Placebo Mean Imputation (PMI)

A while ago, I discussed several simple imputation methods in LOCF, BOCF, WOCF, and MVTF. Recently, I noticed another simple imputation method Placebo Mean Imputation (PMI). This simple imputation method of PMI seems to be used Only for the purpose of sensitivity analyses, not for the primary analysis. It is also true that this approach is mainly used in certain therapeutic areas such as analgesic drug (pain medication) and anti-bacterial drug.  

 

In FDA's Clinical Review document for a chronic pain medication in 2011,  the Placebo mean imputation is described:

 "Placebo mean imputation (PMI): the missing pain measurements for each day after discontinuation were replaced by the mean of all available pain intensity scores for all placebo-treated patients who completed treatment. Therefore if a patient discontinued treatment or recorded their last pain score at Week 8 of the Maintenance Period, the pain intensity score at Week 12 was imputed using the Week 12 mean pain intensity score for all placebo-treated patients who completed treatment. Also a placebo missing pain score at some time-point was imputed by the observed placebo group mean pain intensity at the same time-point"

In FDA's Statistical Review for the same indication, FDA statistician assessed that PMI is not an appropriate approach

"Since the estimated treatment effect is only influenced by the data from patients completing the study, the PMI method is similar to an analysis of completers. Analyzing completers is
problematic since the outcome of patients completing the study may not represent the outcome of patients not completing the study. In the placebo group, patients completing the study are likely to be the less severely afflicted patients; while in the NUCYNTA group, patients completing the study are likely to be the more severely afflicted patients. As a result, the PMI method assigned good scores from the placebo completers to patients dropping out due to adverse events in the treatment group. Based on these reasons, I conclude that the PMI method is not appropriate."

In a briefing document for FDA anti-infective drug advisory committee meeting for a cystic fibrosis drug in 2012, PMI approach was used by FDA statistical reviewer:

“…In this sensitivity analysis, missing values were imputed using the placebo group mean of -0.57% (i.e. the minimum of 0 and the least favorable group mean) as performed in the Reviewer’s primary analysis and other Reviewer analyses.”

The use of these simple imputation approaches is mainly driven by the perception that these approaches provide conservative estimate of the treatment effect (people have shown that this is not always true). This fits into the Intention-to-treat principle to be conservative in estimating the treatment effect in superiority studies. While it is ok to perform the sensitivity analyses using various simple imputation approaches such as LOCF, BOCF, WOCF, and PMI, these imputation methods should not be used as the primary analysis. 

Further Reading: 

• Gual et al (2013) A randomised, double-blind, placebo-controlled, efficacy study of nalmefene, as-needed use, in patients with alcohol dependence 
• Carpenter and Kenward (2007) Missing data in randomised controlled trials — a practical guide
• The National Academy Press (2010) The Prevention and Treatment of Missing Data in Clinical Trials

Replies to Inquiries to FDA on Good Clinical Practice

Good Clinical Practice (GCP) is the law of conducting the clinical trials. Sometimes, interpreting the law is challenging, the same is true in interpreting the GCP and other regulatory guidelines. FDA has set up an Office of Good Clinical Practice and provided  Good Clinical Practice Contacts for helping interpreting the GCP and answering the GCP related questions.

Recently, I just found that the FDA has an email address for questions and answers regarding GCP questions. The original questions along with FDA's responses are posted on the web. While FDA's responses may not directly address the original questions being asked, Replies to Inquiries to FDA on Good Clinical Practice is still a great resource.

•  Replies to Inquiries to FDA on Good Clinical Practice

Monitoring the double-blind study: unblinded pharmacist, unblinded monitor, and drug kit

In many clinical trials with biological products, the investigational products usually need to be reconstituted before the use. If the study is a double-blind study, the reconstitution of the investigational products can not be performed by the investigator in order for the investigator to remain blinded to the treatment assignment.

There are two ways to tackle this issue.

1. Using a third party (unblinded pharmacist).

With this option, the unblinded pharmacist at the investigational site will be the only person who knows the treatment assignment. The unblinded pharmacist will obtain the randomization number and treatment assignment information from the randomization envelope (manual process) or from IRT (interactive response technology including IVR or IWR) system. The unblinded pharmacist will then prepare and reconstitute the study drug, and then give to the investigator for administration.

The unblinded pharmacist must not communicate with the investigator about the subject treatment assignment.

The unblinded pharmacist must maintain the records for drug accountability for auditing and inspections.

With this option, the study team will need to include an unblinded CRA (clinical research associate) for the purpose of checking the drug accountability. In other words, there should be separate roles for two type of clinical monitors:

• Blinded Monitor: A monitor designated to perform site monitoring activities except for pharmacy, drug accountability, and reconciliation of the blinded investigational products.
• Unblinded Monitor: A monitor designated to perform the site monitoring activities for pharmacy, drug accountability, and reconciliation of the blinded investigational products.

 Some of the references for this approach:

• Unblinded Monitoring Programs: Design and Education
• Unblinded Monitoring in HCV Trials
• Integrated monitoring: Setting new standards for the next decade of clinical trial practice

2. Using the blinded drug kit for the investigational product  

With this approach, in addition to the randomization schedule (containing the  randomization number and the treatment assignment), additional list of drug kit numbers (and the linked batch or lot number corresponding to the investigational products) will be generated. Through the packaging, the label for the investigational product kit will contain only the kit number. Investigators can dispense or administer the investigational products to the patients based on the kit number without knowing the actual contents and the treatment assignments.

There is no need to have separate roles for site monitoring (ie, unblinded monitor and blinded monitor). The same monitor can cover the study activities and the pharmacy/drug accountability.

This approach can also be used for pills (non-biological products). It is especially useful when the investigational products are dispensed to the patients for their self-administration.  

A couple of examples for using this approach are listed below:

• Tenecteplase (TNK) study in AMI

“Randomization was to be blocked by study site, based on a SAS-generated code. Treatment assignment was made through a call to a centralized interactive voice response system for a drug kit number. Subjects were considered randomized upon verbal assignment of kit number.”

• A Randomized, Double-Blind, Placebo-Controlled Study of Guanfacine Extended Release in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder

“Patients were randomly assigned to 1 of 3 groups of GXR treatment (2, 3, or 4 mg/day) or placebo, in a 1:1:1:1 ratio. Matching GXR and placebo tablets were provided to patients in the form of weekly prepackaged individual study drug kits, identical in appearance, according to the randomization schedule. Every morning during the double-blind treatment period, patients took a total of 4 tablets, without regard to meals. Patients who completed the screening and washout periods were assigned to the treatment group of the next available drug kit in ascending order of the drug kit number (or randomization number), which was recorded on the case report form.”

Science or Just for Fun? - Correlation between Chocolate Consumption and Winning Nobel Prize

 The New England Journal of Medicine is a very prestigious medical journal with very high impact factor. I am surprised to read an article by Dr Messerli to claim that the chocolate consumption is correlated to winning the Nobel Laureates. See “Chocolate Consumption, Cognitive Function,and Nobel Laureates” at NEJM 367;16 1562-1564.

I hope that the paper was published just for fun, not for serious discussion of the science.

Nothing is wrong about the statistical calculation. The p-value for correlation coefficient is indeed statistically significant. However, this superficial correlation ignores many confounding factors behind this correlation. I guess that it will be embarrassing to find out that even though there is a correlation between the chocolate consumption on national level and the number of Nobel Laureates, the Novel Laureates are those who consumed little or no chocolate.

In my first college lesson about the correlation, the teacher told me that before calculating the correlation coefficient, make sure that we were comparing two things that are related. If we try to claim there is a correlation between the growth of a tree and a growth of a baby, we will certainly be able to establish the correlation, but so what?

An excerpt from J. A. Paulos,Beyond Numeracy gives some examples that the wrong correlations are assumed.

“Children with bigger feet spell better. In areas of the South those counties with higher divorce rates generally have lower death rates. Nations that add fluoride to their water have a higher cancer rate than those that don't. Should we be stretching our children's feet? Are more hedonist articles in Penthouse and Cosmopolitan on the way? Is fluoridation a plot?
……
The odd results above are easily explained in this way. Children with bigger feet spell better because they're older, their greater age bringing about bigger feet and, not quite so certainly, better spelling. Age is a factor in the next example as well since those couples who are older are less likely to divorce and more likely to die than are those from counties with younger demographic profiles. And those nations that add fluoride to their water are generally wealthier and more health-conscious, and thus a greater percentage of their citizens live long enough to develop cancer, which is, to a large extent, a disease of old age.”

Unfortunately, nowadays, many articles in medical journals consciously or unconsciously presents the correlations between two things without further considering the confounding factors or the basis for the correlations. This is why we often see that a conclusion from one study is later totally reversed by the results from the another study.

Fields Arranged by Purity - A Cartoon

See the explanation for this cartoon, please visit: explainxkcd.com

‘As treated’ versus ‘As randomized’ analysis

When we design a non-inferiority trial to support the product registration, the regulatory agencies may suggest the statistical analysis using ‘as treated’ population. In FDA’s Guidance for Industry Non-Inferiority Clinical Trials, it suggested the ‘as-treated’ analysis for primary efficacy endpoint:

“Intent-to-treat (ITT) analyses in superiority trials are nonetheless preferred because they protect against the kinds of bias that might be associated with early departure from the study. In non-inferiority trials, many kinds of problems fatal to a superiority trial, such as non-adherence, misclassification of the primary endpoint, or measurement problems more generally (i.e., “noise”), or many dropouts who must be assessed as part of the treated group, can bias toward no treatment difference (success) and undermine the validity of the trial, creating apparent non-inferiority where it did not really exist. Although an “as-treated” analysis is therefore often suggested as the primary analysis for NI studies, there are also significant concerns with the possibility of informative censoring in an as-treated analysis. It is therefore important to conduct both ITT and as-treated analyses in NI studies. Differences in results using the two analyses will need close examination.”

The term “as treated” means that when we do analysis/summaries, the treatment assignment is based on the actual treatment the patients receive, not the treatment the patients are supposed to receive. The concept of “as treated” should be explained in comparison with “as randomized” – a key component for the concept of “Intention-to-Treat” principle.

Typically, ‘Intention-to-Treat’ population can be simply defined as all patients who are randomized. However, the formal definition of the “Intention-to-Treat” population contains several other concepts:

The formal definition of the "Intention to Treat" is usually referred to the one suggested by Fisher, LD et al. in “Intention to treat in clinical trials in Statistical Issues in Drug Research and Development. Edited by Peace KE (1990)”. Intention to Treat population “Includes all randomized patients in the groups to which they were randomly assigned, regardless of their adherence with the entry criteria, regardless of the treatment they actually received, and regardless of subsequent withdrawal from treatment or deviation from the protocol”.

In other words, ITT analysis includes every subject who is randomized according to randomized treatment assignment. It ignores noncompliance, protocol deviations, withdrawal, and anything that happens after randomization. ITT analysis is usually described as “once randomized, always analyzed”. ITT analysis is referred as ‘as randomized’ - the opposite of the term ‘as treated’.

In majority of cases, if all randomized subjects receive their allocated treatments and if there is no randomization error, the ‘as treated’ analysis will be the same as 'as randomized' analysis.

If some randomized subjects do not receive the randomly alloccated treatment and if there are randomization errors, ‘as treated’ population will be different from ITT population or 'as randomized' population.

The example below can be used to illustrate the difference between the ‘as treated’ and ‘as randomized’. This is an open label, randomized study to compare the treatment effect of r-ProUK (intra-artery prourokinase + heparin) with control (heparin alone) on acute ischemia stroke - PROACT II Study. Eligible subjects were randomized to r-proUK or Control in 2:1 ratio. 180 subjects were randomized (121 subjects to r-proUK group and 59 to Control group). The problem was that in subjects who were randomized to r-proUK group, 13 subjects never received the treatment and in subjects who were randomized to control group, 5 subjects received the wrong treatment (they were supposed to receive control treatment, but instead received r-proUK). 

In this case, to strictly follow the ITT principle, the ITT analysis will include all 121 subjects in r-proUK group and all 59 subjects in Control group - purely based on the randomization, not based on whether or not the randomized subject receive the actual treatment or receive the wrong treatment. The ITT analysis was indeed used in the paper. However, since the PROACT II study was a proof of concept study, the analysis based on 'as treated' population should be used as well. 

With 'as treated' analysis, r-proUK group would have 121 subjects and Control group would have 54 subjects (see the comparison and the calculation below). 

The Number of Subjects for Analysis

	r-proUK	Control
As Randomized	121	59
As Treated	113 (121 randomized subjects - 13 subjects who did not receive treatment + 5 subjects who were randomized to Control group, but received r-proUK)	54 (59 randomized subjects – 5 subjects who were randomized to Control, but received r-proUK)

Typically, the safety analysis should always be based on the 'as treated' population since it really reflects the safety of patients under each treatment.

Fixed covariate, time dependent covariate, time varying covariate, and post-randomization covariate

A covariate is a variable that is possibly predictive of the outcome under study. A covariate may be of direct interest or it may be a confounding or interacting variable. Typically, when we use the term of ‘covariate’, we refer to the fixed covariate – a variable whose value will not change over the time. The fixed covariates include the demographic information (gender, age, race, …) and baseline characteristics (weight, height, baseline measure,…). Since ‘covariate’ typically means ‘fixed covariate’, we often drop the term ‘fixed’.

In contrary to the fixed covariate, time depedent covariate or time varying covariate (exact the same thing) refers to a covariate that is not necessarily constant through the whole study and has different values at different time points.  

There are many examples of the time dependent or time varying covariate in clinical trials or observational studies. For instance, if one wishes to examine the link between area of residence and cancer, this would be complicated by the fact that study subjects move from one area to another. The area of residency could then be introduced in the statistical model as a time-varying covariate.

When we analyze the CT lung densitometry data, we need to deal with the measurement of the lung volume – a reflection of the inspiration level. The CT densitometry measure is negatively correlated with the effort of the inspiration level. If we inhale more air into the lung, the lung density measure will be smaller and vice versa. In order for quantitative CT lung density to be valid endpoint, the corresponding measure of the lung volume (inspiration level) needs to considered and adjusted. If the outcome measure of CT lung density is measured at different times, there will also be corresponding measures of lung volumes at various times. The lung volume measure is considered as a time dependent covariate.

In clinical trials, the timing of the randomization is a critical point. The covariate can be separated into two groups: covariate measured or existed prior to the randomization and covariate measured after the randomization. The fixed covariates are measured or known prior to the randomization. The covariate measured after the randomization is called ‘post-randomization covariate’. The post-randomization covariate is usually also the time-dependent covariate.

When the time dependent covariate exists, we may be very tempted to include the covariate in the statistical analysis model. However, such approach could be biased if the time dependent covariate involves measures post-randomization.

A paper by Chen et al ‘A Note on Postrandomization Adjustment of Covariates” explained the issue:

“Examples include adjusting rescue therapies to explore the underlying difference of treatments which would have been observed in the absence of rescue medication, adjusting the level of patient compliance to estimate the treatment effect at the same compliance level, adjusting a postrandomization biomarker to evaluate the surrogating status of the biomarker to the clinical endpoint, and adjusting an intermediate endpoint or an endpoint other than the primary one to explore the therapeutic mechanism of one intervention.”

In its “ Guideline on adjustment for baseline covariates”, EMA explicitly opposes the use of adjustment for post-baseline covariates. In Section 4.2.5 (covariates affected by the treatment allocation), it states:

“A covariate that may be affected by the treatment allocation (for example, a covariate measured after randomisation such as duration of treatment, level of compliance or use of rescue medication) should not normally be included in the primary analysis of a confirmatory trial. When a covariate is affected by the treatment either through direct causation or through association with another factor, the adjustment may hide or exaggerate the treatment effect. It therefore makes the treatment effect difficult to interpret. However, such covariates (e.g. duration of treatment) might be included in secondary (exploratory) analyses and might offer the sponsor useful insights during the drug development process. Alternatively, subgroup analyses might offer similar insights.”

 The main concern for using the post-randomization covariates is the potential effect of treatment on the covariate measures. The covariates could become outcome measures or dependent variables instead of the independent variables. The post-randomization covariate measures may also be biased if there is inadequate blinding of the treatment allocation. In reality, it is not possible to defend that a post-randomization covariate is really a covariate and not impacted by the treatment. It is also difficult to confirm whether or not if the treatment effect of the experimental drug is mediated through the post-randomization or time-dependent covariates.

FDA does not have any guidance similar to EMA’s guidance on the use of post-randomization covariates. However, it is very rare to see the use of post-randomization covariates in the analysis of the primary efficacy endpoint in NDA or BLA submissions. There may be cases where post-randomization covariates are used in exploratory or ad-hoc analyses or used in analyses for non-randomized, observational studies.  

In sNDA ODAC Briefing document for Proscar, post hoc analysis was performed to include the post-randomization covariates. However, the briefing documents also commented “Adjustment for post-randomization variables (cores and volume) that are affected by treatment can introduce confounding bias and complicate causal interpretation.”

It has been noted that the time-dependent covaraites are more commonly used in survival analysis. See paper by Dr Lin “TIME-DEPENDENT COVARIATES IN THE COX PROPORTIONAL-HAZARDS REGRESSION MODEL”

Dealing with the time-dependent variable is a challenging statistical issue. The fundamental statistical problem is how to properly adjust for post-randomization variables, since, if the treatment has an effect on post-randomization variables, standard adjustment by regression modelling is susceptible to selection bias. The current proposal is to utilize Causal-Effects approach.  Dai et al had a pretty good introduction on this topic in their paper “Partially hidden Markov model for time-varying principal stratification in HIV prevention trials“. For details about the concept of Causal-Effects, the report of ”From Neurons to Neighborhoods: The Science of Early Childhood Development” by National Academy Press is a pretty good resource. 

Concept of "Person Year" in daily life

A while ago, I wrote a blog “Understanding person-year or patient-year”. While the concept of ‘person-year’ or ‘patient-year’ is mainly used in the epidemiology research (especially in the occupational health field) and clinical trials, the same concept may be used in the daily life.

This morning, I read an “Ask Marilyn” column on Parade.com. The question and answer applies the same concept of the ‘person-year’ or ‘patient-year’. With the concept of “person-year”, we need to combine the number of persons and the number of follow-up years in order to do the further calculation/statistical analyses. In the question/answer below, the number of family members and the number of days each family member stays need to be combined before the further calculation.

While 'person-year' may be more frequently used in the research field, the unit of time can be in years, months, days and they also can be called 'Person-time'. Person-time is an estimate of the actual time-at- risk in years, months, or days.

Question:

Nine family members will be renting a vacation property. The fee is $3,600 for 10 days. People will be staying for a varying number of days. I say the first step in figuring what each person owes is to divide the fee by nine. My husband says we should start by dividing the fee by 10—the number of days we have the rental. Who is right?

Marilyn responds:
Neither of you, but the dilemma is common. Here’s a way for anyone to solve this kind of vacation problem with any number of guests, days, etc. I’ll use your case as an example, and I’ll assume a family member who’s there surrounded by loved ones pays the same per day as one who gets the place all to him- or herself.

First, add up the number of days each family member stays. (Let’s say your nine people stay a total of 5+6+6+8+8+9+10+10+10 = 72 days.) Then divide the total rental fee by that figure ($3,600 ÷ 72 days = $50 per “person-day”). Each family member owes that result ($50) multiplied by the number of days he or she stays. So in this example, a person who stays five days owes $250. A person who stays all 10 owes $500.

Laboratory Tests: U.S. Conventional units versus SI units and Their Conversion Factors

In pretty much all clinical trials, laboratory tests are performed as a tool for diagnosing the disease, assessing the safety / tolerability, assessing the pharmacokinetics / pharmacodynamics and so on. A very common issue is the reporting units for laboratory tests. There are two different unit systems: conventional units and SI units.

U.S. Conventional Units (may also be referred as United States customary units): In the United States, most people express distances in inches, feet, yards, or miles. Those units, along with the units we use for speed, volume, and other quantities, are known as the U.S. Conventional System.

SI Units or Système Internationale: The International System of Units (abbreviated SI from French: Le Système international d'unités) is the modern form of the metric system and is the world's most widely used system of measurement, used in both everyday commerce and science. Most scientists and most countries now use SI units. SI units use the meter, the kilogram, the second, and the kelvin. Each base unit measures a different quantity. For example, the meter measures length, and the kilogram measures mass.

The units of these two systems are different, but the quantities they represent do not change. The units have a fixed relationship to each other. The laboratory results reported in U.S. Conventional Units can be converted into the results in SI Units or vice versa. The relationship to convert a value from one system to the other is called conversion factor. Below are some of the websites containing the conversion factors for common laboratory tests. The SI Conversion Calculator or conversion factor table provided by JAMA Author Instructions may be the most common one that has been used.

• SI Conversion Calculator
• Clinical Analyte Unit Conversion 
• SI Units for Clinical DataNormal Laboratory Values from AIDS.org
• AMA manual of style laboratory unit table 
• Conventional Units - International UnitsClinical Chemistry SI Conversion Factors 

Due to the differences between two unit systems, for a clinical trial where the central laboratory services are used, the decision needs to be made on which unit system is used for reporting the laboratory results to the investigators and to the sponsors. If it is a domestic trial in U.S., it is better to report the central laboratory test results in U.S. conventional units to the investigators. If it is multinational trial, it is better to report the central laboratory test results in S.I. units. For laboratory test results transferred to the sponsor, the results in both units can be requested.

The following may also be useful in understanding the different laboratory test units:

Factor Prefix Symbol for Lab Unit
10^12 tetra T
10^9 giga G
10^6 mega M
10^3 kilo k
10^(-3) milli m
10^(-6) micro µ
10^(-9) nano n
10^(-12) pico p
10^(-15) femto f
10^(-18) atto a

1 mL = 1 cc where mL is milliliter and cc stands for cubic centimeter
mg = milli gram = one-thousandth of a gram. 1 g = 1000 mg
1 mg = 1000 mcg or ug, here mc stands for "micro-" meaning "one millionth of".
In cell counts, the results may be reported as #/cumm, here cumm = mm^3 meaning per cubic millimeter.

For microbial measures (such as viral counts and viral loads), the log scale is commonly used. Microbial load (cfu/g or cfu/ml) can be expressed as log10. So, if you have 100,000 microbes that is 5 log, 10,000 microbes is 4 log, 1,000 is 3 log, 100 microbes is 2 log and 10 microbes is 1 log. Now, if you went from 100,000 microbes cfu/g to 10,000 microbes cfu/g that would be a 1 log reduction (5 - 4 log). If you went from 100,000 to 32,000 that would be a 0.5 log reduction (5 - 4.5 log) and so on.
If the microbial population went from 100,000 to 32,000 that would be a 0.5 log reduction (5 log - 4.5 log).
If the microbial population went from 100,000 to 320,000 that would be a 0.5 log increase (5.5 log - 5.0 log)

Periodic Safety Report: DSUR, PSUR, PBRER, ASR, IB, Orphan Designation Annual Report

During the drug development and after the drug is on the market, the sponsor or market authorization holder has obligations to submit the safety information to regulatory agencies periodically. These periodic reports have different requirements and sometimes are confusing. While these reports may be prepared by the regulatory affairs department or pharmacovigilence department, biostatistics group may often be asked to provide the information for these periodic reports.

These reports and their abbreviations could be very confusing especially for those who are not working in the pharmacovigilence department: DSUR, IND Annual Report, ASR, IB, PSUR, and PBRER… If we take a close look at these reports, they may be considered as four types:

• DSUR (replacing IND Annual Report and Annual Safety Report) for drugs under developments
• PBRER (replacing PSUR) for drugs already on the market
• IB is required whenever there is a clinical trial
• Orphan Designation Annual Report is required for the developing product with orphan designation - for rare disease

DSUR:  Development Safety Update Report

According to ICH E2F “Development Safety Update Report”,

The Development Safety Update Report (DSUR) proposed in this guideline is intended to be a common standard for periodic reporting on drugs under development (including marketed drugs that are under further study) among the ICH regions. US and EU regulators consider that the DSUR, submitted annually, would meet national and regional requirements currently met by the US IND Annual Report and the EU Annual Safety Report, respectively, and can therefore take the place of these existing reports

ICH E2F Guideline is finalized in August 2010 and is replacing the previous IND (investigational new drug) Annual Report (in US) and Annual Safety Report (in EU). Other documents regarding DSUR can be found at ICH.org website

• Concept Paper
• Business Plan
• DSUR Examples Commercial Sponsors
• DSUR Examples non-Commercial Sponsors
• Presentation on E2F

IB: Investigator Brochure

According to ICH E6 “Good Clinical Practice”,

The Investigator's Brochure (IB) is a compilation of the clinical and nonclinical data on the investigational product(s) that are relevant to the study of the product(s) in human subjects. Its purpose is to provide the investigators and others involved in the trial with the information to facilitate their understanding of the rationale for, and their compliance with, many key features of the protocol, such as the dose, dose frequency/interval, methods of administration, and safety monitoring procedures. The IB also provides insight to support the clinical management of the study subjects during the course of the clinical trial.

For post marketing commitment clinical trials, the product label (package insert) may be used in place of the investigator brochure since the package insert includes the contents required in the investigator brochure.

Orphan Drug Designation Annual Report 

21CFR Part 316 (Orphan Drugs) contains the specific section (section 316.30) requiring the annual reports of holder of orphan drug designation:

§ 316.30 Annual reports of holder of orphan-drug designation.
Within 14 months after the date on which a drug was designated as an orphan drug and annually thereafter until marketing approval, the sponsor of a designated drug shall submit a brief progress report to the FDA Office of Orphan Products Development on the drug that includes:
(a) A short account of the progress of drug development including a review of preclinical and clinical studies initiated, ongoing, and completed and a short summary of the status or results of such studies.
(b) A description of the investigational plan for the coming year, as well as any anticipated difficulties in development, testing, and marketing; and
(c) A brief discussion of any changes that may affect the orphan-drug status of the product. For example, for products nearing the end of the approval process, sponsors should discuss any disparity between the probable marketing indication and the designated indication as related to the need for an amendment to the orphan-drug designation pursuant to § 316.26.

EU has the similar requirement and sponsors are required to submit to the European Medicines Agency (EMA) every year after their medicine has been granted orphan designation. See EMA Orphan Designation Annual Report.

PSUR: Periodic Safety Update Reports

The term PSUR comes from the previous ICH E2C (R1) “Clinical Safety Data Management: Periodic Safety Update Reports for Marketed Drugs”. ICH guideline E2C has been subsequently revised and renames as Periodic Benefit-Risk Evaluation Report (PBRER) (see below). However, the term PSUR is still used by EMA. See EMA's 'Periodic safety update reports: questions and answers'

PBRER: Periodic Benefit-Risk Evaluation Report

According to the ICH E2C (R2) “PERIODIC BENEFIT-RISK EVALUATION REPORT (PBRER)”,

The Periodic Benefit-Risk Evaluation Report (PBRER) described in this Guideline is intended to be a common standard for periodic benefit-risk evaluation reporting on marketed products (including approved drugs that are under further study) among the ICH regions.

Other supporting documents regarding PBRER are:

• Concept Paper
• Business Plan
• Contribute to the E2C(R2) IWG

E2C (R2) is finalized at November 2012 and is supposed to replace the PSUR. However, EMA has not fully adopted the PBRER and continues to use the term PSUR as defined in its recent guideline “Guideline on good pharmacovigilance practices (GVP) 4 Module VII – Periodic safety update report (Rev 1)”

FDA fully endorsed E2C (R2) and PBRER and issued its guidance “Providing Postmarket Periodic Safety Reports in the ICH E2C(R2) Format (Periodic Benefit-Risk Evaluation Report”

A paper EMA’S NEW PSUR-PBRER from Sentrx.com discussed this confusion.

PBRER vs. DSUR

It is often confusing whether or not a PBRER or DSUR or both are needed. It is commonly understood that PBRER is for a marketed products (including approved drugs that are under further study) and DSUR is for drugs under development (including marketed drugs that are under further study). In US, for the marketed products, if IND is still open, the annual safety report (i.e., DSUR) will be required by FDA.

There are some duplications between the DSUR and PBRER. FDA's guidance "PERIODIC BENEFIT-RISK EVALUATION REPORT (PBRER)" intended to provide some clarifications about PBRER and DSUR. In some situations, both PBRER and DSUR are required (to meet different regulatory requirements). in preparation. However, the effort can be made to minimize the duplicate works.

"This guideline aims to address this duplication and facilitate flexibility by encouraging the use of individual modules, where they pertain to more than one report – to be used at different times, for different authorities, and for different purposes. Therefore, the PBRER has been developed in such a way that content of several sections may be used for sections of other documents as a basis for a modular approach (see Section 1.1). "

To some degree, the DSUR can be considered as a subset of PBRER with focus on the ongoing clinical trials.

SOP, WP, MAPP, SOPP for FDA Internal Staff / Reviewers

In an earlier discussion, I compared the differences between SOPs (Standard Operation Procedures) and WPs (Working Procedures). Pharmaceutical companies, Biotechnology companies, Clinical research organizations, and vendors providing services in clinical trials area must have the established SOPs and these SOPs must be followed by their employees. Adequacy of the SOPs and the compliance with the established SOPs are the key targets when there are audits (either from the sponsor or from the regulatory agencies).

As the regulatory agency for drug, biological products, and device clinical trials and market authorization approvals, FDA also has its established working procedures and FDA review staff should be trained on these working procedures and should follow these procedures. I hope that the compliance of these working procedures within FDA is also be monitored or audited.  

Interestingly, different divisions in FDA use different terminologies for their working procedures (see table below).

CDER (Center for Drug Evaluation and Research)	MAPP	Manual of Policies and Procedures
CBER (Center for Biologics Evaluation and Research)	SOPP	Manual of Standard Operating Procedures and Policies
CDRH (Center for Device and Radiological Health)	Guidance Documents (Medical Devices and Radiation-Emitting Products)

FDA also issues a lot of guidances. According to FDA, “Guidance documents represent FDA's current thinking on a topic.  They do not create or confer any rights for or on any person and do not operate to bind FDA or the public.  You can use an alternative approach if the approach satisfies the requirements of the applicable statutes and regulations.” While these guidances are mainly for industry, some of them are also for FDA Staff and FDA reviewers and perhaps also for investigators, IRB...

To understand what procedures FDA staff / reviewers are following can help the industry in preparing the regulatory submission materials to make sure that the documents FDA reviewers are looking for are included in the submission package.  For example, FDA MAPP 6010.4 “Good Review Practice: Statistical Review Template” can be good reference in preparing the planned analyses and tables. The documents provided “Examples of important statistical issues that may affect the results”

• Breaking the blind
• Unblinded or unplanned interim analyses
• High percentage of dropouts
• Inappropriate imputation for missing values
• Change of primary endpoint during conduct of the trial
• Dropping/adding treatment arms
• Sample size modification
• Inconsistency of results across subgroups
• Type I error inflation due to multiplicity
• Planned and unplanned adaptations
• Non-Inferiority

MAPP 6010.3 Rev. 1 “AttachmentB: Clinical Safety Review of an NDA or BLA” can be a good reference in understanding how the safety data should be presented. The document provides the detail review guidance on safety data including adverse events, vital signs, laboratory data, ECG,… In the section “Standard Analyses and Explorations of Laboratory Data” it specifically discussed what type of laboratory analysis results should be presented and the hypothesis tests for comparing the laboratory results are discouraged.  

In general, this review should include three standard approaches to the analysis of laboratory data, noted as: (1) Analyses Focused on Measures of Central Tendency; (2) Analyses Focused on Outliers or Shifts From Normal to Abnormal; and (3) Marked Outliers and Dropouts for Laboratory Abnormalities. The first two analyses are based on comparative trial data. The third analysis should focus on all subjects in the phase 2 to phase 3 experience. Analyses are intended to be descriptive and should not be thought of as hypothesis testing. P-values or confidence intervals can provide some evidence of the strength of the finding, but unless the trials are designed for hypothesis testing (rarely the case), these data should be thought of as descriptive. Generally, the magnitude of change is more important than the p-value for the difference.

MAPP 6030.9 “Good Review Practice: Good Review Management Principles and Practices for Effective IND Development and Review” discussed the SAP submission during IND:

Statistical analysis plan. Submission of a detailed statistical analysis plan (SAP) in the initial protocol submission for phase 3 protocols is not required by CDER regulations. However, review staff should strongly encourage sponsors to include the SAP in the initial protocol submission, because phase 3 protocols generally include a detailed section devoted to statistical methods that are closely linked to trial design. 

Clinical Trial Regulations in European Union (EU) Countries

For clinical trials in European Union countries, the regulations are mainly based on:

•  European Union (EU) clinical-trial legislation (Directive 2001/20/EC) and EU Legislation – Eudralex Volume 1 “Pharmaceutical Legislation Medicinal Products for Human Use”; Volume 10 “Clinical trials guidelins”
• Regulatory guidelines from The European Medicines Agency (EMA)
• Some guideline documents from The Heads of Medicines Agencies

According to EU Directive 2001/83/EC, “All clinical trials, conducted within the European Community, must comply with the requirements of Directive 2001/20/EC of the European Parliament and of the Council on the approximation of the laws, regulations and administrative provisions of the Member States relating to the implementation of good clinical practice in the conduct of clinical trials on medicinal products for human use. To be taken into account during the assessment of an application, clinical trials, conducted outside the European Community, which relate to medicinal products intended to be used in the European Community, shall be designed, implemented and reported on what good clinical practice and ethical principles are concerned, on the basis of principles, which are equivalent to the provisions of Directive 2001/20/EC. They shall be carried out in accordance with the ethical principles that are reflected, for example, in the Declaration of Helsinki.”

There are two updates to the Directive 2001/20/EC:

• COMMISSION DIRECTIVE 2005/28/EC of 8 April 2005 laying down principles and detailed guidelines for good clinical practice as regards investigational medicinal products for human use, as well as the requirements for authorisation of the manufacturing or importation of such products
• 2012/0192 (COD)  Proposal for a Regulation of the European Parliament and of the Council on clinical trials on medicinal products for human use, and repealing Directive 2001/20/EC

The European Medicines Agency (EMA) is the closest counterpart of US FDA and is the main body in EU to provide the regulatory guidelines for conducting clinical trials.

“The European Medicines Agency relies on the results of clinical trials carried out by pharmaceutical companies to reach its opinions on the authorisation of medicines. Although the authorisation of clinical trials occurs at Member State level, the Agency plays a key role in ensuring that the standards of good clinical practice (GCP) are applied across the European Economic Area in cooperation with the Member States. It also manages a database of clinical trials carried out in the European Union.”

The Heads of Medicines Agencies (HMA) is a network of the Heads of the National Competent Authorities whose organisations are responsible for the regulation of Medicinal Products for human and veterinary use in the European Economic Area. The Heads of Medicines Agencies is supported by working groups covering specific areas of responsibility and by the Heads of Medicines Agencies Management Group and Permanent Secretariat.
The Heads of Medicines Agencies co-operates with the European Medicines Agency and the European Commission in the operation of the European Medicines Regulatory Network (“the Network”).

The guidelines related to statistics are mainly under Clinical efficacy and safety: General and Clinical efficacy and safety: Clinical pharmacology and pharmacokinetics

Some of the guidelines are listed below with comparison to the corresponding FDA guidance.

EMA	FDA
Guideline on adjustment for baseline covariates
Guideline on Missing Data in Confirmatory Clinical Trials
Concept paper on the need for a Guideline on the use of Subgroup Analyses in Randomised Controlled Trials
Guideline on Clinical Trials in Small Populations
Reflection Paper on Methodological Issues in Confirmatory Clinical Trials Planned with an Adaptive Design	Adaptive Design Clinical Trials for Drugs and Biologics
Guideline on Data Monitoring Committees	Establishment and Operation of Clinical Trial Data Monitoring Committees
Guideline on the Choice of the Non-Inferiority Margin	Non-Inferiority Clinical Trials
Points to consider on switching between superiority and non-infeirority	Non-Inferiority Clinical Trials
	Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials
Points to Consider on Multiplicity Issues in Clinical Trials Concept paper on the need for a guideline on multiplicity issues in clinical trials
Reflection Paper on the Regulatory Guidance for the Use of Health Related Quality of Life (HRQL) Measures in the Evaluation of Medical Products	Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims
Points to consider on application with 1. Meta-Analyses; 2. One pivotal study	-          Providing Clinical Evidence of Effectiveness for Human Drug and Biological Products -          Integrated Summary of Effectiveness
Guideline on the Investigation of Bioequivalence	Statistical Approaches to Establishing Bioequivalence

Other EU regulatory guidelines that I have been exposed to are:

•  Clinical efficacy and safety: Blood products (including biotech alternatives)
• Guidance for the format and content of the final study report of non-interventional post-authorisation safety studies
• ENCEPP Considerations on the Definition of Non-Interventional Trials Under the Current legislative Framework (“Clinical Trials Directive” 2001/20/EC)
• Guideline on good pharmacovigilance practices (GVP) Module VIII – Post-authorisation safety studies
• Guidance for Industry on Providing Regulatory Information in Electronic Format: Non-eCTD electronic Submissions (NeeS) for human medicinal products
• TIGes Harmonised Guidance for Non-eCTD electronic Submissions (NeeS) for human medicinal products in the EU
• Dossier Format: Regulatory and procedural guidance
• Multinational Clinical Trials in Europe and the Voluntary Harmonisation Procedure (VHP)
• Guidance document for sponsors for a Voluntary Har-monisation Procedure (VHP) for the assessment of mul-tinational Clinical Trial Applications