Abstract
BackgroundStandard survival analysis fails to give insight into what happens to a patient after a first outcome event (like first relapse of a disease). Multi-state models are a useful tool for analyzing survival data when different treatments and results (intermediate events) can occur. Aim of this study was to implement a multi-state model on data of patients with rectal cancer to illustrate the advantages of multi-state analysis in comparison to standard survival analysis.MethodsWe re-analyzed data from the RCT FOGT-2 study by using a multi-state model. Based on the results we defined a high and low risk reference patient. Using dynamic prediction, we estimated how the survival probability changes as more information about the clinical history of the patient becomes available.ResultsA patient with stage UICC IIIc (vs UICC II) has a higher risk to develop distant metastasis (DM) or both DM and local recurrence (LR) if he/she discontinues chemotherapy within 6 months or between 6 and 12 months, as well as after the completion of 12 months CTx with HR 3.55 (p = 0.026), 5.33 (p = 0.001) and 3.37 (p < 0.001), respectively. He/she also has a higher risk to die after the development of DM (HR 1.72, p = 0.023). Anterior resection vs. abdominoperineal amputation means 63% risk reduction to develop DM or both DM and LR (HR 0.37, p = 0.003) after discontinuation of chemotherapy between 6 and 12 months. After development of LR, a woman has a 4.62 times higher risk to die (p = 0.006). A high risk reference patient has an estimated 43% 5-year survival probability at start of CTx, whereas for a low risk patient this is 79%. After the development of DM 1 year later, the high risk patient has an estimated 5-year survival probability of 11% and the low risk patient one of 21%.ConclusionsMulti-state models help to gain additional insight into the complex events after start of treatment. Dynamic prediction shows how survival probabilities change by progression of the clinical history.
Highlights
Standard survival analysis fails to give insight into what happens to a patient after a first outcome event
The medical history of a cancer patient is very complex and often includes different intermediate events, as for example the development of distant metastasis (DM) or local recurrence (LR) after surgery of the primary tumor, which plays a central role in influencing the survival prognosis
Multi-state models are an extension of classical survival analysis which allows adjustment to the prediction of survival duration of the patient in the course of time by incorporating new information regarding the progression of the medical history and to better understand how prognostic factors influence the different phases of the disease/recovery process [2]
Summary
Standard survival analysis fails to give insight into what happens to a patient after a first outcome event (like first relapse of a disease). The starting state could represent the time of discovery of a disease, the final state usually is death, intermediate states reflect all relevant treatments or disease stages or generally clinical events between starting state and final state. These models are very helpful in clinical decision making because they allow the prognosis of the patient to be updated according to the progression of the disease
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