Survey-based demand studies for pipeline products rarely provide good estimates of timing and order-of-entry effects as they arise mostly as a consequence of real-world prescribing experience. Forecasters often rely on secondary data analogs to model the effects of timing and order-of- entry but these estimates often fail to capture the unique characteristics and market situations for pipeline products. This presentation shares new tools for modeling timing and order-of-entry that combine models based on secondary data with new metrics from survey-based demand research. Accuracy of the methodology is demonstrated with several case studies.