![]() ![]() Log-rank test has been widely used to compare two survival curves between two groups. To have a better understanding of KM curve directly, a transformation of KM curve into difference in survival probability and risk difference can been introduced as absolute measures, which have been highly recommended for clinical decision in comparative studies because of their intuition and practical value. The diversity of KM curve makes it difficult to understand TTE data directly and comprehensively, especially when two survival curves intersect. Our brains have been trained to chronically focus on the rate of decline between groups however, many useful information may be ignored. For example, two curves separate widely from the start to the end of follow-up, or they can track closely at the early stage and separate at the end, or they can crossover at the early stage or at late stage. KM curve with two groups can be presented with various forms. Time-specific survival probability can be estimated from KM curves and median survival time when survival probability drops to 50% or below. Previous studies have presented the application of Kaplan-Meier (KM) curve to analyze TTE data from oxidative medicine. KM curve becomes an essential part in generating evidence-based information on TTE data and has been used for more than 70 years. In these trials, time-to-event (TTE) data was collected, including survival time (until the occurrence of an event of interest, for example, death and progression of multiple myeloma) and status at last observation. Many therapies, including bortezomib and melphalan, have been studied in many trials for untreated multiple myeloma. Antioxidant defense endowed multiple myeloma cells with resistance to high-dose melphalan. Unbalanced production of ROS leads to oxidative stress, and the oxidative stress signaling could contribute to acquired melphalan resistance. These plasma cells overproduce intracellular reactive oxygen species (ROS), resulting in unbalanced redox homeostasis. ![]() Multiple myeloma is a neoplastic disease of plasma cell characterized by the accumulation of clonal cells in the bone marrow. They will provide clear insight in treatment effect and assist clinicians to make decision comprehensively. The 5 complementary plots with KM curve give a broad and straightforward picture of potential time-varying effect. When proportional hazard assumption was not met, estimated hazard ratio from traditional Cox regression was not appropriate, and time-varying hazard ratios could be presented instead of an average and single value. Changed values from landmark analyses were used to inspect conditional treatment effect the turning points could be identified for further landmark analysis. Absolute effects were presented in the 3 plots of difference in survival probability, risk, and restricted mean survival time. Entanglement and intersection of two KM curves would make the 5 complementary plots to fluctuate over time intuitively. We reconstructed individual patient data, and plotted 5 complementary plots (difference in survival probability and risk difference, difference in restricted mean survival time, landmark analyses, and hazard ratio over time), along with KM curve. Three KM curves were identified from published randomized control trials: (a) curves diverged immediately (b) intersected curves with statistical significance and (c) intersected curves without statistical significance. Complementary plots might promote clear insights in time-varying effect from KM curve. However, time-varying effect might be presented in KM curve and cannot be intuitively observed. Kaplan-Meier (KM) curve has been widely used in the field of oxidative medicine and cellular longevity.
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