Inverse probability of censoring weighting (IPCW) is widely used to address dependent censoring in survival analysis. Beyond estimation of the censoring distribution, its implementation requires specifying how inverse probability weights are incorporated into survival estimators. IPCW does not uniquely determine this construction, and different choices can produce different finite-sample behavior even under the same censoring model. In this work, we examine a class of IPCW estimators for the survival function, distinguishing hazard-based from margin-based formulations and time-varying from fixed weighting schemes. We show how these choices affect structural validity, stability, and finite-sample performance. In particular, the weight-changing hazard (WCH) estimator applies time-varying censoring weights within a product-limit construction with risk-set normalization, ensuring monotonicity and boundedness when the weighted risk-set denominators are positive. Through simulation studies under varying levels of covariate-dependent and administrative censoring, we demonstrate that WCH achieves favorable bias--variance tradeoffs and improved finite-sample stability relative to the alternative IPCW constructions examined, particularly in settings with few observed events or heavy censoring. We further illustrate these differences using data from the Women's Interagency HIV Study (WIHS), in which alternative IPCW constructions produced meaningfully different survival curves. Overall, our findings emphasize that the effectiveness of IPCW depends on both the weighting scheme and the estimator formulation. Careful consideration of these choices is therefore important for reliable survival estimation under dependent censoring.

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