Background: Investigators in the Us described large volume water infusion with marked benefits but acknowledged the limitation of male veteran predominance in the study subjects. The aim of this study was to assess the feasibility of large volume water infusion in asian patients undergoing minimal sedation diagnostic colonoscopy in a community setting. Methods: consecutive patients who underwent colonoscopy were randomized to receive large volume (entire colon) (Group a, n=51), limited volume (rectum and sigmoid colon) (Group B, n=51) water infusion, or air insufflation (Group c, n=51). pain during insertion, completion rate, cecal intubation and total procedure times, and patient satisfaction were evaluated. pain and satisfaction were assessed with a 0-10 visual analog scale. Results: The mean pain scores during insertion were lower in the Group a and Group B than in Group c, 3.3±2.4, 3.0±2.2 and 4.4±2.6, respectively (p=0.028 and p=0.004). The completion rates and cecal intubation times were similar among the three groups. The procedure time was significantly longer in Group a than in group c (15.3±5.9 min vs. 13.1±5.4 min, p=0.049). Overall satisfaction with the procedure was greater in Group B than in Group c only (9.7±0.5 vs. 9.4±0.8, p=0.044). Conclusions: Diagnostic colonoscopy with large volume water infusion without air insufflation appears to be feasible in minimally sedated asian patients in a community setting. Measures to improve the outcome further are discussed.

This paper considers quantile regression analysis based on semi-competing risks data in which a non-terminal event may be dependently censored by a terminal event. The major interest is the covariate effects on the quantile of the non-terminal event time. Dependent censoring is handled by assuming that the joint distribution of the two event times follows a parametric copula model with unspecified marginal distributions. The technique of inverse probability weighting (IPW) is adopted to adjust for the selection bias. Large-sample properties of the proposed estimator are derived and a model diagnostic procedure is developed to check the adequacy of the model assumption. Simulation results show that the proposed estimator performs well. For illustrative purposes, our method is applied to analyze the bone marrow transplant data in [1].

Medical studies often involve semi-competing risks data, which consist of two types of events, namely terminal event and non-terminal event. Because the non-terminal event may be dependently censored by the terminal event, it is not possible to make inference on the non-terminal event without extra assumptions. Therefore, this study assumes that the dependence structure on the non-terminal event and the terminal event follows a copula model, and lets the marginal regression models of the non-terminal event and the terminal event both follow time-varying effect models. This study uses a conditional likelihood approach to estimate the time-varying coefficient of the non-terminal event, and proves the large sample properties of the proposed estimator. Simulation studies show that the proposed estimator performs well. This study also uses the proposed method to analyze AIDS Clinical Trial Group (ACTG 320).

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