Home Mathematics An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma
Article
Licensed
Unlicensed Requires Authentication

An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma

  • Massimo Bilancia EMAIL logo , Andrea Nigri , Barbara Cafarelli and Danilo Di Bona
Published/Copyright: June 25, 2024
The International Journal of Biostatistics
From the journal The International Journal of Biostatistics Volume 20 Issue 2

Abstract

Asthma is a disease characterized by chronic airway hyperresponsiveness and inflammation, with signs of variable airflow limitation and impaired lung function leading to respiratory symptoms such as shortness of breath, chest tightness and cough. Eosinophilic asthma is a distinct phenotype that affects more than half of patients diagnosed with severe asthma. It can be effectively treated with monoclonal antibodies targeting specific immunological signaling pathways that fuel the inflammation underlying the disease, particularly Interleukin-5 (IL-5), a cytokine that plays a crucial role in asthma. In this study, we propose a data analysis pipeline aimed at identifying subphenotypes of severe eosinophilic asthma in relation to response to therapy at follow-up, which could have great potential for use in routine clinical practice. Once an optimal partition of patients into subphenotypes has been determined, the labels indicating the group to which each patient has been assigned are used in a novel way. For each input variable in a specialized logistic regression model, a clusterwise effect on response to therapy is determined by an appropriate interaction term between the input variable under consideration and the cluster label. We show that the clusterwise odds ratios can be meaningfully interpreted conditional on the cluster label. In this way, we can define an effect measure for the response variable for each input variable in each of the groups identified by the clustering algorithm, which is not possible in standard logistic regression because the effect of the reference class is aliased with the overall intercept. The interpretability of the model is enforced by promoting sparsity, a goal achieved by learning interactions in a hierarchical manner using a special group-Lasso technique. In addition, valid expressions are provided for computing odds ratios in the unusual parameterization used by the sparsity-promoting algorithm. We show how to apply the proposed data analysis pipeline to the problem of sub-phenotyping asthma patients also in terms of quality of response to therapy with monoclonal antibodies.

Keywords: severe eosinophilic asthma; clustering algorithms; partitioning around medoids (PAM); logistic regression with interactions; group-Lasso
Corresponding author: Massimo Bilancia, Department of Precision and Regenerative Medicine and Jonian Area (DiMePRe-J), University of Bari Aldo Moro, Bari, Italy, E-mail: 

Acknowledgment

The authors would like to thank the editor-in-chief and the associate editor as well as two anonymous reviewers for their valuable comments. The manuscript has been thoroughly reviewed and improved with their help and support.

  1. Research ethics: We performed a retrospective analysis of all patients with SEA who commenced treatment between May 2020 and June 2022 at 12 Italian tertiary referral asthma centers. Informed consent for the retrospective analysis was obtained from all subjects. The involved asthma centers used a shared anonymized database to collect clinical, functional, and biological data. Ethical approval was obtained from each local Ethic Committee (coordinator center: Catania, Italy; document number, 33/2020/PO–14th/April/2020).

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved submission.

  3. Competing interests: The authors declare that are not in any situation which could give rise to a conflict of interest.

  4. Research funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

  5. Data availability: The data are available in anonymous form on request due to privacy/ethical restrictions.

References

1. Reddel, HK, Bacharier, LB, Bateman, ED, Brightling, CE, Brusselle, GG, Buhl, R, et al.. Global initiative for asthma strategy 2021: executive summary and rationale for key changes. Eur Respir J 2022;59:2102730. https://doi.org/10.1183/13993003.02730-2021.Search in Google Scholar PubMed PubMed Central

2. Cao, Y, Chen, S, Chen, X, Zou, W, Liu, Z, Wu, Y, et al.. Global trends in the incidence and mortality of asthma from 1990 to 2019: an age-period-cohort analysis using the global burden of disease study 2019. Front Public Health 2022;10:1036674. https://doi.org/10.3389/fpubh.2022.1036674.Search in Google Scholar PubMed PubMed Central

3. Reddel, HK, Taylor, DR, Bateman, ED, Boulet, LP, Boushey, HA, Busse, WW, et al.. An official American thoracic society/European respiratory society statement: asthma control and exacerbations. Am J Respir Crit Care Med 2009;180:59–99. https://doi.org/10.1164/rccm.200801-060st.Search in Google Scholar PubMed

4. Bel, EH. Clinical phenotypes of asthma. Curr Opin Pulm Med 2004;10:44–50. https://doi.org/10.1097/00063198-200401000-00008.Search in Google Scholar PubMed

5. Porpodis, K, Tsiouprou, I, Apostolopoulos, A, Ntontsi, P, Fouka, E, Papakosta, D, et al.. Eosinophilic asthma, phenotypes-endotypes and current biomarkers of choice. J Personalized Med 2022;12:1093. https://doi.org/10.3390/jpm12071093.Search in Google Scholar PubMed PubMed Central

6. Corren, J, Du, E, Gubbi, A, Vanlandingham, R. Variability in blood eosinophil counts in patients with eosinophilic asthma. J Allergy Clin Immunol Pract 2021;9:1224–31.e9. https://doi.org/10.1016/j.jaip.2020.10.033.Search in Google Scholar PubMed

7. Di Bona, D, Crimi, C, D’Uggento, AM, Benfante, A, Caiaffa, MF, Calabrese, C, et al.. Effectiveness of benralizumab in severe eosinophilic asthma: distinct sub-phenotypes of response identified by cluster analysis. Clin Exp Allergy 2022;52:312–23. https://doi.org/10.1111/cea.14026.Search in Google Scholar PubMed PubMed Central

8. FitzGerald, JM, Bleecker, ER, Menzies-Gow, A, Zangrilli, JG, Hirsch, I, Metcalfe, P, et al.. Predictors of enhanced response with benralizumab for patients with severe asthma: pooled analysis of the SIROCCO and CALIMA studies. Lancet Respir Med 2018;6:51–64. https://doi.org/10.1016/s2213-2600(17)30344-2.Search in Google Scholar PubMed

9. Harvey, ES, Langton, D, Katelaris, C, Stevens, S, Farah, CS, Gillman, A, et al.. Mepolizumab effectiveness and identification of super-responders in severe asthma. Eur Respir J 2020;55:1902420. https://doi.org/10.1183/13993003.02420-2019.Search in Google Scholar PubMed

10. Bourdin, A, Chanez, P. Clustering in asthma: why, how and for how long? Eur Respir J 2013;41:1247–8. https://doi.org/10.1183/09031936.00003313.Search in Google Scholar PubMed

11. Marcinkevičs, R, Vogt, JE. Interpretable and explainable machine learning: a methods-centric overview with concrete examples. WIREs Data Min Knowl Disc 2023;13:e1493. https://doi.org/10.1002/widm.1493.Search in Google Scholar

12. Forero, R, Nahidi, S, De Costa, J, Mohsin, M, Fitzgerald, G, Gibson, N, et al.. Application of four-dimension criteria to assess rigour of qualitative research in emergency medicine. BMC Health Serv Res 2018;18:120. https://doi.org/10.1186/s12913-018-2915-2.Search in Google Scholar PubMed PubMed Central

13. Lange, T, Roth, V, Braun, ML, Buhmann, JM. Stability-based validation of clustering solutions. Neural Comput 2004;16:1299–323. https://doi.org/10.1162/089976604773717621.Search in Google Scholar PubMed

14. Linardatos, P, Papastefanopoulos, V, Kotsiantis, S, Explainable, AI. A review of machine learning interpretability methods. Entropy 2020;23:18. https://doi.org/10.3390/e23010018.Search in Google Scholar PubMed PubMed Central

15. Molnar, C. Interpretable machine learning, 2nd ed; 2022. Available from: https://christophm.github.io/interpretable-ml-book.Search in Google Scholar

16. Hosmer, DW, Lemeshow, S, Sturdivant, RX. Applied logistic regression, 3rd ed Hoboken, New Jersey: John Wiley & Sons, Inc.; 2013.10.1002/9781118548387Search in Google Scholar

17. Lim, M, Hastie, T. Learning interactions via hierarchical group-lasso regularization. J Comput Graph Stat 2015;24:627–54. https://doi.org/10.1080/10618600.2014.938812.Search in Google Scholar PubMed PubMed Central

18. Haldar, P, Pavord, ID, Shaw, DE, Berry, MA, Thomas, M, Brightling, CE, et al.. Cluster analysis and clinical asthma phenotypes. Am J Respir Crit Care Med 2008;178:218–24. https://doi.org/10.1164/rccm.200711-1754oc.Search in Google Scholar PubMed PubMed Central

19. Kim, TB, Jang, AS, Kwon, HS, Park, JS, Chang, YS, Cho, SH, et al.. Identification of asthma clusters in two independent Korean adult asthma cohort. Eur Respir J 2008;41:1308–14. https://doi.org/10.1183/09031936.00100811.Search in Google Scholar PubMed

20. Deliu, M, Sperrin, M, Belgrave, D, Custovic, A. Identification of asthma subtypes using clustering methodologies. Pulm Ther 2016;2:19–41. https://doi.org/10.1007/s41030-016-0017-z.Search in Google Scholar PubMed PubMed Central

21. Moore, WC, Meyers, DA, Wenzel, SE, Teague, WG, Li, H, Li, X, et al.. Identification of asthma phenotypes using cluster analysis in the severe asthma research program. Am J Respir Crit Care Med 2010;181:315–23. https://doi.org/10.1164/rccm.200906-0896oc.Search in Google Scholar PubMed PubMed Central

22. Wu, W, Bleecker, E, Moore, W, Busse, WW, Castro, M, Chung, KF, et al.. Unsupervised phenotyping of severe asthma research program participants using expanded lung data. J Allergy Clin Immunol 2014;133:1280–8. https://doi.org/10.1016/j.jaci.2013.11.042.Search in Google Scholar PubMed PubMed Central

23. Howard, R, Rattray, M, Prosperi, M, Custovic, A. Distinguishing asthma phenotypes using machine learning approaches. Curr Allergy Asthma Rep 2015;15:38. https://doi.org/10.1007/s11882-015-0542-0.Search in Google Scholar PubMed PubMed Central

24. Raherison-Semjen, C, Parrat, E, Nocent-Eijnani, C, Mangiapan, G, Prudhomme, A, Oster, JP, et al.. FASE-CPHG study: identification of asthma phenotypes in the French severe asthma study using cluster analysis. Respir Res 2021;22:136. https://doi.org/10.1186/s12931-021-01723-x.Search in Google Scholar PubMed PubMed Central

25. Robinson, PN, Mungall, CJ, Haendel, M. Capturing phenotypes for precision medicine. Cold Spring Harbor Mol Case Stud 2015;1:a000372. https://doi.org/10.1101/mcs.a000372.Search in Google Scholar PubMed PubMed Central

26. Huang, Z. Clustering large data sets with mixed numeric and categorical values. In: The first Pacific-Asia conference on knowledge discovery and data mining; 1997:21–34 pp.Search in Google Scholar

27. Huang, Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Discov 1998;12:283–304.10.1023/A:1009769707641Search in Google Scholar

28. Gower, JC. A general coefficient of similarity and some of its properties. Biometrics 1971;27:882–907. https://doi.org/10.2307/2528823.Search in Google Scholar

29. D’Orazio, M. Distances with mixed type variables some modified Gower’s coefficients, preprint 2021. http://arxiv.org/abs/2101.02481.Search in Google Scholar

30. Jin, X, Han, J. K-medoids clustering. In: Encyclopedia of machine learning. Boston, MA: Springer US; 2011:564–5 pp.10.1007/978-0-387-30164-8_426Search in Google Scholar

31. Schubert, E, Rousseeuw, PJ. Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms. Inf Syst 2021;101:101804. https://doi.org/10.1016/j.is.2021.101804.Search in Google Scholar

32. Botyarov, M, Miller, EE. Partitioning around medoids as a systematic approach to generative design solution space reduction. Res Eng 2022;15:100544. https://doi.org/10.1016/j.rineng.2022.100544.Search in Google Scholar

33. Reynolds, AP, Richards, G, de la Iglesia, B, Rayward-Smith, VJ. Clustering rules: a comparison of partitioning and hierarchical clustering algorithms. J Math Model Algorithm 2006;5:475–504. https://doi.org/10.1007/s10852-005-9022-1.Search in Google Scholar

34. Maechler, M, Rousseeuw, P, Struyf, A, Hubert, M, Hornik, K. Cluster: cluster analysis basics and extensions, R package version 2.1.6; 2023. Available from: https://cran.r-project.org/web/packages/cluster/index.html.Search in Google Scholar

35. Yan, EW, Jian, A, Yan, L, HongGang, W. Optimization of k-medoids algorithm for initial clustering center. J Phys Conf Ser 2020;1487:012011. https://doi.org/10.1088/1742-6596/1487/1/012011.Search in Google Scholar

36. Brock, G, Pihur, V, Datta, S, Datta, S. clValid: an R package for cluster validation. J Stat Software 2008;25:1–22. https://doi.org/10.18637/jss.v025.i04.Search in Google Scholar

37. Shutaywi, M, Kachouie, NN. Silhouette analysis for performance evaluation in machine learning with applications to clustering. Entropy 2021;23:759. https://doi.org/10.3390/e23060759.Search in Google Scholar PubMed PubMed Central

38. Tibshirani, R, Walther, G, Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B Stat Methodol. 2001;63:411–23. https://doi.org/10.1111/1467-9868.00293.Search in Google Scholar

39. Lengyel, A, Botta-Dukát, Z. Silhouette width using generalized mean – a flexible method for assessing clustering efficiency. Ecol Evol 2019;9:13231–43. https://doi.org/10.1002/ece3.5774.Search in Google Scholar PubMed PubMed Central

40. von Luxburg, U. Clustering stability: an overview. Found Trends® Mach Learn 2010;2:235–74. https://doi.org/10.1561/2200000008.Search in Google Scholar

41. Liu, T, Yu, H, Blair, RH. Stability estimation for unsupervised clustering: a review. WIREs Comput Stat 2022;14:e1575. https://doi.org/10.1002/wics.1575.Search in Google Scholar PubMed PubMed Central

42. Hennig, C. Cluster-wise assessment of cluster stability. Comput Stat Data Anal 2007;52:258–71. https://doi.org/10.1016/j.csda.2006.11.025.Search in Google Scholar

43. Yu, H, Chapman, B, Di Florio, A, Eischen, E, Gotz, D, Jacob, M, et al.. Bootstrapping estimates of stability for clusters, observations and model selection. Comput Stat 2019;34:349–72. https://doi.org/10.1007/s00180-018-0830-y.Search in Google Scholar

44. Jaccard, J. Interaction effects in logistic regression. London, UK: Sage Publications, Inc.; 2001.10.4135/9781412984515Search in Google Scholar

45. Chen, JJ. Communicating complex information: the interpretation of statistical interaction od multiple logistic regression analysis. Am J Publ Health 2003;93:1376–7. https://doi.org/10.2105/ajph.93.9.1376-a.Search in Google Scholar PubMed PubMed Central

46. Norton, EC, Wang, H, Ai, C. Computing interaction effects and standard errors in logit and probit models. STATA J 2004;4:154–67. https://doi.org/10.1177/1536867x0400400206.Search in Google Scholar

47. Ai, C, Norton, EC. Interaction terms in logit and probit models. Econ Lett 2003;80:123–9. https://doi.org/10.1016/s0165-1765(03)00032-6.Search in Google Scholar

48. Tibshirani, R. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B Stat Methodol 1996;58:267–88. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.Search in Google Scholar

49. Zou, H. The adaptive lasso and its oracle properties. J Am Stat Assoc 2006;101:1418–29. https://doi.org/10.1198/016214506000000735.Search in Google Scholar

50. Gauraha, N. Introduction to the LASSO: a convex optimization approach for high-dimensional problems. Resonance 2018;23:439–64. https://doi.org/10.1007/s12045-018-0635-x.Search in Google Scholar

51. Bien, J, Taylor, J, Tibshirani, R. A Lasso for hierarchical interactions. Ann Stat 2013;41:1111–41. https://doi.org/10.1214/13-aos1096.Search in Google Scholar PubMed PubMed Central

52. Yuan, M, Lin, Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B Stat Methodol 2006;68:49–67. https://doi.org/10.1111/j.1467-9868.2005.00532.x.Search in Google Scholar

53. Jacob, L, Obozinski, G, Vert, JP. Group lasso with overlap and graph lasso. In: Proceedings of the 26th annual international conference on machine learning. New York, NY, USA: ACM; 2009:433–40 pp.10.1145/1553374.1553431Search in Google Scholar

54. Hastie, T, Tibshirani, R, Wainwright, M. Statistical learning with sparsity: the lasso and generalizations. Boca Raton, FL: Chapman & Hall/CRC; 2015.10.1201/b18401Search in Google Scholar

55. Holguin, F, Cardet, JC, Chung, KF, Diver, S, Ferreira, DS, Fitzpatrick, A, et al.. Management of severe asthma: a European respiratory society/American thoracic society guideline. Eur Respir J 2020;55:1900588. https://doi.org/10.1183/13993003.00588-2019.Search in Google Scholar PubMed

56. Schatz, M, Kosinski, M, Yarlas, AS, Hanlon, J, Watson, ME, Jhingran, P. The minimally important difference of the asthma control test. J Allergy Clin Immunol 2009;124:719–23.e1. https://doi.org/10.1016/j.jaci.2009.06.053.Search in Google Scholar PubMed

57. Crimi, C, Ferri, S, Campisi, R, Crimi, N. The link between asthma and bronchiectasis: state of the art. Respiration 2020;99:463–76. https://doi.org/10.1159/000507228.Search in Google Scholar PubMed

58. Bakakos, A, Schleich, F, Bakakos, P. Biological therapy of severe asthma and nasal polyps. J Personalized Med 2022;12:976. https://doi.org/10.3390/jpm12060976.Search in Google Scholar PubMed PubMed Central

59. Batool, F, Hennig, C. Clustering with the average silhouette width. Comput Stat Data Anal 2021;158:107190. https://doi.org/10.1016/j.csda.2021.107190.Search in Google Scholar

60. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2024. Available from: https://www.R-project.org/.Search in Google Scholar

61. Lim, M, Hastie, T. Glinternet: learning interactions via hierarchical group-Lasso regularization, R package version 1.0.12; 2021. Available from: https://cran.r-project.org/web/packages/glinternet/index.html.Search in Google Scholar

62. Zhao, P, Yu, B. On model selection consistency of lasso. J Mach Learn Res 2006;7:2541–63.Search in Google Scholar

63. Kammer, M, Dunkler, D, Michiels, S, Heinze, G. Evaluating methods for Lasso selective inference in biomedical research: a comparative simulation study. BMC Med Res Methodol 2022;22:206. https://doi.org/10.1186/s12874-022-01681-y.Search in Google Scholar PubMed PubMed Central

64. Taylor, J, Tibshirani, RJ. Statistical learning and selective inference. Proc Natl Acad Sci 2015;112:7629–34. https://doi.org/10.1073/pnas.1507583112.Search in Google Scholar PubMed PubMed Central

65. Tian, X, Taylor, J. Selective inference with a randomized response. Ann Stat 2018;46:679–710. https://doi.org/10.1214/17-aos1564.Search in Google Scholar

66. Lee, JD, Sun, DL, Sun, Y, Taylor, JE. Exact post-selection inference, with application to the lasso. Ann Stat 2016;44:907–27. https://doi.org/10.1214/15-aos1371.Search in Google Scholar

67. Loftus, JR. Selective inference after cross-validation, preprint 2015. https://arxiv.org/abs/1511.08866.Search in Google Scholar

68. Panigrahi, S, MacDonald, PW, Kessler, D. Approximate post-selective inference for regression with the group LASSO. J Mach Learn Res 2023;24:1–49.Search in Google Scholar

69. Meinshausen, N, Bühlmann, P. Stability selection. J R Stat Soc Ser B Stat Methodol 2010;72:417–73. https://doi.org/10.1111/j.1467-9868.2010.00740.x.Search in Google Scholar

70. DiCiccio, TJ, Efron, B. Bootstrap confidence intervals. Stat Sci 1996;11:189–228. https://doi.org/10.1214/ss/1032280214.Search in Google Scholar

71. Ricciardolo, F, Sprio, A, Baroso, A, Gallo, F, Riccardi, E, Bertolini, F, et al.. Characterization of T2-low and T2-high asthma phenotypes in real-life. Biomedicines 2021;9:1648. https://doi.org/10.3390/biomedicines9111684.Search in Google Scholar PubMed PubMed Central

72. Annunziato, F, Romagnani, C, Romagnani, S. The 3 major types of innate and adaptive cell-mediated effector immunity. J Allergy Clin Immunol 2015;135:626–35. https://doi.org/10.1016/j.jaci.2014.11.001.Search in Google Scholar PubMed

73. Lund, S, Walford, H, Doherty, T. Type 2 innate lymphoid cells in allergic disease. Curr Immunol Rev 2014;9:214–21. https://doi.org/10.2174/1573395510666140304235916.Search in Google Scholar PubMed PubMed Central

74. Gautam, Y, Johansson, E, Mersha, TB. Multi-omics profiling approach to asthma: an evolving paradigm. J Personalized Med 2022;12:66. https://doi.org/10.3390/jpm12010066.Search in Google Scholar PubMed PubMed Central

75. Held, L, Holmes, CC. Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Anal 2006;1:145–68. https://doi.org/10.1214/06-ba105.Search in Google Scholar

76. Farcomeni, A. Bayesian constrained variable selection. Stat Sin 2010;20:1043–62.Search in Google Scholar

77. Dabney, AR. Classification of microarrays to nearest centroids. Bioinformatics 2005;21:4148–54. https://doi.org/10.1093/bioinformatics/bti681.Search in Google Scholar PubMed

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/ijb-2023-0061).

Received: 2023-06-04
Accepted: 2024-05-27
Published Online: 2024-06-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Random forests for survival data: which methods work best and under what conditions?
  4. Flexible variable selection in the presence of missing data
  5. An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma
  6. MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination
  7. Detecting differentially expressed genes from RNA-seq data using fuzzy clustering
  8. Hypothesis testing for detecting outlier evaluators
  9. Response to comments on ‘sensitivity of estimands in clinical trials with imperfect compliance’
  10. Commentary
  11. Comments on “sensitivity of estimands in clinical trials with imperfect compliance” by Chen and Heitjan
  12. Research Articles
  13. Optimizing personalized treatments for targeted patient populations across multiple domains
  14. Statistical models for assessing agreement for quantitative data with heterogeneous random raters and replicate measurements
  15. History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome
  16. Revisiting incidence rates comparison under right censorship
  17. Ensemble learning methods of inference for spatially stratified infectious disease systems
  18. The survival function NPMLE for combined right-censored and length-biased right-censored failure time data: properties and applications
  19. Hybrid classical-Bayesian approach to sample size determination for two-arm superiority clinical trials
  20. Estimation of a decreasing mean residual life based on ranked set sampling with an application to survival analysis
  21. Improving the mixed model for repeated measures to robustly increase precision in randomized trials
  22. Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data
  23. A modified rule of three for the one-sided binomial confidence interval
  24. Kalman filter with impulse noised outliers: a robust sequential algorithm to filter data with a large number of outliers
  25. Bayesian estimation and prediction for network meta-analysis with contrast-based approach
  26. Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods
https://doi.org/10.1515/ijb-2023-0061
Keywords for this article
severe eosinophilic asthma; clustering algorithms; partitioning around medoids (PAM); logistic regression with interactions; group-Lasso

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Random forests for survival data: which methods work best and under what conditions?
  4. Flexible variable selection in the presence of missing data
  5. An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma
  6. MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination
  7. Detecting differentially expressed genes from RNA-seq data using fuzzy clustering
  8. Hypothesis testing for detecting outlier evaluators
  9. Response to comments on ‘sensitivity of estimands in clinical trials with imperfect compliance’
  10. Commentary
  11. Comments on “sensitivity of estimands in clinical trials with imperfect compliance” by Chen and Heitjan
  12. Research Articles
  13. Optimizing personalized treatments for targeted patient populations across multiple domains
  14. Statistical models for assessing agreement for quantitative data with heterogeneous random raters and replicate measurements
  15. History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome
  16. Revisiting incidence rates comparison under right censorship
  17. Ensemble learning methods of inference for spatially stratified infectious disease systems
  18. The survival function NPMLE for combined right-censored and length-biased right-censored failure time data: properties and applications
  19. Hybrid classical-Bayesian approach to sample size determination for two-arm superiority clinical trials
  20. Estimation of a decreasing mean residual life based on ranked set sampling with an application to survival analysis
  21. Improving the mixed model for repeated measures to robustly increase precision in randomized trials
  22. Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data
  23. A modified rule of three for the one-sided binomial confidence interval
  24. Kalman filter with impulse noised outliers: a robust sequential algorithm to filter data with a large number of outliers
  25. Bayesian estimation and prediction for network meta-analysis with contrast-based approach
  26. Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2026 De Gruyter Brill
Downloaded on 1.1.2026 from https://www.degruyterbrill.com/document/doi/10.1515/ijb-2023-0061/html
Scroll to top button