Prediction intervals are commonly used in meta-analysis with random-effects models. One widely used method, the Higgins–Thompson–Spiegelhalter prediction interval, replaces the heterogeneity parameter with its point estimate, but its validity strongly depends on a large sample approximation. This is a weakness in meta-analyses with few studies. We propose an alternative based on bootstrap and show by simulations that its coverage is close to the nominal level, unlike the Higgins–Thompson–Spiegelhalter method and its extensions. The proposed method was applied in three meta-analyses.

Background: Recent studies have shown that immune-related adverse events (irAEs) caused by immune checkpoint inhibitors were associated with clinical benefit in patients with melanoma or lung cancer. In advanced gastric cancer (AGC) patients, there have been few reports about the correlation between irAEs and efficacy of immune checkpoint inhibitors. In this study, we retrospectively investigated the correlation between irAEs and efficacy in AGC patients treated with nivolumab.Methods: The subjects of this study were AGC patients received nivolumab monotherapy between January 2015 and August 2018. IrAEs were defined as those AEs having a potential immunological basis that required close follow-up, or immunosuppressive therapy and/or endocrine therapy. We divided the patients who received nivolumab into two groups based on occurrence of irAEs; those with irAEs (irAE group) or those without (non-irAE group). We assessed the efficacy in both groups.Results: Of the 65 AGC patients that received nivolumab monotherapy, 14 developed irAEs. The median time to onset of irAEs was 30.5 days (range 3–407 days). Median follow-up period for survivors was 32 months (95% CI, 10.8 to 34.5). The median progression-free survival was 7.5 months (95% CI, 3.6 to 11.5) in the irAE group and 1.4 months (95% CI, 1.2 to 1.6) in the non-irAE group (HR = 0.11,p < 0.001). The median overall survival was 16.8 months (95% CI, 4.4 to not reached) in the irAE group and 3.2 months (95% CI, 2.2 to 4.1) in the non-irAE group (HR = 0.17,p < 0.001). Multivariate analysis demonstrated that number of metastatic sites ≥2 (HR = 2.15; 95% CI, 1.02 to 4.54), high ALP level (HR = 2.50; 95% CI, 1.27 to 4.54), and absence of irAEs (HR = 9.54, 95% CI, 3.34 to 27.30 for yes vs. no) were associated with a poor prognosis. The most frequent irAEs was diarrhea/colitis (n = 5). Grade 3 adverse events were observed in 6 patients; hyperglycemia (n = 2), diarrhea/colitis (n = 1), adrenal insufficiency (n = 1), aspartate aminotransferase increased (n = 1), peripheral motor neuropathy (n = 1). There were no grade 4 or 5 adverse events related to nivolumab.Conclusions: Development of irAEs was associated with clinical benefit for AGC patients receiving nivolumab monotherapy.

Background: Prior to the approval of sorafenib, hepatic arterial infusion chemotherapy (HAIC) was offered to patients with advanced hepatocellular carcinoma (HCC) in East Asia, particularly Japan. According to the Japanese guidelines, HAIC is recommended as one of the treatment options in patients without extrahepatic metastasis (EHM). Methods: The present cohort study compared the use of HAIC and sorafenib on outcomes of patients with advanced HCC. Consecutive patients with advanced HCC who received HAIC or sorafenib as a first-line systemic therapy were enrolled from 10 Japanese institutions. The primary outcomes were overall survival (OS) in patients with macrovascular invasion (MVI), but without EHM, and OS in patients without both MVI and EHM. Results: Between 2009 and 2016, 2,006 patients were enrolled (541 HAIC patients, 1,465 sorafenib patients). After propensity score matching, the OS of patients with MVI but without EHM was significantly longer in the HAIC group compared with the sorafenib group (10.1 vs. 9.1 months for the HAIC and sorafenib groups, respectively; n = 170 for each group; hazard ratio [HR] 0.668; 95% confidence interval [95% CI] 0.475–0.935; p = 0.018). There was no significant difference in OS between patients without both MVI and EHM (12.2 vs. 15.4 months for the HAIC and sorafenib groups, respectively; n = 76 in each cohort after propensity score matching; HR 1.227; 95% CI 0.699–2.155; p = 0.475). Conclusion: HAIC is a potential front-line treatment choice in a subpopulation of patients with advanced HCC with MVI but without EHM.

Biomarkers are becoming increasingly important for streamlining drug discovery and development. In addition, biomarkers are widely expected to be used as a tool for disease diagnosis, personalized medication, and surrogate endpoints in clinical research. In this paper, we highlight several important aspects related to study design and statistical analysis for clinical research incorporating biomarkers. We describe the typical and current study designs for exploring, detecting, and utilizing biomarkers. Furthermore, we introduce statistical issues such as confounding and multiplicity for statistical tests in biomarker research.

Recently, a body shape index (ABSI) was reported to predict all-cause mortality independently of body mass index (BMI) in Americans. This study aimed to evaluate whether ABSI is applicable to Japanese adults as a predictor for development of diabetes, hypertension, and dyslipidemia. We evaluated the predictive power of ABSI in a retrospective cohort study using annual health examination data from Chiba City Hall in Japan, for the period 2008 to 2012. Subjects included 37,581 without diabetes, 23,090 without hypertension, and 20,776 without dyslipidemia at baseline who were monitored for disease incidence for 4 years. We examined the associations of standardized ABSI, BMI, and waist circumference (WC) at baseline with disease incidence by logistic regression analyses. Furthermore, we conducted a case-matched study using the propensity score matching method. Elevated BMI, WC, and ABSI increased the risks of diabetes and dyslipidemia [BMI-diabetes: odds ratio (OR) = 1.26, 95% confidence interval (95%CI) = 1.20−1.32; BMI-dyslipidemia: OR = 1.15, 95%CI = 1.12−1.19; WC-diabetes: OR = 1.24, 95%CI = 1.18−1.31; WC-dyslipidemia: OR = 1.15, 95%CI = 1.11−1.19; ABSI-diabetes: OR = 1.06, 95%CI = 1.01−1.11; ABSI-dyslipidemia: OR = 1.04, 95%CI = 1.01−1.07]. Elevated BMI and WC, but not higher ABSI, also increased the risk of hypertension [BMI: OR = 1.32, 95%CI = 1.27−1.37; WC: OR = 1.22, 95%CI = 1.18−1.26; ABSI: OR = 1.00, 95%CI = 0.97−1.02]. Areas under the curve (AUCs) in regression models with ABSI were significantly smaller than in models with BMI or WC for all three diseases. In case-matched subgroups, the power of ABSI was weaker than that of BMI and WC for predicting the incidence of diabetes, hypertension, and dyslipidemia. Compared with BMI or WC, ABSI was not a better predictor of diabetes, hypertension, and dyslipidemia in Japanese adults.