000			01403nam a2200229 i 4500
001			9789811262302
005			20250414083508.0
008			240416t2023 xxu e 001 0 eng
020			_a9789811262302
037			_bWorld Scientific Publishing _cUSD 148.00
037			_fBB
041			_aeng
072		7	_aMED090000 _2bisacsh
100	1		_4A01 _aButler, Richard J
245	1	0	_aAdvanced Statistics For Health Research
260	2		_bWorld Scientific _c2023
520			_bAdvanced Statistics for Health Research provides a rigorous geometric understanding of models used in the analysis of health data, including linear and non-linear regression models, and supervised machine learning models. Models drawn from the health literature include: ordinary least squares, two-stage least squares, probits, logits, Cox regressions, duration modeling, quantile regression and random forest regression. Causal inference techniques from the health literature are presented including randomization, matching and propensity score matching, differences-in-differences, instrumental variables, regression discontinuity, and fixed effects analysis. Codes for the respective statistical techniques presented are given for STATA, SAS and R.
521			_aProfessional and scholarly
540			_aFor sale with non-exclusive rights _bWORLD
700	1		_4A01 _aButler, Matthew J
700	1		_4A01 _aWilson, Barbara L
999			_c10021 _d10021