Question

use your own words, don't copy and paste, don't use handwriting, please. i need references please

Discuss the applications of Binary Logistic Regression in Clinical Research using the case study (Application of Binary Logistic Regression in Clinical Research) in a brief manner with a maximum length of two pages.

Answer 1

A COMMONLY USED STATISTICAL TOOL THAT IS BINARY LOGISTIC REGRESSION USED TO ANALYSE DATA IN HEALTH CARE, IT ISTHE TECHNIQUE USED TO MEASURE CONNECTION BETWEEN VARIABLES AND THE PREDICTION OF FUTURE VALUES.

IT THE COMMONLY USED TOOL IN HEALTH CARE AND ALSO IT CONTAINS TWO VARIABLES THAT IS OUTCOME AND THE PREDICTOR, IT IS USED TO MEASURE THE CHANGES IN OUTCOME WITH THE CHANGE IN PREDICTOR.

WHEN CONTINUOUS OUTCOME VARIABLE IS SEEN THEN SIMPLE LINEAR REGRESSION IS USED, FOR EXAMPLE WHEN WE WANT TO PREDICT BODY MASS INDEX WITH THE HELP OF INCREASED CHOLESTROL THEN HERE SIMPLE LINEAR REGRESSION IS USED (CONTINUOUS) BUT IN MOST OF THE CASES OUTCOME VARIABLE THAT IS BASED ON CATEGORY, FOR EXAMPLE, IF YOU WANT TO CHECK THE EFFECT OF SMOKING ON CARDIOVASCULAR DISEASES THEN THE OUTCOME WILL HAVE TWO CATEGORY THAT IS YES OR NO, IN THIS TYPE OF CASES IT IS NOT VALID TO USED SIMPLE LINEAR EQUATION AND HERE BINARY LOGISTIC REGRESSION IS USED.

DEPENDS UPON THE NATURE OF OUTCOME LOGISTIC REGRESSION CAN BE OF MANY TYPES SUCH AS, BINOMIAL, MULTINOMIAL AND ORDINAL.

WHEN THE OUTCOME OF LOGISTIC REGRESSION IS TWO ( SUCH AS DISEASE IS PRESENT OR DISEASE IS ABSENT, YES OR NO, ETC.) THAN BINARY LOGISTIC REGRESSION IS APPLIED, IF THERE IS MORE THAN 2 OR THREE OUTCOME IS PRESENT SUCH AS ( X, Y, Z, MILD, PATIENT A, PATIENT BAND PATIENT C) ETC, AND ALSO WHEN THESE OUTCOMES ARE NOT PRESENT IN ORDER THAN MULTIPLE LOGISTIC REGRESSION IS APPLIED.

WHEN THE VARIABLE OUTCOME ARE PRESENT IN ORDER SUCH AS GOOD, BETTER, BEST, EXCELLENT ) IN WHICH ORDINAL LOGISTIC REGRESSION IS APPLIED.

TURIN ET AL has CONDUCTED THE RESEARCH STUDIES REGARDING PRESENCE OF STROKE AND THE INVOLVEMENT OF SEASON AND THE OUTCOME OF LOGISTIC REGRESSION WAS 4 AND THAT WAS IN SUMMER, IN WINTER, IN SPRING AND IN THE AUTUMN SEASON.

ESTIMATION OF BINARY LOGISTIC REGRESSION IS BASED ON THE MAXIMUM LIKELIHOOD ESTIMATION, MLE IS STARTS WITH A GUESS AND PUTS WEIGHT ON EACH OF THE PREDICTOR VARIABLE AND THE CONTINUOUS ADJUSTMENT TO THE COEFFICIENT IS DONE UNTIL THERE IS NO NEED OF FURTHER IMPROVEMENT IN ORDER TO PREDICT THE VALUE OF THE OUTCOME VARIABLE.