Bayesian hierarchical evaluation of dose-response for peanut allergy in clinical trial screening

ABSTRACT Thirteen patients with prolonged secondary amenorrhoea and 5 with anovular cycles each provided sufficient data to establish their individual sensitivity to FSH. The parameters used were based on the rise in excretion of oestriol and of pregnanediol. The sensitivity expressed as the ED 50 (that dose which is effective in 50 per cent of times it is given) varied between patients over at least a sevenfold range but the difference between the 2 groups of patients was not significant. After adjusting for differences between patients a dose-response relationship was established and it was found that a dose equal to half the ED 50 would be expected to produce a response in less than Io per cent of the times it is given while a dose equal to twice the ED 50 would be expected to produce a response in more than go per cent of the times it is given. Such a dose would be likely to produce an excessive response. An excessive response occurred three times in 140 months of treatment. Sixteen of the I8 patients became pregnant. The 2 who failed gave up treatment prematurely. A test of individual patient's sensitivity to FSH was established which depended on single injections of the hormone mixed with HCG. The test was repeated at 2 to 3 weekly intervals with increasing dosages until a positive response was obtained as judged by an increase in excretion of oestriol. The rate at which the dose should be increased was investigated and a 30 per cent rate was selected. When the effective test dose was established a standard schedule of dosages was recommended for treatment using 3 equal doses of FSH in 8 days and HCG on the tenth.

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Bayesian hierarchical dose-response meta-analysis of epidemiological studies: Modeling and target population prediction methods

Environment International ◽

10.1016/j.envint.2020.106111 ◽

2020 ◽

Vol 145 ◽

pp. 106111 ◽

Cited By ~ 1

Author(s):

Bruce Allen ◽

Kan Shao ◽

Kevin Hobbie ◽

William Mendez ◽

Janice S. Lee ◽

...

Keyword(s):

Dose Response ◽

Meta Analysis ◽

Target Population ◽

Epidemiological Studies ◽

Prediction Methods ◽

Bayesian Hierarchical ◽

Population Prediction

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Two-stage Bayesian hierarchical modeling for blinded and unblinded safety monitoring in randomized clinical trials

BMC Medical Research Methodology ◽

10.1186/s12874-020-01097-6 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Junhao Liu ◽

Jo Wick ◽

Renee’ H. Martin ◽

Caitlyn Meinzer ◽

Dooti Roy ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Drug Safety ◽

Safety Data ◽

Safety Monitoring ◽

Safety Signal ◽

Two Stage ◽

Bayesian Hierarchical ◽

Potential Safety ◽

Stage 1

Abstract Background Monitoring and reporting of drug safety during a clinical trial is essential to its success. More recent attention to drug safety has encouraged statistical methods development for monitoring and detecting potential safety signals. This paper investigates the potential impact of the process of the blinded investigator identifying a potential safety signal, which should be further investigated by the Data and Safety Monitoring Board with an unblinded safety data analysis. Methods In this paper, two-stage Bayesian hierarchical models are proposed for safety signal detection following a pre-specified set of interim analyses that are applied to efficacy. At stage 1, a hierarchical blinded model uses blinded safety data to detect a potential safety signal and at stage 2, a hierarchical logistic model is applied to confirm the signal with unblinded safety data. Results Any interim safety monitoring analysis is usually scheduled via negotiation between the trial sponsor and the Data and Safety Monitoring Board. The proposed safety monitoring process starts once 53 subjects have been enrolled into an eight-arm phase II clinical trial for the first interim analysis. Operating characteristics describing the performance of this proposed workflow are investigated using simulations based on the different scenarios. Conclusions The two-stage Bayesian safety procedure in this paper provides a statistical view to monitor safety during the clinical trials. The proposed two-stage monitoring model has an excellent accuracy of detecting and flagging a potential safety signal at stage 1, and with the most important feature that further action at stage 2 could confirm the safety issue.

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