Abstract In prognosis studies with time-to-event outcomes, the survivals of groups with high/low biomarker expression are often estimated by the Kaplan–Meier method, and the difference between groups is measured by the hazard ratios (HRs). Since the high/low expressions are usually determined by study-specific cutoff values, synthesizing only HRs for summarizing the prognostic capacity of a biomarker brings heterogeneity in the meta-analysis. The time-dependent summary receiver operating characteristics (SROC) curve was proposed as a cutoff-free summary of the prognostic capacity, extended from the SROC curve in meta-analysis of diagnostic studies. However, estimates of the time-dependent SROC curve may be threatened by reporting bias in that studies with significant outcomes, such as HRs, are more likely to be published and selected in meta-analyses. Under this conjecture, this paper proposes a sensitivity analysis method for quantifying and adjusting reporting bias on the time-dependent SROC curve. We model the publication process determined by the significance of the HRs and introduce a sensitivity analysis method based on the conditional likelihood constrained by some expected proportions of published studies. Simulation studies showed that the proposed method could reduce reporting bias given the correctly-specified marginal selection probability. The proposed method is illustrated on the real-world meta-analysis of Ki67 for breast cancer.

ABSTRACT The summary receiver operating characteristic (SROC) curve has been recommended as one important meta-analytical summary to represent the accuracy of a diagnostic test in the presence of heterogeneous cutoff values. However, selective publication of diagnostic studies for meta-analysis can induce publication bias (PB) on the estimate of the SROC curve. Several sensitivity analysis methods have been developed to quantify PB on the SROC curve, and all these methods utilize parametric selection functions to model the selective publication mechanism. The main contribution of this article is to propose a new sensitivity analysis approach that derives the worst-case bounds for the SROC curve by adopting nonparametric selection functions under minimal assumptions. The estimation procedures of the worst-case bounds use the Monte Carlo method to approximate the bias on the SROC curves along with the corresponding area under the curves, and then the maximum and minimum values of PB under a range of marginal selection probabilities are optimized by nonlinear programming. We apply the proposed method to real-world meta-analyses to show that the worst-case bounds of the SROC curves can provide useful insights for discussing the robustness of meta-analytical findings on diagnostic test accuracy.

The coronavirus disease (COVID-19) pandemic has greatly altered peoples' daily lives, and it continues spreading as a crucial concern globally. Knowledge, attitudes, and practices (KAP) toward COVID-19 are related to individuals' adherence to government measures. This study evaluated KAP toward COVID-19 among university students in Japan between May 22 and July 16, 2020, via an online questionnaire, and it further investigated the associated determining KAP factors. Among the eligible respondents (n = 362), 52.8% were female, 79.0% were undergraduate students, 32.9% were students whose major university subjects were biology-related, 35.4% were from the capital region, and 83.7% were Japanese. The overall KAP of university students in Japan was high. All respondents (100%) showed they possessed knowledge on avoiding enclosed spaces, crowded areas, and close situations. Most respondents showed a moderate or higher frequency of washing their hands or wearing masks (both at 96.4%). In addition, 68.5% of respondents showed a positive attitude toward early drug administration. In the logistic regressions, gender, major subjects, education level, nationality, residence, and psychological factors (private self-consciousness and extroversion) were associated with knowledge or attitudes toward COVD-19 (p < 0.05). In the logistic and multiple linear regressions, capital regions, high basic knowledge, high information acquisition, correct information explanations contributed positively to preventative action (p < 0.05). Non-capital regions, male gender, non-bio-backgrounds, high public self-consciousness, high advanced knowledge, incorrect information explanations, and high extroversion contributed negatively to self-restraint (p < 0.05). Moreover, self-restraint was decreasing over time. These findings clarify the Japanese university students' KAP and the related factors in the early period of the COVID-19 pandemic, and they may help university managers, experts, and policymakers control the future spread of COVID-19 and other emerging infections.

Dapagliflozin is a first-in-class oral sodium glucose co-transporter 2 (SGLT2) inhibitor. It is often used in combination with conventional anti-diabetic drugs such as metformin, glimepiride, and insulin in treating type 2 diabetes (T2D). It not only reduces glucose reabsorption in the kidney but also increases renal glucose excretion. Some studies found the actions of dapagliflozin independent of insulin and free from risk of weight gain. This meta-analysis aims to evaluate whether dapagliflozin is synergistic with other anti-diabetic drugs without risk of weight gain. This meta-analysis will include the randomized controlled trials (RCT) evaluating the efficacy of dapagliflozin as an add-on drug in treating T2D for >8 weeks with the outcome measures glycosylated hemoglobin (HbA1c), fasting plasma glucose (FPG) and body weight. Information of relevant RCTs will be retrieved from major databases including PubMed, Cochrane Library, Embase, ClinicalTrials.gov, and Google Scholar according to a pre-specified search strategy. Google and manual search will find other unpublished reports and supplementary data. Eligible RCTs will be selected according to pre-specified inclusion and exclusion criteria. Data will be extracted and input into a pre-formatted spreadsheet. The Cochrane risk of bias tool will be used to assess the quality of the eligible RCTs. Meta-analysis based on the random-effects model will be conducted to compare the changes of HbA1c (%), FPG (mmol/L), and body weight (kg) between dapagliflozin arm and placebo arm. Publication bias will be evaluated with a funnel plot and the Egger’s test. Heterogeneity will be assessed with the I² statistics. Sensitivity analysis will be conducted on follow-up periods. The evidential quality of the findings will be assessed with the GRADE profiler. The findings of this meta-analysis will be important to clinicians, patients, and health policy-makers regarding the use of dapagliflozin in T2D treatment. PROSPERO registration number: CRD42013005034