1 Introduction
Interval censored failure time data can be found in many areas of research including animal carcinogenicity experiments, demographical, epidemiological, financial, medical, sociological, machine reliability, and injury risk studies (Collett, 2015; Crowder et al., 1994; Klein et al., 2016; Ozturk et al., 2018; Sun, 2006; Yoganandan et al., 2016). Typical examples of interval censored data are found in medical or health studies with periodic followup protocols, with examples including timetotumor or timetoinfection studies (e.g. appearance of lung cancer tumors; breast cancer studies with 46 month followup times; timing of HIV infection in AIDS cohort studies with followup every 6 months) (Sun, 2006). Another example of interval censored failure time data can be found in the dosing steps of an oral food challenge studies to diagnose food allergy Taylor et al. [2009]
. However, the previously available methods for parametric modelling of interval censored failure data have been limited to single model approaches. In other fields of risk assessment, i.e. chemical risk assessment, single model selection and rejection has been acknowledged as a suboptimal approach and a more advanced, stateoftheart method preferred for parametric modelling is model averaging (EFSA Scientific Committee, 2017; US EPA et al., 2019). Current model averaging methods available for chemical benchmark dose risk assessment are limited to a single dose or time point and corresponding model averaging techniques for interval censored data have not been previously available. In this study, we present a novel Bayesian methodology developed for interval censored failure data to combine parametric survival estimates with frailty components based upon posterior predictive stacking, where the weights are formed to maximize posterior predictive accuracy. The case study of oral food challenge data from food allergic patients is utilized to demonstrate the utility of the method.
In studying food allergies, oral food challenge studies slowly increase the food protein dose until the individual presents allergic symptoms to a specific exposure amount (mg food protein). Figure (1) shows an example of such data for peanut allergies and represents 17 different clinical studies. Each study can range in size, from a small (10 to 30 participants) to a large (over 200 participants) dosetofailure experiment with participants who present with objective, externally observable symptoms. To more accurately estimate the survival curve, separate studies are combined to estimate an exposed dose that represents a predefined, specific risk to the population. Risk assessors are typically interested in finding allergic populationbased eliciting doses (EDs) where less than or of the allergic population would experience an objective allergic reaction (ED01, ED05 respectively). Such data are most appropriately analyzed using survival techniques [Crevel et al., 2007, Taylor et al., 2009, 2014, Dano et al., 2015] , but to accurately represent the population’s dosetofailure distribution, especially in the lower tails, use of a single survival distribution presents several challenges.
Most critically, we are concerned with survival regression methods that provide an accurate representation of the dosetofailure curve along with appropriate uncertainties being accurately represented. Traditionally, a nonparametric estimator, such as the KaplanMeier [Turnbull, 1976], can be used for this task. However, food challenge data is not readily available for all foods, geographic locations, age groups or other subpopulations. Thus, the data sets of interest can typically rely on less than observations where the true doseatfailure is only known to be in an interval (i.e., the data are interval censored). Nonparametric estimators, based on such limited data, may not be able to accurately capture dosetofailure distribution in the tails of the failure distribution.
Parametric failure time models are an alternative to nonparametric survival models if they fit the data well. In many cases, this is not the case, and these models may not be flexible enough, especially in the tails of the failure distribution, to describe a given data set. As a consequence, estimation using any single model may result in bias and estimates whose parameters do not fully reflect the true uncertainty. With this in mind, a number of authors have considered flexible parametric survival models using mixing distributions. Lambert et al. Lambert et al. [2004] used a mixture distribution consisting of a Gompertz hazard function and a single accelerated failure time (AFT) component. Komarek and Lesaffare Komárek and Lesaffre [2008] developed an AFT model with an overspecified number of mixture components. In both cases, the resulting time to failure distribution is a convex sum of individual failure curves, which is more flexible than a single parametric model, but these approaches develop a new class of models to describe the data, under the assumption the increased flexibility will accurately describe the observed data. As a consequence, given appropriate data, the methodologies may suffer from the same issues as single failure time distributions. Instead of creating a new survival model, we take the approach that it is possible to estimate the survival function as a weighted sum of the posterior distribution of existing parametric failure models. As our approach is a general methodology it could incorporate any failure model, which includes those of Lambert et al. and Komarek and Lesaffare.
Unlike the above mixture approaches, we estimate failure distributions individually and focus on estimation where the failure distribution is representable as a convex sum of these estimates. This is often accomplished using Bayesian model averaging [Raftery et al., 1997, Hoeting et al., 1999]
(BMA). However, when the true data generating mechanism is not in the suite of models considered BMA may not be appropriate. BMA is known to choose the model that is closest in KullbackLeibler divergence to the observed data generating mechanism
[Yao et al., 2018], which implies that the estimate will asymptotically be from one model. In our problem, there is no expectation that any of the parametric survival curves represent the true survival function, implying the estimate will be chosen by a single incorrect model that does not fully incorporate all of the uncertainty.Instead of picking the best model that represents the data, we are interested in finding a set of weights that optimize posterior predictive inference. Stacking [Wolpert, 1992, Breiman, 1996, LeBlanc and Tibshirani, 1996] produces estimates based upon multiple models, but focuses on predicting new observations based upon some scoring rule. Here point estimates and uncertainty in that estimate are constructed using a weighted average of estimates, where weights are formed by minimizing the squared error scoring rule in a leaveoneout cross validation. Originally used in frequentist settings, stacking has been given a Bayesian motivation by Clyde and Iverson Clyde and Iversen [2013]
. They show that weights can be interpreted as maximizing the expected utility under several loss functions for the prediction of new data; Le and Clarke
Le et al. [2017] show that the stacking solution is the Bayes solution asymptotically, but, these results limit stacking to averaging point predictions. Yao et al. Yao et al. [2018] extend these results making stacking applicable to estimating the posterior predictive distribution. We apply Yao et al. to stack posterior predictive distributions estimated from parametric failure time modeling. This results in a flexible methodology to estimate the failure distribution using any fit parametric model, where no single model is expected to get weight as the sample size increases. The methodology is relatively straightforward to implement and extend using a new parametric failure model in STAN [Carpenter et al., 2017], and we provide an R package that implements the modeling for the data we consider.As any parametric model can be used with this methodology, we include frailty components through random effects to account for studytostudy heterogeneity. This approach has a long history of use in both semiparametric [Klein, 1992] and parametric [Vaida and Xu, 2000, Yamaguchi et al., 2002, Komárek et al., 2007, Crowther et al., 2014] timetoevent data to represent frailty components. In our data, our effects enter the model as study specific random effects, where the frailties are thought to arise through: differing protocols, differing participant recruitment, differing cumulative dosing designs, differing symptom interpretations by clinicians and nurses, and an array of possible regional genetic or environmental differences present in each study’s population. In addition to frailty components with standard right and left censoring, we include the possibility that the failure is only known in an interval to reflect the complicated nature of our data problem. This is a flexible approach to analyzing failure time data that arise from a diverse set of circumstances.
The manuscript is organized by describing the general model framework, studying the method through a simulation, and showing the utility of the method by applying it to two food allergen dosetofailure databases. Example databases for the manuscript include a large peanut allergy food challenge database Taylor et al. [2014] and much smaller sesame allergy food challenge database Dano et al. [2015] formed from multiple studies at different centers.
2 Stacked Survival Regression
To model these data, assume there are studies and each study has observations, where . For subject in study we observe a failure in the interval , where the set represents study specific intervals where the failure can be observed, cumulative dose intervals in our case. For , and for one has For studies, we observe with total observations and wish to accurately estimate the underlying generating process.
Let
be a cumulative distribution function (CDF), with
a function of defining the location and a scale parameter. Given the failure model and noting , the observed data likelihood for is(1) 
where
is a vector of covariates.
For a given (1) forms a key component for inference on the failure distribution, and may include fixed and random effects. In our problem, is a vector of indicator variables that indicate the observation’s study and is related to through the identity or log link of where and is a fixed intercept. That is, for study given CDF or which is used to define a random effect model over the study centers.
When random effects are included in the model, inference on the population average survivor function may be of interest. This quantity is estimated by marginalizing over , i.e.,
(2) 
For many survival functions, analytic evaluation of this integral is not possible. In what follows, we estimate it using Markov Chain Monte Carlo (MCMC) methods.
The above describes a likelihood model on a specific failure distribution that can be used in Bayesian or frequentist inference. Given a CDF, there is no expectation that any parametric failure distribution will be representative of the underlying process. In what follows, we use Bayesian stacking to estimate the failure distribution from multiple parametric models that can be analyzed individually using (1) as well as prior assumptions on using Bayesian stacking.
2.1 Bayesian Stacked Survival
For inference based upon an individual survival function, we place a prior over and and estimate the posterior distribution using MCMC. Inference on a single model may under represent the true uncertainty around the failure distribution. Consequently, we perform multimodel inference using Bayesian stacking.
Assume there are individual failure distributions, i.e., , , estimated using MCMC. We wish to find an optimal predictive distribution for and based upon these models. To do this, we use Yao et al. [2018] to estimate a set of optimal weights that places and in the convex hull of the posterior predictive densities. That is we find and estimate the predictive density for new failure time as
(3) 
where is the posterior predictive distribution for model
As discussed in Yao et al. [2018], the weights are found by
where is the leaveoneout predictive distribution computed using PSISLOO Vehtari et al. [2017] and is the space weights such that By estimating the posterior predictive distribution in (3) a large number of plausible failure time distributions can be investigated, and uncertainty in the true failure distribution can be appropriately expressed. Further, this can be readily implemented in off the shelf software, which allows for straightforward implementation of the approach.
2.2 Estimation
Individual model estimation proceeds using MCMC and the NoUTurn Sampler (NUTS) [Hoffman and Gelman, 2014] implemented in STAN Carpenter et al. [2017]. These posterior samples are used to estimate the stacked survival function. With (3) sampled using MCMC, there are several survival functions that may be of interest. If it is believed that an individual study may be representative of a susceptible subpopulation, i.e., a representative of a group that may be particularly susceptible to failure, then the expected survival distribution for a given study can be computed as
where and is shorthand to represent sampled parameter vectors for all M models given the data , that is these values are available from the sampling output. This gives the point wise posterior predicted survival curve for an individual study with information shared between studies through the parameters.
To estimate the population average survival function, the frailty components should be accounted for, which is accomplished by integrating out the random effects, i.e.
which is estimable over MCMC iterations.
All estimates form the pointwise survival curve estimates. To compute the survival curve over the interval
, we estimate the pointwise curve over a fine grid of points and use a spline function to interpolate between points. Specific timetofailure probabilities are then computed from this curve. For example, in our application the eliciting dose (
) causing a group or population to have percent failure, i.e.is computed from the spline estimate. Further, posterior predictive summaries of this value , e.g., distribution quantiles, can also be computed similarly as above.
3 Bayesian Specification
3.1 Failure Distributions
The above specification is general and may include any density; however, for this analysis, we choose each from distributions that are either common in parametric survival regression, i.e., the Weibull distribution and LogGaussian (LogNormal) and LogLogistic, or whose mean is easily represented as a linear or exponential function of
additionally we include the generalized Pareto and LogLaplace, also known as the log doubleexponential, distribution, which have heavy tails, and are not typically used in the survival literature. These choices give a broad range of survival functions that are applicable to many contexts, but the methodology is easily extended to other models if necessary. Table (
1 ) gives the five parametric distributions chosen in this specification. All of the distributions are determined by a and a scale parameter .Distribution  

Weibull  
Generalized Pareto  
LogGaussian  
LogLogistic  
LogLaplace 
3.2 Prior Specifications
We place diffuse priors over a range of plausible shapes for each distribution defined above. For any given analysis, the observed failure times are typically on different scales. For example, cancer recurrence times may be in the tens of years where as cumulative dosetofailure data may be in the thousands of micrograms; specifying a prior for a given failure distribution on a given scale may not translate to another scale. To create a general approach, we scale all failure times to be in the interval. Diffuse priors are subsequently defined relative to this interval.
Table (2) describes the priors for the each model. For each survival function, defines mean of the random effects distribution placed over in Table (1), where and
is a hyperparamter on the variance of the random effect specific to the distribution. For a given model, the dispersion parameter
is given a prior that is diffuse over the interval . For example in the Weibull model, the shape parameter is log normal allowing a large range of shapes in this range, but for the LogGaussian this parameter is given a distribution.Model  z  

Weibull  
Generalized Pareto  
LogGaussian  
LogLogistic  
LogLaplace 
represents the normal distribution with mean
,is the inversegamma distribution with parameters
and , andis the lognormal distribution with logmean
and standarddeviation Additionally,is an indicator function truncating the random variable
to be in the interval4 Simulation
We investigated the performance of this approach through simulation. For this study, we use the priors defined above, and, as we are concerned with inference in the tail of the dosetofailure curve for food allergy related purposes, we look at estimating the doses associated with and failure rates. We compare the proposed approach to a Weibull model with a random effect term specified for each study.
For the simulation, data are generated from two underlying true failure distributions,
which represent mixture distribution from two distinct failure models dependent on study . Here, IG
is the CDF of the inverseGaussian distribution having expectation
and scale and logSkewTis the CDF of log of the skewT distribution, that is if
then where is the center parameter, is the dispersion parameter, and 3 is the skew parameter. By defining the failure distribution to be a mixture distribution, the study ensures the true failure distribution is not in the suite of distributions fit to the data; however, is close to the Weibull model that is fit to the data as a comparison. This allows a comparison of the robustness of inference when the model is slightly missspecified. Further, is a case where the model is completely missspecified, which allows a comparison of the methodologies in cases expected to arise in practice.To accommodate studytostudy variability, multiple study centers are included in each simulation. For each iteration and distribution/study, is drawn from a logGaussian distribution with logstandard deviation of and logmean of and for the Weibull, inverseGaussian, and logSkew T distributions respectively. This process was done 200 times for n =
study centers. For each study center, a random number of subjects were observed, where the total number of subjects was drawn from a Poisson distribution with mean
. Additionally, the interval censoring locations were considered random for each study. Here 10 dosing regimes were drawn from a logGaussian distribution with log mean of 1 and logstandard deviation of 1.4. To ensure these doses were not close to zero, i.e. there was appreciable positive probability of a left censoring event, 0.75 was added to each tested dose. The dosetofailure was estimated for probabilities and , and the mean squared integrated error for the proposed approach and the Weibull approach are computed.Table (3) gives the ratio of the mean squared error (MSE) between the two approaches. Values less than one indicate the proposed approach has less mean squared error, while asterisks indicate that ratio is significantly different at the level. In the case of the inverseGaussian/logSkew T case, which is the case the model is completely misspecified, the proposed approach has significantly less MSE than the Weibull approach. Further, the improvement increases as the number of study centers increases, which indicates that the difference in the MSE improves as more study centers are added. In the Weibull/inverseGaussian case, a more nuanced result is seen. Here the MSE is approximately the same between the two approaches, when and but does favor fitting the single Weibull model when for smaller sample with the Weibull random effects model appearing to have slightly less MSE although this value is never significantly different at the level across the simulation conditions.
InverseGaussian/SkewT  Weibull/InverseGaussian  
Number of study centers  Number of study centers  
Failure Probability  5  15  30  5  15  30 
0.53*  0.43*  0.29*  1.05  1.02  1.33  
0.59*  0.47*  0.38*  0.99  0.98  1.07  
0.76*  0.68*  0.62*  0.98  1.02  1.30 
5 Food Allergy Data
We apply the proposed approach to study two cumulative dosetofailure failure distributions for populations exposed to sesame and peanut allergies. These databases and corresponding frequentist analyses using accelerated failure time models are described in Taylor et al. [2014] and Dano et al. [2015]. For each individual study in a given database, known or suspected allergic participants were recruited and exposed to increasing doses of the allergenic food protein. For a given dose, if no objective symptoms were observed after a set time period, the dose was increased to the next dosing step until the first objective symptom(s) of an allergic reaction was observed or the maximum predetermined experimental dose was reached. When objective symptoms were presented, the study was stopped, and the participant was marked as having an objective allergic reaction between the previous cumulative dose with no objective symptoms (NOAEL, No Observed Adverse Effect Level) and the current cumulative dose with observed objective symptoms (LOAEL, Lowest Observed Adverse Effect Level). Each individual study in a given database recruited participants from diverse geographic populations and followed its own internal protocol determining the nature and severity of symptoms required to terminate the experiment. As a result, individual studies exhibit marked heterogeneity. We estimate the entire failure distribution and utilize the entire distribution in certain quantitative risk assessment scenarios. However, for the purposes of derivation of eliciting doses which possibly could be used later as Reference Doses such as those proposed the the Allergen Bureau of Australia and New Zealand’s Voluntary Incidental Trace Allergen Labelling (VITAL) program Taylor et al. [2014], we are interested in the cumulative protein dose that would result in an objective allergic reaction in less than or of the allergic population (i.e., ED01 or ED05) which is important for risk assessment purposes. For our purposes, the two databases analyzed represent a small database (three studies with less than 40 participants for sesame) and a large database (17 studies with more than 700 participants for peanut) to show the utility of the proposed approach. Analyses for these databases use the same prior distributions as those in the simulation study.
5.1 Peanut dosetofailure
Taylor et al. [2014] reported 750 peanutallergic individuals with 30 leftcensored and 132 rightcensored datapoints. Using accelerated failure time models, cumulative ED01 values were reported as 0.13 mg peanut protein for the LogLogistic distribution and 0.28 mg peanut protein for the LogGaussian distribution. Accelerated failure time ED05s were 1.4 and 1.5 mg peanut protein, respectively, with ED10s of 4.1 and 3.8 mg peanut protein. Based upon the available data and expert judgement, there was no biological reason to distinguish between the results of either distribution and the VITAL Scientific Expert Panel recommended a Reference Dose of 0.2 mg peanut protein to guide application of precautionary allergen labeling and achieve the desired protection level for of the the allergic population Taylor et al. [2014]. However, this Reference Dose has come into question as the process did not rely on a single dose distribution. Reanalysis of the Taylor et al. [2014] peanut database with the proposed approach in this manuscript results in a stacked survival estimate ED01 of 0.2 mg peanut protein, an ED05 of 3.3 mg peanut protein and an ED10 of 10.9 mg peanut protein. Unsurprisingly, the stacked survival estimate values are quite close to the previous accelerated failure time results, and the stacked survival estimate ED01 of 0.2 mg peanut protein is the same as the previously suggested Taylor et al. [2014] VITAL reference dose of 0.2 mg peanut protein. The Weibull, Generalized Pareto and LogLaplace distributions were the 3 models giving the most weight to the ED estimation for the peanut reanalysis. The Generalized Pareto and LogLaplace distributions were not previously available for Taylor et al. [2014] or Dano et al. [2015], demonstrating the added value of the new approach and reinforcing the conservative nature of the previous estimates.
5.2 Sesame dosetofailure
Dano et al. [2015] reported 35 sesameallergic individual datapoints. Using accelerated failure time models, the lower confidence interval estimates for the cumulative ED05 values were reported as 0.4 mg sesame protein for the LogLogistic distribution, 0.6 mg sesame protein for the LogGaussian distribution, and 0.1 mg sesame protein for the Weibull distribution. Accelerated failure time ED05s were 2.1, 2.4, and 1.0 mg sesame protein, respectively. Based on a similar but slightly smaller database, the VITAL Scientific Expert Panel utilized the lower confidence interval of the ED05 to recommend a Reference Dose of 0.2 mg sesame protein Taylor et al. [2014]. Reanalysis of the Dano et al. [2015] sesame database with the proposed approach in this manuscript results in a stacked survival estimate ED05 of 5.1 mg sesame protein, with a lower confidence interval of 0.6 mg sesame protein. The stacked survival estimate values are quite close to the previous accelerated failure time results, and the stacked survival estimate of the lower confidence interval estimate for the ED05 of 0.6 mg sesame protein is slightly higher that the previously suggested reference dose of 0.2 mg sesame protein, and it is in 0.1 to 0.6 mg sesame protein range from the 3 accelerated failure time model estimates. The LogLaplace distribution, which was not previously available for Taylor et al. [2014] or Dano et al. [2015], was model giving the most weight to the ED estimation for the sesame reanalysis, again demonstrating the added value of the new approach.
6 Discussion
We have presented a general framework for survival regression based upon stacking, and have applied it to several dosetofailure datasets where it is of interest to determine levels of exposure that do not typically elicit a response from a majority of the sensitized population. Although we have assumed random effects for our model to account for studytostudy variability, other modifications of this approach can be envisioned. For example, if necessary, it is possible given the framework to include fixed effects. In such a case, survival functions given these covariates can be constructed from the posterior predictive distribution.
The major benefit of this approach is that it can incorporate any parametric failure distribution. Extensions to the model are straight forward, and individual models can be added to the code provided in the appendix. Flexible survival models, such as those described by Lambert et al. [2004] can be included providing an additional layer of flexibility. Finally, though we have shown the applicability of the methodology with regard to clinical data from the field of food allergy, it is anticipated that the method has utility outside of this domain. Additional possible domains of application could include medical related timetotumor research, carcinogenicity experiments in animals, large epidemiological studies, machine maintenance studies, estimation of dietary exposure to contaminants or other fields where interval censored data is present. For example, in the machine reliability literature, where accurate estimates of the failure times in the tail are important, this methodology may be a useful addition.
Acknowledgements
The authors would like to thank Randall Smith and Drs Dustin Long and Michael Pennell for comments on an earlier version of the manuscript. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention.
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