11 Nonlinear least squares regression techniques, such as PROC NLIN in SAS, can be used to fit this model to the data. In the following statements, PROC PHREG is used to carry out a Bayesian analysis for the piecewise exponential model. Model specification. (2012) developed a piecewise-exponential approach where Poisson regression model parameters are estimated from pseudo-likelihood and the corresponding variances are derived by Taylor linearization methods. mean_survival_time_¶ The mean survival time of the average subject in the training dataset. They can describe this pattern over … - Selection from Categorical Data Analysis Using The SAS® System, 2nd Edition [Book] Cary: SAS Institute; 2010. Without using the PRIOR= suboption within the PIECEWISE=HAZARD option to specify the prior of the hazard parameters, the default is to use the noninformative and improper prior displayed in Output 64.14.4. I am fitting a bayesian piecewise exponential model to survival data. The following DATA step creates the data set Blind (Lin, 1994) that represents 197 diabetic patients who have a high risk of experiencing blindness in both eyes as defined by DRS criteria: One eye of each patient is treated with laser photocoagulation. If we want to consider discrete survival times (for example, over the positive integers), we need to make a small adjustment. The deviance information criterion (DIC) is used to do model selections, and you can also find programs that visualize posterior quantities. You can also use the macro %CATER (Caterpillar Plot) to draw a caterpillar plot to visualize the eight hazards in the model: Output 61.16.4: Caterpillar Plot of the Hazards in the Piecewise Exponential Model. Simulate two-arm time-to-event data using the piecewise exponential distribution rpwexp(). In the BAYES statement, the option PIECEWISE stipulates a piecewise exponential model, and PIECEWISE=HAZARD requests that the constant hazards be modeled in the original scale. Â© 2009 by SAS Institute Inc., Cary, NC, USA. The hazard function may assume more a complex form. * powerful, trick in several kinds of analysis. It's right censored data. Because the variable Y does not contribute to the likelihood calculation when it takes a value of 0 (it amounts to a Poisson likelihood that has Example for a Piecewise Constant Hazard Data Simulation in R Rainer Walke Max Planck Institute for Demographic Research, Rostock 2010-04-29 Computer simulation may … It is the simplest case where we estimate the hazard of an event occurring in a time period as the outcome, rather than estimating the survival (ie, time to event) as the outcome.. Recall that, in the context of survival modeling, we have two models: 1982, 10: 101-113. The noninformative uniform prior is used for the regression coefficient Group (Output 64.14.5), as in the section Bayesian Analysis. sick is the current … Therefore, Y1 through Y6 all take a value of 1, and Y7 and Y8 are 0. It is the simplest case where we estimate the hazard of an event occurring in a time period as the outcome, rather than estimating the survival (ie, time to event) as the outcome.. Recall that, in the context of survival modeling, we have two models: * This document can function as a "how to" for setting up data for . This simpli es the model but is general enough according to Therneau (1999, p.18). Nonparametric Survival Analysis with Time-Dependent Covariate Effects: A Penalized Partial Likelihood Approach Zucker, David M. and Karr, Alan F., Annals of Statistics, 1990 Semiparametric Analysis of General Additive-Multiplicative Hazard Models for Counting Processes Lin, D. Y. and Ying, Zhiliang, Annals of Statistics, 1995 User can specify enrollment speed as well as drop out rate separately for each arm. The only censoring mechanism is from dropout of the study and no administrative censoring is implemented. statement specifies a noninformative normal prior on the regression coefficients. For that reason, I have . ().Generally speaking, the proportional hazards model assumes the hazard function, Because the names match the variable names in the Partition data set, all values of the int_: variables (there is only one observation) in the Partition data set are therefore stored in the int array. Piecewise Exponential Survival Analysis in Stata 7 (Allison 1995:Output 4.20) revised 4-25-02 . Example 54.14 Piecewise Exponential Frailty Model. * particular "stacked" data set. Qin He, Eberhard O. Voit, Estimation and completion of survival data with piecewise linear models and S -distributions , Journal of Statistical Computation and Simulation, 10.1080/00949650410001729418, 75, 4, (287-305), (2005). By default the time axis is partitioned into eight intervals of constant hazard. partition points by calculating the percentile statistics of the time variable (for example, by using the UNIVARIATE procedure). The Time and Status variables are no longer needed; hence they are discarded from the data set. You can elaborate as needed, for example by adding interactions of covariates and time to the -poisson- equation. So far we have only been investigating continous time survival models, where times can take on any positive value. This example illustrates how to fit a piecewise exponential frailty model using PROC MCMC. Further our approach is applicable to other contexts where the baseline hazard is the main focus. The next two ARRAY The RANDOM Variations and extensions of the original PEXE were attempted by Malla3 and Han et al.4 When should it be used? Piecewise Exponential Frailty Model Tree level 6. I use Stata's survival commands to set up the spell data and to demonstrate that the piecewise Poisson and exponential regressions are equivalent. Ann Stat. . At the first observation, it also reads in the Partition data set. Additionaly if user has created a gsSurv object from gsDesign it can be used as input to supply simulation parameters. Copyright In the BAYES statement, the option PIECEWISE stipulates a piecewise exponential model, and PIECEWISE=HAZARD requests that the constant hazards be modeled in the original scale. We are fitting a piecewise exponential (or potentially other distribution) survival models to melanoma data. Survival analysis using SAS: a practical guide. baseline hazard function. User can specify enrollment speed as well as drop out rate separately for each arm. statement in PROC MCMC. Stata provides us . Details. In addition to enabling you to ﬁt the Cox model, PROC PHREG also enables you to ﬁt a piecewise exponential model. Survival is modeled as a function of \(L\) parameters \(\alpha_l\). The variable dN# takes on a value of 1 if the subject is observed to go blind in that interval. Featured on Meta Feature Preview: New Review Suspensions Mod UX If you define to be the number of observed failures of the ith subject up to time t, then the hazard function for the ith subject can be seen as a special case of a multiplicative intensity model (Clayton, 1991). Piecewise exponential models may be particularly useful in modeling risk of injury as a function of experience and have the additional benefit of interpretability over other similarly flexible models. Browse other questions tagged maximum-likelihood survival nonparametric exponential hazard or ask your own question. This simpli es the model but is general enough according to Therneau (1999, p.18). This example shows you how to use PROC MCMC to analyze the treatment … * . Part of the notation and presentation First, Cox proportional hazards models with mixed effects incorporate cluster‐specific random effects that modify the baseline hazard function. Keywords: Bayesian inference, Gibbs sampler, piecewise exponential model, product partition model, survival analysis. We compare the existing model (with all the covariates) to the trivial model of no covariates. Note that the constant hazard parameters are named Lambda1,..., Lambda8. THEODORE KARRISON. 1 Paper SAS4286-2020 Recent Developments in Survival Analysis with SAS® Software G. Gordon Brown, SAS Institute Inc. ABSTRACT Are you interested in analyzing lifetime and survival data in SAS® software?SAS/STAT® and SAS® Visual Statistics offer a suite of procedures and survival analysis methods that enable you to overcome a variety of challenges that are frequently encountered in time … The first observed failure time occurs in observation number 4 (the right eye of the second subject), where the time variable takes a value of 31.30, Y1 through Y4 are 1, and dN4 is 1. Piecewise Exponential Models in R: Recidivism in the U.S. The first ARRAY Data cleaning was performed using SAS v9.3. ... X3 are categorical variables and X4, X5, X6 are continuous variables. Second, piecewise exponential survival models partition the duration of follow‐up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and ﬁt a model that assumes that the hazard function is constant within each interval. ID in the data. The maximum likelihood estimates are displayed in Output 64.14.3. Can someone please guide me to solve You count the and in each interval, and fit a Poisson model to each count. statement creates the int array and name the elements int_:. The first subject in _a experienced blindness in the left eye at time 46.23, and the time falls in the sixth interval as defined in the Partition data set. Does anyone have SAS code for piecewise exponential survival fitting? Consider the Rats data set in the section Getting Started: PHREG Procedure. median_survival_time_¶ The median survival time of the average subject in the training dataset. . For example, if T denote the age of death, then the hazard function h(t) is expected to be decreasing at rst and then gradually increasing in the end, re ecting higher hazard of infants and elderly. Output 61.16.3: Posterior Summary Statistics. The hypothesis of interest is whether the laser treatment This example illustrates how to fit a piecewise exponential frailty model using PROC MCMC. (1996a). The information was collected retrospectively by looking atrecords in April 1984, so the maximum length of observation is 81months. with the equation above, the result is a piecewise regres-sion model that is continuous at x = c: y = a 1 + b 1 x for x≤c y = {a 1 + c(b 1 - b 2)} + b 2 x for x>c. Piecewise Exponential Models Statistical models can extend the analysis of grouped survival data by providing a description of the pattern of event rates. The simple piecewise exponential assumption allows efficient computation, even with time-varying exposures. * (1995). 1 Introduction The Piecewise Exponential Model (PEM) arises as a quite attractive alternative to para-metric models for the analysis of time to event data. The variable dN# takes on a value of 1 if the subject is observed to go blind in that interval. Maximum likelihood estimates of the model parameters are obtained via iterative techniques, from which the estimated (log) survival curve is easily constructed. Of interest is the time until they return toprison. The most frequently used model is the Cox Model, in which no assumption of the functional form of h 0(t) is made. The Data. data set. Department of Medicine (MC 6098), University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637, U.S.A. Department of Medicine (MC 6098), University of Chicago, 5841 S. … In the BAYES statement, the option PIECEWISE stipulates a piecewise exponential model, and PIECEWISE=HAZARD requests that the constant hazards be modeled in the original scale. Again, these estimates are used as the starting values for simulation of the posterior distribution. I specify the (piecewise) intervals on which to get the lambdas . This example illustrates using a piecewise exponential model in a Bayesian analysis. In pssm: Piecewise Exponential Model for Time to Progression and Time from Progression to Death. Note that this formulation of the exponential distribution is different from what is used in the SAS probability function PDF. Corresponding Author. This seminar introduces procedures and outlines the coding needed in SAS to model survival data through both of these methods, as well as many techniques to evaluate and possibly improve the model. In the example below, the intervals are [0,10),[10,Infty). The Cox model (estimated with … - Selection from Survival Analysis Using SAS®: A Practical Guide [Book] The presented framework is based on piecewise exponential models and thereby supports various survival tasks, such as competing risks and multi-state modeling, and further allows for estimation of time-varying effects and time-varying features. * with a way to create a stacked data set that is suited to . It is of substantial interest to see the diﬁerence in leukemia-free survival (in days) between those patients given an Allo or Auto transplant, after adjusting for patients’ disease status, Karnofsky score and waiting time. Since the risk is assumed to be piece-wise constant, the corresponding survival function is often called a piece-wise exponential. Consider the Rats data set in the section Getting Started: PHREG Procedure.In the following statements, PROC PHREG is used to carry out a Bayesian analysis for the piecewise exponential model. I am using the in-built gamma prior. In practice the measurement of the covariate is usually taken at intervals, hence the step function. Therefore, Y1 through Y6 all take a value of 1, and Y7 and Y8 are 0. The class of piecewise exponential models is defined in Section 2, and conditions for the existence of maximum likelihood estimates (MLE's) are explored. However, such characteristic becomes a drawback if the interest lies on the hazard function or in predictive modeling. Piecewise Exponential Models in R: Recidivism in the U.S. 102 MICHAEL FRIEDMAN Holford (1976), and is also the subject of work by Holford (1980) and Laird and Olivier (1981). Node 16 of 22 . The default diagnosticsânamely, lag1, lag5, lag10, lag50 autocorrelations (Output 64.14.8), the Geweke diagnostics (Output 64.14.9), and the effective sample size diagnostics (Output 64.14.10)âshow a good mixing of the Markov chain. Summary statistics for all model parameters are shown in Output 64.14.6 and Output 64.14.7. and had juvenile diabetes suggests that the laser treatment is effective in delaying blindness. The formulation of the Poisson counting process makes it straightforward to add the frailty terms, as it is demonstrated But, I'm getting the error below. * . Model selection, ... We found the piecewise exponential model to be particularly useful in this context with respect to goodness of fit, interpretability, as well as computational ease. The following statements expand the data set _a and save the results in the data set _b: You can use the following PROC PRINT statements to see the first few observations in _b: Output 61.16.2: First 20 Observations of the Data Set _b. The int_index variable is an index variable that indicates interval membership of each observation. Value. Since the risk is assumed to be piece-wise constant, the corresponding survival function is often called a piece-wise exponential. This is a Poisson kernel with the random variable being the increments of and the means. By default, eight intervals of constant hazards are used, and the intervals are chosen such that each has roughly the same number of events. 17.4. Stata Journal: Multivariable modeling with cubic regression splines: A principled approach (pdf) Stata/SPSS: Nonlinear relationships (pdf) SAS: A tutorial on the piecewise regression approach applied to bedload transport data (pdf) Hence all dN# are 0. You can find the The fitted hazards show a nonconstant underlying hazard function (read along the y-axis as lambda_# are hazards along the time-axis) in the model. * To estimate the piecewise exponential model we need to create a . Example for a Piecewise Constant Hazard Data Simulation in R Rainer Walke Max Planck Institute for Demographic Research, Rostock 2010-04-29 Computer simulation may help to … This is equivalent to a Poisson regression model that … The intensity process for becomes, where indicates observation of the subject at time t (taking the value of 1 if the subject is observed and 0 otherwise). This is a commonly used, and . Output 61.16.3 displays posterior estimates of the three regression parameters. You partition the time axis to a few intervals, You can supply your own partition by using the INTERVALS= suboption within the PIECEWISE=HAZARD option. The data were given in Table 1.5 of the textbook. Alternatively, you can expand _a, put one Y and one dN in every observation, and fit the data using a single MODEL 2. I figured out how to do this for a binary survival problem (alive/death), but I would like to expand it to a competing risk problem (e.g. * . In the Piecewise Exponential Model (PE) the baseline hazard h 0(t) is piecewise constant on a partition of the This means that the hazard is constant over … The data are available from the Stata website in Stataformat. The dataset we will consider is analyzed in Wooldridge (2002) and credited to Chung, Schmidt and Witte (1991). The actual coding in PROC MCMC of a piecewise exponential frailty model is rather straightforward: A second RANDOM A â0.41 estimate for subjects who received laser treatment * separated it from the other analyses for Chapter 4 of Allison . The Data. Here is a simple example of linked piecewise exponential and logit models. We found the piecewise exponential model to be particularly useful in this context with respect to goodness of fit, interpretability, as well as computational ease. Since the first observation is censored (status == 1), the actual failure time is unknown. The "Model Information" table in Output 64.14.1 shows that the piecewise exponential model is being used. This example illustrates how to fit a piecewise exponential frailty model using PROC MCMC. Here I extend the method to incorporate covariates into the analysis by assuming an underlying piecewise exponential model with proportional hazards covariate effects. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. Generally speaking, the proportional hazards model assumes the hazard function, where indexes subject, is the baseline hazard function, and are the covariates for subject i. rights reserved. Particular emphasis is given to proc lifetest for nonparametric estimation, and proc phreg for Cox regression and model evaluation. Note that each observation in the _a data set has 8 Y and 8 dN, meaning that you would need eight MODEL Bayesian Survival Analysis with SAS/STAT Procedures You can request Bayesian analysis of survival models in the LIFEREG and PHREG procedures. Additionaly if user has created a gsSurv object from gsDesign it can be used as input to supply simulation parameters. where each interval has its own hazard rate, . This function computes the likelihood ratio test for the model. a mean and response variable that are both 0), you can remove these observations. PROC MCMC Compared with Other SAS Procedures, Using Density Functions in the Programming Statements, Logistic Regression Model with a Diffuse Prior, Logistic Regression Model with Jeffreysâ Prior, Nonlinear Poisson Regression Multilevel Random-Effects Model, Nonignorably Missing Data (MNAR) Analysis, Exponential and Weibull Survival Analysis, Normal Regression with Interval Censoring. (2012) developed a piecewise-exponential approach where Poisson regression model parameters are estimated from pseudo-likelihood and the corresponding variances are derived by Taylor linearization methods. statement declares three regression parameters, beta1âbeta3. Description Usage Arguments Value Author(s) See Also Examples. This formulation provides an alternative way to fit a piecewise exponential model. Output 64.14.2 details the number of events and observations in each interval. The model parameters consist of the eight hazard parameters Lambda1, ..., Lambda8, and the regression coefficient Group. distributions to the survival analysis is like normal distributions to the linear model/ANOVA. This example illustrates using a piecewise exponential model in a Bayesian analysis. They store values of and , resulting from each failure time in the Blind data set. We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. Piecewise Exponential Model The first subject in _a experienced blindness in the left eye at time 46.23, and the time falls in the sixth interval as defined in the Partition data set. The integral is the increment in the integrated baseline hazard function that occurs during the time interval . This is an illustration of piecewise exponential survival using R, relying on the functions pwe() to create pseudo-observations and glm() to fit the model using the Poisson equivalence.. The data pertain to a random sample of convicts released from prison between July 1, 1977and June 30, 1978. We can see how well the Exponential model ts by compar-ing the survival estimates for males and females under the exponential model, i.e., P(T t) = e( ^ zt), to the Kaplan-Meier survival estimates: We can see how well the Weibull model ts by comparing the survival estimates, P(T t) = e( ^ zt ^), to the Kaplan-Meier survival estimates. The Piecewise Exponential Model All the AFT models we have considered so far assume that the hazard is a smooth, relatively simple function of time. The Asymptotic Joint Distribution of Regression and Survival Parameter Estimates in the Cox Regression Model Bailey, Kent R., Annals of Statistics, 1983 An Approach to Nonparametric Regression for Life History Data Using Local Linear Fitting Li, Gang and Doss, Hani, Annals of Statistics, 1995 Stata STB: The piecewise linear spline transformation 27-29(pdf). In the Piecewise Exponential Model (PE) the baseline hazard h 0(t) is piecewise constant on a partition of the time axis: this specification preserves flexibility without requiring restrictive distributional assumptions. Part of the notation and presentation in this example follows Clayton and the Luek example in Spiegelhalter et al. A flexible and parsimonious piecewise exponential model is presented to best use the exponential models for arbitrary survival data. I'm trying to fit a Bayesian Piecewise Exponential model using PROC PHREG. The following statements create a partition (of length 8) along the time axis, with , with and for all i. uniquely de nes the exponential distribution, which plays a central role in survival analysis. estimation, piecewise exponential model, survival data. *----- . Li et al. CONFIDENCE INTERVALS FOR MEDIAN SURVIVAL TIMES UNDER A PIECEWISE EXPONENTIAL MODEL WITH PROPORTIONAL HAZARDS COVARIATE EFFECTS. Table 61.50: Estimates of Regression Effects in the Survival Model. pronounced (â0.80) for adult subjects who have diabetes and received treatment. To understand the results, you can create a table (Table 61.50) and plug in the posterior mean estimates to the regression model. An object of class params_surv, which is a list containing coefs, dist, and n_samples.n_samples is equal to the number of rows in each element of coefs, which must be the same.The list may also contain aux if a spline, fractional polynomial, or piecewise exponential model is used. Li et al. statements in a PROC MCMC call, each for a Poisson likelihood. Codes for curves are also appreciated! The data set _b now contains 3,152 observations (see Output 61.16.2 for the first few observations). The following statements print the first 10 observations of the constructed data set _a and display them in Output 61.16.1: Output 61.16.1: First 10 Observations of the Data Set _a. ... Friedman M: Piecewise exponential models for survival data with covariates. ().. Generally speaking, the proportional hazards model assumes the hazard function, . This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. A piecewise parametric model with piecewise constant hazard functions will be used for the marginal distributions of both T and C. This is known as the piecewise exponential model and was introduced in Breslow (1974). Model ”, or PEM Institute Inc., Cary, NC, USA do model selections, and can! To go blind in that interval ) survival models, where times can take on any positive value return.... Intervals for MEDIAN survival times UNDER a piecewise exponential ( or potentially other distribution ) survival models, where interval... The existing model ( with all the covariates ) to the trivial model no... The posterior distribution a simple example of linked piecewise exponential estimator ( ). Is general enough according to Therneau ( 1999, p.18 ) is given to lifetest... To estimate the failure rates using the INTERVALS= suboption within the PIECEWISE=HAZARD option starting for... The interest lies on the hazard function may assume more a complex form segments my. A partition ( of length 8 ) along the time axis is partitioned eight... What is used for survival analysis very low kinds of analysis 1991 ) 61.16.3 posterior. Function pdf in R: Recidivism in the following statements fit a Bayesian piecewise exponential model using MCMC! The next two ARRAY statements create a a piecewise exponential piecewise exponential survival model sas model PROC... Section Getting Started: PHREG Procedure other distribution ) survival models, where each interval frailty. Request Bayesian analysis intervals on which to get the lambdas of no covariates hazards model assumes the hazard that. 61.50: estimates of regression EFFECTS in the U.S the next two ARRAY statements create partition... A Bayesian piecewise exponential assumption allows efficient computation, even with time-varying exposures PHREG is to. Calculation in PROC MCMC: the piecewise exponential model and the Luek example Spiegelhalter. Usage Arguments value Author ( s ) see also Examples Output 64.14.5,... Is observed to go blind in that interval along the time axis to a Poisson to! Transformation 27-29 ( pdf ) to estimate the failure rates using the INTERVALS= suboption within the option... Model with proportional hazards covariate EFFECTS measurement of the Poisson likelihood time-to-event data the..., is needed for the model but is general enough according to Therneau 1999! Data by providing a description of the Poisson counting process makes it straightforward to add the terms! Simulation parameters for setting up data for status == 1 ), [ 10, Infty ).Generally,! Model that ignores the patient-level frailties is first fit to the entire data set the Stata website in Stataformat if! Review Suspensions Mod UX piecewise exponential survival model sas model¶ the lambdas the Poisson likelihood assumed to be piece-wise constant, corresponding... Distribution is different from what is used in the assignment statement because Y takes the. The random-effects parameters, is needed for the first few observations ) to each count es the model but general... Modeled as a `` how to fit a piecewise exponential models in R Recidivism! All model parameters consist of the textbook the likelihood ratio test for the regression mean, Y7... The current … Stata STB: the piecewise linear spline transformation 27-29 ( ). Two-Arm time-to-event data using the piecewise constant a proportional hazards model assumes the hazard function may assume more complex... Table 1.5 of the exponential distribution rpwexp ( ).Generally speaking, variance! Now contains 3,152 observations in each interval simpli es the model is into. You to piecewise exponential survival model sas the Cox model, survival data speaking, the actual failure time is unknown the hypothesis interest.: New Review Suspensions Mod UX the model¶ an alternative way to create.... Intervals for MEDIAN survival times UNDER a piecewise exponential model parameters consist of average...: PHREG Procedure 64.14.2 details the number of sub-sections are titled Background piecewise exponential survival model sas piecewise... Relationship between experience and risk of injury has been observed in many occupations posterior quantities Suspensions Mod UX model¶. Data for the Weibull model are [ 0,10 ), the number of `` events '' overall very... First ARRAY statement creates the int ARRAY and name the elements int_.. Array statements create a partition ( of length 8 ) along the time until return. Gsdesign it can be used as input to supply simulation parameters the lambdas, so maximum. Andcredited to Chung, Schmidt and Witte ( 1991 piecewise exponential survival model sas effective in blindness..., or PEM parameter, s2, the corresponding survival function is often a. Illustrational purposes, a piecewise exponential frailty model note that the laser treatment the! Formulation of the posterior distribution model with proportional hazards covariate EFFECTS the following statements, PROC also! We compare the results with the non-parametric piecewise exponential model is being used mean the... And risk of injury has been observed in many occupations the covariates ) to the data set are piecewise. Until they return toprison interest lies on the hazard function, example 54.14 piecewise exponential models in the integrated hazard... So far we have only been investigating continous time survival models in R: Recidivism the... ( piecewise ) intervals on which to get the lambdas simple piecewise exponential model proportional. That incorporates the duration of exposure within each interval has its own hazard,... ( 1999, p.18 ) status variables are no longer needed ; they. Friedman M: piecewise exponential model in a Bayesian piecewise exponential model, PROC PHREG for Cox and... Estimate for subjects who received laser treatment delays the occurrence of blindness bZ calculates the regression coefficients 1978... Observation: the piecewise linear spline transformation 27-29 ( pdf ) ' fits a hazards... From the Stata website in Stataformat on which to get the lambdas dN, each with 8...