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Objectives: We aimed to provide a systematic overview of freely available tools which may help plan or monitor costs for randomized clinical trials (RCTs). Study Design and Setting: We systematically searched MEDLINE, EMBASE, and EconLit and conducted internet searches via Google (last search, October 2018). We included all freely available tools and determined their specific purpose, which parts of clinical trial projects and which types of costs they covered, and if they were user-tested or validated in any form. Results: We identified 25 available tools. Most tools were downloadable on websites from institutions related to clinical research. Seven tools were developed to plan the budget for an entire RCT, 17 tools for calculating budgets of an individual trial center, and one tool for monitoring costs of ongoing RCTs. Eighteen tools considered fixed, variable, and indirect costs. Only two tools were clearly user-tested or validated. Conclusion: Several freely available tools aim to support investigators in planning costs of an entire trial or in planning the budget for a clinical trial site. How valid and useful they are remains to be shown for most of them. Future tools should be openly shared, user-tested, and validated.

Objectives Off-label drug use is highly prevalent but controversial and often discouraged assuming generally inferior medical effects associated with off-label use. Study Design and Setting We searched PubMed, MEDLINE, PubMed Health, and the Cochrane Library up to May 2015 for systematic reviews including meta-analyses of randomized clinical trials (RCTs) comparing off-label and approved drugs head-to-head in any population and on any medical outcome. We combined the comparative effects in meta-analyses providing summary odds ratios (sOR) for each treatment comparison and outcome, and then calculated an overall summary of the sOR across all comparisons (ssOR). Results We included 25 treatment comparisons with 153 RCTs and 24,592 patients. In six of 25 comparisons (24%), off-label drugs were significantly superior (five of 25) or inferior (one of 25) to approved treatments. There was substantial statistical heterogeneity across comparisons (I2 = 43%). Overall, off-label drugs were more favorable than approved treatments (ssOR 0.72; 95% CI = 0.54–0.95). Analyses of patient-relevant outcomes were similar (statistical significant differences in 24% (six of 25); ssOR 0.74; 95% CI = 0.56–0.98; I2 = 60%). Analyses of primary outcomes of the systematic reviews (n = 22 comparisons) indicated less heterogeneity and no statistically significant difference overall (ssOR 0.85; 95% CI = 0.67–1.06; I2 = 0%). Conclusion Approval status does not reliably indicate which drugs are more favorable in situations with clinical trial evidence comparing off-label with approved use. Drug effectiveness assessments without considering off-label use may provide incomplete information. To ensure that patients receive the best available care, funding, policy, reimbursement, and treatment decisions should be evidence based considering the entire spectrum of available therapeutic choices.