appear to break down rather fast, the robust estimator behaves very well. It is worth to emphasize that the type of contamination here is not the worst possible one, so that there is no reason to expect the good performance of the RD estimators to extend to other contamination schemes.

Lastly, we carry out a third simulation experiment in order to detect what the breakdown points of the RD estimators may be under a different type of model departure. As a model for the contaminating distribution, we chose point masses on the line. This artificially introduces outliers in the sample as shown in Figure 1, where the red observations in the first plot are replaced by the blue-colored points shown in the second plot. This graphical illustration shows that the contaminating distribution is very different from the proposed Additive Hazards Model and it may thus have the hability to severely affect the estimates. If, instead, we have contaminated by large values of z (high leverage) or large values of t (outliers), keeping the other variable unchanged, the potential harmfull effects are greatly diminished. This is because we can see in the first (uncontaminated) plot many of such points. The simulation results of 200 replicates are shown in Table 3, for

sample a size. We observe that the breakdown point seems to be about 30% for and at about 10% for. This is intuitive as the constant m regulates the trimming, given our choice of the weight function.

Intuitively, the finite sample breakdown point of an estimator is the largest proportion of contaminated observations, and a method can resist before the estimates become nonsensical, which usually means that the estimate drifts away towards zero of infinity, or in general towards the boundaries of a parameter space. Equ- ivalently, its functional version is called the asymptotic breakdown point and it measures the largest proportion of contamination. A statistical functional could tolerate before becoming nonsensical in the same sense (e.g. Maronna, Martin and Yohai 2006 [12] for formal definitions). It is noteworthy that either in its finite sample or in its asymptotic version, calculating a breakdown point requires identifying the worst possible type of con- tamination. This would depend on the joint distribution of the triplet, which is only partially specified in the Additive Hazards Model. Therefore, the breakdown point cannot be calculated explicitly. Nor is it feasible to provide reasonable bounds. For that reason, it is not possible to give a numeric value for the breakdown point and its assesment via simulations becomes illustrative. An extensive investigation of the breakdown point under different distributions and under different weight functions via simulation is a subject of further research.

We thank the editor and the referee for their comments. This work has been financed in part by UNLP Grants PPID/X003 and PID/X719. Julieta Ferrario further wishes to thank Tadeus Bednarski for generously sharing otherwise electronically unavailable manuscripts.

EnriqueE. Álvarez,JulietaFerrario, (2015) Robust Differentiable Functionals for the Additive Hazards Model. Open Journal of Statistics,05,631-644. doi: 10.4236/ojs.2015.56064

Proof of Lemma 1. Rearranging, becomes

So that substracting,

Let. Since, by A1),. Define further

, , and

. Since,.

To simplify notation, let, W: =W(u,z), , and further, , ,. Note that and, so that . Thus,

where after distributing the inner brackets, through are each of the integral terms above. By assumption

A2), we can choose large enough values, so that is bounded and it includes

the support of any function in. Observe further that includes K.

Take and. Then,

where and. The integral within the argument

where in order to simplify notation, we introduced the operator.

Denote also the set by and the integrals by, so that

Since we chose the large enough,. By iterative integration by parts,

In consequence,

Hence for all,

which is bounded because of A1) and A2). i.e. for some constant,. Similarly by integration by parts,

Thus

so that. By assumption A2) this integral is bounded. i.e. for all

, for some finite constant. A similar application of integration by parts and the assumptions gives that for all, for some finite constant. Lastly, we apply the same methodology to the integrals

to claim that for some finite constants and, and. Also for

, which closed and bounded, we have that. With the above bounds, we now

focus on the terms through

Similar calculations hold for the other’s. Detailed calculations can be found in Juieta Ferrario’s Ph.D. dissertation [15]. Finally, joining all bounds together we can assert that

For the last assertion of the Lemma, substitute H by in the functions and to obtain that and. With that, , which cancels out all but the first two terms in

, which entails by Fisher-consistency that.

Proof of Lemma 2. For any fixed and,

which is independent of. With the above we express

So substracting,.

Proof of Lemma 3. Express

where and the other integrals are defined similarly. Following the same

arguments as in Lemma 1, relying on integration by parts and Assumptions A1)-A2), we see that all the terms

are, which finishes the proof. Detailed calculations are shown in [15].

Proof of Theorem 1. By Lemmas 1 and 3, for all and, there exists some constant so that

Also by Lemma 2, , so that adding to (16) we get

, for. Since by Assumption A3),

is finite and positive, this ensures that

Take now and define a continuous function by

for some fixed, and. Thus by (17) we have that

So for sufficiently small, the ball is a subset of B. Also, by

Equation (18), if some, its image too. By Brouwer’s fixed point theorem, there exists for which, i.e.

which implies that.

Proof of Theorem 2. Since,

Also by Lemmas 1 and 2 respectively, for all and,

Adding the above Equations in (19) we get

Note that by Theorem 1, for sufficiently small, there exists so that

and. i.e.

Now since by Assumption A3) is nonzero and bounded on, premultiplying by the inverse we get

Proof of Theorem 3. Decompose. By assumption

and by Glivenko-Cantelli’s Theorem. Then by Theorem 2,

Since is linear and, the conclusion follows by the classical Central Limit’s Theorem.