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\"San Francisco's Queen of Vice uncovers the story of one of the most skilled, high-priced, and corrupt abortion entrepreneurs in America. Even as Prohibition was the driving force behind organized crime, abortions became the third-largest illegal enterprise as state and federal statutes combined with changing social mores to drive abortionists into hiding. Inez Brown Burns, a notorious socialite and abortionist in San Francisco, made a fortune providing her services to desperate women throughout California. Beginning in the 1920s, Burns oversaw some 150,000 abortions until her trial and conviction brought her downfall. In San Francisco's Queen of Vice, Lisa Riggin tells the story of the rise and fall of San Francisco's \"abortion queen\" and explores the rivalry between Burns and the city's newly elected district attorney, Edmund G. \"Pat\" Brown (father of the present governor of California). Pledging to clean up the graft-ridden city, Brown exposed the hidden yet not-so-secret life of backroom deals, political payoffs, and corrupt city cops. Through the arrest, prosecution, and conviction of Burns, Brown used his success as a stepping-stone for his political rise to California's governor's mansion. Featuring an array of larger-than-life characters, Riggin shows how Cold War domestic ideology and the national quest to return to a more traditional America quickly developed into a battle against internal decay. Based on a combination of newspaper accounts, court records, and personal interviews, San Francisco's Queen of Vice reveals how the drama played out in the life and trial of one of the wealthiest women in California history\"-- Provided by publisher.

Historical controls (HCs) can be used for model parameter estimation at the study design phase, adaptation within a study, or supplementation or replacement of a control arm. Currently on the latter, there is no practical roadmap from design to analysis of a clinical trial to address selection and inclusion of HCs, while maintaining scientific validity. This paper provides a comprehensive roadmap for planning, conducting, analyzing and reporting of studies using HCs, mainly when a randomized clinical trial is not possible. We review recent applications of HC in clinical trials, in which either predominantly a large treatment effect overcame concerns about bias, or the trial targeted a life-threatening disease with no treatment options. In contrast, we address how the evidentiary standard of a trial can be strengthened with optimized study designs and analysis strategies, emphasizing rare and pediatric indications. We highlight the importance of simulation and sensitivity analyses for estimating the range of uncertainties in the estimation of treatment effect when traditional randomization is not possible. Overall, the paper provides a roadmap for using HCs.

Comparator arms in randomized clinical trials may be impractical and/or unethical to assemble in rare diseases. In the absence of comparator arms, evidence generated from external control studies has been used to support successful regulatory submissions and health technology assessments (HTA). However, conducting robust and rigorous external control arm studies is challenging and despite all efforts, residual biases may remain. As a result, regulatory and HTA agencies may request additional external control analyses so that decisions may be made based upon a body of supporting evidence.This paper introduces external control studies and provides an overview of the key methodological issues to be considered in the design of these studies. A series of case studies are presented in which evidence derived from one or more external controls was submitted to regulatory and HTA agencies to provide support for the consistency of findings.

•Single-arm trials that informed treatment approval by the FDA-AA have modest effect sizes.•The lower bound of the 95% CI of 53% of approved treatments crossed the line of null effect.•The effect sizes were not larger in the studies that were followed by the FDA-required confirmatory randomized trials. To describe effect sizes of single-arm clinical trials that supported AA approvals. We reviewed all the single-arm approvals granted by the FDA-AA pathway between June1992 to December2020. Two independent reviewers identified single-arm studies and extracted data from FDA Full-Medical Reviews. We performed a meta-analysis to estimate the effect sizes and compared it between studies that met post-approval FDA requirements for RCTs with those that did not. From the total of 254 approvals, single arm clinical trials describing effects of 54 drugs for 72 clinical indications were evaluated. The effect size estimated was OR:2.22(CI95%:1.76-2.81) [relative risk (RR) = 1.63(95CI% 1.38-1.92)]; 53% of treatments had a lower 95% CI bound crossing the null effect. Effect size did not differ between the treatments that met the FDA requirement for conducting post-approval RCTs. Treatment effects observed in the FDA AA single-arm studies was modest and can be to ascribed to bias.

Introduction: Virtual Control Groups (VCGs) represent the concept of using historical control data from legacy animal studies to replace concurrent control group (CCG) animals. Based on the data curation and sharing activities of the Innovative Medicine Initiatives project eTRANSAFE (enhancing TRANSlational SAFEty Assessment through Integrative Knowledge Management) the ViCoG working group was established with the objectives of i) collecting suitable historical control data sets from preclinical toxicity studies, ii) evaluating statistical methodologies for building adequate and regulatory acceptable VCGs from historical control data, and iii) sharing those control-group data across multiple pharmaceutical companies. During the qualification process of VCGs a particular focus was put on the identification of hidden confounders in the data sets, which might impair the adequate matching of VCGs with the CCG. Methods: During our analyses we identified such a hidden confounder, namely, the choice of the anesthetic procedure used in animal experiments before blood withdrawal. Anesthesia using CO 2 may elevate the levels of some electrolytes such as calcium in blood, while the use of isoflurane is known to lower these values. Identification of such hidden confounders is particularly important if the underlying experimental information (e.g., on the anesthetic procedure) is not routinely recorded in the standard raw data files, such as SEND (Standard for Exchange of Non-clinical Data). We therefore analyzed how the replacement of CCGs with VCGs would affect the reproducibility of treatment-related findings regarding electrolyte values (potassium, calcium, sodium, and phosphate). The analyses were performed using a legacy rat systemic toxicity study consisting of a control and three treatment groups conducted according to pertinent OECD guidelines. In the report of this study treatment-related hypercalcemia was reported. The rats in this study were anesthetized with isoflurane. Results: Replacing the CCGs with VCGs derived from studies comprising both anesthetics resulted in a shift of control electrolyte parameters. Instead of the originally reported hypercalcemia the use of VCG led to fallacious conclusions of no observed effect or hypocalcemia. Discussion: Our study highlights the importance of a rigorous statistical analysis including the detection and elimination of hidden confounders prior to the implementation of the VCG concept.

Purpose In the context of clinical research, there is an increasing need for new study designs that help to incorporate already available data. With the help of historical controls, the existing information can be utilized to support the new study design, but of course, inclusion also carries the risk of bias in the study results. Methods To combine historical and randomized controls we investigate the Fill-it-up-design, which in the first step checks the comparability of the historical and randomized controls performing an equivalence pre-test. If equivalence is confirmed, the historical control data will be included in the new RCT. If equivalence cannot be confirmed, the historical controls will not be considered at all and the randomization of the original study will be extended. We are investigating the performance of this study design in terms of type I error rate and power. Results We demonstrate how many patients need to be recruited in each of the two steps in the Fill-it-up-design and show that the family wise error rate of the design is kept at 5 % . The maximum sample size of the Fill-it-up-design is larger than that of the single-stage design without historical controls and increases as the heterogeneity between the historical controls and the concurrent controls increases. Conclusion The two-stage Fill-it-up-design represents a frequentist method for including historical control data for various study designs. As the maximum sample size of the design is larger, a robust prior belief is essential for its use. The design should therefore be seen as a way out in exceptional situations where a hybrid design is considered necessary.

Tongue pressure (TP) decreases significantly after esophagectomy in esophageal cancer patients (ECPs). Meanwhile, 2 weeks of gum-chewing training (GCT) significantly increased TP in healthy university students. We examined whether perioperative GCT would decrease the proportion of patients exhibiting a decline in TP at 2 weeks postoperatively, and prevent postoperative complications, in thoracic ECPs (TECPs). This was a single-center interventional study, and nonrandomized study with a historical control group (HCG). TECPs who underwent first-stage radical esophagectomy were recruited. Thirty-two patients of 40 in the gum-chewing group (GCG) were completed perioperative GCT in 3 times daily. Propensity score matching was performed with covariates related to TP including preoperative age, sex, body mass index, and the repetitive saliva swallowing test result, and yielded a matched cohort of 25 case pairs. Eleven GCG patients [44.0%] exhibited significantly lower TP at 2 weeks postoperatively than before esophagectomy was significantly fewer than that of 19 patients [76.0%] in the HCG. The median number of fever days (≥ 38.0 °C) in the 2 weeks after esophagectomy in the GCG was significantly fewer than those in the HCG. Perioperative GCT may prevent postoperative TP decline and postoperative dysphagia-related complications after esophagectomy.

Background Clinical trials assessing new treatment effects require a control group to compare the pure treatment effects. However, in clinical trials on regenerative medicine, rare diseases, and intractable diseases, it may be ethically difficult to assign participants to the control group. In recent years, the use of historical control data has attracted attention as a method for supplementing the number of participants in the control group. When combining historical control data with new randomized controlled trial (RCT) data, the assessment of heterogeneity using outcome data is not sufficient. Therefore, several statistical methods that consider participant outcomes and baseline characteristics, including the propensity score (PS) method have been proposed. Methods We propose a new method considering “information on whether the data are RCT data or not” in the PS model when combining the RCT and historical control data. The performance of the proposed method in estimating the treatment effect is evaluated using simulation data. Results When the distribution of covariates is similar between the RCT and historical control data, not much difference in performance is found between the proposed and conventional methods to estimate the treatment effect. On the other hand, when the distribution of covariates is not similar between the two kinds of data, the proposed method shows higher performance. Conclusions Even when it is not known whether RCT and historical control data are similar, the proposed PS model is useful to estimate the treatment effect appropriately in RCTs using historical control data.

Cancers of the mesothelium, such as malignant mesothelioma (MM), historically have been attributed solely to exposure to asbestos. Recent large scale genetic and genomic functional studies now show that approximately 20% of all human mesotheliomas are causally linked to highly penetrant inherited (germline) pathogenic mutations in numerous cancer related genes. The rarity of these mutations in humans makes it difficult to perform statistically conclusive genetic studies to understand their biological effects. This has created a disconnect between functional and epidemiological studies. However, since the molecular pathogenesis of MM in mice accurately recapitulates that of human disease, this disconnect between functional and epidemiological studies can be overcome by using inbred mouse strains that harbor mutation(s) in genes involved in the disease. Most mouse studies have focused on the effect of asbestos exposure, leaving the effects of genetic mutations in the absence of exposure understudied. Here, using existing peer-reviewed studies, we investigate the rate of spontaneous MM among mice with and without germline genetic mutations, in the absence of asbestos exposure. We leveraged these published data to generate a historical control dataset (HCD) to allow us to improve statistical power and account for genetic heterogeneity between studies. Our Bayesian analyses indicate that the odds of spontaneous MM among germline BAP1 mutant mice is substantially larger than that of wildtype mice. These results support the existing biological study findings that mesotheliomas can arise in the presence of pathogenic germline mutations, independently of asbestos exposure.

We aimed to map the characteristics of single-arm trials (SAT), report the Food and Drug Administration (FDA) transparency in presenting historical control, and to assess the confirmatory randomized controlled trials (RCTs). This metaresearch included a review of all oncology indication approved using SAT by FDA-AA (FDA—Accelerated Approval) from 1992 to 2020. Two independent reviewers identified SAT, extracted data from FDA full medical reviews for historical controls reported and MEDLINE for searching for confirmatory RCT published. Of 254 FDA-AA approvals, 119 (47%) were approved for oncologic indications using SAT. Fifty-four drugs for 72 oncology indications were for leukemia, lymphoma, lung cancer, urothelial cancer, multiple myeloma, and thyroid cancer. Overall, 37 (52%) treatments were converted into regular approval. Of these, 17 (46%) were based on confirmatory RCTs using overall survival (OS) as an outcome. Five indications were withdrawn from the market. Most trials outcomes were blindly assessed by independent research committees. Median trial sample size was 105 patients (min:8 to max:532). The FDA did not fully specify historical control selection in 75% of cases. The granting of FDA-AAs based on SAT in oncology is increasing with more target drugs approved over time. Transparency in historical control reporting is necessary. •The granting of FDA-AAs based on SAT in oncology is increasing, mostly target drugs.•Indications were mainly leukemia, lymphoma, lung cancer, and urothelial cancer.•Historical control data are often not clearly reported.•Transparency in historical control election and analysis are needed.