Drug names: brand name, proprietary name, trade name, generic name, and sponsor's drug code

Every Saturday, my favorite radio show is "Wait Wait... Don't Tell Me! | WUNC". In yesterday's episode, the host made fun of the brand name ('Comirnaty') of Pfizer/BioNTech Covid (Listen: 46.17 minutes).

Covid vaccines from Pfizer/BioNTech, Moderna, and J&J have become the household name in just about a year or so and have been administered to millions and millions of people under FDA's Emergency Use Authorization (EUA). Under the EUA, these vaccines are just called and differentiated by the company's name: Pfizer vaccine, Moderna vaccine, and J&J vaccine. However, when the vaccine (or any drug) is formally approved, according to the regulation, it must have a brand name - in the case of Pfizer/BioNTech Covid vaccine, the brand name is now 'Comirnaty'.

The meaning behind the name 'Comirnaty': Comirnaty is an agglomeration of the words “Covid-19 immunity” and “mRNA,” the latter indicating the technology that makes the vaccine work. As a whole, the word is intended to evoke “community,”

This brings a question about the drug names: the difference between the brand name and the generic name and how to record the drug names in clinical trials. According to the CDASH, the case report form will include a form for concomitant medication including the questions like

"What was the term for the medication/therapy taken?

or

"What was the term for the medication taken?"

Concomitant medications used by the study participants prior to the trial or during the trial period should all be recorded. Usually, there is no restriction on the medication term to be recorded. Either brand name or generic name can be recorded. 

The brand name of a medication is the name given by the company that makes the drug and is usually easy to say for sales and marketing purposes. The brand name may also be called the trade name. In FDA's guidance, the brand name is called proprietary name. The proprietary name of a drug product is its brand name. 

The generic name, on the other hand, is the name of the active ingredient. Generic drugs are copies of brand-name drugs that have exactly the same dosage, intended use, effects, side effects, route of administration, risks, safety, and strength as the original drug. In other words, their pharmacological effects are exactly the same as those of their brand-name counterparts. In FDA's guidance, the generic name may be called 'nonproprietary name' or 'proper name' for biological products. For biological products, the term proper name means the nonproprietary name designated by FDA in the license for a biological product licensed under the PHS Act. See also 105 21 CFR 600.3(k).

For drugs that make it all the way through development, testing, and regulatory acceptance, the pharmaceutical company then gives the drug a trade name, which is a standard term in the pharmaceutical industry for a brand name, trademark name, or proprietary name. The proprietary name must be approved by the FDA and included in the product label. FDA has its guidance for industry to govern the brand name approval process "Best Practices in DevelopingProprietary Names for HumanPrescription Drug Products".

During the development stage, sponsors of the clinical trials may use their code for the compound or drug in the development. These drug codes will not be used when a drug is approved. For example, the famous drug from Merck 'pembrolizumab' has its own code MK-3475 that can be used in the clinical development stage and registered in clinicaltrials.gov, but will not be used as the name once the product is approved or marketed. Sponsor's drug code: MK-3475 - > generic name: pembrolizumab -> brand name: Keytruda. Similarly, Gilead's COVID-19 treatment drug has sponsor's drug code GS-5734 -> generic name: remdesivir -> Veklery. Pfizer/BioNTech's COVID-19 vaccine has sponsor's drug code: BNT16b2 -> generic name: COVID-19 Vaccine, mRNA -> brand name (proprietary name): COMIRNATY.

Here are some examples of the sponsor's drug code, generic drug name, brand drug name, and the corresponding indications.

When recording the drug name in clinical trials, either brand name or generic name can be recorded, but not the drug code used by the sponsors. 

Before the statistical analyses of the concomitant medication data, the data management group will perform appropriate medical coding activities to map the recorded brand name or generic name to the corresponding ATC classifications according to WHODrug-Global dictionary. 

Single Imputation Methods for Missing Data: LOCF, BOCF, LRCF (Last Rank Carried Forward), and NOCB (Next Observation Carried Backward)

The missing data is always an issue when analyzing the data from clinical trials. The missing data handling has been moved toward the model-based approaches (such as multiple imputation and mixed model repeated measures (MMRM)). The single imputation methods, while being heavily criticized and cast out, remain as practical approaches for handling the missing data, especially for sensitivity analyses.

Single imputation methods replace a missing data point by a single value and analyses are conducted as if all the data were observed. The single value used to fill in the missing observation is usually coming from the observed values from the same subject - Last Observation Carried Forward (LOCF), Baseline Observation Carried Forward, and Next Observation Carried Backward (NOCB, the focus of this post). The single value used to fill in the missing observation can also be derived from other sources: Last Rank Carried Forward (LRCF), Best or Worst Case Imputation (assigning the worst possible value of the outcome to dropouts for a negative reason (treatment failure) and the best possible value to positive dropouts (cures)), Mean value imputation, trimmed mean,…Single imputation approaches also include regression imputation, which imputes the predictions from a regression of the missing variables on the observed variables; and hot deck imputation, which matches the case with missing values to a case with values observed that is similar with respect to observed variables and then imputes the observed values of the respondent.

In this post, we discussed the single imputation method of LOCF, BOCF, LRCF, and NOCB (the focus of this post). 

Last Observation Carried Forward (LOCF): A single imputation technique that imputes the last measured outcome value for participants who either drop out of a clinical trial or for whom the final outcome measurement is missing. LOCF is usually used in the longitudinal study design where the outcome is measured repeatedly at pre-specified intervals. LOCF usually requires there is at least one post-baseline measure. The LOCF is the widely used single imputation method.

Baseline Observation Carried Forward (BOCF): A single imputation technique that imputes the baseline outcome value for participants who either drop out of a clinical trial or for whom the final outcome measurement is missing. BOCF is usually used in a study design with perhaps only one post-baseline measure (i.e., the outcome is only measured at the baseline and at the end of the study).

Last Rank Carried Forward (LRCF): The LRCF method carries forward the rank of the last observed value at the corresponding visit to the last visit and is the non-parametric version of LOCF. However, unlike the LOCF that is based on the observation from the same subject, for the LRCF method, the ranks come from all subjects with non-missing observations at a specific visit.  From the early visits to the later visits, the number of missing values will be different, the constant ranking, carried forward, and re-ranking will be needed. Here are some good references for LRCF:

• O’Brien et al (2005) Semi-parametric and non-parametric methods for clinical trials with incomplete data
• Peter Christenson An Alternative to Last-Observation-Carried-Forward: Last-Rank-Carried-Forward
• Fan et al (2015) Methods for Missing Data Handling in Randomized Clinical Trials With Nonnormal Endpoints With Application to a Phase III Clinical Trial

LRCF is thought to have the following features:

In a paper by Jing et al, the LRCF was used for missing data imputation: 

"...The last rank carried forward or last observation carried forward was assigned to patients who withdrew prematurely from the study or study drug for other reasons or who did not perform the 6-minute walk test for any reason not mentioned above (eg, missed visit), provided that the patient performed at least 1 postbaseline 6-minute walk test.

Next Observation Carried Backward (NOCB): NOCB is a similar approach to LOCF but works in the opposite direction by taking the first observation after the missing value and carrying it backward. NOCB may also be called Next Value Carried Backward (NVCB) or Last Observation Carried Backward (LOCB).

NOCB may be useful in handling the missing data arising from the external control group, from Real-World Data (RWD), Electronic health records (EHRs) where the outcome data collection is usually not structured and not according to the pre-specified visit schedule. 

I can foresee that the NOCB may also be an approach in handing the missing data due to the COVID-19 pandemic. Due to the COVID-19 pandemic, subjects may not be able to come to the clinic for the outcome measure at the end of the study. The outcome measure may be performed at a later time beyond the visit window allowance. Instead of having a missing observation for the end of the study visit, the NOCB approach can be applied to carry the next available outcome measure backward. 

The NOCB approach, while not popular, can be found in some publications and regulatory approval documents. Here are some examples: 

In an article by Wyles et al (2015, NEJM) Daclatasvir plus Sofosbuvir for HCV in Patients Coinfected with HIV-1, "Missing response data at post-treatment week 12 were inferred from the next available HCV RNA measurement with the use of a next-value-carried-backward approach."

In BLA 761052 of Brineura (cerliponase alfa) Injection Indication(s) for Late-Infantile Neuronal Ceroid Lipofuscinosis Type 2 (CLN2)- Batten Disease, the NOCB was used to handle the missing data for comparison to the data from a natural history study. 

Because intervals between clinical visits vary a lot in Study 901, the agency recommended performing analyses using both the last available Motor score and next observation carried backward (NOCB) for the intermediate data points although the former one is determined as the primary. 

In FDA Briefing Document for Endocrinologic and Metabolic Drugs Advisory Committee Meeting for NDA 210645, Waylivra (volanesorsen) injection for the treatment of familial chylomicronemia syndrome, NOCF was used as one of the sensitivity analyses:

Similar planned (prespecified) analyses using different variables, such as slightly different endpoint definitions (e.g. worst maximum pain intensity versus average maximum pain intensity), or imputation methods for missing data (next observation carried backward versus imputation of zero for missing values) did not demonstrate treatment differences.

 Missing values were pre-specified to be imputed using Next Observation Carried Back (NOCB); i.e., if a patient did not complete the questionnaire for several weeks, the next value entered was assumed to have occurred during all intervening (missing) weeks.

 Missing data for any post-baseline visit will be imputed by using Next Observation Carried Back (NOCB) if there is a subsequent score available. Missing data after the last available score of each patient will not be imputed.

in NDA 212157 of Celecoxib Oral Solution for Treatment of acute migraine, the NOCB was used for sensitivity analysis

Headache Pain Freedom at 2 hours - Sensitivity Analysis

To analyze the missing data for the primary endpoint, Dr. Ling performed an analysis analyzing patients who took rescue medications as nonresponders and then also imputing missing data at the 2-hour time point using the next available time point of information (Next Observation Carried Backward (NOCB)) or a worst-case type of imputation (latter not shown in table).

Single imputation methods are generally not recommended for the primary analysis because of the following disadvantages (issues): 

• Single imputation usually does no provides an unbiased estimate
• Inferences (tests and confidence intervals) based on the filled-in data can be distorted by bias if the assumptions underlying the imputation method are invalid
• Statistical precision is overstated because the imputed values are assumed to be true.
• Single imputation methods risk biasing the standard error downwards by ignoring the uncertainty of imputed values. Therefore, the confidence intervals for the treatment effect calculated using single imputation methods may be too narrow and give an artificial impression of precision that does not really exist.  
• the single imputation method such as LOCF, NOCB, and BOCF do not reflect MAR (missing at random) data mechanisms.

Further Readings:

• EMA's Guideline on Missing Data in Confirmatory Clinical Trials
• US National Research Council: The Prevention and Treatment of Missing Data in Clinical Trials 
• Missing Data Approaches in eHealth Research: Simulation Study and a Tutorial for Nonmathematically Inclined Researchers 
• John Lachin (2016) Fallacies of last observation carried forward analyses
• Missing Value Imputation by Last Observation Carried Forward - R package
• Kenward and Molenberghs (2009) Last Observation Carried Forward: A Crystal Ball? 
• Engels and Diehr (2003) Imputation of missing longitudinal data: a comparison of methods
• 
  

The fate of confirmatory clinical trials for Remdesivir for treatment of COVID-19

Remdesivir, as the first highly touted drug to treat COVID-19 patients, has now been approved for emergency use authorization in several countries. The focus of fighting COVID-19 seems to shift to the safe and effective vaccine development. Unfortunately, the efficacy of Remdesivir has not been confirmed due to the flaws in the study design (for example, no placebo control) or due to the issues in the study conduct (for example, early discontinuation of the study resulted in underpowered studies) .

• Despite the Hype, Gilead’s Remdesivir Will Do Nothing to End the Coronavirus Pandemic
• Remdesivir alone is not enough, researchers conclude in first major Covid-19 trial of the drug
• Data Monitoring for the Chinese Clinical Trials of Remdesivir in Treating Patients with COVID-19 During the Pandemic Crisis
• Gilead to end coronavirus drug trials, adding to access worry: researchers

In a previous post, six pivotal studies were listed for Remdesivir. These six studies were also listed in the article by Singh et al Remdesivir in COVID-19: A critical review of pharmacology, pre-clinical and clinical studies.

The table below listed the status (fate) of these studies.

Protocol Title	Study Features	Fate of Studies
A Phase 3 Randomized Study to Evaluate the Safety and Antiviral Activity of Remdesivir (GS-5734™) in Participants With Severe COVID-19	Gilead Sciences Phase III, initially planned for 400 subjects, then increased to 2400, and then 6000 subjects Two  arms: Standard of care + Remdesivir for 5 days, Standard of care + Remdesivir for 10 days	Enrolment was stopped early after 397 subjects were randomized.   Results were published in NEJM (Goldman et al “Remdesivir for 5 or 10 Days in Patients with Severe Covid-19“ Conclusion: “In patients with severe Covid-19 not requiring mechanical ventilation, our trial did not show a significant difference between a 5-day course and a 10-day course of remdesivir. With no placebo control, however, the magnitude of benefit cannot be determined.” It is noted that the study design was flawed and should have included a third arm with Standard of Care without Remdesivir. The data from this study was further compared to the external control group and results were announced in a recent press release "Comparative Analysis of Phase 3 SIMPLE-Severe Study and Real-World Retrospective Cohort of Patients Diagnosed with Severe COVID-19 Receiving Standard of Care" to show the statistically significant reduction in mortality in Remdesivir group.
A Phase 3 Randomized Study to Evaluate the Safety and Antiviral Activity of Remdesivir (GS-5734™) in Participants With Moderate COVID-19 Compared to Standard of Care Treatment	Gilead Sciences Phase III, initially planned for 600 subjects, then increased to 1600 subjects Three arms: Remdesivir for 5 days, Remdesivir for 10 days, Standard of care	The study is active, but not recruiting new patients. The enrolment has stopped. The results have not been published yet. Gilead announces results in a press release “Gilead Announces Results From Phase 3 Trial of Remdesivir in Patients With Moderate COVID-19” Study Demonstrates 5-Day Treatment Course of Remdesivir Resulted in Significantly Greater Clinical Improvement Versus Treatment with Standard of Care Alone Data Add to Body of Evidence from Prior Studies Demonstrating Benefit of Remdesivir in Hospitalized Patients with COVID-19 The results were later published in JAMA "Effect of Remdesivir vs Standard Care on Clinical Status at 11 Days in Patients with Moderate Covid-19" Also see the articles “More evidence remdesivir helps some coronavirus patients”  “Gilead's remdesivir shows modest improvement in moderate COVID-19 patients” “A slice of half-baked remde­sivir da­ta takes a dark turn — but we're still wait­ing for a de­fin­i­tive study”
A Phase 3 Randomized, Double-blind, Placebo-controlled Multicenter Study to Evaluate the Efficacy and Safety of Remdesivir in Hospitalized Adult Patients With Mild and Moderate 2019-nCoV Respiratory Disease.	Capital Medical University/Chinese Academy of Medical Sciences Phase III, 308 Subjects Two arms: Remdesivir, placebo Mainland China only	The study was suspended (The epidemic of COVID-19 has been controlled well at present, no eligible patients can be recruited).  The results have not been published yet.  See “Second trial testing Gilead's remdesivir in COVID-19 suspended in China”
A Phase 3 Randomized, Double-blind, Placebo-controlled, Multicenter Study to Evaluate the Efficacy and Safety of Remdesivir in Hospitalized Adult Patients With Severe 2019-nCoV Respiratory Disease.	Capital Medical University Phase III, 453 Subjects Two arms: Remdesivir, placebo Mainland China only	The study was terminated after 237 patients were enrolled and randomly (158 in remdesivir and 79 in placebo) (The epidemic of COVID-19 has been controlled well in China, no eligible patients can be enrolled at present.) Results were published at Lancet Wang et al “Remdesivir in adults with severe COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial” Conclusion: “In this study of adult patients admitted to hospital for severe COVID-19, remdesivir was not associated with statistically significant clinical benefits. However, the numerical reduction in time to clinical improvement in those treated earlier requires confirmation in larger studies.”
A Multicenter, Adaptive, Randomized Blinded Controlled Trial of the Safety and Efficacy of Investigational Therapeutics for the Treatment of COVID-19 in Hospitalized Adults	National Institute of Allergy and Infectious Diseases (NIAID) Phase II, planned 440 Subjects (protocol specified 394 subjects), actual enrolment: 1063 subjects at the time of DMC review) Two arms: Placebo, Remdesivir with additional arms to be added Multi-National: US, Japan, South Korea, Singapore	The study was stopped after the interim analyses.  Preliminary results were published in NEJM by Beigel et al. Remdesivir for the Treatment of Covid-19 - Preliminary Report Conclusion: “Remdesivir was superior to placebo in shortening the time to recovery in adults hospitalized with Covid-19 and evidence of lower respiratory tract infection.” The results from this study were the basis for FDA to issue Emergency Use Authorization for Remdesivir. Subsequently, several other countries followed suit. It is disappointing that the study was stopped after inconclusive or not convincing results from the interim analyses. There was no mention if there was a pre-specified stopping rule and whether the boundaries for stopping the study had been crossed.  Also see: Inside the NIH’s controversial decision to stop its big remdesivir study The final report was later published in NEJM (on Oct 8). Results were better than those reported in the preliminary report (median recovery time was shorten by 5 days). The final report concluded "Our data show that remdesivir was superior to placebo in shortening the time to recovery in adults who were hospitalized with Covid-19 and had evidence of lower respiratory tract infection."
Multi-centre, Adaptive, Randomized Trial of the Safety and Efficacy of Treatments of COVID-19 in Hospitalized Adults	Institut National de la Santé Et de la Recherche Médicale, France Phase III, 3100 Subjects Four arms: Remdesivir, Lopinavir/ritonavir, Interferon Beta-1A, Hydroxychloroquine, Standard of care France Only	This study is funded by WHO and is called DIsCoVeRy in clinicaltrials.gov and SOLIDARITY trial in ISRCTN registration. The interim results were published in a paper "Repurposed antiviral drugs for COVID-19; interim WHO SOLIDARITY trial results". It concludes "These Remdesivir, Hydroxychloroquine, Lopinavir and Interferon regimens appeared to have little or no effect on hospitalized COVID-19, as indicated by overall mortality, initiation of ventilation and duration of hospital stay. The mortality findings contain most of the randomized evidence on Remdesivir and Interferon, and are consistent with meta-analyses of mortality in all major trials." The results were disputed by the manufacturer of Remdesirvir Gilead.

Covid-19 Vaccine: Is 50% Vaccine Efficacy (VE) Too Low?

Ever since FDA issued its guidance “Development and Licensure of Vaccines to Prevent COVID-19,” to help facilitate timely development of safe, effective COVID-19 Vaccines, the question arose whether the threshold of 50% vaccine efficacy (VE) was set too low.

• FDA issues COVID-19 vaccine guidance, setting 50% effectiveness threshold
• A COVID-19 Vaccine May Be Only 50% Effective. Is That Good Enough?
• COVID-19 vaccine trials should seek worthwhile efficacy

As cited in an article “FDA to Require 50 Percent Efficacy for COVID-19 Vaccines”:

Gregory Poland, the director of the Mayo Vaccine Research Group, tells Reuters the efficacy guidelines are standard compared to other vaccines. “They look pretty much like influenza vaccine guidelines,” Poland says. “I don’t think that’s a high bar. I think that’s a low to . . . appropriate bar for a first-generation COVID-19 vaccine.” The effectiveness of the annual flu shot, for example, generally ranges between 40 percent and 60 percent, according to The Washington Post.

Peter Hotez, a vaccine expert at the Baylor College of Medicine, tells the Post the 50 percent threshold is low, a sign that the FDA recognizes “our first vaccine won’t be our best.” Ultimately, he says, vaccine developers should aim for 70–75 percent efficacy. 

If we just look at the face meaning of the 50% VE, it does look like the bar is low. Some people may interpret 50% VE as that the COVID-19 vaccine will only need to be effective in 50% of people – which is not true. Even in the FDA’s announcement about the issuance of its guidance, the statement about the requirement of 50% VE was incorrectly stated:

“The guidance also discusses the importance of ensuring that the sizes of clinical trials are large enough to demonstrate the safety and effectiveness of a vaccine. It conveys that the FDA would expect that a COVID-19 vaccine would prevent disease or decrease its severity in at least 50% of people who are vaccinated.”

Let’s see how the vaccine efficacy is calculated and what the 50% VE means.

According to Wikipedia, Vaccine efficacy (VE) is the percentage reduction of disease in a vaccinated group of people compared to an unvaccinated group, using the most favorable conditions.

The outcome data (vaccine efficacy) generally are expressed as a proportionate reduction in disease attack rate (AR) between the unvaccinated (ARU) and vaccinated (ARV), or can be calculated from the relative risk (RR) of disease among the vaccinated group.

The basic formula is written as:

VE=(ARU-ARV) / ARU * 100%

with

VE = Vaccine efficacy,

ARU = Attack rate of unvaccinated people,

ARV = Attack rate of vaccinated people.

An alternative, equivalent formulation of vaccine efficacy

VE=1-RR

where RR is the relative risk of developing the disease for vaccinated people compared to unvaccinated people.

In the actual calculation of VE, we will need to consider the total exposure time (usually measured by the total person-time). One person observed for one year = 1 person-year; one person observed for 3 months = 0.25 person year. the total person-time in year (or total person-years) will be the summation of person-years across all participants in the vaccine group and similarly across all participants in the placebo group. 

The point estimate of the VE can be written as:

If the clinical trial has a 1:1 randomization ratio (all participants are randomized equally into the vaccine group and the placebo group), the 'total person-time' in the vaccine group will be approximately equal to the 'total person-time' in the placebo group, the point estimate of VE can then be easily calculated as:

 

If we know the number of cases (here COVID-19 cases) in the vaccine group and in the placebo group, we can easily calculate the VE.  For example, if the total number of cases is 150 (50 cases observed in the vaccine group and 100 cases observed in the placebo group), the VE will be 1 - (50/100) = 0.5 = 50%.

If an interim analysis is performed after a total of 75 cases are observed, VE will be 50% if 25 cases are observed in the vaccine group and 50 cases are observed in the placebo group. 

Here is a comparison of VE calculations from three Phase III protocols of COVID-19 vaccines:

 

	Moderna	Pfizer	AstraZeneca
Primary efficacy endpoint	VE will be estimated with 1 - HR (mRNA-1273 vs placebo) using a Cox proportional hazard regression model with treatment group as a fixed effect and adjusting for stratification factor	VE will be estimated by 100 × (1 – IRR), where IRR is the  calculated ratio of confirmed COVID-19 illness per 1000 person-years follow-up in the active vaccine group to the corresponding illness rate in the placebo group 7 days after the last dose.	VE is calculated as RRR = 100*(1-relative risk), which RRR is the incidence of infection in the vaccine group relative to the incidence of infection in the control group expressed as a percentage.
Statistical model for calculating the VE and its 95% confidence interval	Cox proportional hazard model	Beta-binomial model	Modified Poisson regression model with robust variance
Sample size (number of volunteers to be recruited)	30,000	43,998	33,000
Number of cases needed to be observed	151	164	150

With the COVID-19 pandemic is still not under control in the US and a large number of volunteers participating in these phase III clinical trials, we hope that the total number of COVID-19 cases can be easily reached so that we can have a readout about the vaccine's efficacy. All three studies have included at least one interim analysis to have a possible readout much earlier. 

In addition to the requirement of at least 50% VE, FDA guidance also requires that the lower bound of the 95% confidence interval of the VE must be greater than 30%. 

The sample size (the number of COVID-19 infection cases) is largely dictated by this criterion of 30% for the lower bound of 95% CI. Otherwise, with 3 cases (1 case in the vaccine group and 2 cases in the placebo group), we would have a point estimate of VE = 50% to meet the requirement. 

50% VE implies that the vaccine can decrease the risk of COVID-19 cases by 50%. Comparing with other clinical trials, the 50% reduction is substantial and meaningful. In Moderna's trial, the VE will be estimated using the Cox proportional hazard model where the time to the first case of COVID-19 infection is also considered. In order to meet the criteria of at least 50% VE, the estimated hazard ratio (HR) needs to be equal to or less than 0.5. In oncology trials or in other clinical trials with time to event variables, if we can have an HR of 0.5 or lower, we will claim that the experimental treatment can reduce the risk of death or event by at least 50% - a result to die for. 

I agree with the statement about the COVID-19 vaccine from a Lancet paper:

“A vaccine that has 50% efficacy could appreciably reduce incidence of COVID-19 in vaccinated individuals, and might provide useful herd immunity. Hence, although efficacy far greater than 50% would be better, efficacy of about 50% would represent substantial progress.”

SARS-CoV-2 (the virus causing COVID-19) has a very high R0 (2.5 according to the table below) which estimates the speed at which a disease is capable of spreading in a population. We hope that we will have a vaccine that will meet the efficacy requirements of at least 50% VE in point estimate and at least 30% VE in the lower bound of 95% confidence interval. With an effective vaccine and the majority of people being vaccinated, we may be able to drop the transmissibility R0 below 1 to prevent the spread of the SARS-CoV-2.

     Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics

How to interpret the risk ratio? - A controversy related to FDA's EUA announcement of convalescent plasma in treatment of Covid-19

This past Sunday, FDA Issues Emergency Use Authorization for Convalescent Plasma as Potential Promising COVID–19 Treatment, Another Achievement in Administration’s Fight Against Pandemic. The EUA (emergency use authorization) was mainly based on a study conducted by Mayo Clinic and the study results were described in the paper (not yet peer-reviewed yet) uploaded to Medrxiv.

Joyner 2020 “Effect of Convalescent Plasma on Mortality among Hospitalized Patients with COVID-19: Initial Three-Month Experience”.

 Issuance of EUA for convalescent plasma immediately draw some criticize about not having strong evidence to support the EUA approval and about the timing of the EUA announcement (right before the Republic National Convention) – We will leave this to others to debate.

One thing related to the statistics is how to present or interpret the results from Mayo Clinic’s study. In the paper, the author stated the hypotheses for analyzing the data from this study that has no concurrent control group.  

“We hypothesized, based on historical data that earlier administration of convalescent plasma with high antibody levels would be associated with reduced mortality. To address this hypothesis, we evaluated seven and 30-day mortality in 35,322 hospitalized adults transfused with COVID-19 convalescent plasma by asking two questions. First, was earlier treatment of patients with convalescent plasma after diagnosis of COVID-19 associated with reduced mortality compared to later treatment in the course of disease? Second, were higher antibody levels in the transfused convalescent plasma associated with reduced mortality?”

For the statistical analyses, the study cohort "was stratified into categories based on the days from COVID-19 diagnosis to plasma transfusion, including: 0, 1-3, 4-10, and 11 or more days and for some graphical presentations and analyses, dichotomized into 0-3 vs. 4 or more days". The relative risk was calculated for each sub-group for mortality for patients who received convalescent plasma with IgG S/co greater than 18.45 (high antibody level group) vs. patients that received less than 4.62 S/Co (low antibody level group). The pooled, or common, relative risk for 7-day and 30-day mortality were then calculated. The paper concluded, "the pooled relative risk of mortality among patients transfused with high antibody level plasma units was 0.65 [0.47-0.92] for 7 days and 0.77 [0.63-0.94] for 30 days compared to low antibody level plasma units."  For 7-day mortality, the forest plot was depicted below:

 

How to interpret the pooled relative risk of 0.65? FDA commissioner, Dr. Hahn, was criticized for overstating the efficacy results because of his interpretation of this relative risk. 

“In the optimal patients … treated with convalescent plasma at the highest titers, there was a 35% improvement in survival, which is a significant clinical benefit,” Hahn said during the press conference, noting that, “This clearly meets the criteria that we’ve established for emergency use authorization.”
 
Hahn went on to say that, “A 35% improvement in survival is a pretty substantial clinical benefit. What that means is—and if the data continue to pan out—100 people who are sick with COVID-19, 35 would have been saved because of the administration of plasma.”

Dr. Hahn's statement immediately drew a lot of criticizes and he had to come up to apologize for overstating the treatment effect of convalescent plasma in the treatment of COVID-19. 

• Lawmakers, experts raise questions after convalescent plasma EUA
• FDA chief apologizes for overstating plasma effect on virus
• FDA's Hahn Apologizes For Overselling Plasma's Benefits As A COVID-19 Treatment
• Covid-19 Live Updates: 2 F.D.A. Public Relations Experts Are Fired After Plasma Fiasco
• Is convalescent plasma safe and effective? We answer the major questions about the Covid-19 treatment

What is the right way to state the relative risk of 0.65? Well, relative risk is relative and can't be stated as an absolute benefit. CDC’s website “An Introduction to Applied Epidemiology and Biostatistics” has a chapter about the relative risk. It explains how the relative risk is calculated and how to interpret the results. 

In general, a risk ratio greater than 1.0 indicates an increased risk for the group in the numerator, usually the exposed group. A risk ratio of less than 1.0 indicates a decreased risk for the exposed group, indicating that perhaps exposure actually protects against disease occurrence.

With the convalescent plasma study by Mayo Clinic, the relative risk is calculated as the ratio of "the risk of 7-day mortality in patients receiving convalescent plasma with high antibody level" divided by "the risk of 7-day mortality in patients receiving convalescent plasma with low antibody level". A relative ratio of 0.65 (less than 1.0) indicates the benefit of receiving convalescent plasma with high antibody level - an indication of dose-response. 

It would be correct to state:

"The risk ratio of 0.65 indicates that COVID-19 patients who received convalescent plasma with high antibody level were only 65% as likely to die in 7 days as were patients who received convalescent plasma with low antibody level"

or

"The risk ratio of 0.65 indicates that the convalescent plasma with high antibody level reduced the risk of 7-day mortality by 35%"

Blinding and Masking Issue in Covid-19 Vaccine Clinical Trials

Clinical trials for Covid-19 vaccine development are moving into the critical late phase stage. The front runners right now are Moderna (in collaboration with NIAIH), Oxford University (in collaboration with AstraZeneca), and BioNTech (in collaboration with Pfizer). All three had published the positive results from their phase 1/2 studies to demonstrate that the Covid-19 vaccines can generate utilizing antibodies against SARS-COV-2 virus and vaccines are tolerable and generally safe in healthy volunteers. 

• Moderna Phase 1 results: Jackson et al (2020) An mRNA Vaccine against SARS-CoV-2 — Preliminary Report NEJM 
• Oxford/AstraZeneca Phase 1/2 results:  Folegatti et al (2020) Safety and immunogenicity of the ChAdOx1 nCoV-19 vaccine against SARS-CoV-2: a preliminary report of a phase 1/2, single-blind, randomised controlled trial. Lancet
• BioNTech/Pfizer Phase 1/2 results: Mulligan et al (2020) Phase 1/2 Study to Describe the Safety and Immunogenicity of a COVID-19 RNA Vaccine Candidate (BNT162b1) in Adults 18 to 55 Years of Age: Interim Report. medRxiV

The confirmatory studies are about to begin to demonstrate the efficacy and safety of the Covid-19 vaccines. The requirements for study design, efficacy endpoint, and safety endpoints are laid out in FDA's guidance "Development and Licensure of Vaccines to Prevent COVID-19"

Moderna is supposed to announce the start of phase 3 study next week. The other two will follow. The phase 3 studies from these three companies have already been registered in clinicaltrials.gov. The table below lists key parameters from these three studies. BioNTch/Pfizer had phase 1/2/3 studies combined in the same study protocol where the results from the phase 1 portion of the study have been published (see above Mulligan et al) 

	Moderna/NIAIH	Oxford/AstraZeneca	BioNTech/Pfizer
Protocol Title	A Phase 3, Randomized, Stratified, Observer-Blind, Placebo-Controlled Study to Evaluate the Efficacy, Safety, and Immunogenicity of mRNA-1273 SARS-CoV-2 Vaccine in Adults Aged 18 Years and Older	A Phase 2/3 Study to Determine the Efficacy, Safety and Immunogenicity of the Candidate Coronavirus Disease (COVID-19) Vaccine ChAdOx1 nCoV-19	A Phase 1/2/3, Placebo-Controlled, Randomized, Observer-Blind, Dose-Finding Study to Evaluate the Safety, Tolerability, Immunogenicity, and Efficacy of SARS-COV-2 RNA Vaccine Candidates Against COVID-19 in Healthy Adults
Phase	Phase 3	Phase 2/3	Phase 1/2/3
Sample Size	30,000	10,260	32,000
Treatment Groups	mRNA-1273 Placebo	ChAdOx1 nCoV-19 (Abs 260) MenACWY vaccine ChAdOx1 nCoV-19 (Abs 260) + 2.2x10^10vp (qPCR) boost Two dose MenACWY vaccine ChAdox1 n-CoV-19 (Abs 260) vaccine low dose ChAdOx1 nCoV-19 (qPCR) ChAdOx1 nCoV-19 plus 5x10^10vp boost (qPCR)	BNT162b1 BNT162b2 BNT162b3 Placebo
Age Groups	18 years and older	18 years or older 18-55 years 70 years and older 5-12 years inclusive	18-55 years of age 65-85 years of age 18-85 years of age
Number of Doses	100 microgram 2 doses (on day 1 and day 29)	1 or 2 doses	Low, low-mid, mid, or high doses 1 or 2 doses
Randomization	Randomized	Randomized	Randomized
Control Group	Placebo [0.9% sodium chloride (normal saline) injection]	MenACWY vaccine (also named Menveo or Nimenrix)	Placebo [a sterile saline solution for injection (0.9% sodium chloride injection, in a 0.5-mL dose)]
Blinding/Masking	Quadruple (Participant, Care Provider, Investigator, Outcome Assessor)	Single (Participant)	Triple (Participant, Care Provider, Investigator)

With the side-by-side comparison, we can see the clear difference in selecting the control group and how the blinding/masking is handled. In studies by Moderna and BioNTech, the control group is a placebo consisting of only the normal saline. But the quadruple and the triple masking (beyond the double-blind) are used to prevent the potential unblinding. 

 In the study by Oxford, the control group is another vaccine, MenACWY vaccine that is approved for protecting against meningococcal disease (meningitis and blood poisoning (septicaemia)) caused by serogroups A, C, W, and Y. The single blinding is used and the participants (volunteers) will not know whether they receive Covid-19 vaccine or MenACWY vaccine. 

In order to prevent potential unblinding - the participants become knowing which treatment they have received, using an active vaccine such as MenACWY that have been approved to be safe seems to be better and more adequate. In the publication of their phase 1 study results, they explained why it's necessary to use MenACWT vaccine as control. 

"MenACWY was used as a comparator vaccine to maintain blinding of participants who experienced local or systemic reactions, since these reactions are a known association with viral vector vaccinations. Use of saline as a placebo would risk unblinding participants as those who had notable reactions would know they were in the ChAdOx1 nCoV-19 vaccine group."

Placebo with saline as the control group is acceptable to FDA. In FDA's guidance "Development and Licensure of Vaccines to Prevent COVID-19", it says "Later phase trials, including efficacy trials, should be randomized, double-blinded, and placebo controlled" even though there is no mention about the requirement for the component of the placebo. 

With placebo (saline) as the control group, no matter whether the triple or quadruple blinding is used, there is still a potential unblinding by the participants because the participants can guess which treatment (Covid-19 vaccine or placebo) they have received based on the adverse events they may experience.

The published early phase results indicate that participants receiving Covid-19 vaccine experience more frequent adverse events in local injection site reactions and systemic reactions. BioNTech/Pfizer study says: 

"pain at the injection site was the most frequent prompted local reaction, reported after Dose 1 by 58.3% (7/12) in the 10 μg, 100.0% (12/12 each) in the 30 μg and 100 μg BNT162b1 groups, and by 22.2% (2/9) of placebo recipients. After Dose 2, pain was reported by 83.3% and 100.0% of BNT162b1 recipients at the 10 μg and 30 μg dose levels, respectively, and by 16.7 % of placebo recipients."

"Reports of fatigue and headache were more common in the BNT162b1 groups compared to placebo. Additionally, chills, muscle pain, and joint pain were reported among BNT162b1 recipients and not in placebo recipients."

After vaccination, participants may be able to guess they have received Covid-19 if they experience adverse events such as local injection site pain and systemic side effects such as fatigue, headache, chills, muscle pain,... They will be able to guess (pretty accurately) that they have received Placebo (saline) if they don't experience any local reactions or systemic side effects. 

If participants become aware of the treatment they have received, will it have an impact on their behavior? Will participants knowing to receive Covid-19 vaccine feel they have some protection, therefore maybe let loose their guard against Covid-19? I hope this will not be the case, otherwise, the biases induced by the behavior change because of the potential unblinding will have an impact on the efficacy results (most likely toward the null hypothesis of no difference).