comparison of generalized estimating equations and quadratic inference functions in superior versus inferior ahmed glaucoma valve implantation

background: the celebrated generalized estimating equations (gee) approach is often used in longitudinal data analysis. while this method behaves robustly against misspecification of the working correlation structure, it has some limitations on efficacy of estimators, goodness-of-fit tests and model selection criteria. the quadratic inference functions (qif) is a new statistical methodology that overcomes these limitations. methods: we administered the use of qif and gee in comparing the superior and inferior ahmed glaucoma valve (agv) implantation, while our focus was on the efficiency of estimation and using model selection criteria; we compared the effect of implant location on intraocular pressure (iop) in refractory glaucoma patients. we modeled the relationship between iop and implant location, patient's sex and age, best corrected visual acuity, history of cataract surgery, preoperative iop and months after surgery with assuming unstructured working correlation. results: 63 eyes of 63 patients were included in this study; 28 eyes in inferior group and 35 eyes in superior group. the gee analysis revealed that preoperative iop has a significant effect on iop (p = 0.011). however, qif showed that preoperative iop, months after surgery and squared months are significantly associated with iop after surgery (p < 0.05). overall, estimates from qif are more efficient than gee (re = 1.272). conclusions: in the case of unstructured working correlation, the qif is more efficient than gee. there were no considerable difference between these locations; our results confirmed previously published works which mentioned it is better that glaucoma patients undergo superior agv implantation. keywords: longitudinal data, generalized estimating equation, quadratic inference function, ahmed glaucoma valve implantation. abstract background: the celebrated generalized estimating equations (gee) approach is often used in longitudinal data analysis. while this method behaves robustly against misspecification of the working correlation structure, it has some limitations on efficacy of estimators, goodness-of-fit tests and model selection criteria. the quadratic inference functions (qif) is a new statistical methodology that overcomes these limitations. methods: we administered the use of qif and gee in comparing the superior and inferior ahmed glaucoma valve (agv) implantation, while our focus was on the efficiency of estimation and using model selection criteria; we compared the effect of implant location on intraocular pressure (iop) in refractory glaucoma patients. we modeled the relationship between iop and implant location, patient's sex and age, best corrected visual acuity, history of cataract surgery, preoperative iop and months after surgery with assuming unstructured working correlation. results: 63 eyes of 63 patients were included in this study; 28 eyes in inferior group and 35 eyes in superior group. the gee analysis revealed that preoperative iop has a significant effect on iop (p = 0.011). however, qif showed that preoperative iop, months after surgery and squared months are significantly associated with iop after surgery (p < 0.05). overall, estimates from qif are more efficient than gee (re = 1.272). conclusions: in the case of unstructured working correlation, the qif is more efficient than gee. there were no considerable difference between these locations; our results confirmed previously published works which mentioned it is better that glaucoma patients undergo superior agv implantation. keywords: longitudinal data, generalized estimating equation, quadratic inference function, ahmed glaucoma valve implantation.