Monday, December 28, 2020

120-Day Safety Update or 4-Month Safety Update - The Requirement for NDA/BLA

After the new drug application (NDA) or biological license application (BLA) is submitted by the sponsor and is accepted by FDA, FDA reviewers will take 10 months (regular review) or 6 months (expedited review) to review the submission package and issue a decision on or before the decision date (PDUFA date). FDA reviewers will evaluate marketing applications for efficacy and safety and consider benefit and risk and will expect to receive a complete application at the time of filing (exclusive of the 120-day safety update).

It is very possible that during the 6-10 month review time, the sponsor will have additional data to supplement the already submitted NDA/BLA package. The regulatory pathway for providing additional data to the FDA is through so-called ‘120-Day Safety Update’, also referred to as ‘4-Month Safety Update, 4MSU).

The ‘120-Day Safety Update’ or ‘4-Month Safety Update” is specified as a requirement in Code of Federal Regulations - 21CFR314.50.


The 120-Day Safety Update contains any new safety information learned about the drug that may reasonably affect the statement of contraindications, warnings, precautions, and adverse reactions in the draft drug labeling.

The report must be received by the FDA within 120 days of drug approval submission (receipt by the FDA of the New Drug Application (NDA), comprising the CTD/Integrated Summary Report) to avoid triggering an extension of the review clock.

The 120-Day Safety Update Report is mandated for submission to the FDA 120 days after submission of the NDA/BLA, and is intended to provide a summary update of any new safety data gathered by the sponsor since the data cut-off for the NDA submission documents, which could have been as far back as 6 months prior to the NDA submission date. In effect, the 120-Day Safety Update report could represent almost 1 year’s worth of new safety data, which needs to be reviewed by the authorities to ensure there has been no change in the product’s recorded safety profile. This is particularly important for medications intended for long-term treatment.

The 120-Day Safety Update is focused on additional safety data. If additional data is collected for efficacy variables, the efficacy information can also be included - but in general, it is for the summary propose and there is no inferential statistics needed. 

The data to be included in the 120 Day Safety Update can include:

  • The long-term follow-up data from the on-going clinical trials
  • Open-label extension studies with patients rolled over from the pivotal studies (usually the double-blinded controlled studies)
  • Additional data from later time points and from newly enrolled patients
  • Newly initiated clinical trials

Depending on the type of data to be included in the 120 Day Safety Update, the submission package could be just a written report or a full submission package (including the report; post-text tables, listings, figures; the data sets; define documents; SDRG/ADRG, etc.).

There are a lot of examples of 120 Day Safety Update from market applications. Here are some examples:

Briefing Document for Advisory Committee Meeting on Novo Nordisk’s Insulin degludec/liraglutide (IDegLira) for Treatment to Improve Glycemic Control in Adults with Type 2 Diabetes Mellitus. The NDA submission was based on two pivotal trials. Two pivotal trials (Trial 3697 in patients inadequately controlled on OAD treatment and Trial 3912 in patients inadequately controlled on basal insulin treatment) were designed to assess the contribution of the individual components of the combination to its primary efficacy effect (i.e., overall glycemic control). Additional data from other ongoing studies and the studies initiated after the data cut for NDA submission were submitted to the NDA as ‘120 Day Safety Update’:

The NDA submitted to the FDA had a data cut-off of 31 March 2015. Additional blinded safety data from two phase 3 trials that were ongoing at the time of NDA submission (Trials 4119 and 4056) as well as from a trial that was subsequently initiated (Trial 4185) was submitted to the FDA in a 120 Day Safety Update with a cut-off date of 30 September 2015. A brief overview of the ongoing trials included in the 120-day safety update is provided in Table 1–1. The 120-day safety update included available blinded safety data from these trials on deaths, other serious adverse events, pregnancies (including updates for pregnancies reported as ongoing in the NDA) and adverse events leading to withdrawal. 

Sunovion Pharmaceuticals NDA of Latuda for treatment of major depressive episodes associated with bipolarI disorder in pediatric patients aged 10 and older. The NDA submission was mainly based on the pivotal study (Study D1050326). Subjects who completed Study D0150326 was recruited into an open-label study (D1050302). As a 120-day safety date, the date from the open label study was submitted to support the NDA.

Study D1050302 is a 104-week open-label trial designed to assess the long-term safety profile of lurasidone (dosed 20-80 mg per day) in pediatric patients recruited from the pediatric schizophrenia (Study D1050301), bipolar depression (Study D1050326), and autism trials. This study was scheduled for completion last December, 2017. The Applicant submitted preliminary data for 619 patients participating in this trial with a cutoff date of October, 2016. Additionally, the 120-day safety update submitted with this application focused on the available safety data from 305 patients recruited from Study D1050326 with a cutoff date of May, 2017. It should be noted that although the final report for Study D1050302 has not been submitted for review, the Applicant presented acceptable long-term data to make an approval determination for this sNDA, including lurasidone exposure of 153 patients for ≥ 52 weeks.

Clinical Review for BLA for Mepolizumab for Add-on maintenance treatment of severe asthma. The data from long-term open-label studies were not available at the time of BLA preparation but was submitted to FDA as a 120-Day Safety Update.

This safety review primarily relies on data from three placebo-controlled studies in a severe asthma population: MEA112997 (Study 97), MEA115588 (Study 88) and MEA115575 (Study 75) as these studies most closely approximate the patient population to receive mepolizumab in the clinical practice. Within this review, the pooled database for these studies is referred to as the Placebo-Controlled Severe Asthma Studies (PCSA). Longer term safety data are provided by two open-label studies, MEA115666 (Study 66), MEA115661 (Study 61). These studies were ongoing at the time of the BLA submission with updated data provided to the Division in a 120-day safety update. The data from this safety update used a cutoff date of October 27, 2014 and provides cumulative review of the data for the studies ongoing at the time of BLA submission25 .

Tuesday, December 08, 2020

COA (Clinical Outcome Assessment): PRO, ClinRO, PerfRO, ObsRO, eCOA, and TECOA

Clinical Outcome Assessment (COA) has triggered multiple acronyms: PRO, ClinRO, PerfRO, and ObsRO

According to FDA's website, these acronyms are defined as the following: 

PRO - patient-reported outcome
A type of clinical outcome assessment. A measurement based on a report that comes directly from the patient (i.e., study subject) about the status of a patient’s health condition without amendment or interpretation of the patient’s response by a clinician or anyone else. A PRO can be measured by self-report or by interview provided that the interviewer records only the patient’s response. Symptoms or other unobservable concepts known only to the patient can only be measured by PRO measures. PROs can also assess the patient perspective on functioning or activities that may also be observable by others. PRO measures include:
  • Rating scales (e.g., numeric rating scale of pain intensity or Minnesota Living with Heart Failure Questionnaire for assessing heart failure)
  • Counts of events (e.g., patient-completed log of emesis episodes or micturition episodes)
Specifically for PRO, FDA has a guidance "Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims". PRO can be further separated into generic (such as SF-36, EQ-5D, ...) and disease-specific PROs (SGRQ for COPD, PAH-SYMPACT for pulmonary arterial hypertension, ...). 

ObsRO - observer-reported outcome

A type of . A  based on a report of observable signs, events or behaviors related to a patient’s health condition by someone other than the patient or a health professional. Generally, ObsROs are reported by a parent, caregiver, or someone who observes the patient in daily life and are particularly useful for patients who cannot report for themselves (e.g., infants or individuals who are cognitively impaired). An  measure does not include medical judgment or interpretation. ObsRO measures include:

  • Rating scales, such as:
    • Acute Otitis Media Severity of Symptoms scale (AOM-SOS), a measure used to assess signs and behaviors related to acute otitis media in infants
    • Face, Legs, Activity, Cry, Consolability scale (FLACC), a measure used to assess signs and behaviors related to pain
  • Counts of events (e.g., observer-completed log of seizure episodes)

ObsRO is often used in rare diseases, in pediatric diseases, or in diseases that the patients may lose self-control and patients can not detect the signs/symptoms on their own (such as seizure, stroke).  For patients who cannot respond for themselves (e.g., infants or cognitively impaired), observer reports should include only those events or behaviors that can be observed. As an example, observers cannot validly report an infant’s pain intensity (a symptom) but can report infant behavior thought to be caused by pain (e.g., crying). For example, in the assessment of a child’s functioning in the classroom, the teacher is the most appropriate observer. Examples of ObsROs include a parent report of a child’s vomiting episodes or a report of wincing thought to be the result of pain in patients who are unable to report for themselves.

Additional examples are OsRO-Celiac Disease Daily Symptom Diary (ObsRO-CDSD©), the Pediatric Quality of Life Inventory™,  the Edmonton Symptom Assessment System Revised (ESAS-r), ....

ClinRO - clinician-reported outcome

A type of . A  based on a report that comes from a trained health-care professional after observation of a patient’s health condition. Most  measures involve a clinical judgment or interpretation of the observable signs, behaviors, or other manifestations related to a disease or condition. ClinRO measures cannot directly assess symptoms that are known only to the patient. ClinRO measures include:

  • Reports of particular clinical findings (e.g., presence of a skin lesion or swollen lymph nodes) or clinical events (stroke, heart attack, death, hospitalization for a particular cause), which can be based on clinical observations together with  data, such as electrocardiogram (ECG) and creatine phosphokinase (CPK) results supporting a myocardial infarction
  • Rating scales, such as:
    • Psoriasis Area and Severity Index (PASI) for  of severity and extent of a patient’s psoriasis
    • Hamilton Depression Rating Scale (HAM-D) for  of depression

The majority of neurological assessment tools are falling into this category. additional examples are INCAT (Inflammatory Neuropathy Cause and Treatment), Guillian-Barre Syndrome disability score, MRC sum score...

PerfRO - performance outcome

A type of clinical outcome assessment. A  based on standardized task(s) actively undertaken by a patient according to a set of instructions. A  assessment may be administered by an appropriately trained individual or completed by the patient independently. PerfO assessments include:

  • Measures of gait speed (e.g., timed 25 foot walk test using a stopwatch or using sensors on ankles)
  • Measures of memory (e.g., word recall test) 
For example, the frequently used outcome measures such as the six-minute walking test (6MWT), cardio-pulmonary exercise test (CPET), Grip strength, ... are falling into PerfRO. 

FDA created a division "Division of Clinical Outcome Assessment (CDOA)" with a mission of Integrating the patient voice into drug development through COA endpoints that are meaningful to patients, valid, reliable and responsive to treatment."

For a scale that has not bee validated before and is intended to be used as the primary efficacy outcome measure in a clinical development program, CDER has two pathways for reviewing COAs:
  • The CDER COA Qualification Program or
  • Under an individual drug development program
With new technology development, we are now coming up with new terms: eCOA - electronic Clinical Outcome Assessment and TECOA - Technology Enabled COA. 

eCOA is to use electronic means to collect the COA data for example, electronic diaries. TECOA is a measurement that comes directly but passively from the patient using technology. 

eCOA has the following benefits:
  • Improved protocol compliance
  • Improved data integrity (real-time, timestamped entries, encrypted, protected data, and ability to integrate sources)
  • decrease hidden cost of paper (monitoring & querying data entry, recruitment due to non-compliance of paper, regulatory submission risk)
  • Better patient experience (intuitive user interface, ability to work offline/online, can be used on patients' devices to fit their lifestyle)
  • Improved operational efficiencies (shortened study timelines, reduced manual efforts, easily make mid-study changes, gather real-time insights)
  • Better regulatory guidance (regulators support and encourage eCOA use, willing to include outcomes in product labels). 


Sunday, December 06, 2020

Multiple Imputation: Imputation Model versus Analysis Model

Multiple imputation has become more and more popular in handling the missing data in clinical trials. Multiple imputation inference involves three distinct phases:

  • The missing data are filled in m times to generate m complete data sets. This step is through the imputation model and can be implemented using SAS Proc MI
  • The m complete data sets are analyzed by using standard procedures. This step is through the analysis model – depending on nature of the outcome variable, the analysis model can be ANCOVA (analysis of covariance), MMRM (mixed model repeated measures), Logistic regression, GEE (generalized estimating equation), GENMOD (generalized linear model),… The analysis model is also the primary model for analyzing the corresponding outcome variable.
  • The results from the m complete data sets are combined for the inference. This step is using Robin’s rule and can be implemented with SAS Proc MIANALYZE

For both the imputation model and the analysis model will need to include a list of explanatory or independent variables, but for different purposes. The list of explanatory or independent variables in the imputation model is to impute the missing values; the list of explanatory or independent variables in the analysis model are covariates as part of the standard statistical models. Here are some comparisons for the variables used in the imputation model and analysis model:

  • The covariates included in the analysis model must also be included in imputation model
  • The imputation model can include additional auxiliary variables including those variables that are not used as covariates in the analysis model
  • The number of variables used in imputation model is greater than or equal to the number of variables in analysis model
  • The imputation model can include variables measured after the randomization (such as secondary outcomes, concomitant medication use, compliance data). However, for analysis model, “variables measured after randomisation and so potentially affected by the treatment should not be included as covariates in the primary analysis.”
  • For longitudinal data or repeated measures, the outcome measures at early time points will be included in the imputation model.
  • If the variables used in the analysis model are transformed, the transformed variable should also be used in the imputation model
  • If the interaction term is used in the analysis model, it should also be included in the imputation model - this can make the imputation model pretty complicated though. 

In many publications, multiple imputation was stated as the method for handling the missing data, however, the details about the imputation model (i.e., which variables are included in the imputation model) were not usually described. 

While there is no clear guidance about the variables included in the imputation model, it is important to pre-specify the list of variables included in the imputation model especially if the auxiliary variables or variables not included in the analysis model. 

Below are some excerpts from the literature about the imputation model and analysis model.


Imputation Model, Analytic Model and Compatibility :

When developing your imputation model, it is important to assess if your imputation model is “congenial” or consistent with your analytic model. Consistency means that your imputation model includes (at the very least) the same variables that are in your analytic or estimation model. This includes any transformations to variables that will be needed to assess your hypothesis of interest. This can include log transformations, interaction terms, or recodes of a continuous variable into a categorical form, if that is how it will be used in later analysis. The reason for this relates back to the earlier comments about the purpose of multiple imputation. Since we are trying to reproduce the proper variance/covariance matrix for estimation, all relationships between our analytic variables should be represented and estimated simultaneously. Otherwise, you are imputing values assuming they have a correlation of zero with the variables you did not include in your imputation model. This would result in underestimating the association between parameters of interest in your analysis and a loss of power to detect properties of your data that may be of interest such as non-linearities and statistical interactions. 

Auxiliary variables are variables in your data set that are either correlated with a missing variable(s) (the recommendation is r > 0.4) or are believed to be associated with missingness. These are factors that are not of particular interest in your analytic model , but they are added to the imputation model to increase power and/or to help make the assumption of MAR more plausible. These variables have been found to improve the quality of imputed values generate from multiple imputation. Moreover, research has demonstrated their particular importance when imputing a dependent variable and/or when you have variables with a high proportion of missing information (Johnson and Young, 2011; Young and Johnson, 2010; Enders , 2010).

You may a priori know of several variables you believe would make good auxiliary variables based on your knowledge of the data and subject matter. Additionally, a good review of the literature can often help identify them as well. However, if your not sure what variables in the data would be potential candidates (this is often the case when conducting secondary data analysis), you can uses some simple methods to help identify potential candidates.

In a presentation of “multiple imputations” by Adrienne D. Woods

Which variables should you include as predictors in the imputation model?

  • Any variables you plan to use in later analyses (including controls)
  • General advice: use as many as possible (could get unwieldy!)
  • Although, some (i.e., Kline, 2005; Hardt, Herke, & Leonhart, 2012) believe that this introduces more imprecision, especially if the auxiliary variable explains less than 10% of the variance in missingness on Y… thoughts?
  • Know your analysis model beforehand and include at least all analysis variables in imputation model (including interaction terms)

FDA’s Statistical Review for Vantrela (hydrocodone bitartrate) extended-release tablets in Management of pain severe

Analysis model:

"The primary efficacy endpoint of trial 3103 was change from baseline to week 12 in the weekly average of worst pain intensity (WPI). The primary analysis was ANCOVA model with baseline WPI, randomized treatment, opioid status, and center as covariates. The intent-to-treat analysis population, defined as all randomized patients, was used for the primary efficacy analysis."

Imputation model:

"The applicant performed multiple imputation on the week 12 missing data for the primary analysis. The imputation model included randomized treatment, opioid status, baseline and postbaseline WPI values while subjects in the active-drug treatment group who discontinued study drug because of an adverse event, were treated as if they were in the placebo group and their missing data were imputed based on the observed placebo subjects' data."

FDA's Statistical Review for EUCRISA™ (crisaborole) topical ointment, 2% for Atopic Dermatitis mentioned the imputation model for missing dichotomized outcome variable. 

The protocol specified the primary imputation method to be the multiple imputation (MI) approach. For each treatment arm separately, missing data was imputed using the Markov Chain Monte Carlo (MCMC) method. The protocol specified the following two sensitivity analyses for the handling of missing data:

· Repeated-measures logistic regression model (GEE), with dichotomized ISGA success as the dependent variable and treatment, analysis center, and visit (i.e., Days 8, 15, 22, and 29) as independent factors. In this analysis, data from all post-baseline visits will be included with no imputation for missing data.

· Model-based multiple imputation method to impute missing data for the dichotomized ISGA data. The imputation model (i.e., logistic regression) will include treatment and analysis center.

Kaifeng Lu et al (2010) Multiple Imputation Approaches for the Analysis of Dichotomized Responses in Longitudinal Studies with Missing Data pointed out the issue if the analysis model is different from the imputation model. 

Despite its conceptual simplicity and flexibility, the above MI procedure is not valid for the analysis of dichotomized responses because Rubin’s variance estimator is biased when the analysis model is different from the imputation model (Meng, 1994; Robins and Wang, 2000). This is true even when the imputation and analysis models are compatible, e.g. when the treatment is the only effect in the logistic regression model.

Ian R. White  et al (2012) Including all individuals is not enough: lessons for intention-to-treat analysis

In some cases, an MI procedure can be improved by including in the imputation model ‘auxiliary variables’ that are not in the analysis model [36, Chapter 4]: auxiliary variables in a randomised trial might be secondary outcomes or compliance summaries. MI then produces estimates of the treatment effect that are genuinely different from a likelihoodbased analysis, by incorporating information on individuals with missing outcome but observed values of auxiliary variables. However, in our experience, the contribution to such an analysis of individuals missing the outcome of interest is moderate unless correlations between the outcome and one or more auxiliary variables are substantial [37].

Michael Spratt et al (2010) Strategies for Multiple Imputation in Longitudinal Studies

Where there are nontrivial amounts of missing data in covariates, both preliminary analyses and imputation models will become more complex. An MAR assumption may often become more plausible after the inclusion in the imputation model of additional variables that are not in our analysis model (because they are on the causal pathway, for example). Thus, multiple imputation models should typically be more complex than the analysis model. Including variables that are not related to the variable being imputed in the imputation models may slightly decrease efficiency but should not cause bias (29, 31). Model diagnostics should be used to highlight any implausibility in the imputed values. For example, the distributions of observed and imputed data should be compared and the plausibility of any differences examined. Imputation models should also preserve the structure of the analysis model (32). For example, where the substantive analysis exploits the hierarchical nature of longitudinal data (e.g., using a multilevel model), the imputation model should be similarly structured. Here, the longitudinal nature of the data allowed us to include variables (previous wheezing) that predicted the values of the variable with the most missing data (wheeze at 81 months) in imputation models.

Jochen Hard et al (2012) Auxiliary variables in multiple imputation in regression with missing X: a warning against including too many in small sample research

  • An additional advantage of MI over CC (complete-case analysis) is the possibility of including information from auxiliary variables into the imputation model. Auxiliary variables are variables within the original data that are not included in the analysis, but are correlated to the variables of interest or help to keep the missing process random [MAR: 1]. Little [6] has calculated the amount of decrease in variance of a regression coefficient Y on X1 when a covariate X2 is added that has no missing data. White and Carlin [7] have extended this proof to more than one covariate. In practice however, it is likely that auxiliary variables themselves will have missing data.

EMA Guideline on Missing Data in Confirmatory Clinical Trials mentioned the multiple imputation as an approach to handle the missing data with MAR assumption, however, it did not mention anything about the imputation model.   

Panel on Handling Missing Data in Clinical Trials; National Research Council  (2010) The Prevention and Treatment of Missing Data in Clinical Trials

Multiple imputation methods address concerns about (b) “simple imputation is generally not true because the methods do not always yield conservative effect estimators, and standard errors and confidence interval widths can be underestimated when uncertainty about the imputation process is neglected.”  and enable the use of large amounts of auxiliary information.

An important advantage of multiple imputation in the clinical trial setting is that auxiliary variables that are not included in the final analysis model can be used in the imputation model. For example, consider a longitudinal study of HIV, for which the primary outcome Y is longitudinal CD4 count and that some CD4 counts are missing. Further, assume the presence of auxiliary information V in the form of longitudinal viral load. If V is not included in the model, the MAR condition requires the analysis to assume that, conditional on observed CD4 history, missing outcome data are unrelated to the CD4 count that would have been measured; this assumption may be unrealistic. However, if the investigator can confidently specify the relationship between CD4 count and viral load (e.g., based on knowledge of disease progression dynamics) and if viral load values are observed for all cases, then MAR implies that the predictive distribution of missing CD4 counts given the observed CD4 counts and viral load values is the same for cases with CD4 missing as for cases with CD4 observed, which may be a much more acceptable assumption.

Meyer et al (2020) Statistical Issues and Recommendations for Clinical Trials Conducted During the COVID-19 Pandemic

Multiple imputation (MI) methodology (Rubin, 1987) may be helpful in this respect as it allows inclusion of auxiliary variables (both pre- and post-randomization) in the imputation model while utilizing the previously planned analysis model. Multiple imputation with auxiliary variables may be used for various types of endpoints, including continuous, binary, count, and time-to-event and coupled with various inferential methods in the analysis step.

Thomas R Sullivan et al (2018) Should multiple imputation be the method of choice for handling missing data in randomized trials?

In the first stage of MI, multiple values (m > 1) for each missing observation are independently simulated from an imputation model. For missing data restricted to the outcome, the imputation model would typically regress observed values of Y on X and T. Additional auxiliary variables that are not in the analysis model can also be added to the imputation model to improve the prediction of missing values.

In applying MI, the repeated measurements of the outcome are usually treated as distinct variables in the imputation model. Where interest lies in the treatment effect at the final time point, the analysis model need not include the intermediate outcome measures; following imputation a comparison of final time point results is sufficient. In this case, the intermediate measures operate as auxiliary variables, assisting with the prediction of missing values at the final time point and making the MAR assumption more plausible. Other auxiliary variables, for instance measures of compliance or related outcomes, can also be added to the imputation model as required. If data are collected but more likely to be missing following treatment discontinuation, an indicator variable for discontinuation may also be valuable as an auxiliary variable. The ability to incorporate auxiliary variables, both for univariate and multivariate outcomes, is considered one of the key strengths of MI.

Thus in settings where MI is adopted, we recommend imputing by randomized group; compared to MI overall, this approach offers greater robustness at little cost. The approach is also consistent with general recommendations for over- rather than under-specifying imputation models. It should be noted that imputing by group only protects against bias in estimating the ATE if effect modifiers are included in the imputation model.

One of the strengths of MI is its ability to easily incorporate variables of different types (e.g. continuous, binary) in the imputation model, whether for univariate or multivariate data. An added benefit of including all outcomes in a single imputation model is that associations between related outcomes can aid imputation. Another appealing feature of MI is its ability to be implemented under an assumption that data are MNAR. This property makes MI well suited to undertaking sensitivity analyses around a primary assumption that data are MAR, and as a primary method of analysis in settings where data are believed to be MNAR. One such setting is RCTs where participants cannot followed up after discontinuing treatment. If all observed data are ‘on-treatment’, a MAR assumption entails estimating the effect of treatment had all participants remained on their assigned treatment.27 However, for a de facto type estimand (such as ITT), it may be more appropriate to assume that data are MNAR. In this situation, reference based sensitivity analyses have been proposed, which at present require the use of MI.2

Interaction terms are not suggested.

Although the bias of MI overall could be eliminated by including the interaction term in the imputation model (results not shown), this may not be an obvious strategy if subgroup analyses are not of interest.

Simon Grund et al (2018) Multiple Imputation of Missing Data for Multilevel Models: Simulations and Recommendations

A crucial point in the application of MI to multilevel data is that the imputation model not only includes all relevant variables, but also that it “matches” the model of interest (i.e., the substantive analysis model; see Meng, 1994; Schafer, 2003). In other words, the imputation model must capture the relevant aspects of the analysis model, making the imputation model at least as general as (or more general than) the analysis model. If the imputation model is more restrictive than the analysis
model, then imputations are generated under a simplified set of assumptions, and the results of subsequent analyses may be misleading.

Protocol for: Hatemi G, Mahr A, Ishigatsubo Y, et al. Trial of apremilast for oral ulcers in Behçet’s syndrome. N Engl J Med 2019;381:1918-28. DOI: 10.1056/NEJMoa1816594