OBJECTIVES: Measuring individual patient’s true preferences(trade-offs) across treatment outcomes and assessing them precisely in a consistent manner is essential for patient centered care (PCA). However, the recognition of individual heterogeneity by current DCE studies usually confounds estimates of the aggregated (population) mean and variance of random uncertainty, ignoring the patient’s specific scale of random components based on their clinical condition and other personal characteristics. The purpose of this study is twofold: 1. Recognize individual level estimation techniques based on systematic literature review, tracing back to basic linear probability estimation since the inception of discrete choice model. 2. Provide empirical evidence by simulation, which tests the applicability of estimation techniques for PCA in terms of decision rules(assumptions), convergence status, statistical efficiency and accuracy. METHODS: Systematic review of individual level estimation techniques of DCE was conducted to determine the use of linear probability estimation and nested logit estimation. Simulation was constructed for 1000 pseudo patients in the routine care setting under two most common scenarios of individual decision rules (lexicographic and compensatory). Based on previous qualitative results from clinical setting, each pseudo patient answered 6-12 question in a well-balanced orthogonal DCE design, including five treatment outcome attributes and each attribute has two levels. RESULTS: Considerable heterogeneity remains, as evidenced by the range of the estimates and standard deviation. The linear probability estimation yielded the similar pattern of coefficients indicating trade-offs as nested logit estimation did, while the logit exhibited poorer convergence and statistical power. CONCLUSIONS: Since we are not aiming at predicting choice probability but understanding patient’s trade-offs of treatment outcomes, linear estimation can be served better at individual level. The preliminary results are the subject of ongoing research, further research are needed because the applicability of estimation techniques in PCA is conditioned on survey design and clinical environments as well.