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Supplemental material, sj-docx-1-mdm-10.1177_0272989X231184247 for Survival Extrapolation Incorporating General Population Mortality Using Excess Hazard and Cure Models: A Tutorial by Michael J. Sweeting, Mark J. Rutherford, Dan Jackson, Sangyu Lee, Nicholas R. Latimer, Robert Hettle and Paul C. Lambert in Medical Decision Making

Additional file 1. Appendix.

Supplemental material, sj-docx-1-mdm-10.1177_0272989X231184247 for Survival Extrapolation Incorporating General Population Mortality Using Excess Hazard and Cure Models: A Tutorial by Michael J. Sweeting, Mark J. Rutherford, Dan Jackson, Sangyu Lee, Nicholas R. Latimer, Robert Hettle and Paul C. Lambert in Medical Decision Making

Additional file 2 Estimates of 5-year RS and LLE for setting 2 and scenario J for the Gotland region.

Additional file 2.

Additional file 3: Supplementary Figure 2.

Additional file 1. Supplementary Material 1: Original vs Simulated Covariate Distributions

Additional file 1 Estimates of 5-year RS and LLE for setting 2 and scenarios F-H for the Gotland region.

Additional file 3 Graphical comparisons of the bootstrap-based and conventional estimates of 5-year RS, LLE and their CIs for all scenarios.

Additional file 2: Supplementary Figure 1.

Additional file 1 Poisson regression and the Pohar Perme estimator.

Additional file 1 Stata code for predictions. A Word document (.docx) with Stata code to analyse the HAI data. It formats the los.data dataset (once imported from the R package etm), fits the parametric models and obtains the predictions from the different approaches (Aalen-Johansen estimates, “Exp” model, “AIC” model and “RP(4)” model). The predictions include all those calculated in this paper: transition probabilities, AM and PAF and expected length of stay.

Additional file 6 List of HTML tag elements used in MSMplus app. This file contains a table with all the HTML tag elements that are used in MSMplus app (PDF file 463KB).

Additional file 1 Stata code for deriving the multi-state structure summary information file and the analysis results input file for mSMplus. Stata code for fitting the multistate models on the EBMT dataset and for deriving the Summary information and the Analysis results input files for the web tool MSMplus using the Stata commands msboxes and predictms. (PDF file 404KB)

Additional file 3 Manually creating a analysis results file to provide to mSMplus. Guidance in creating an Analysis results CSV file to provide as input to MSMplus. (PDF file 522KB).

Additional file 3 Additional figures. A PDF including supplementary figures from the HAI data analysis.

Additional file 3 Manually creating a analysis results file to provide to mSMplus. Guidance in creating an Analysis results CSV file to provide as input to MSMplus. (PDF file 522KB).

Additional file 2 Further details of the analysis approaches. A PDF giving details about how the Aalen-Johansen estimates were calculated and showing the functional forms of the transition rates for the three parametric approaches.

Additional file 6 List of HTML tag elements used in MSMplus app. This file contains a table with all the HTML tag elements that are used in MSMplus app (PDF file 463KB).

Additional file 2 Stata implementation of the models.

Additional file 2 R code for deriving the multi-state structure summary information file and the analysis results input file for mSMplus. Installing of the github R package MSMplus. Examples of creating json files in R as input for MSMplus via the functions msboxes_R for the summary information file and functions flexjson, mstatejson and msmjson. (PDF file 429KB)

As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population-level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multi-state models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, by allowing is to directly quantify the excess risk. This paper proposes a combination of these two frameworks, providing one approach to address (1)-(3). Using recently developed methods in multi-state modeling, we incorporate estimation of excess hazards into a multi-state model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user-friendly Stata software with accompanying example code.