Survival prediction models: an introduction to discrete-time modeling
Background Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed...
Saved in:
| Published in | BMC medical research methodology Vol. 22; no. 1; pp. 1 - 18 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
26.07.2022
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2288 1471-2288 |
| DOI | 10.1186/s12874-022-01679-6 |
Cover
| Abstract | Background
Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes.
Methods
Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment.
Results
Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set.
Conclusions
We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository. |
|---|---|
| AbstractList | Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes.BACKGROUNDPrediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes.Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment.METHODSDiscrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment.Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set.RESULTSUsing publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set.We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository.CONCLUSIONSWe present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository. Background Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes. Methods Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment. Results Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set. Conclusions We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository. Background Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes. Methods Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment. Results Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set. Conclusions We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository. Keywords: Cox proportional hazards, Machine learning, Random survival forest, Time-to-event Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes. Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment. Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set. We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository. Background Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes. Methods Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment. Results Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set. Conclusions We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository. Abstract Background Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes. Methods Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment. Results Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set. Conclusions We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository. |
| ArticleNumber | 207 |
| Audience | Academic |
| Author | Ghosh, Debashis Suresh, Krithika Severn, Cameron |
| Author_xml | – sequence: 1 givenname: Krithika orcidid: 0000-0001-7785-3536 surname: Suresh fullname: Suresh, Krithika email: krithika.suresh@cuanschutz.edu organization: Department of Biostatistics and Informatics, Colorado School of Public Health – sequence: 2 givenname: Cameron surname: Severn fullname: Severn, Cameron organization: Child Health Biostatistics Core Department of Pediatrics, Section of Endocrinology, School of Medicine, University of Colorado Anschutz Medical Campus – sequence: 3 givenname: Debashis surname: Ghosh fullname: Ghosh, Debashis organization: Department of Biostatistics and Informatics, Colorado School of Public Health |
| BookMark | eNp9Ustq3DAUNSWlebQ_0JWhm26c6mG9uiiEkCaBQBdt10KWr1wNtjSV7IH8fTVxaDOhBC0krs6Dczmn1VGIAarqPUbnGEv-KWMiRdsgQhqEuVANf1Wd4FbghhApj568j6vTnDcIYSEpf1MdUyYlRZScVFffl7TzOzPW2wS9t7OPoZ5iD2P-XJtQ-zCn2C_rfI5177NNMEMz-wlWoA_D2-q1M2OGd4_3WfXz69WPy5vm7tv17eXFXWOZbOfGEakIlkwYQhG2zHCHRddRSzGSWElrnJOMUmaZo5Ij1SPrCgTKt-tcS8-q21W3j2ajt8lPJt3raLx-GMQ0aJNmb0fQILuegkRFFFoA3HWdtEhJbEVPOyqK1pdVa7t0E_QWSlIzHoge_gT_Sw9xpxXFvCWoCHx8FEjx9wJ51lNZDoyjCRCXrAlXjCjKFCvQD8-gm7ikUFaliUBUcU7EE9RgSgAfXCy-di-qLwQupqht96jz_6DK6WHytjTE-TI_IJCVYFPMOYH7mxEjvS-SXoukS5H0Q5E0LyT5jGT9bPYtKG5-fJlKV2ouPmGA9C_sC6w_VVHcdg |
| CitedBy_id | crossref_primary_10_1016_j_anbehav_2025_123102 crossref_primary_10_1016_j_envres_2024_118812 crossref_primary_10_3390_math11040819 crossref_primary_10_1016_j_drugalcdep_2024_111402 crossref_primary_10_1093_eurjcn_zvad023 crossref_primary_10_1111_cdoe_13018 crossref_primary_10_1016_j_ejca_2024_115106 crossref_primary_10_3390_cancers16142525 crossref_primary_10_3390_cancers15194897 crossref_primary_10_1001_jamanetworkopen_2024_44593 crossref_primary_10_17749_2070_4909_farmakoekonomika_2024_237 crossref_primary_10_1186_s13054_024_05021_y crossref_primary_10_1093_ofid_ofae740 crossref_primary_10_1109_TITS_2024_3454768 crossref_primary_10_1289_EHP14783 crossref_primary_10_1002_hsr2_1212 crossref_primary_10_1093_jssam_smae037 crossref_primary_10_1371_journal_pmed_1004516 crossref_primary_10_1371_journal_pone_0314725 crossref_primary_10_1001_jama_2023_27029 crossref_primary_10_1016_j_cie_2023_109530 crossref_primary_10_1001_jamanetworkopen_2023_38315 crossref_primary_10_1016_j_drugalcdep_2025_112552 crossref_primary_10_1017_elr_2024_50 crossref_primary_10_3390_cancers16203527 crossref_primary_10_1001_jamanetworkopen_2023_18919 crossref_primary_10_1001_jamanetworkopen_2024_61958 crossref_primary_10_1136_bmjph_2024_001130 crossref_primary_10_1177_08982643241310296 crossref_primary_10_1371_journal_pmed_1004395 crossref_primary_10_1001_jamanetworkopen_2023_32780 crossref_primary_10_1016_j_csda_2025_108161 crossref_primary_10_1177_11297298231203356 crossref_primary_10_2340_17453674_2024_41911 crossref_primary_10_1097_MNM_0000000000001646 crossref_primary_10_1161_CIRCHEARTFAILURE_124_012343 crossref_primary_10_1186_s12933_024_02571_x crossref_primary_10_1016_j_ekir_2024_11_017 crossref_primary_10_1016_j_scitotenv_2023_166304 crossref_primary_10_1093_ajh_hpae033 crossref_primary_10_1093_radadv_umae030 crossref_primary_10_3233_SJI_230075 crossref_primary_10_1080_10705511_2024_2432598 crossref_primary_10_1016_j_cccb_2023_100179 |
| Cites_doi | 10.1007/978-1-4614-7138-7 10.1002/sim.4780140108 10.1111/j.2517-6161.1972.tb00899.x 10.1109/72.623209 10.1177/1471082X1001100503 10.1080/01621459.1993.10476296 10.2307/2529360 10.1002/wics.1529 10.1177/0962280213515571 10.7717/peerj.6257 10.1145/2939672.2939778 10.1093/biostatistics/kxy006 10.1145/2939672.2939857 10.1002/sim.1655 10.1002/sim.4780131202 10.1007/978-1-4614-6849-3 10.1177/1471082X17748084 10.1111/rssa.12611 10.1016/j.artmed.2011.06.006 10.1145/3214306 10.1214/08-AOAS169 10.1609/aaai.v32i1.11842 10.1093/biomet/asm037 10.1002/(SICI)1097-0258(19980530)17:10<1169::AID-SIM796>3.0.CO;2-D 10.1201/9781138384484 10.1002/9781118646106 10.1002/bimj.201200045 10.1186/s12874-018-0482-1 10.3390/cancers12102802 10.1371/journal.pcbi.1006076 10.1002/sim.4780091214 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 10.1007/978-1-4757-3294-8 10.1186/1471-2105-9-14 10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529::AID-SIM274>3.0.CO;2-5 10.1093/aje/kwt312 10.1002/bimj.200610301 10.1007/s10985-021-09532-6 10.1111/j.0006-341X.2005.030814.x 10.1097/EDE.0b013e3181c30fb2 10.2307/2531894 10.1007/978-3-030-67664-3_10 10.1214/09-SS047 10.1023/A:1007379606734 10.1093/biostatistics/kxj011 10.1002/sim.6729 10.1097/01.ju.0000094764.56269.2d 10.1186/s12874-018-0650-3 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2022 COPYRIGHT 2022 BioMed Central Ltd. 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022. The Author(s). |
| Copyright_xml | – notice: The Author(s) 2022 – notice: COPYRIGHT 2022 BioMed Central Ltd. – notice: 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022. The Author(s). |
| DBID | C6C AAYXX CITATION 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.1186/s12874-022-01679-6 |
| DatabaseName | Springer Nature OA Free Journals CrossRef ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Open Access Full Text |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1471-2288 |
| EndPage | 18 |
| ExternalDocumentID | oai_doaj_org_article_e8bd3e808cae4ee1bbb8c0981c7d3b37 PMC9316420 A711640445 10_1186_s12874_022_01679_6 |
| GeographicLocations | Iran |
| GeographicLocations_xml | – name: Iran |
| GrantInformation_xml | – fundername: National Institutes of Health grantid: CA129102 funderid: http://dx.doi.org/10.13039/100000002 – fundername: American Cancer Society grantid: ACS IRG #16-184-56 funderid: http://dx.doi.org/10.13039/100000048 – fundername: ; grantid: CA129102 – fundername: ; grantid: ACS IRG #16-184-56 |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 6PF 7X7 88E 8FI 8FJ AAFWJ AAJSJ AASML AAWTL ABDBF ABUWG ACGFO ACGFS ACIHN ACUHS ADBBV ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GROUPED_DOAJ GX1 HMCUK IAO IHR INH INR ITC KQ8 M1P M48 MK0 M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SMD SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX ALIPV CITATION PMFND 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c584t-f28921857a2301c5a6f17bb3c3108198caff85335c5f38609d0cff17e108fbf43 |
| IEDL.DBID | M48 |
| ISSN | 1471-2288 |
| IngestDate | Wed Aug 27 01:28:20 EDT 2025 Thu Aug 21 14:10:36 EDT 2025 Fri Sep 05 05:54:40 EDT 2025 Fri Jul 25 08:34:29 EDT 2025 Tue Jun 17 21:34:19 EDT 2025 Tue Jun 10 20:32:19 EDT 2025 Tue Jul 01 04:30:59 EDT 2025 Thu Apr 24 23:02:05 EDT 2025 Sat Sep 06 07:35:36 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Time-to-event Cox proportional hazards Random survival forest Machine learning |
| Language | English |
| License | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c584t-f28921857a2301c5a6f17bb3c3108198caff85335c5f38609d0cff17e108fbf43 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-7785-3536 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1186/s12874-022-01679-6 |
| PMID | 35883032 |
| PQID | 2703966275 |
| PQPubID | 42579 |
| PageCount | 18 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_e8bd3e808cae4ee1bbb8c0981c7d3b37 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9316420 proquest_miscellaneous_2695293595 proquest_journals_2703966275 gale_infotracmisc_A711640445 gale_infotracacademiconefile_A711640445 crossref_primary_10_1186_s12874_022_01679_6 crossref_citationtrail_10_1186_s12874_022_01679_6 springer_journals_10_1186_s12874_022_01679_6 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2022-07-26 |
| PublicationDateYYYYMMDD | 2022-07-26 |
| PublicationDate_xml | – month: 07 year: 2022 text: 2022-07-26 day: 26 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London |
| PublicationTitle | BMC medical research methodology |
| PublicationTitleAbbrev | BMC Med Res Methodol |
| PublicationYear | 2022 |
| Publisher | BioMed Central BioMed Central Ltd BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: BMC |
| References | BD Ripley (1679_CR17) 2001; 237 H He (1679_CR70) 2013 E Giunchiglia (1679_CR47) 2018 1679_CR49 KT Tanner (1679_CR74) 2021; 184 M Kuhn (1679_CR38) 2013 AD Shah (1679_CR56) 2014; 179 TA Gerds (1679_CR69) 2006; 48 M Berger (1679_CR46) 2018; 18 B Efron (1679_CR61) 1997; 92 M Schmid (1679_CR73) 2021; 13 PJ Heagerty (1679_CR62) 2005; 61 RM Ripley (1679_CR15) 2004; 23 W Thompson Jr (1679_CR45) 1977; 33 J Bergstra (1679_CR57) 2012; 13 TM Therneau (1679_CR5) 2000 P Wang (1679_CR2) 2019; 51 H Kvamme (1679_CR34) 2021; 27 FM Khan (1679_CR12) 2008 RB D’Agostino (1679_CR28) 1990; 9 EW Steyerberg (1679_CR60) 2010; 21 MW Kattan (1679_CR1) 2003; 170 P Blanche (1679_CR63) 2013 J Snoek (1679_CR59) 2012; 25 JL Katzman (1679_CR42) 2018; 18 MF Gensheimer (1679_CR32) 2019; 7 1679_CR35 DJ Stekhoven (1679_CR55) 2012; 28 J Friedman (1679_CR37) 2001 1679_CR72 1679_CR71 X Qiu (1679_CR19) 2020; 10 E Graf (1679_CR68) 1999; 18 SF Brown (1679_CR16) 1997; 8 P Blanche (1679_CR64) 2019; 20 MR Segal (1679_CR10) 1988; 44 KP Murphy (1679_CR39) 2012 1679_CR25 DG Kleinbaum (1679_CR3) 2010 1679_CR23 G Tutz (1679_CR26) 2016 M Schmid (1679_CR30) 2016; 35 1679_CR24 1679_CR21 M LeBlanc (1679_CR44) 1993; 88 R Caruana (1679_CR50) 1997; 28 1679_CR22 J Lambert (1679_CR66) 2016; 25 1679_CR20 C-N Yu (1679_CR51) 2011; 24 TA Gerds (1679_CR41) 2021 M Du (1679_CR18) 2020; 12 I Bou-Hamad (1679_CR9) 2011; 5 T Hothorn (1679_CR11) 2006; 7 I Bou-Hamad (1679_CR29) 2011; 11 H Binder (1679_CR6) 2008; 9 H Ishwaran (1679_CR8) 2008; 2 D Faraggi (1679_CR14) 1995; 14 CM Bishop (1679_CR40) 2006 V Van Belle (1679_CR13) 2011; 53 1679_CR58 JA Sidey-Gibbons (1679_CR36) 2019; 19 GW Brier (1679_CR67) 1950; 78 1679_CR54 T Ching (1679_CR43) 2018; 14 1679_CR52 1679_CR53 P Blanche (1679_CR65) 2013; 55 E Biganzoli (1679_CR31) 1998; 17 JD Singer (1679_CR27) 1993; 18 G James (1679_CR48) 2013 HH Zhang (1679_CR7) 2007; 94 DR Cox (1679_CR4) 1972; 34 K Liestbl (1679_CR33) 1994; 13 |
| References_xml | – volume-title: Risk Assessment and Evaluation of Predictions year: 2013 ident: 1679_CR63 – volume-title: An Introduction to Statistical Learning, vol. 112 year: 2013 ident: 1679_CR48 doi: 10.1007/978-1-4614-7138-7 – volume: 14 start-page: 73 issue: 1 year: 1995 ident: 1679_CR14 publication-title: Stat Med doi: 10.1002/sim.4780140108 – volume: 34 start-page: 187 issue: 2 year: 1972 ident: 1679_CR4 publication-title: J R Stat Soc Ser B Methodol doi: 10.1111/j.2517-6161.1972.tb00899.x – volume: 8 start-page: 1071 issue: 5 year: 1997 ident: 1679_CR16 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.623209 – volume: 11 start-page: 429 issue: 5 year: 2011 ident: 1679_CR29 publication-title: Stat Model doi: 10.1177/1471082X1001100503 – volume-title: Survival Analysis, vol. 3 year: 2010 ident: 1679_CR3 – volume: 88 start-page: 457 issue: 422 year: 1993 ident: 1679_CR44 publication-title: J Am Stat Assoc doi: 10.1080/01621459.1993.10476296 – volume: 33 start-page: 463 issue: 3 year: 1977 ident: 1679_CR45 publication-title: Biometrics doi: 10.2307/2529360 – volume: 13 start-page: 1529 issue: 5 year: 2021 ident: 1679_CR73 publication-title: Wiley Interdiscip Rev Comput Stat doi: 10.1002/wics.1529 – volume: 13 start-page: 281 issue: 2 year: 2012 ident: 1679_CR57 publication-title: J Mach Learn Res – volume: 25 start-page: 2088 issue: 5 year: 2016 ident: 1679_CR66 publication-title: Stat Methods Med Res doi: 10.1177/0962280213515571 – ident: 1679_CR21 – volume-title: 2008 Eighth IEEE International Conference on Data Mining year: 2008 ident: 1679_CR12 – ident: 1679_CR25 – volume: 7 start-page: 6257 year: 2019 ident: 1679_CR32 publication-title: PeerJ doi: 10.7717/peerj.6257 – ident: 1679_CR72 – ident: 1679_CR71 doi: 10.1145/2939672.2939778 – volume: 20 start-page: 347 issue: 2 year: 2019 ident: 1679_CR64 publication-title: Biostatistics doi: 10.1093/biostatistics/kxy006 – volume-title: Modeling Discrete Time-to-event Data year: 2016 ident: 1679_CR26 – ident: 1679_CR35 – ident: 1679_CR52 doi: 10.1145/2939672.2939857 – volume: 10 start-page: 2311 year: 2020 ident: 1679_CR19 publication-title: Front Oncol – volume: 23 start-page: 825 issue: 5 year: 2004 ident: 1679_CR15 publication-title: Stat Med doi: 10.1002/sim.1655 – volume-title: The Elements of Statistical Learning, vol. 1 year: 2001 ident: 1679_CR37 – volume-title: International Conference on Artificial Neural Networks year: 2018 ident: 1679_CR47 – volume: 13 start-page: 1189 issue: 12 year: 1994 ident: 1679_CR33 publication-title: Stat Med doi: 10.1002/sim.4780131202 – ident: 1679_CR20 – volume-title: Applied Predictive Modeling, vol. 26 year: 2013 ident: 1679_CR38 doi: 10.1007/978-1-4614-6849-3 – volume: 18 start-page: 322 issue: 3-4 year: 2018 ident: 1679_CR46 publication-title: Stat Model doi: 10.1177/1471082X17748084 – ident: 1679_CR24 – ident: 1679_CR49 – volume: 184 start-page: 3 issue: 1 year: 2021 ident: 1679_CR74 publication-title: J R Stat Soc Ser A (Stat Soc) doi: 10.1111/rssa.12611 – volume: 53 start-page: 107 issue: 2 year: 2011 ident: 1679_CR13 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2011.06.006 – volume: 92 start-page: 548 issue: 438 year: 1997 ident: 1679_CR61 publication-title: J Am Stat Assoc – volume: 51 start-page: 1 issue: 6 year: 2019 ident: 1679_CR2 publication-title: ACM Comput Surv (CSUR) doi: 10.1145/3214306 – volume: 2 start-page: 841 issue: 3 year: 2008 ident: 1679_CR8 publication-title: Ann Appl Stat doi: 10.1214/08-AOAS169 – ident: 1679_CR53 doi: 10.1609/aaai.v32i1.11842 – volume: 18 start-page: 155 issue: 2 year: 1993 ident: 1679_CR27 publication-title: J Educ Stat – volume: 94 start-page: 691 issue: 3 year: 2007 ident: 1679_CR7 publication-title: Biometrika doi: 10.1093/biomet/asm037 – volume: 17 start-page: 1169 issue: 10 year: 1998 ident: 1679_CR31 publication-title: Stat Med doi: 10.1002/(SICI)1097-0258(19980530)17:10<1169::AID-SIM796>3.0.CO;2-D – volume-title: Medical Risk Prediction Models: With Ties to Machine Learning (1st Ed.) year: 2021 ident: 1679_CR41 doi: 10.1201/9781138384484 – volume-title: Imbalanced Learning: Foundations, Algorithms, and Applications year: 2013 ident: 1679_CR70 doi: 10.1002/9781118646106 – volume: 55 start-page: 687 issue: 5 year: 2013 ident: 1679_CR65 publication-title: Biom J doi: 10.1002/bimj.201200045 – ident: 1679_CR23 – volume: 18 start-page: 1 issue: 1 year: 2018 ident: 1679_CR42 publication-title: BMC Med Res Methodol doi: 10.1186/s12874-018-0482-1 – volume: 12 start-page: 2802 issue: 10 year: 2020 ident: 1679_CR18 publication-title: Cancers doi: 10.3390/cancers12102802 – volume: 25 start-page: 2951 year: 2012 ident: 1679_CR59 publication-title: Adv Neural Inf Process Syst – volume: 14 start-page: 1006076 issue: 4 year: 2018 ident: 1679_CR43 publication-title: PLoS Comput Biol doi: 10.1371/journal.pcbi.1006076 – volume: 9 start-page: 1501 issue: 12 year: 1990 ident: 1679_CR28 publication-title: Stat Med doi: 10.1002/sim.4780091214 – volume: 78 start-page: 1 issue: 1 year: 1950 ident: 1679_CR67 publication-title: Mon Weather Rev doi: 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 – volume-title: Modeling Survival Data: Extending the Cox Model year: 2000 ident: 1679_CR5 doi: 10.1007/978-1-4757-3294-8 – volume: 24 start-page: 1845 year: 2011 ident: 1679_CR51 publication-title: Adv Neural Inf Process Syst – volume: 9 start-page: 1 issue: 1 year: 2008 ident: 1679_CR6 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-14 – volume: 18 start-page: 2529 issue: 17-18 year: 1999 ident: 1679_CR68 publication-title: Stat Med doi: 10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529::AID-SIM274>3.0.CO;2-5 – volume: 179 start-page: 764 issue: 6 year: 2014 ident: 1679_CR56 publication-title: Am J Epidemiol doi: 10.1093/aje/kwt312 – ident: 1679_CR58 – volume: 48 start-page: 1029 issue: 6 year: 2006 ident: 1679_CR69 publication-title: Biom J doi: 10.1002/bimj.200610301 – volume-title: Pattern recognition and machine learning year: 2006 ident: 1679_CR40 – volume: 27 start-page: 710 issue: 4 year: 2021 ident: 1679_CR34 publication-title: Lifetime Data Anal doi: 10.1007/s10985-021-09532-6 – volume: 61 start-page: 92 issue: 1 year: 2005 ident: 1679_CR62 publication-title: Biometrics doi: 10.1111/j.0006-341X.2005.030814.x – volume: 21 start-page: 128 issue: 1 year: 2010 ident: 1679_CR60 publication-title: Epidemiol (Camb, Mass) doi: 10.1097/EDE.0b013e3181c30fb2 – volume: 44 start-page: 35 issue: 1 year: 1988 ident: 1679_CR10 publication-title: Biometrics doi: 10.2307/2531894 – ident: 1679_CR22 – volume: 28 start-page: 112 issue: 1 year: 2012 ident: 1679_CR55 publication-title: Biostatistics – ident: 1679_CR54 doi: 10.1007/978-3-030-67664-3_10 – volume: 5 start-page: 44 year: 2011 ident: 1679_CR9 publication-title: Stat Surv doi: 10.1214/09-SS047 – volume: 28 start-page: 41 issue: 1 year: 1997 ident: 1679_CR50 publication-title: Mach Learn doi: 10.1023/A:1007379606734 – volume: 237 start-page: 255 year: 2001 ident: 1679_CR17 publication-title: Clin Appl Artif Neural Netw – volume: 7 start-page: 355 issue: 3 year: 2006 ident: 1679_CR11 publication-title: Biostatistics doi: 10.1093/biostatistics/kxj011 – volume-title: Machine Learning: a Probabilistic Perspective year: 2012 ident: 1679_CR39 – volume: 35 start-page: 734 issue: 5 year: 2016 ident: 1679_CR30 publication-title: Stat Med doi: 10.1002/sim.6729 – volume: 170 start-page: 6 issue: 6S year: 2003 ident: 1679_CR1 publication-title: J Urol doi: 10.1097/01.ju.0000094764.56269.2d – volume: 19 start-page: 1 issue: 1 year: 2019 ident: 1679_CR36 publication-title: BMC Med Res Methodol doi: 10.1186/s12874-018-0650-3 |
| SSID | ssj0017836 |
| Score | 2.5575106 |
| Snippet | Background
Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making... Background Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making... Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important... Abstract Background Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in... |
| SourceID | doaj pubmedcentral proquest gale crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Algorithms Classification Cox proportional hazards Data analysis Datasets Discrete-time systems Health Sciences Machine learning Medical research Medicine Medicine & Public Health Neural networks Optimization techniques Probability Random survival forest Research Article Software Statistical Theory and Methods statistics and modelling Statistics for Life Sciences Survival analysis Theory of Medicine/Bioethics Time-to-event Treatment outcome |
| SummonAdditionalLinks | – databaseName: DOAJ Open Access Full Text dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NT90wDLcmDmiXicHQCmwqEtIOEJE2bZrsBhMIIbHLhsQtar40JNSH3sf_Pztt31bQ4LJr4yipa8d2Y_8McCRrr-hCjJVSS1ZVtWetKASLlZV1E6Nr0j_dm-_y6ra6vqvv_mr1RTlhPTxwz7jToKwXQXHl2lCFUFhrleNaFa7xwopUR841H4Op4f6AahPGEhklTxcFwbozylyntHvN5MQMJbT-52fy8zzJJ5elyQZdbsG7wXnMz_pNv4c3oduGzZvhenwHLn6sUPNRdvLHOT0kpuep183ia952-T2lpfseLzZfznIqyZ2j18yow3xPiOt-gNvLi5_frtjQJ4E5dB-WLGLQVBKmU4vxROHqVsaisVY4dN3Q4CPnYkSrLGpXR6Ek1567iCQBh6ONldiFjW7WhY-QS4n-q41OqCCqMlRo_iMX3ovYWBe9zqAY2WbcACJOvSweTAomlDQ9qw2y2iRWG5nB8XrOYw-h8SL1OX2NNSXBX6cHKBRmEArzmlBk8IW-pSElxe25dqg1wJckuCtz1hQYJnIUzQwOJpSoXG46PEqDGZR7YUo8JXUCzs_gcD1MMylhrQuzFdJIXZep6jmDZiJFkzebjnT3vxLAtxa4fMkzOBnl7c_i_-bc3v_g3D68LZOaNKi9B7CxnK_CJ3S7lvZz0rDfNJEpIg priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3fa9cwED90gvgiOhWrc3Qg-KBhbdMmqS-yycYQ5osOvm-h-aWD0X79_vj_vUvT76jDvTYX0lzukrvk7nMA70XjFD2IsUq0gtV141jHS85CbUQjQ7Ay3ulefhcXV_W3RbNIF27rFFY57Ylxo3aDpTvy4wpFs41o5V-WfxhVjaLX1VRC4yE8itBlKM9ysXO4SspQmBJllDhelwTuzih-nYLvWyZmh1HE7L-7M9-NlvznyTSeROfP4GkyIfOTcc2fwwPf78Pjy_RI_gLOfmxR_1GC8uWKPhLr81jxZv057_r8moLT3Ygam2-GnBJzV2g7M6ozPxLiuC_h6vzs59cLlqolMItGxIYFdJ0qQnbq0KsobdOJUEpjuEUDDo99ZbsQ8GzmjW0CV6JoXWEDknhsDibU_BXs9UPvX0MuBFqxJliuPK8rX6MREAruHA_S2ODaDMqJbdomKHGqaHGjo0uhhB5ZrZHVOrJaiww-7vosRyCNe6lPaTV2lASCHT8Mq1866ZT2yjjuVYFT87X3pTFG2aJVpZWOGy4z-EBrqUlV8fdslzIOcJIEeqVPZInOYoECmsHBjBJVzM6bJ2nQScXX-lYgMzjaNVNPClvr_bBFGtE2Vcx9zkDOpGg2s3lLf_07wny3HIevigw-TfJ2O_j_Offm_n99C0-qqAAStfMA9jarrX-HZtXGHEbd-Qti2x8S priority: 102 providerName: ProQuest – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1baxUxEB60BfFF2qq49sIKgg8a3Gw2l-1bLS1FqC9a6FvY3LAge8q5_P_OZPecuq0KviYTspnMZCY7M18A3isZDAXEWK1axZpGBtYJLlhqnJI6Ja_zP93Lb-riqvl6La9HmByqhfk9fs-N-rzgBMjOKOecEuZbpp7CtuRC5cCsOt1EDKgaYV0U88dxE8OT8fkfn8KPMyMfhEez1TnfgReju1ieDPu7C09ivwfPLseA-Es4-75CXUdpKW_n1EhsLvPrNovjsuvLG0pEDwNCbLmclVSEO0c_mdGb8gMhzvsKrs7PfpxesPFlBObRYViyhNekmlCcOrxBcC87lbh2Tnh01tDEG9-lhHZYSC-TMKpqQ-UTkkTsTi414jVs9bM-voFSKfRYXfLCRNHUsUGDnyoRgkja-RTaAviabdaPsOH0esUvm68PRtmB1RZZbTOrrSrg42bM7QCa8U_qL7QbG0oCvM4NKAd21B8bjQsimgqXFpsYuXPO-Ko13OsgnNAFfKC9tKSW-Hm-G6sLcJEEcGVPNMeLYYXCWMDBhBLVyU-719JgR3Ve2BrPxTZD5RfwbtNNIylFrY-zFdKoVta5zrkAPZGiycqmPf3Nzwzp3Qqcvq4K-LSWt_vJ_865t_9Hvg_P66wQGjXzALaW81U8RJdq6Y6yLt0BDz8X9g priority: 102 providerName: Springer Nature |
| Title | Survival prediction models: an introduction to discrete-time modeling |
| URI | https://link.springer.com/article/10.1186/s12874-022-01679-6 https://www.proquest.com/docview/2703966275 https://www.proquest.com/docview/2695293595 https://pubmed.ncbi.nlm.nih.gov/PMC9316420 https://doaj.org/article/e8bd3e808cae4ee1bbb8c0981c7d3b37 |
| Volume | 22 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3da9swED_6AWMvY5_Maxc8GOxh02ZbtiQPxmhCShmkjG6BsBdhydJWKE6bD9j--90pdorXri-GWGcrOt1Jd9bd7wBei6JWdCDGMlEKludFzSqecuZzIwrpvZXhm-7kVJxM8y-zYrYDXbmjloHLW107qic1XVy8_3315zMq_Keg8Ep8WKYE2s4oLp2C6ksmdmEfdyZBztgkvz5VoIyFkG0kU5ZlSnVJNLe-o7dRBTz_m6v2zUjKf45Twy51_BAetOZlfLSRh0ew45rHcG_SHqA_gfG3Na4NKF3x5YJu0rTEoRrO8mNcNfE5Ba7XG0TZeDWPKWl3gXY1oxr0G0Ls9ylMj8ffRyesraTALBoYK-bRrcoI9alCjyO1RSV8Ko3hFo07NAmUrbzHfZsXtvBciaSsE-uRxGGzNz7nz2CvmTfuOcRCoIVrvOXK8TxzORoIPuF1zb001tdlBGnHNm1bmHGqdnGhg7uhhN6wWiOrdWC1FhG83T5zuQHZuJN6SLOxpSSA7HBjvvipW33TTpmaO5Xg0FzuXGqMUTYpVWplzQ2XEbyhudQkWPj3bNVmI-AgCRBLH8kUHckEhTeCwx4lqp_tN3fSoDvp1Rmuo2WA1o_g1baZnqSQtsbN10gjyiILedERyJ4U9UbWb2nOfwUI8JJj91kSwbtO3q47_z_nXtw9lAO4nwUFkKi5h7C3WqzdSzS5VmYAu3ImB7A_HJ9-PcNfIzEahM8Xg6BheD0b_vgLOxkpMg |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtR3batRA9FC3oL6IV4xWjaD4oEOTTDJJBJFWt2xtdxFtoW_TzE0Lkl33gvhTfqPnTJItsdi3vmZOMpkz5zZzbgAvRGYKcoixRJSCpWlmWMVjzlyqRJY7p3N_pzueiNFx-ukkO9mAP10uDIVVdjLRC2oz1XRHvp0gaZa-Wvn72U9GXaPIu9q10GjI4sD-_oVHtsW7_Y-4vy-TZG949GHE2q4CTKOyXTKHR4yEKiBVaH3HOquEi3OluEZDB9VjoSvnUIfxTGeOFyIqTaQdglgcdsqlHL97DTZTymgdwObucPL5y9pvQTkRXWpOIbYXMZWTZxQxT-H-JRM99ee7BFzUBRfjM_9x0nrdt3cbbrVGa7jTUNkd2LD1Xbg-bt3y92D4dYUSB2k2nM3pIW126HvsLN6GVR2eUTi8aerUhstpSKnAc7TWGXW2bwBx3vtwfCWYfACDelrbhxAKgXazcpoXlqeJTdHscBE3hrtcaWfKAOIObVK3xcuph8YP6Q8xhZANqiWiWnpUSxHA6_U7s6Z0x6XQu7Qba0gqu-0fTOffZMvF0hbKcFtEuDSbWhsrpQodlUWsc8MVzwN4RXspSTjg7-mqzXHARVKZLbmTx3g8jZAlAtjqQSJT6_5wRw2yFSoLec4CATxfD9ObFChX2-kKYUSZJT7bOoC8R0W9lfVH6rPvvrB4yXH6JArgTUdv55P_H3OPLv_XZ3BjdDQ-lIf7k4PHcDPxzJCjbNiCwXK-sk_QqFuqpy0nhXB61cz7FwHTXHU |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9QwDLdgSBMviE9RGKxISDxAtLZJ05S3se00PjYhwaS9Rc3XmIR6p7ve_4-dtgfdYBKviaM0jh07tf0LwGtZOkUBMVbIWjIhSscannMWhJFlFYKt4j_dk1N5fCY-nZfnf1Txx2z3MSTZ1zQQSlPb7S1c6FVcyb1VTjDtjDLRKY2-ZvI23BFk-ihcKw82cQSqURhLZf46bmKOImr_9bP5er7klaBptEWz-3BvcCLT_X7XH8At3z6E7ZMhTP4Ijr6t8QRAGUoXS2ok5qfxzZvV-7Rp00tKT3c9bmzazVMqzV2i98zopfmeEOd9DGezo-8Hx2x4L4FZdCM6FvDyVBC2U4P3ityWjQx5ZQy36MKh4Ve2CQGtMy9tGbiSWe0yG5DEY3cwQfAnsNXOW_8UUinRjzXBcuW5KLxANyBk3DkeKmODqxPIR7ZpO4CJ05sWP3W8VCipe1ZrZLWOrNYygbebMYseSuNG6g-0GxtKgsGODfPlhR60SntlHPcqw6V54X1ujFE2q1VuK8cNrxJ4Q3upSVnx82wz1BzgIgn2Su9XOV4XMxTRBHYmlKhkdto9SoMelHylCzwt6wign8CrTTeNpMS11s_XSCPrsojVzwlUEymarGza017-iEDfNcfpiyyBd6O8_Z7835x79n_ku7D99XCmv3w8_fwc7hZRNypU3R3Y6pZr_wJ9rs68jGr1C14lIyo |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Survival+prediction+models%3A+an+introduction+to+discrete-time+modeling&rft.jtitle=BMC+medical+research+methodology&rft.au=Suresh%2C+Krithika&rft.au=Severn%2C+Cameron&rft.au=Ghosh%2C+Debashis&rft.date=2022-07-26&rft.pub=BioMed+Central+Ltd&rft.issn=1471-2288&rft.eissn=1471-2288&rft.volume=22&rft.issue=1&rft_id=info:doi/10.1186%2Fs12874-022-01679-6&rft.externalDocID=A711640445 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2288&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2288&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2288&client=summon |