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Purpose: Insulin-like growth factor-I (IGF-I) reflects hepatic synthetic function and plays an important role in the development and progression of various cancers. In this study, we investigated whether pretreatment serum IGF-I levels predict time-to-recurrence (TTR) and overall survival (OS) in patients with early-stage hepatocellular carcinoma after curative treatment. Experimental Design: Consecutive patients with hepatocellular carcinoma who had undergone surgical resection, radiofrequency ablation, or percutaneous ethanol injection as curative treatments of early hepatocellular carcinoma were included from two prospective cohorts and the training set (n = 101) and the validation set (n = 91) were established. Serum samples were collected before treatment and the levels of IGF-I and IGF-binding protein-3 (IGFBP-3) were analyzed with regard to their associations with recurrence and survival. Results: In the training set, patients with low IGF-I levels showed significantly shorter TTR [median, 14.6 months; 95% confidence interval (CI), 1.8–27.5] than patients with high IGF-I levels (median, 50.8 months; 95% CI, 36.9–64.7; P < 0.001) during a median follow-up period of 52.4 months. In the multivariate analysis, low levels of IGF-I were an independent predictor of recurrence (HR, 2.49; 95% CI, 1.52–4.08; P < 0.001). Furthermore, together with high-serum α-fetoprotein and multiple tumors, low levels of IGF-I remained an independent predictor of poorer survival (HR, 8.00; 95% CI, 1.94–33.01; P = 0.004). Applied to the independent validation set, low-serum IGF-I levels maintained their prognostic value for shorter TTR and OS. Conclusions: Low-baseline IGF-I levels independently correlated with shorter TTR and poorer survival in patients with early-stage hepatocellular carcinoma after curative treatment. Clin Cancer Res; 19(15); 4218–27. ©2013 AACR.

We consider the recent history functional linear models, relating a longitudinal response to a longitudinal predictor where the predictor process only in a sliding window into the recent past has an effect on the response value at the current time. We propose an estimation procedure for recent history functional linear models that is geared towards sparse longitudinal data, where the observation times across subjects are irregular and the total number of measurements per subject is small. The proposed estimation procedure builds upon recent developments in literature for estimation of functional linear models with sparse data and utilizes connections between the recent history functional linear models and varying coefficient models. We establish uniform consistency of the proposed estimators, propose prediction of the response trajectories and derive their asymptotic distribution leading to asymptotic point-wise confidence bands. We include a real data application and simulation studies to demonstrate the efficacy of the proposed methodology.

We propose a variant of historical functional linear models for cases where the current response is affected by the predictor process in a window into the past. Different from the rectangular support of functional linear models, the triangular support of the historical functional linear models and the point-wise support of varying coefficient models, the current model has asliding window support into the past. This idea leads to models that bridge the gap between varying coefficient models and functional linear (historic)models. By utilizing one-dimensional basis expansions and one-dimensional smoothing procedures, the proposed estimation algorithm is shown to have better performance and to be faster than the estimation procedures proposed for historical functional linear models.