Estimating the Fisher’s Scoring Matrix Formula from Logistic Model

This paper proposes a matrix approach to estimating parameters of logistic regression with a view to estimating the effects of risk factors of gestational diabetic mellitus (GDM). The proposed method of maximum likelihood estimation (MLE) unlike other methods of estimating parameters of non-linear regression is simpler and convergence of parameters is quicker. The odds ratio obtained from the logistic regression were used to interpret the effects of these risk factors on GDM where obesity and F.H as risk factors, were positively associated with GDM. The proposed method was seen to compare favorably with other known methods.

[1]  D. Sloane,et al.  An Introduction to Categorical Data Analysis , 1996 .

[2]  J A Swets,et al.  Better decisions through science. , 2000, Scientific American.

[3]  M. Pepe.  The Statistical Evaluation of Medical Tests for Classification and Prediction , 2003 .

[4]  Chris Lloyd,et al.  Using Smoothed Receiver Operating Characteristic Curves to Summarize and Compare Diagnostic Systems , 1998 .

[5]  R. Moses.  Screening for gestational diabetes mellitus , 1992, The Medical journal of Australia.

[6]  H. Ishimoto,et al.  Cutoff value of 1 h, 50 g glucose challenge test for screening of gestational diabetes mellitus in a Japanese population. , 2003, Diabetes research and clinical practice.

[7]  K. Zou,et al.  Two transformation models for estimating an ROC curve derived from continuous data , 2000 .

[8]  Z. Hasan,et al.  Obesity related complications in 100 obese subjects and their age matched controls. , 2006, JPMA. The Journal of the Pakistan Medical Association.

[9]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[10]  D. Sherer,et al.  Glucose challenge test threshold values in screening for gestational diabetes among black women. , 2006, American journal of obstetrics and gynecology.

[11]  T. Cai,et al.  Semi-parametric estimation of the binormal ROC curve for a continuous diagnostic test. , 2004, Biostatistics.

[12]  David R. Cox.  The analysis of binary data , 1970 .

[13]  Margaret Sullivan Pepe,et al.  Distribution-free ROC analysis using binary regression techniques. , 2002, Biostatistics.

[14]  A. Agresti.  An introduction to categorical data analysis , 1997 .