The FDA's Final Rule on Expedited Safety Reporting: Statistical Considerations

Wittes, Janet; Crowe, Brenda; Chuang‐Stein, Christy; Guettner, A.; Hall, David B.; Jiang, Qi; Odenheimer, Daniel J.; Xia, H. Amy; Kramer, Judith M.

doi:10.1080/19466315.2015.1043395

Cited by 23 publications

(17 citation statements)

References 13 publications

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“…The event rate ratio distributions differ substantially between the approaches because the first two approaches force overlap of the distributions while the third approach explicitly incorporates the possibility that there may be an acceptable degree of risk inflation for the test group. Figure 2 displays the control charts for Event 1 (top panels) and Event 2 (bottom panels) corresponding to the three approaches based on the binomial model using expression (2). As expected, the control charts for Approaches 1 and 2 are similar, while the control charts for Approach 3 reflect acceptance of limited elevated risk.…”

Section: Binomial Modelmentioning

confidence: 60%

“…These trials usually are blinded, so that conventional statistical methods for explicit identification of test-control differences with respect to adverse event risk cannot be applied. Nonetheless, it would be useful, and recently has become a regulatory mandate, [1] (see also [2]) to monitor the cumulative frequency of reports of various adverse events in order to identify possible toxicity differences between the test and control groups without unblinding ongoing trials. Various tools can be used (e.g., [3][4][5]).…”

Section: Introductionmentioning

confidence: 99%

“…The InvertCDF.fn < -function(X,cdfX,pctpts = c(.9,.95)) { nq < -length(pctpts) y < -abs(outer(cdfX,pctpts,"-")) z < -apply(y,2,min) w < -X%*%(y==t(array(z,c(nq,length(X))))) return(w) } NTNC < -rbind(NTNC) TotN < -apply(NTNC,1,sum) K < -dim(NTNC) [1] nq < -length(pctpts) result < -array(0,c(K,nq + 2)) sumry < -vector("list",K) xlimL < -qnbinom(eps,abcd [1],abcd [ ), break, s < -s + 1) } cfS < -sum(fS) fS < -fS/cfS sfs < -as.data.frame(cbind(S,fS,cumsum(fS))) dimnames(sfs)[ [2]] < -c("S","denS","cdfS") quants < -InvertCDF.fn(sfs$S,sfs$cdfS,pctpts = pctpts) meanS < -round(S%*%fS,1) sumry[[i]] < -list(meanS = meanS,SfS = sfs,quants = quants)…”

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confidence: 99%

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Control charts for monitoring accumulating adverse event count frequencies from single and multiple blinded trials

Gould

2016

Statist. Med.

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Conventional practice monitors accumulating information about drug safety in terms of the numbers of adverse events reported from trials in a drug development program. Estimates of between-treatment adverse event risk differences can be obtained readily from unblinded trials with adjustment for differences among trials using conventional statistical methods. Recent regulatory guidelines require monitoring the cumulative frequency of adverse event reports to identify possible between-treatment adverse event risk differences without unblinding ongoing trials. Conventional statistical methods for assessing between-treatment adverse event risks cannot be applied when the trials are blinded. However, CUSUM charts can be used to monitor the accumulation of adverse event occurrences. CUSUM charts for monitoring adverse event occurrence in a Bayesian paradigm are based on assumptions about the process generating the adverse event counts in a trial as expressed by informative prior distributions. This article describes the construction of control charts for monitoring adverse event occurrence based on statistical models for the processes, characterizes their statistical properties, and describes how to construct useful prior distributions. Application of the approach to two adverse events of interest in a real trial gave nearly identical results for binomial and Poisson observed event count likelihoods. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Binomial Modelmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Control charts for monitoring accumulating adverse event count frequencies from single and multiple blinded trials

Gould

2016

Statist. Med.

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“…For example, although it is possible that an investigational drug may be causing febrile neutropenia, report of a single instance in a study in which the investigational drug is added on to cytotoxic chemotherapy is uninformative. Aggregate reporting is the only way to determine that common or anticipated adverse events are potentially causally related to the investigational drug (3,4).…”

Section: Resultsmentioning

confidence: 99%

The Majority of Expedited Investigational New Drug Safety Reports Are Uninformative

Jarow

Casak

Chuk

et al. 2016

Clinical Cancer Research

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Sponsors of human drug and biologic products subject to an investigational new drug (IND) application are required to distribute expedited safety reports of serious and unexpected suspected adverse reactions to participating investigators and the FDA to assure the protection of human subjects participating in clinical trials. On September 29, 2010, the FDA issued a final rule amending its regulations governing expedited IND safety reporting requirements that revised the definitions used for reporting and clarified when to submit relevant and useful information to reduce the number of uninformative reports distributed by sponsors.

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“…The 2015 draft guidance is still under review, following the FDA public meeting held in March, 2018 about the Rule and associated guidance. The process for examining accumulating safety data and the entity that should conduct this examination has been discussed at numerous public meetings and in publications (Wittes et al, 2015). If a trial has a DMC in place, should the data review and corresponding decisions about safety reporting be added to the DMC responsibilities, or should a separate safety review committee be established for this purpose?…”

Section: Us Fda Considerationsmentioning

confidence: 99%

The changing landscape of data monitoring committees—Perspectives from regulators, members, and sponsors

Bhattacharyya¹,

Gallo

Crisp³

et al. 2018

Biometrical J

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Data Monitoring Committees (DMCs) are an integral part of clinical drug development. Their use has evolved along with changing study designs and regulatory expectations, which has associated statistical and ethical implications. Although there is guidance from the different regulatory agencies, there are opportunities to bring more consistency to address practical issues of establishing and operating a DMC. Challenging issues include defining the scope of DMC decisions, the regulatory requirements and expectations, the perceived independence of DMCs, the specific focus primarily on safety, etc. Wider use of adaptive clinical trial designs in recent years introduce additional challenges in terms of trial governance and the complexity of DMC activities. A panel comprised of clinical and statistical experts from across academia, industry, and regulatory agencies shared their experience and thoughts on the importance of these aspects and offered perspectives on the future of the DMCs. This paper documents the thinking from the panel session at the CEN-ISBS conference held in Vienna, Austria, 2017. K E Y W O R D Sadaptive design, clinical trial, data monitoring committee (DMC), EMA, futility, regulatory guideline, US FDA 1232

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The FDA's Final Rule on Expedited Safety Reporting: Statistical Considerations

Cited by 23 publications

References 13 publications

Control charts for monitoring accumulating adverse event count frequencies from single and multiple blinded trials

Control charts for monitoring accumulating adverse event count frequencies from single and multiple blinded trials

The Majority of Expedited Investigational New Drug Safety Reports Are Uninformative

The changing landscape of data monitoring committees—Perspectives from regulators, members, and sponsors

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