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Background: Limited efficacy has been observed when using opioids to treat neuropathic pain. Lidocaine patches reduce neuropathic pain in postherpetic neuralgia, but their benefits for cancer-related neuropathic pain remain unclear. This study aimed to investigate a treatment for cancer-related neuropathic pain. Methods: We conducted a prospective, open-label, single-arm study to assess the efficacy and safety of lidocaine transdermal patches in patients experiencing localized, superficial, neuropathic cancer pain. Terminal cancer patients already receiving opioid treatment participated in the 3-day study. The primary endpoint was pain intensity evaluated by the numerical rating scale (NRS). The secondary endpoints were the pain relief score and the quality of analgesic treatment. Results: The results showed a significant difference in the median NRS over 3 days (Kruskal–Wallis test, p < 0.0001). The median NRS pain intensity from Day 1 to Day 3 was 4.0 with 95% C.I. (3.3, 5.0), 3.0 (2.5, 3.5), and 2.6 (2.0, 3.0), respectively. The difference between the median NRS pain intensities of any 2 days was significant (Wilcoxon signed-rank test, p < 0.0001). The generalized estimating equation (GEE) estimation model showed significant differences between the NRS pain intensities on any 2 days. There was no significant difference in the pain relief score or the quality of analgesic treatment. Conclusions: In this study, the 5% lidocaine transdermal patch reduced the NRS pain intensity in neuropathic cancer patients already receiving opioid treatment. Treatment of localized and superficial neuropathic pain caused by cancer was well tolerated and effective. [ABSTRACT FROM AUTHOR]

Recurrent event and terminal event data commonly arise in clinical and observational studies. To evaluate the efficacy of a treatment effect for both types of events, a composite endpoint has been used as a possible assessment, particularly when faced with high costs and a longer follow‐up study. To model recurrent event processes complicated by the existence of a terminal event, joint frailty modeling has been typically employed. In this study, the objective was to develop some target‐driven response adaptive randomization strategies using a composite endpoint based on joint frailty modeling. We first implemented a balanced randomized design and then investigated the response adaptive randomization. The former is intuitively first adopted while the latter is expected to be desirable and ethical in terms of allocating more subjects to the more effective treatment. The results show that the proposed procedures using a composite endpoint are capable of reducing the number of trial participants who receive inferior treatment while simultaneously reaching a desired optimal target as compared to a balanced randomized design. The R shiny application for calculating the sample size and allocation probabilities is also available. Finally, two clinical trials were used as pilot datasets to introduce the proposed procedures. [ABSTRACT FROM AUTHOR]

Recurrent event responses are frequently encountered during clinical trials of treatments for certain diseases, such as asthma. The recurrence rates of different treatments are often compared by applying the negative binomial model. In addition, a balanced treatment-allocation procedure that assigns the same number of patients to each treatment is often applied. Recently, a response-adaptive treatment-allocation procedure has been developed for trials with recurrent event data, and has been shown to be superior to balanced treatment allocation. However, this response-adaptive treatment allocation procedure is only applicable for the comparison of two treatments. In this paper, we derive response-adaptive treatment-allocation procedures for trials which comprise several treatments. As pairwise comparisons and multiple comparisons with a control are two common multiple-testing scenarios in trials with more than two treatments, corresponding treatment-allocation procedures for these scenarios are also investigated. The redesign of two clinical studies illustrates the clinical benefits that would be obtained from our proposed response-adaptive treatment-allocation procedures. [ABSTRACT FROM AUTHOR]

Sample size determination is essential during the planning phases of clinical trials. To calculate the required sample size for paired right-censored data, the structure of the within-paired correlations needs to be pre-specified. In this article, we consider using popular parametric copula models, including the Clayton, Gumbel, or Frank families, to model the distribution of joint survival times. Under each copula model, we derive a sample size formula based on the testing framework for rank-based tests and non-rank-based tests (i.e., logrank test and Kaplan- Meier statistic, respectively). We also investigate how the power or the sample size was affected by the choice of testing methods and copula model under different alternative hypotheses. In addition to this, we examine the impacts of paired-correlations, accrual times, follow-up times, and the loss to follow-up rates on sample size estimation. Finally, two real-world studies are used to illustrate our method and R code is available to the user. [ABSTRACT FROM AUTHOR]