Studies that reported hepatitis B virus (HBV) reactivation in rheumatoid arthritis (RA) patients have caused attention of disease-modifying antirheumatic drug (DMARD)-related HBV reactivation. Most of the studies were focused on HBV reactivation risk of biologic DMARDs; insufficient data are available to identify the exact risk of conventional DMARD (c-DMARD)-related HBV reactivation. This prospective study aimed to investigate the risk of HBV reactivation in HBV-infected RA patients who received c-DMARDs. A total of 476 RA patients were screened in this prospective non-randomized, non-controlled study. HBV-infected patients characterized by hepatitis B surface antigen (HBsAg) positive or HBsAg negative/anti-hepatitis B core antigen (anti-HBc) positive were analyzed for HBV DNA, followed with HBV DNA monitoring scheduled every 3 months, serum alanine aminotransferase test at 2-month intervals, or more frequently. Prevalence of HBsAg positive and HBsAg negative/anti-HBc positive was 6.51 and 51.1 %, respectively, among the 476 RA patients. Among 211 patients (23 patients were HBsAg positive and 188 patients were HBsAg negative/anti-HBc positive) who received c-DMARDs without antiviral prophylactic treatment, 4 patients developed HBV reactivation. Both HBsAg positive and HBsAg negative/anti-HBc positive patients have the possibility of developing HBV reactivation. There was no correlation between HBV reactivation and any specific c-DMARD. Glucocorticoid coadministration and negative anti-hepatitis B surface antigen (anti-HBs) at baseline showed correlation with reactivation. In conclusion, it would be rational to initiate antiviral prophylaxis according to risk stratification rather than universal prophylaxis for HBV-infected RA patients. Conventional DMARDs are relatively safe to HBV-infected patients with low reactivation risk (low HBV DNA level, no GCs administration, and anti-HB positive).

Cancer immunotherapy trials have two special features: a delayed treatment effect and a cure rate. Both features violate the proportional hazard model assumption and ignoring either one of the two features in an immunotherapy trial design will result in substantial loss of statistical power. To properly design immunotherapy trials, we proposed a piecewise proportional hazard cure rate model to incorporate both delayed treatment effect and cure rate into the trial design consideration. A sample size formula is derived for a weighted log‐rank test under a fixed alternative hypothesis. The accuracy of sample size calculation using the new formula is assessed and compared with the existing methods via simulation studies. A real immunotherapy trial is used to illustrate the study design along with practical consideration of balance between sample size and follow‐up time.

A challenge arising in cancer immunotherapy trial design is the presence of a delayed treatment effect wherein the proportional hazard assumption no longer holds true. As a result, a traditional survival trial design based on the standard log-rank test, which ignores the delayed treatment effect, will lead to substantial loss of statistical power. Recently, a piecewise weighted log-rank test is proposed to incorporate the delayed treatment effect into consideration of the trial design. However, because the sample size formula was derived under a sequence of local alternative hypotheses, it results in an underestimated sample size when the hazard ratio is relatively small for a balanced trial design and an inaccurate sample size estimation for an unbalanced design. In this article, we derived a new sample size formula under a fixed alternative hypothesis for the delayed treatment effect model. Simulation results show that the new formula provides accurate sample size estimation for both balanced and unbalanced designs. KEYWORDS cancer clinical trial, delayed treatment effect, piecewise weighted log-rank test, sample size Pharmaceutical Statistics. 2019;1-12.wileyonlinelibrary.com/journal/pst

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Recently, molecularly targeted agents and immunotherapy have been advanced for the treatment of relapse or refractory cancer patients, where disease progression-free survival or event-free survival is often a primary endpoint for the trial design. However, methods to evaluate two-stage single-arm phase II trials with a time-to-event endpoint are currently processed under an exponential distribution, which limits application of real trial designs. In this paper, we developed an optimal two-stage design, which is applied to the four commonly used parametric survival distributions. The proposed method has advantages compared with existing methods in that the choice of underlying survival model is more flexible and the power of the study is more adequately addressed. Therefore, the proposed two-stage design can be routinely used for single-arm phase II trial designs with a time-to-event endpoint as a complement to the commonly used Simon's two-stage design for the binary outcome.