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Background: Prognostic markers use an individual’s characteristics at a given time to predict future disease events, with the ultimate goal of guiding medical decision-making. If an accurate prediction can be made, then a prognostic marker could be used clinically to identify those subjects at greatest risk for future adverse events and may be used to define populations appropriate for targeted therapeutic intervention. Often, a marker is measured at a single baseline time point such as disease diagnosis, and then used to guide decisions at multiple subsequent time points. However, the performance of candidate markers may vary over time as an individual’s underlying clinical status changes. Methods: We provide an overview and comparison of modern statistical methods for evaluating the time-varying accuracy of a baseline prognostic marker. We compare approaches that consider cumulative versus incident events. Additionally, we compare the common approach of using hazard ratios obtained from Cox proportional hazards regression to more recently developed approaches using time-dependent receiver operating characteristic (ROC) curves. The alternative statistical summaries are illustrated using a multiple myeloma study of candidate biomarkers. Results: We found that time-varying HRs, HR(t), using local linear estimation revealed time trends more clearly by directly estimating the association at each time point t, compared to landmark analyses, which averaged across time ≥ t. Comparing area under the ROC curve (AUC) summaries, there was close agreement between AUCC/D(t, t + 1) which defines cases cumulatively over 1-year intervals and AUCI/D(t) which defines cases as incident events. HR(t) was more consistent with AUCI/D(t), as estimation of these measures is localized at each time point. Conclusions: We compared alternative summaries for quantifying a prognostic marker’s time-varying performance. Although landmark-based predictions may be useful when patient predictions are needed at select times, a focus on incident events naturally facilitates evaluating trends in performance over time.

@article{bansal_comparison_2019-1, title = {A comparison of landmark methods and time-dependent {ROC} methods to evaluate the time-varying performance of prognostic markers for survival outcomes}, volume = {3}, issn = {2397-7523}, url = {https://diagnprognres.biomedcentral.com/articles/10.1186/s41512-019-0057-6}, doi = {10.1186/s41512-019-0057-6}, abstract = {Background: Prognostic markers use an individual’s characteristics at a given time to predict future disease events, with the ultimate goal of guiding medical decision-making. If an accurate prediction can be made, then a prognostic marker could be used clinically to identify those subjects at greatest risk for future adverse events and may be used to define populations appropriate for targeted therapeutic intervention. Often, a marker is measured at a single baseline time point such as disease diagnosis, and then used to guide decisions at multiple subsequent time points. However, the performance of candidate markers may vary over time as an individual’s underlying clinical status changes. Methods: We provide an overview and comparison of modern statistical methods for evaluating the time-varying accuracy of a baseline prognostic marker. We compare approaches that consider cumulative versus incident events. Additionally, we compare the common approach of using hazard ratios obtained from Cox proportional hazards regression to more recently developed approaches using time-dependent receiver operating characteristic (ROC) curves. The alternative statistical summaries are illustrated using a multiple myeloma study of candidate biomarkers. Results: We found that time-varying HRs, HR(t), using local linear estimation revealed time trends more clearly by directly estimating the association at each time point t, compared to landmark analyses, which averaged across time ≥ t. Comparing area under the ROC curve (AUC) summaries, there was close agreement between AUCC/D(t, t + 1) which defines cases cumulatively over 1-year intervals and AUCI/D(t) which defines cases as incident events. HR(t) was more consistent with AUCI/D(t), as estimation of these measures is localized at each time point. Conclusions: We compared alternative summaries for quantifying a prognostic marker’s time-varying performance. Although landmark-based predictions may be useful when patient predictions are needed at select times, a focus on incident events naturally facilitates evaluating trends in performance over time.}, language = {en}, number = {1}, urldate = {2021-04-28}, journal = {Diagnostic and Prognostic Research}, author = {Bansal, Aasthaa and Heagerty, Patrick J.}, month = dec, year = {2019}, pages = {14}, file = {Bansal and Heagerty - 2019 - A comparison of landmark methods and time-dependen.pdf:/Users/neil.hawkins/Zotero/storage/CPLIKZPR/Bansal and Heagerty - 2019 - A comparison of landmark methods and time-dependen.pdf:application/pdf}, }

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