Abstract Background & Objectives: The use of complex sampling designs in data collection of some studies such as case-control studies is becoming common, yet this method has often been ignored in analysis. Ordinary logistic regression is used to estimate the coefficients of model, instead. The purpose of the present study is to evaluate the fitting of weighted logistic regression compared to that of ordinary (OLR) and post stratified (PSLR) logistic regression. Materials and Methods: The data used in this study were collected using complex sampling method among urban women in four provinces of Iran. Age, educational level, BMI, marital age, number of pregnancies and abortions, total and vaginal number of deliveries were considered in this research. Dependent variable was pelvic organ prolapse. Ordinary, post-stratified and weighted logistic regression was fitted to the data. To compare models’ goodness of fit, ROC curve analysis was used. Results: In post-stratified and weighted methods the standard errors of estimates were almost equal and both larger than that of ordinary method. In ordinary method, number of vaginal deliveries, age, employment and BMI showed statistically significant association with prolapsed pelvic. Area under ROC curve in ordinary, post-stratified and weighted methods obtained as 0.75, 0.72 and 0.73, respectively. Conclusion: Results showed that although weighting reduces the bias of estimates by adjusting sampling errors, it does not necessarily shrink their variance.