Upversion of medical coding dictionaries (MedDRA and WHO-DD)

In clinical trials, adverse events, medical histories, concomitant medications (drug names) are usually entered as the free text fields (verbatim terms) in the case report forms. Data in free text format can not be systematically summarized and analyzed, medical coding becomes necessary to convert the free text fields into categories.

Adverse events and medical histories are coded using MedDRA (Medical Dictionary for Regulatory Activities). Drug names (generic or brand names) are coded using WHO-DD (World Health Organization – Drug Dictionary). Early dates, there are different dictionaries available for performing the medical coding. Nowadays, the coding dictionaries are fixed on MedDRA and WHO-DD.

Both MedDRA and WHO-DD dictionaries are updated periodically - specifically, MedDRA is updated twice a year and WHO-DD is updated four times a year.

When we do a clinical trial, we will start with the latest version of the coding dictionaries. The clinical studies usually take several years to complete. By the time we reach the completion of the study, we will do the database lock (i.e., no data changes after the database lock). The coding dictionaries selected for use at the beginning of the study will be several years old and become obsolete.

One way to resolve this issue is to perform the upversion (or up-version, upversioning) of the medical dictionaries. For a clinical trial (especially a trial with extended study duration), it is good to update the medical coding dictionaries periodically. If upversion can not be performed periodically during the study, it is better to do an upversion at least at the end of the study (before the database lock).

Historically, there was no requirement/mandate for upversion, different companies had different practice in terms of the upversion. Here is the survey result about the upversoin in practice.



However, upversion will soon become a mandate for both MedDRA and WHO-DD dictionaries.

There are two Federal Registers pertinent to the upversion of the MedDRA and WHO-DD (or WHODG) dictionaries: one for MedDRA and one for WHO-DD. Notice that WHO-DD is now called WHODG (World Health Organization Drug Global) in the Federal Register.

Electronic Study Data Submission; Data Standards; Support for Version Update of the Medical Dictionary for Regulatory Activities

“Generally, the studies included in a submission are conducted over many years and may have used different MedDRA versions to code adverse events. The expectation is that sponsors or applicants will use the most current version of MedDRA at the time of study start. However, there is no requirement to recode earlier studies. The transition date for support and requirement to use the most current version of MedDRA is March 15, 2018. Although the use of the most current version is supported as of this Federal Register notice and sponsors or applicants are encouraged to begin using it, the use of the most current version will only be required in submissions for studies that start after March 15, 2019….”

Electronic Study Data Submission; Data Standards; Support for Version Update of World Health Organization Drug Global

“FDA currently supports the use of WHODG for the coding of concomitant medications in studies submitted to CBER or CDER in NDAs, ANDAs, BLAs, and certain INDs in the electronic common technical document format. Generally, the studies included in a submission are conducted over many years and may have used different WHODG versions to code concomitant medications. The expectation is that sponsors and applicants will use the most current B3-format annual version of WHODG at the time of study start. However, there is no requirement to recode earlier studies. The transition date for support of the most current B3- format annual version of WHODG is March 15, 2018. Although the use of the current B3-format annual version of WHODG is supported as of this Federal Register notice and sponsors or applicants are encouraged to begin using it, the use of the most current B3- format annual version will only be required in submissions for studies that start after March 15, 2019.”

Additional Readings:

• Upversioning MedDRA Dictionary - Insights from a Seasoned Coder
• Challenges of differentMedDRA versions for integrated data and database
• B3/C3 formats upversioning

Retire Statistical Significance and p-value?

In the March issue of The American Statistician, there was a special issue with 43 papers about “Statistical Inference in the 21st Century: A World Beyond p < 0.05”. The discussion about using the p-values was picked up by the scientific communities and triggered a lot of discussions. Some of the articles were provocative: “Retire Statistical Significance”, “Abandon / Retire Statistical Significance”. American Statistician Association’s president, Karen Kafadar, has also discussed this issue in his ‘president’s corner’.

• P-values: To Own or Not to Own?
• Statistics and Unintended Consequences

For a long time, statisticians have been cautioned about the misuse of the p-values.

• Don’t become the slave of the p-values
• Don’t base your conclusions solely on whether an association or effect was found to be “statistically significant” (i.e., the pvalue passed some arbitrary threshold such as p < 0.05).
• Don’t believe that an association or effect exists just because it was statistically significant.
• Don’t believe that an association or effect is absent just because it was not statistically significant.
• Don’t believe that your p-value gives the probability that chance alone produced the observed association or effect or the probability that your test hypothesis is true.
• Don’t conclude anything about scientific or practical importance based on statistical significance (or lack thereof).

The intention of the special issue is to trigger a healthy debate about the p-values and statistical significance, trigger the development of better methods, and provide the educations about the appropriate use and interpretation of the p-values. However, there is a danger of the unintended consequences: non-statisticians may be confused about what to do. Worse, “by breaking free from the bonds of statistical significance” as the editors suggest and several authors urge, researchers may read the call to “abandon statistical significance” as “abandon statistical methods altogether”.

The drug development relies on the clinical trials to demonstrate the substantial evidence about the efficacy and the substantial evidence comes from adequate and well-controlled investigations.

“evidence consisting of adequate and well-controlled investigations, including clinical investigations, by experts, qualified by scientific training and experience to evaluate the effectiveness of the drug involved, on the basis of which it could fairly and responsibly be concluded by such experts that the drug will have the effect it purports or is represented to have under the conditions of use prescribed, recommended, or suggested in the labeling or proposed labeling thereof”

For a common disease, two pivotal studies (with each showing a statistical significance at alpha = 0.05) have been the requirement for FDA (see FDA guidance "Providing Clinical Evidence of Effectiveness for Human Drug and Biological Products")

FDA has applied more flexibilities in evaluating the evidence for drugs/biological products for treating rare diseases especially those with unmet medical needs. Frank Sasinowski has two articles discussing this issue.

• Quantum of Effectiveness Evidence in FDA’s Approval of OrphanDrugs
• Quantum of Effectiveness Evidence in FDA’s Approval of Orphan Drugs: Update, July 2010 to June 2014

If we need to avoid the overuse and misuse of p-values, we will need to start with the changes in the statute of the laws and changes in regulatory science.

In addition, the scientific journals and editors may judge the value of a paper based on the significance of the results and favors the studies with statistical significance for publication. However, this may be changed now. On July 18. 2019 issue of New England Journal of Medicine (NEJM), an editorial paper was published "New Guidelines for Statistical Reporting in the Journal".

"The new guidelines discuss many aspects of the reporting of studies in the Journal, including a requirement to replace P values with estimates of effects or association and 95% confidence intervals when neither the protocol nor the statistical analysis plan has specified methods used to adjust for multiplicity."

With NEJM leading the way, other journals may follow. We will see more reporting of the confidence intervals and less reporting of the p-values. 

Generate Real-World Data (RWD) and Real-World Evidence (RWE) for Regulatory Purposes

Real-world data (RWD) and real-world evidence (RWE) have been hot topics in the drug development field and in the statistical field. With the new technologies, electronic health records, and big data, it is no surprise that RWD and RWE are much discussed to be a new way to revolutionize the clinical trial design and the regulations.

FDA has its dedicated webpage for 'Real-World Evidence' and issued several guidelines for using real-world evidence to support the regulatory approvals. 

What is the Definition of RWD and RWE?

Real-world data (RWD) are the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources. RWD can come from a number of sources, for example:

• Electronic health records (EHRs)
• Claims and billing activities
• Product and disease registries
• Patient-generated data including in home-use settings
• Data gathered from other sources that can inform on health status, such as mobile devices

Real-world evidence (RWE) is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD. RWE can be generated by different study designs or analyses, including but not limited to, randomized trials, including large simple trials, pragmatic trials, and observational studies (prospective and/or retrospective).

Last week, Duke Margolis Center for Health Policy organized a symposium titled "Leveraging Randomized Clinical Trials to Generate Real-World Evidence for Regulatory Purposes". All presentation slides and the video recordings are available for free.

Day 1 focused on the study design using RWD and RWE for efficacy measure (presentation slides, presentation video)

Day 2 focused on safety monitoring using RWD and RWE (presentation slides, presentation video)

This is not the first Duke Margolis Center for Health Policy organizes the event for this topic. Here are some previous events.

Second Annual Duke-Margolis Conference on Real-World Data and Evidence

Enhancing the Application of Real-World Evidence In Regulatory Decision-Making DAY 1

Public Workshop: A Framework for Regulatory Use of Real-World Evidence

Additional Reading: 

• Real-World Evidence — What Is It and What Can It Tell Us? NEJM 2018
• FDA guidance (2018) "Use of Real-World Evidence to Support Regulatory Decision-Makingfor Medical Devices"

• FDA Discusses RWD, RWE With Industry, Academia
• Good practices for real‐world data studies of treatment and/or comparative effectiveness: Recommendations from the joint ISPOR‐ISPE Special Task Force on real‐world evidence in health care decision making Pharmacoepidemiology and Drug Safety and Value in Health
• Real-World Evidence and Real-World Data for Evaluating Drug Safety and Effectiveness
• Gilead and Novartis Seek to Expand What FDA Considers as Real-World Data and Real-World Evidence

Regulations for Clinical Trials, Drug Development, Drug Approvals in China

The article by Bill Wang and  Alistair Davidson "An overview of major reforms in China’s regulatory environment" summarized the background about the regulatory environment in China. 

It is widely recognized that China is currently the second largest pharmaceutical market in the world. Historically the regulatory environment in China has been considered a highly challenging one, with: (1) major issues in the areas of comparative quality between international standards and some local products and manufacturers; (2) a timeframe for review and approval of new drugs that is longer than most major countries; and (3) a lack of capacity in the regulatory bodies that has resulted in a backlog of applications. In August 2015, the China State Council issued “Opinions on Reforming the Review and Approval System for Drugs and Medical Devices.” This was partly a result of dialogue with the local and international pharma industry that, for many years, has been pressing for major regulatory reform.1 The overarching intention of this was to “promote the structural adjustment, transformation and upgrade of the pharmaceutical industry and bring marketed products up to international standards in terms of efficacy, safety and quality, so as to better meet the public needs for drugs.” The main practical aims are to: (1) eliminate the existing backlog of registration applications; (2) establish an environment for maximizing the quality of generic drugs; (3) create a framework in China that encourages research and development of new drugs in line with global development; and (4) improve the quality and increase transparency of the review and approval process.

 Additional discussions about the regulatory environment in China for clinical trials and drug development can be found here:

• An Analysis Of Regulatory Reforms In China's Pharmaceutical Market
• New CFDA Requirements and its Implementation
• Q&A: Regulatory Reform in China
• The rewards of regulatory change Launching innovative biopharma in China

Chinese regulatory agencies have published a flurry of regulations about the clinical trials and drug approval processes. Unfortunately, all regulatory guidance and policies are in Chinese. 

In the US, NIH’s ClinRegs website contains the English version of the updates about the Chinese regulations in clinical trials and drug development:

ClinRegs: Aggregating clinical research regulations from around the globe

Regulatory Authorities in China and the US 

China	US
国家药品监督管理局（National Medical Product Administration, NMPA） It has just launched its English version website at http://subsites.chinadaily.com.cn/nmpa/drugs.html	Food and Drug Administration (FDA)
国家药品监督管理局药品审评中心 (Center for Drug Evaluation)	Center for Drug Evaluation and Research (CDER) Center for Biological Evaluation and Research (CBER)
国家药品监督管理局医疗器械技术审评中心 (Center for Medical Device Evaluation)	Center for Device and Radiological Health

Guidance, Guideline, and Policies Regarding Clinical Trials, Drug Development, Drug Approval (Links to the Chinese version and the translation of the titles in English)

上市后个例安全性报告（ICSRs）E2B（R3）实施指南 2019/05	Guidelines for Post-marketing individual case safety reporting (ICSRs) E2B (R3)
药物研发与技术审评沟通交流管理办法 2018/09	Communications for Drug Development and Technical Evaluation (Trial)
接受药品境外临床试验数据的技术指导原则 2018/07 解读	Guidance for Accepting Data from Foreign Clinical Trials
药品试验数据保护实施办法 2018/04	Data Protection in Clinical Trials for Drug
关于鼓励药品创新实现优先审评审批的意见 2017/12 图解	Priority Review & Approval Procedure
药品注册受理审查指南 2017/11	Guidelines for Drug Application / Registration Submission
关于调整进口药品注册管理有关事项的决定 2017/10	Decisions on the Adjustment of Imported Drug Registration
成人用药数据外推至儿科人群的 技术指导原则 2017/05	Pediatric Extrapolation
药物临床试验的一般考虑指导原则 2017/01	General Considerations to Clinical Trials for Drug
仿制药质量和疗效一致性评价临床 有效性试验一般考虑 2016/09 图解政策	Bioequivalence Evaluation for Generic Drugs
药物临床试验数据管理与统计分析的 计划和报告指导原则 2016/07	Data Management Planning and Reporting of Statistical Analysis
药物临床试验的生物统计学指导原则 2016/06	Biostatistics Principles for Clinical Trials
临床试验数据管理工作技术指南 2016/07	Data Management Procedure for Clinical Trials
儿科人群药物临床试验技术指导原则 2016/03	Clinical Trials in Pediatric Population
关于开展药物临床试验数据自查核查工作的公告 2015/07	Self-inspection of Clinical Trial Data
临床试验的电子数据采集技术指导原则 2015/07	Electronic Data Capture for Clinical Trials
国际多中心临床试验指南 2015/01	Multi-regional Clinical Trial
关于药物临床试验信息平台的公告 2013/09	Clinical Trial Registry http://www.chinadrugtrials.org.cn/
药品不良反应报告和监测管理办法 2011/07	Adverse Drug Reaction Reporting and Monitoring
药物临床试验质量管理规范 2003/09	Guidance for Quality Control in Clinical Trials