Monday, July 22, 2019

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:

Thursday, July 18, 2019

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’.

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.
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

Sunday, July 14, 2019

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:

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:

Regulatory Authorities in China and the US 

国家药品监督管理局National Medical Product Administration, NMPA
It has just launched its English version website at

国家药品监督管理局药品审评中心 (Center for Drug Evaluation)

国家药品监督管理局医疗器械技术审评中心 (Center for Medical Device Evaluation)

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

Guidelines for Post-marketing individual case safety reporting (ICSRs) E2B (R3)
Communications for Drug Development and Technical Evaluation (Trial)
Guidance for Accepting Data from Foreign Clinical Trials
Data Protection in Clinical Trials for Drug

Priority Review & Approval Procedure

Guidelines for Drug Application / Registration Submission
Decisions on the Adjustment of Imported Drug Registration
Pediatric Extrapolation

General Considerations to Clinical Trials for Drug

Bioequivalence Evaluation for Generic Drugs
Data Management Planning and Reporting of Statistical Analysis
Biostatistics Principles for Clinical Trials

Data Management Procedure for Clinical Trials
Clinical Trials in Pediatric Population
Self-inspection of Clinical Trial Data
Electronic Data Capture for Clinical Trials
Multi-regional Clinical Trial
Clinical Trial Registry
Adverse Drug Reaction Reporting and Monitoring
Guidance for Quality Control in Clinical Trials