Mathematical Sciences
2011-04-01
false
2019-04-10T16:54
The aim of the current work was to evaluate graphical diagnostics for assessment of the fit of logistic regression models. Assessment of goodness of fit of a model to the data set is essential to ensure the model provides an acceptable description of the binary variables seen. For logistic regression the most common diagnostic used for this purpose is binning the data and comparing the empirical probability of the occurrence of a dependent variable with the model predicted probability against the mean covariate value in the bin. Although intuitively appealing this method, which we term simple binning, may not have consistent properties for diagnosing model problems. In this report we describe and evaluate two different diagnostic procedures, random binning and simplified Bayes marginal model plots. These procedures were assessed via simulation under three different designs. Design 1: studies which were balanced on binary variables and a continuous covariate. Design 2: studies that were balanced on binary variables but unbalanced on the continuous covariate. Design 3: studies that were unbalanced on both the binary variables and the covariate. Each simulated study consisted of 500 individuals. Thirty studies were simulated. The covariate of interest was dose which could range from 0 to 20 units. The data were simulated with the dose being related to the outcome according to an E (max) model on the logit scale. A logit E (max) model (correct model) and a logit linear model (wrong model) were fitted to all data sets. The performance of the above diagnostics, in addition to simple binning, was compared. For all designs the proposed diagnostics performed at least as well and in many instances better than simple binning. In case of design 1 random binning and simple binning are identical. In the case of designs 2 and 3 random binning and simplified Bayes marginal model plots were superior in assessing the model fit when compared to simple binning. For the examples tested, both random binning and simplified Bayesian marginal model plots performed acceptably.
https://scigraph.springernature.com/explorer/license/
Evaluation of graphical diagnostics for assessing goodness of fit of logistic regression models
205-222
http://link.springer.com/10.1007%2Fs10928-010-9189-6
research_article
2011-04
en
articles
Statistics
b5588f40091198ff0104db2b87bfc306f079b8cdc9d278ff650be4d0ef57e0e1
readcube_id
Duffull
Stephen B.
21153868
pubmed_id
Probability
Logistic Models
Data Interpretation, Statistical
Models, Statistical
doi
10.1007/s10928-010-9189-6
2168-5789
1567-567X
Journal of Pharmacokinetics and Pharmacodynamics
Venkata V.
Pavan Kumar
School of Pharmacy, University of Otago, Dunedin, New Zealand
University of Otago
Springer Nature - SN SciGraph project
101096520
nlm_unique_id
2
Data Display
Computer Simulation
dimensions_id
pub.1038068526
38