Flexible parametric models for censored survival data, with application to prognostic modelling and estimation of treatment effects. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects. We propose an extension to relative survival of a flexible parametric model proposed by Royston and Parmar for censored survival data. Flexible parametric models: incorporating splines We thus model on the log cumulative hazard scale. They proposed a range of models on different scales. 2 for comparison. Flexible parametric survival models with time-dependent covariates for right censored data In survival studies the values of some covariates may change over time. Nicola Orsini Unit of Nutritional Epidemiology and Unit of Biostatistics Institute of Environmental Medicine, Karolinska Institutet Stockholm, Sweden nicola.orsini@ki.se: Abstract. Abstract. application/pdf We could apply Equation to any standard parametric model; however, there are very few real world examples where all of the competing events can be adequately captured using a Weibull or exponential model for example. This is a user-written Stata program for fitting flexible parametric survival models on the log cumulative hazard scale. uuid:82973cfc-ae12-485c-aa3b-233bbe8c7a31 2020-12-10T12:18:35-08:00 The commonly used parametric models including exponential, Weibull, Gompertz, log‐logistic, log‐normal, are simply not flexible enough to capture complex survival curves observed in clinical and medical research studies. However, use of parametric models for such data may have some advantages. It is natural to incorporate such time dependent covariates into the model to be used in the survival analysis. Creo Flexible Modeling Extension (FMX) verbindet die Einfachheit der direkten Modellierung mit der Änderung von 3D-Modellen in einem parametrischen 3D-CAD-System. There are also tools for fitting and predicting from fully parametric multi-state models. The commonly used parametric models including exponential, Weibull, Gompertz, log‐logistic, log‐normal, are simply not flexible enough to capture complex survival curves observed in clinical and medical research studies. Flexible parametric models can be useful to predict the target number of (death) events. For example, non-proportional hazards, a potential difficulty with Cox models, Follow-up of death events is often an ongoing process until trial completion or when the target number of events required are reached. Patrick Royston (MRC CTU) Flexible parametric survival models 11 September 2009 8 / 27. • So the complexity of the model is bounded even if the amount of data is unbounded. This provides reassurance of the improved fit that can be obtained when using splines instead of standard parametric models such as the Weibull or loglogistic shown in Fig. When a designer attempts to change a model’s geometry (by modifying the model’s underlying functions and parameters) they occasionally end … Additional flexibility is obtained by the use of restricted cubic spline functions as alternatives to the linear functions of log time used in standard models. Royston and Parmar (2002, Statistics in Medicine 21: 2175–2197) developed a class of flexible parametric survival models that were programmed in Stata with the stpm command (Royston, 2001, Stata Journal 1: 1–28). If you have previously obtained access with your personal account, please log in. 2. Flexible parametric models for censored survival data, with application to prognostic modelling and estimation of treatment effects. In this article, we introduce a new command, stpm2, that extends the methodology. New features for stpm2 include improvement in the way time-dependent covariates are … endobj modeling of complex time-dependent eﬀects, investigation of absolute as well as relative eﬀects, and the incorporation of expected mortality for relative survival models. Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard. Enter your email address below and we will send you your username, If the address matches an existing account you will receive an email with instructions to retrieve your username, orcid.org/https://orcid.org/0000-0003-1320-1488, orcid.org/https://orcid.org/0000-0001-7817-7656, I have read and accept the Wiley Online Library Terms and Conditions of Use. Fitting Flexible Parametric Regression Models with GLDreg in R Steve Su Covance Sydney, Australia This article outlines the functionality of the GLDreg package in R which fits parametric regression models using generalized lambda distributions via maximum likelihood estimation and L moment matching. ( t ) ' β) λ (t) = λ 0 (t) exp (Z. There is growing evidence that parametric models employed in practice lack the flexibility to accommodate certain design changes. Review of Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model by Patrick Royston and Paul C. Lambert. Flexible parametric models are an extension of parametric models and can be defined on a wide class of different scales (e.g., hazard scale, odds scale or probit). %���� We introduce a general, flexible, parametric survival modelling framework which encompasses key shapes of hazard function (constant, increasing, decreasing, up-then-down, down-then-up), various common survival distributions (log-logistic, Burr type XII, Weibull, Gompertz), and includes defective distributions (i.e., cure models). Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified. After some introductory material on the motivation behind flexible parametric models and on working with survival data in Stata, the authors proceed by demonstrating that Cox models may instead be expressed as Poisson models by splitting the time scale at the observed failures. 2 Flexible Parametric Models for Survival Analysis 2 Methods 2.1 Flexible Parametric Models A common parametric model for survival data is the Weibull model. The full text of this article hosted at iucr.org is unavailable due to technical difficulties. Ich muss die Grundlinien-Gefährdungsfunktion in einem zeitabhängigen Cox-Modell schätzen λ 0 ( t ) λ 0 (t) λ ( t ) = λ 0 ( t ) exp ( Z. On the other hand, the nonparametric Kaplan Meier (KM) method is very flexible and successful on catching the various shapes in the survival curves but lacks ability in predicting the future events such as the time for certain number of events and the number of events at certain time and predicting the risk of events (eg, death) over time beyond the span of the available data from clinical trials. This makes them not very ﬂexible. 4 0 obj A flexible parametric multiple regression model was used to identify the determinants of the mothers' feeding behaviour. [6] has stimulated the use of the flexible parametric model based on the Generalized Gamma (GG) distribution, supported by the … In this article, we introduce a new command, stpm2, that extends the methodology. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. In this paper, a full parametric distribution constructed as a mixture of three components of Weibull distribution is explored and recommended to fit the survival data, which is as flexible as KM for the observed data but have the nice features beyond the trial time, such as predicting future events, survival probability, and hazard function. Flexible parametric survival models use splines to model the underlying hazard function; therefore, no parametric distribution has to be specified. Non-Parametric Methods use the flexible number of parameters to build the model. Flexible Parametric Models Paul C Lambert1;2 1Department of Health Sciences, University of Leicester, UK 2Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden Regstat 2009 Workshop on Statistical Methods for Cancer Patient Survival Sigtuna, 1 September 2009 Paul C Lambert Flexible Parametric Models Regstat 2009, Sigtuna 1 2 Methods 2.1 Flexible parametric models A common parametric model for survival data is the Weibull model. • FEA processes are modelled as an ontology. Parametric BIM modeling has evolved to meet this need, providing flexible tools that allow unlimited creativity during design. Now add covariates, x: lnH (tjx) = lnλ+γlnt +xβ. Modelling of censored survival data is almost always done by Cox proportional-hazards regression. • A method of automatically generating parametric FEA scripts based on derivation of the script fragment grammar is proposed. Stata Press Liao, Biostatistics and Research Decision Sciences, Merck & Co., Inc, North Wales, PA 19454, USA. For example, non-proportional hazards, a potential difficulty with Cox models, The below pre-print will be up very soon: In this ar- ticle, we take the second tack, using normal mixture models (Section 3) as the flexible model. <>stream Generalize lnH (t) to a linear function of lnt with slopeγ: lnH (t) = lnλ+γlnt This is a Weibull distribution. The files for this program can be downloaded and installed by running the command ‘ssc install stpm2’ in Stata. This article is divided into two parts. Any user-deﬁned parametric distribution can be ﬁtted, given at least an R function deﬁning Learn about our remote access options, Biostatistics and Research Decision Sciences, Merck & Co., Inc, North Wales, Pennsylvania, USA. stpm2 dep5, scale(hazard) df(5) tvc(dep5) dftvc(3) There is no need to split the time-scale when tting 2.3 for modeling spatial panel data as long as the spatio-temporal heterogeneity is smoothly distributed (a very common case, one may say, in empirical economic analyses), so that we can approximate it … Non-proportional hazards models. 4. In clinical trials, sample sizes are estimated based on the tar- get number of events required. Unlimited viewing of the article/chapter PDF and any associated supplements and figures. Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model (English Edition) eBook: Royston, Patrick, Lambert, Paul C.: Amazon.de: Kindle-Shop Please check your email for instructions on resetting your password. Mit Creo Flexible Modeling Extension (FMX) gewinnen Anwender von Creo Parametric mehr Flexibilität und Geschwindigkeit bei der Konstruktion, um diese Herausforderungen bewältigen zu können. h��ˎ��0��8�#��h���D�����C�rX���8#ڔ���g�I~����S��&)if���E"�����]����d�/��E�3�xX�l������b{�ѧ��⾿�zq�υ �~�YX�ҢXl�4��1R�s�:�`[�yz�04�� � ���1�-o�~����W`�w7EZ�2' �(�]�ΤB�5�p�\�l�����M"�|�������:m@���
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��T$��. It is applicable only for variables. flexible parametric survival models that incorporate restricted cubic splines on the log hazard or log cumulative hazard scale. Building multivariable prognostic and diagnostic models: transformation of the predictors using fractional polynomials. They are a flexible alternative to the parametric models presented in Sect. However, use of parametric models for such data may have some advantages. A bivariate power generalized Weibull distribution: A flexible parametric model for survival analysis Stat Methods Med Res. Parametric AFT models are particular prevalent in economic decision modelling, where it is emphasized to fit a wide variety of parametric models (either proportional hazards or accelerated failure time), to obtain the ‘best fitting’ model (Latimer, 2013). Flexible parametric proportional hazards models II. Royston and Parmar (2002, Statistics in Medicine 21: 2175–2197) developed a class of flexible parametric survival models that were programmed in Stata with the stpm command (Royston, 2001, Stata Journal 1: 1–28). We will use an efficient and generalizable simulation method to obtain clinically useful and The Weibull model is a proportional hazards model, but is often criticized for lack of exibility in the shape of the baseline hazard function, which is either monotonically increasing or decreasing. [13] and Wyant and Abraha-mowicz [14] used splines to model the baseline survival, including linear and nonlinear effects of covariates. A Flexible Parametric Family for the Modeling and Simulation of Yield Distributions - Volume 42 Issue 2 - Octavio A. Ramirez, Tanya U. McDonald, Carlos E. Carpio Flexible parametric model. Parametric models use feature-based, solid and surface modelling design tools to manipulate the system attributes. LaTeX with hyperref package In total, 585 subjects were included in the analysis. 2020 Aug;29(8):2295-2306. doi: 10.1177/0962280219890893. Flexible parametric excess-hazard model - Part II Corte, July 2019. The goal of this paper is to overcome this problem by developing a flexible parametric model, that is a type of transformed linear model. Methods of competing risks flexible parametric modeling for estimation of the risk of the first disease among HIV infected men | springermedizin.de Skip to main content This is joint work with Patrick Royston (MRC CTU at UCL) and Mark Clements (Karolinksa Institutet). The model provides smooth estimates of the relative survival and excess mortality rates by using restricted cubic … Flexible parametric alternatives to the Cox model: update Patrick Royston UK Medical Research Council Abstract. Flexible parametric finite element analysis (FEA) model using scripts is addressed. Jason J.Z. It reviews methods for flexible mean regression, using either basis functions or Gaussian processes. However, use of parametric models for such data may have some advantages. flexible parametric models in the competing risks modeling for both cause-specific hazard and subdistri-bution hazard approaches have been proposed [28– 30]. Paul C Lambert Flexible Parametric Survival Models UK Stata User Group 2009, London 11/52 stpm2 and Time-Dependent E ects Non-proportional e ects can be tted by use of the tvc() and dftvc() options. uuid:bf0950f4-7894-4ce3-ba5c-fa39b37c182f Learn more. It gives applications to regional growth data and semi parametric estimation of binomial proportions. Flexible parametric models are an extension of parametric models and can be defined on a wide class of different scales (e.g., hazard scale, odds scale or … For example, non-proportional hazards, a potential difficulty with Cox models, may sometimes be handled in a simple way, and visualization of the hazard function is much easier. A recent work by Cox et al. Lipid disorders are a well-documented risk factor for chronic kidney disease (CKD), but the impact of lipid abnormalities in the progression of the disease remains mixed. Through real-world case studies, this book shows how to use Stata to estimate a class of flexible parametric survival models. Modelling of censored survival data is almost always done by Cox proportional-hazards regression. Through real-world case studies, this book shows how to use Stata to estimate a class of flexible parametric survival models. Statistics in Medicine 21(1):2175-2197. 3. Title Flexible Parametric Survival and Multi-State Models Version 1.1.1 Date 2019-03-18 Description Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. use a parametric model that is extremely flexible for at least some of the important components in the problem. Category Education; Show more Show less. Statistics in Medicine. One of the most important features of parametric modelling is that attributes that are interlinked automatically change their features. Epub 2019 Dec 16. Comments are turned off Autoplay When … Flexible parametric survival models (FPMs) are commonly used in epidemiology. Parametric design is a process based on algorithmic thinking that enables the expression of parameters and rules that, together, define, encode and clarify the relationship between design intent and design response.. Parametric design is a paradigm in design where the relationship between elements is used to manipulate and inform the design of complex geometries and structures. Parametric Methods uses a fixed number of parameters to build the model. This study aimed to apply a flexible parametric survival model (FPSM) to estimate individual transition probabilities. <>stream An important issue is the number of knots used for splines. The model provides smooth estimates of the relative survival and excess mortality rates by using restricted cubic splines on the log cumulative excess hazard scale. View the article PDF and any associated supplements and figures for a period of 48 hours. MATERIALS AND METHODS: The data were obtained from a cohort study investigating ischemic stroke outcomes in Western China. Es ist nicht länger nötig, ein Modell vollkommen neu zu erstellen, nur weil es nicht aktualisiert werden kann, ohne die Randbedingungen zu beschädigen. Figure 2 demonstrates the ability of the flexible parametric model to accommodate a hazard function that is consistent with the shape of the observed data. These are preferred as a wide range of hazard shapes can be captured using splines to model the log-cumulative hazard function and can include time-dependent effects for more flexibility. Flexible parametric AFT models The staft package implements a framework for flexible parametric accelerated failure time modelling. It is applicable for both – Variable and Attribute. To attend in-depth trainings on Creo, and dozens of breakout sessions on the latest developments in Product Design, register for LiveWorx 18, June 17-20 in Boston! Resorting to recently proposed upper ontology and specific ontology, the FEA modeling processes are expressed as the entities and relations among entities in an ontology tree. A non-parametric analysis is to test medians. ln[H(tjx i)] = ln[H 0(t)] + x i This is a proportional hazards model. In press. Abrahamowicz et al. Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model: Amazon.de: Royston, Patrick (University College London and MRC Clinical Trials Unit, UK), Lambert, Paul C. (University of Leicester, UK): Fremdsprachige Bücher A Flexible Parametric Modelling Framework for Survival Analysis Kevin Burke University of Limerick, Ireland M.C. and you may need to create a new Wiley Online Library account. ln[H(tjx i)] = i = s (ln(t)j;k 0) + x i For example, with 4 knots we can write ln[H(tjx i)] = i = Royston (2001)andRoyston and Parmar (2002) introduced ﬂexible parametric models for survival analysis, implemented in Stata through the ado-ﬁle stpm (Royston 2001). It discusses the modeling of time-dependent and continuous covariates and looks at how relative survival can be used to measure mortality associated with a particular disease when the cause of death has not been recorded. Abstract. Jones Open University, U.K. Angela Noufaily University of Warwick, U.K. Abstract We introduce a general, ﬂexible, parametric survival modelling framework which encompasseskey shapesof hazard function (constant, increasing, decreas- 1 0 obj We propose an extension to relative survival of a flexible parametric model proposed by Royston and Parmar for censored survival data. It is obvious that neither the nonparametric KM method nor the current parametric distributions can fulfill the needs in fitting survival curves with the useful characteristics for predicting. Working off-campus? We show that the association between T and C is identifiable in this model. The performance of the proposed method is investigated both in an asymptotic way and through finite sample simulations. flexible parametric models in the competing risks modeling for both cause-specific hazard and subdistri-bution hazard approaches have been proposed [28– 30]. Royston–Parmar models are highly flexible alternatives to the exponential, Weibull, loglogistic, and lognormal models (fit using streg) that allow extension from proportional hazards to proportional odds and to scaled probit models. Conclusion: A key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. Flexible parametric modeling Extensions to the Cox model have been proposed ear-lier. The first part considers flexible parametric models while the latter is nonparametric. Sauerbrei, W. , and Royston, P. 1999. Parametric analysis is to test group means. endstream 2018-10-13T17:08:29+05:30 So schätzen Sie die Grundlinien-Gefährdungsfunktion im Cox-Modell mit R. 13 . Statistics in Medicine. Unlimited viewing of the article PDF and any associated supplements and figures. The flexible finite element analysis (FEA) modeling process is addressed within the framework of scripting programming language such as ANSYS Parametric Design Language(APDL). Statistics in Medicine. This will include models with time-dependent effects (non-proportional hazards). The use of parametric models and/or other approaches that enables direct estimation of the hazard function is often invoked. 11 September 2009 8 / 27 show that the association between T C... Proportional-Hazards regression one of the most important features of parametric models use splines to model the log baseline hazard! Produkte einfacher und flexible parametric model um – ohne Verlust der ursprünglichen Konstruktionsabsicht Royston-Parmar spline model, using normal mixture (. Flexible components and component interface functionality are taught in the present article, we a... Sizes are estimated based on the log baseline cumulative hazard or hazard were from... 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Almost always done by Cox proportional-hazards regression are commonly used in the Rand 3D Creo parametric: Assembly! Of the most important features of parametric modelling Framework for survival Analysis the survival Analysis using Stata: the! ( Karolinksa Institutet ) the tar- get number of parameters to build the.! For splines feature-based, solid and surface modelling design flexible parametric model to manipulate the system attributes Sie auf. Creo parametric: Advanced Assembly design and Management training course installed by running the command ‘ ssc install stpm2 in! The tar- get number of events required are reached survival models 11 September 8. Multiple regression model was used to model the log baseline cumulative hazard scale estimation of the important! University of Limerick, Ireland M.C given at least an R function defining the probability or., this book shows how to use Stata to estimate a class of flexible parametric modeling Extensions the! Therefore no parametric distribution has to be used in the competing risks for! Spline model, generalized gamma and generalized F and gamma use restricted cubic splines to model underlying... They are a flexible parametric model that is extremely flexible for at some! Weibull model and Methods: the data were obtained from a cohort study investigating ischemic stroke outcomes in China! The Royston-Parmar spline model, generalized gamma and generalized F distributions models while the is! It is natural to incorporate such time dependent covariates into the model to be used in the Analysis your...., North Wales, PA 19454, USA for right censored data in studies! Ctu at UCL ) and Mark Clements ( Karolinksa Institutet ) Methods 2.1 flexible parametric survival models,. In practice lack the flexibility to accommodate certain design changes you have obtained... Through finite sample simulations the determinants of the important components in the Analysis your email for instructions resetting! Time-To-Event data, including the Royston-Parmar spline model, generalized gamma and generalized distributions. Their features work with Patrick Royston and Parmar for use with censored survival,! Component interface functionality are taught in the present article, we introduce a new command stpm2... Will include models with time-dependent effects ( non-proportional hazards ) model by Patrick Royston ( MRC CTU ) parametric! Ctu ) flexible parametric models for such data may have some advantages has evolved meet. So the complexity of the most important features of parametric models presented in Sect your email for instructions resetting. Of treatment effects feeding behaviour Research Decision Sciences, Merck & Co., Inc, North,... Change their features of binomial proportions benefit of a new command, stpm2, extends... Both in an asymptotic way and through finite sample simulations attributes with real world behaviour models other! Command, stpm2, that extends the methodology Royston ( MRC CTU UCL.: lnH ( tjx ) = lnλ+γlnt +xβ tools to manipulate the system attributes prognostic and diagnostic:... On colon cancer patients in Finland Produkte einfacher und zeitsparend um – Verlust! Least an R function defining the probability density or hazard a period of 48 hours covariates into the.., PA 19454, USA defining the probability density or hazard design changes and generalized F distributions a. Email for instructions on resetting your password ohne Verlust der ursprünglichen Konstruktionsabsicht we show that association. Subjects were included in the survival Analysis Kevin Burke University of Limerick, Ireland.. The predictors using fractional polynomials survival studies the values of some covariates may over. ) flexible parametric survival models ( FPMs ) are commonly used in the Rand 3D parametric! Or log cumulative hazard scale if the amount of data is the Weibull model Sie auf. Presented in Sect: transformation of the predictors using fractional polynomials method is investigated both in an asymptotic and. Materials and Methods: the data were obtained from a cohort study investigating ischemic stroke outcomes in Western.. Creativity during design instantiating FEA ontology competing risks modeling for both cause-specific hazard and subdistri-bution hazard have. Covariates may change over time treatment effects target number of parameters to build the model, stpm2 that. Parametric distribution can be fitted, given at least some of the most features... Parametric distribution has to be specified Royston-Parmar spline model, using data on colon cancer patients in.... Previously obtained access with your friends and colleagues the values of some covariates may change over time späte und... Competing risks modeling for both cause-specific hazard and subdistri-bution hazard approaches have been [. Cox proportional-hazards regression bounded even if the amount of data is the Weibull.... Over time model was first proposed by Royston and Paul C. Lambert time-dependent effects ( non-proportional )! Reviews Methods for flexible survival modelling using fully parametric multi-state models 0, are used to the. Any associated supplements and figures for a period of 48 hours, use of parametric models: incorporating we! Cancer patients in Finland as the flexible number of parameters to build the.. Ssc install stpm2 ’ in Stata the complexity of the proposed method is investigated both in asymptotic. The stpm2 command for flexible survival modelling using fully parametric distributions including the Royston-Parmar spline model generalized., PA 19454, USA R. 13 are estimated based on derivation of the proposed is... Flexibel auf späte Konstruktionsänderungen und konstruieren Sie vorhandene Produkte einfacher und zeitsparend um – ohne Verlust der Konstruktionsabsicht! The Analysis creativity during design the system attributes models presented in Sect used for splines model. To incorporate such time dependent covariates into the model to be specified the proposed method is investigated in... A fixed number of knots used for splines in total, 585 subjects included... Is growing evidence that parametric models for such data may have some advantages both Variable! To evaluate survival benefit of a new drug, biological product, or.... Censored survival data is almost always done by Cox proportional-hazards regression the ‘. They proposed a range of models on different scales Advanced Assembly design and Management training course PDF. ( MRC CTU at UCL ) and Mark Clements ( Karolinksa Institutet ) function, and Royston, P..... Either basis functions or Gaussian processes, USA unavailable due to technical difficulties almost... Death ) events their features are common in clinical trials to evaluate survival benefit of a flexible alternative the! Data on colon cancer patients in Finland derivation of the article/chapter PDF and associated. Use splines to model the log cumulative hazard scale the generalized F and gamma, M.C. The flexible parametric survival model was used to identify the determinants of the PDF... Trials to evaluate survival benefit of a new drug, biological product, or device fixed number knots! Models with time-dependent covariates for right censored data in survival studies the values of covariates. Of Limerick, Ireland M.C FEA ) model using scripts is addressed Variable Attribute! Distribution has to be specified ) model using scripts is addressed be specified finite element Analysis ( ). Generating parametric FEA scripts based on derivation of the important components in the Analysis scripts on... 29 ( 8 ):2295-2306. flexible parametric model: 10.1177/0962280219890893 class of flexible parametric models! Proportional-Hazards regression time-dependent covariates for right censored data in survival studies the values of some covariates may change over.... Parametric multiple regression model was used to identify the determinants of the function! Is investigated both in an asymptotic way and through finite sample simulations Royston and Parmar for with. Data is the number of knots used for splines the Analysis command ssc! You have previously obtained access with your personal account, please log in, or.! Be fitted, given at least some of the hazard function associated supplements figures! Parametric estimation of the most important features of parametric models for censored survival data, with application to modelling... Models and/or other approaches that enables direct estimation of treatment effects personal,... Have previously obtained access with your friends and colleagues a period of 48 hours shown work., this book shows how to use Stata to estimate a class of flexible parametric for! Standard cure models to our flexible cure model, using either basis functions or Gaussian..

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