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Growth Mixture Modeling: The New Statistical Kid on the Block?
Journal of Cardiac Failure Vol. 21 No. 6 2015
Editorial Comment
Growth Mixture Modeling: The New Statistical Kid on the Block? STEIN ØRN, MD, PhD,1 AND MAGNE THORESEN, PhD2 Stavanger and Oslo, Norway
There has been remarkable development in diagnostic modalities and treatment options in patients with heart failure during the past decades. Major achievements have been accomplished by means of large randomized clinical trials asking binary questions that have been evaluated against hard end points. Clinicians are encouraged because everybody understands the questions asked and the answers given. However, real life is not binary, and answers are not always revealed by clinical outcomes and hard end points. Real life consists of shades of gray. Guidelines and findings from the large randomized clinical trials need to be adapted to fit the needs of individual patients. Moreover, as prognosis improves as a consequence of improved treatment strategies, it is increasingly challenging to design hard end point trials. Surrogates, often various biomarkers, are increasingly used as end points. The number of available biomarkers has increased substantially, revealing complex physiologic and pathophysiologic systems that interact in subtle ways.1 In line with an increasing need to tailor therapy, and an increasing number of potentially useful biomarkers, there is an increased need for more advanced statistical methods.2 These methods may permit a better identification of patient subgroups that respond to therapy, but to clinicians they may appear to be as incomprehensible as the biologic systems that they attempt to describe. The study ‘‘Identifying Biomarker Patterns and Predictors of Inflammation and Myocardial Stress’’ by Creber et al, published in the current issue of the Journal, uses
growth mixture modeling to identify unique biomarker patterns of N-terminal proeB-type natriuretic peptide (NT-proBNP) and high-sensitivity C-reactive protein (hsCRP).3 The profiles of the biomarkers were assessed for 12 months in a random sample of 320 participants from the biomarker substudy of HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training). HF-ACTION was a randomized clinical trial of exercise training versus usual care in patients with stable chronic heart failure. In the present study, 3 statistically independent biomarker patterns of NT-proBNP and hsCRP were identified. With the use of growth mixture modeling, the authors found that ‘‘exercise therapy was protective for reducing the frequency of membership in the elevated/ worsening biomarker pattern, indicating that exercise may be helpful in delaying the progression of heart failure.’’ The search for subgroups of patients who respond to a specific treatment is important. The use of growth mixture modeling for this purpose is interesting and makes a welcome addition to clinical literature.4,5 However, growth mixture modeling is a highly explorative technique, and one should be cautious when interpreting the results. Can the authors claim that the present study supports the view that exercise delays the progression of heart failure, and what on earth is ‘‘growth mixture modeling’’ anyway?! In a longitudinal study with repeated measurements, timedependent changes in biomarkers (or any other measurements) are of interest. When analyzing longitudinal data from individual patients, measurements from each patient vary around a mean trajectory. Some patients may have a constantly high biomarker level, some patients may have a steep increase and then level off, and some patients may have a more gradual increase or decrease. Such interindividual variation is most often modeled by the use of random effects in a mixed model. In a growth mixture model, the idea is that different patterns of individual trajectories (patterns of random effects) are governed by some underlying, latent class membership, ie, patients with different trajectories belong to different classes or groups, and the aim of a growth mixture model is to identify these groups. In the study by Creber et al, there are only 3 repeated measurements. This makes estimation of the random effects
From the 1Cardiology Department, Stavanger University Hospital, Stavanger, Norway and 2Oslo Center of Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway. Manuscript received April 30, 2015; revised manuscript received April 30, 2015; revised manuscript accepted May 1, 2015. Reprint requests: Stein Ørn, MD, PhD, Cardiology Department, Stavanger University Hospital, Nymansveien 159, Stavanger 4015, Norway. Tel: 004745219653; Fax: 004751519921. E-mail: drsteinorn@hotmail. com See page 447 for disclosure information. 1071-9164/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.cardfail.2015.05.003
446
Editorial Comment
rather uncertain. Furthermore, the method is built on an assumption about multivariate normality. The clustering stage is about searching for deviations from normality, as one assumes clustering to appear as a multimodal distribution. Deviation from normality can also occur for other reasons, eg, nonnormal variables. In the present study, the biomarkers that are being modeled are log transformed to achieve normality, but it is unclear to what extent this was successful. Also in this respect, it was probably a wise decision to merge 2 of the 3 clusters originally found. The authors have made an attempt to characterize the clusters by running a logistic regression toward a number of possible predictors. This is useful in a clinical context, if the clusters can be accurately characterized. The authors find that the treatment is a significant predictor, which is as expected. However, the goal of this analysis is to characterize subgroups of patients who respond to treatment. Based on this, it could be argued that it would be more meaningful to run the analysis on the treated (randomized to physical exercise) group only. In conclusion, the important contribution of the present study is to bring the methodology of growth mixture modeling to the attention of the heart failure community. However, to validate the usefulness of the established clusters, a true clinical outcome is needed.
Ørn and Thoresen
447
P.S. If you are a clinician and you are still confused, you are probably not alone.
Disclosures None.
References 1. Orn S, Aukrust P. The prediction of adverse cardiac remodelling following myocardial infarction: defining the need for a dynamic multimarker approach. Heart 2012;98:1112e3. 2. Manhenke C, Ørn S, von Haehling S, Wollert KC, Ueland T, Aukrust P, et al. Clustering of 37 circulating biomarkers by exploratory factor analysis in patients following complicated acute myocardial infarction. Int J Cardiol 2013;166:729e35. 3. Creber RM, Lee CS, Margulies K, Riegel B. Identifying biomarker patterns and predictors of inflammation and myocardial stress. J Card Fail 2015;21:439e45. 4. Muthen B, Shedden K. Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics 1999;55:463e9. 5. Ram N, Grimm KJ. Growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups. Int J Behav Dev 2009;33:565e76.
Editorial Comment
Growth Mixture Modeling: The New Statistical Kid on the Block? STEIN ØRN, MD, PhD,1 AND MAGNE THORESEN, PhD2 Stavanger and Oslo, Norway
There has been remarkable development in diagnostic modalities and treatment options in patients with heart failure during the past decades. Major achievements have been accomplished by means of large randomized clinical trials asking binary questions that have been evaluated against hard end points. Clinicians are encouraged because everybody understands the questions asked and the answers given. However, real life is not binary, and answers are not always revealed by clinical outcomes and hard end points. Real life consists of shades of gray. Guidelines and findings from the large randomized clinical trials need to be adapted to fit the needs of individual patients. Moreover, as prognosis improves as a consequence of improved treatment strategies, it is increasingly challenging to design hard end point trials. Surrogates, often various biomarkers, are increasingly used as end points. The number of available biomarkers has increased substantially, revealing complex physiologic and pathophysiologic systems that interact in subtle ways.1 In line with an increasing need to tailor therapy, and an increasing number of potentially useful biomarkers, there is an increased need for more advanced statistical methods.2 These methods may permit a better identification of patient subgroups that respond to therapy, but to clinicians they may appear to be as incomprehensible as the biologic systems that they attempt to describe. The study ‘‘Identifying Biomarker Patterns and Predictors of Inflammation and Myocardial Stress’’ by Creber et al, published in the current issue of the Journal, uses
growth mixture modeling to identify unique biomarker patterns of N-terminal proeB-type natriuretic peptide (NT-proBNP) and high-sensitivity C-reactive protein (hsCRP).3 The profiles of the biomarkers were assessed for 12 months in a random sample of 320 participants from the biomarker substudy of HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training). HF-ACTION was a randomized clinical trial of exercise training versus usual care in patients with stable chronic heart failure. In the present study, 3 statistically independent biomarker patterns of NT-proBNP and hsCRP were identified. With the use of growth mixture modeling, the authors found that ‘‘exercise therapy was protective for reducing the frequency of membership in the elevated/ worsening biomarker pattern, indicating that exercise may be helpful in delaying the progression of heart failure.’’ The search for subgroups of patients who respond to a specific treatment is important. The use of growth mixture modeling for this purpose is interesting and makes a welcome addition to clinical literature.4,5 However, growth mixture modeling is a highly explorative technique, and one should be cautious when interpreting the results. Can the authors claim that the present study supports the view that exercise delays the progression of heart failure, and what on earth is ‘‘growth mixture modeling’’ anyway?! In a longitudinal study with repeated measurements, timedependent changes in biomarkers (or any other measurements) are of interest. When analyzing longitudinal data from individual patients, measurements from each patient vary around a mean trajectory. Some patients may have a constantly high biomarker level, some patients may have a steep increase and then level off, and some patients may have a more gradual increase or decrease. Such interindividual variation is most often modeled by the use of random effects in a mixed model. In a growth mixture model, the idea is that different patterns of individual trajectories (patterns of random effects) are governed by some underlying, latent class membership, ie, patients with different trajectories belong to different classes or groups, and the aim of a growth mixture model is to identify these groups. In the study by Creber et al, there are only 3 repeated measurements. This makes estimation of the random effects
From the 1Cardiology Department, Stavanger University Hospital, Stavanger, Norway and 2Oslo Center of Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway. Manuscript received April 30, 2015; revised manuscript received April 30, 2015; revised manuscript accepted May 1, 2015. Reprint requests: Stein Ørn, MD, PhD, Cardiology Department, Stavanger University Hospital, Nymansveien 159, Stavanger 4015, Norway. Tel: 004745219653; Fax: 004751519921. E-mail: drsteinorn@hotmail. com See page 447 for disclosure information. 1071-9164/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.cardfail.2015.05.003
446
Editorial Comment
rather uncertain. Furthermore, the method is built on an assumption about multivariate normality. The clustering stage is about searching for deviations from normality, as one assumes clustering to appear as a multimodal distribution. Deviation from normality can also occur for other reasons, eg, nonnormal variables. In the present study, the biomarkers that are being modeled are log transformed to achieve normality, but it is unclear to what extent this was successful. Also in this respect, it was probably a wise decision to merge 2 of the 3 clusters originally found. The authors have made an attempt to characterize the clusters by running a logistic regression toward a number of possible predictors. This is useful in a clinical context, if the clusters can be accurately characterized. The authors find that the treatment is a significant predictor, which is as expected. However, the goal of this analysis is to characterize subgroups of patients who respond to treatment. Based on this, it could be argued that it would be more meaningful to run the analysis on the treated (randomized to physical exercise) group only. In conclusion, the important contribution of the present study is to bring the methodology of growth mixture modeling to the attention of the heart failure community. However, to validate the usefulness of the established clusters, a true clinical outcome is needed.
Ørn and Thoresen
447
P.S. If you are a clinician and you are still confused, you are probably not alone.
Disclosures None.
References 1. Orn S, Aukrust P. The prediction of adverse cardiac remodelling following myocardial infarction: defining the need for a dynamic multimarker approach. Heart 2012;98:1112e3. 2. Manhenke C, Ørn S, von Haehling S, Wollert KC, Ueland T, Aukrust P, et al. Clustering of 37 circulating biomarkers by exploratory factor analysis in patients following complicated acute myocardial infarction. Int J Cardiol 2013;166:729e35. 3. Creber RM, Lee CS, Margulies K, Riegel B. Identifying biomarker patterns and predictors of inflammation and myocardial stress. J Card Fail 2015;21:439e45. 4. Muthen B, Shedden K. Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics 1999;55:463e9. 5. Ram N, Grimm KJ. Growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups. Int J Behav Dev 2009;33:565e76.