Subgroup identification based on the Logistic model / 南方医科大学学报
Journal of Southern Medical University
; (12): 1503-1508, 2018.
Article
de Zh
| WPRIM
| ID: wpr-772134
Bibliothèque responsable:
WPRO
ABSTRACT
We propose a subgroup identification method based on the Logistic model for data from a two-arm clinical trial with dichotomous outcome variables.In this method, binary Logistic regression models are established for each group to calculate the outcome probabilities of each patient for comparison.According to the established rules, the patients are classified into their corresponding subgroups to establish a multinomial Logistic regression model.We simulated the false rate, correct judgment rate, coincidence rate and model correct judgment rate for different sample sizes and carried out an example analysis.The results of simulation showed that for different sample sizes, the false rates of this method were below 0.07 and the correct judgment rates were all above 0.75 with adequate coincidence rates and model correct judgment rates, demonstrating the effectiveness and reliability of the proposed method for subgroup identification.
Mots clés
Texte intégral:
1
Base de données:
WPRIM
Sujet principal:
Simulation numérique
/
Modèles logistiques
/
Reproductibilité des résultats
/
Essais cliniques comme sujet
/
Taille de l'échantillon
Type d'étude:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limites:
Humans
Langue:
Zh
Journal:
Journal of Southern Medical University
Année:
2018
Type de document:
Article