Unequal intra-group variance in trajectory classification.
Stat Med
; 37(28): 4155-4166, 2018 12 10.
Article
em En
| MEDLINE
| ID: mdl-30073693
ABSTRACT
Classifying patients into groups according to longitudinal series of measurements (ie, trajectory classification) has become frequent in clinical research. Most classification models suppose an equal intra-group variance across groups. This assumption is sometimes inappropriate because measurements in diseased subjects are often more heterogeneous than in healthy ones. We developed a new classification model for trajectories that uses unequal intra-group variance across groups and evaluated its impact on classification using simulations and a clinical study. The classification and typical trajectories were estimated using the classification Expectation Maximization (EM) algorithm to maximize the classification likelihood, the log-likelihood being profiled during the Maximization (M) step of the algorithm. The simulations showed that assuming equal intra-group variance resulted in a high misclassification rate (up to 50%) when the real intra-group variances were different. This rate was greatly reduced by allowing intra-group variances to be different. Similar classification was obtained when the real intra-group variances were equal, except when the total sample size and the number of repeated measurements were small. In a randomized trial that compared the effect of low vs standard cyclosporine A dose on creatinine levels after cardiac transplantation, the classification model with unequal intra-group variance led to more meaningful groups than with equal intra-group variance and showed distinct benefits of low dose. In conclusion, we recommend the use of a classification model for trajectories that allows for unequal intra-group variance across groups except when the number of repeated measurements and total sample size are small.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Interpretação Estatística de Dados
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Resultado do Tratamento
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Variação Biológica da População
Tipo de estudo:
Clinical_trials
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article