Detecting change points and monitoring biomedical data

Time-dependent effect of non-Hodgkin's lymphoma grade on disease-free survival of relapsed/refractory patients treated with high-dose chemotherapy plus autotransplantation

Evaluation of time to event outcomes usually is examined by the Kaplan-Meier method and Cox proportional hazards models. We developed a modified statistical model based on histologic grade and other variables to describe the time-dependent outcome for autologous stem cell transplant (autotransplant) performed for non-Hodgkin's lymphoma (NHL) based on histologic grade and other variables. One hundred and fourteen relapsed or refractory NHL patients were treated using BCNU 600 mg/m2, etoposide 2400 mg/m2, and cisplatin 200 mg/m2 IV followed by autotransplant. Median age was 53.5 (range: 25-70) years, 78 patients had aggressive NHL and 36 indolent NHL. Seventy-five patients received involved-field radiotherapy just prior to transplant. At a median follow-up of 33 (range: 3 to 118) months, the estimated 5-year Kaplan-Meier probabilities of overall survival and disease-free survival were 61% and 51%, respectively. Cox proportional hazards model analysis showed that proportionality did not hold for lymphoma grade, indicating that the relationship between the grade and disease-free survival differed over time. By piece-wise Cox model, the relative risk for experiencing relapse or death after 1 year in patients with indolent compared with patients with aggressive NHL was 2.81 (p=0.019) with 95% confidence interval (1.19, 6.65). The time-dependent effect of lymphoma grade on disease-free survival suggests the need for early (within first year) incorporation of novel therapeutic approaches in management of patients with indolent NHL undergoing autotransplant.

Permutation tests for joinpoint regression with applications to cancer rates

The identification of changes in the recent trend is an important issue in the analysis of cancer mortality and incidence data. We apply a joinpoint regression model to describe such continuous changes and use the grid-search method to fit the regression function with unknown joinpoints assuming constant variance and uncorrelated errors. We find the number of significant joinpoints by performing several permutation tests, each of which has a correct significance level asymptotically. Each p-value is found using Monte Carlo methods, and the overall asymptotic significance level is maintained through a Bonferroni correction. These tests are extended to the situation with non-constant variance to handle rates with Poisson variation and possibly autocorrelated errors. The performance of these tests are studied via simulations and the tests are applied to U.S. prostate cancer incidence and mortality rates.