Monday, February 22, 2021

Randomization Using Envelopes In Randomized, Controlled, and Blinded Clinical Trials

I read an article by Clark et al “Envelope use and reporting in randomized controlled trials: A guide for researchers”. The article reminds me of the old times when envelopes were the popular ways for randomization and blinding (treatment concealment). In the 1990s and 2000s, for randomized, blinded clinical trials, the concealed envelope is the only way for the investigator to do the emergency unblinding (or code breaking) and sometimes the way to administer the randomization for single-blinded studies.

In Berende et al (2016, NEJM) “Randomized Trial of Longer-Term Therapy for Symptoms Attributed to Lyme Disease”, the study protocol described the following procedure for "unblinding of randomization" where sealed envelopes were used.  

I used to be an unblinded statistician to prepare the randomization schedule (including the randomization envelopes) for clinical trials. The following procedures will need to be followed:

  • Based on the study protocol, develop the randomization specifications describing randomization ratio, stratification factors, block size, the number of randomization codes, recipients of the randomization schedule, or code-break envelopes
  • Generate the dummy randomization schedule for the study team to review and approval
  • Replace the random seed to generate the final randomization schedule (a list of all randomized assignments)
  • Prepare the randomization envelopes (randomization number, stratification factors outside the envelope, and treatment assignment inside the envelope)
  • QC the randomization envelopes (to make sure that inside/outside information matches the randomization schedule
  • Shipping and tracking

For double-blinded studies, both the investigator and the patient are blinded to the treatment assignment. The randomization schedule will usually be sent to a third party (for example, the pharmacist) who is unblinded to the treatment assignment and can prepare the study drug for dispensing or administration. The third-party (for example, the pharmacist) must not be involved in other aspects of the clinical trial conduct. The concealed envelopes can be sent to the investigators for emergency unblinding. If there is a medical emergency requiring the unblinding of an individual subject, the investigator can open the code break envelope to reveal the treatment assignment for the specific subject.

For single-blinded studies, the investigator is unblinded to the treatment assignment and the patient is blinded to the treatment assignment. The randomization schedule and/or the randomization envelopes can be sent to the investigators.

Nowadays, randomization through envelopes is obsolete. The randomization procedures are integrated into the overall CTM (clinical trial material)  management process through the IRT (interactive response technologies). In the last 20 years, the randomization process has shifted from randomization envelopes -> IVRS (interactive voice response system) -> IWRS (Interactive Web Response System) - > IRT.

With IRT, the randomization schedule will be sent to the IRT vendor and uploaded into the IRT system. The study team members can be assigned different levels of access to the IRT system depending on their roles in the study. The investigators and pharmacovigilance personnel can be granted the emergency access code for them to gain the access to the treatment assignment in IRT when necessary.  

However, in some situations, randomization envelopes may still the best way for implementing the randomization.

In a study by Chetter et al “A Prospective, Randomized, MulticenterClinical Trial on the Safety and Efficacy of a Ready-to-Use Fibrin Sealant as an Adjunct to Hemostasis during Vascular Surgery”, the randomization occurred in the operation room and only after the target bleeding site (TBS) was identified after the surgical procedure. There would not be ideal for the surgeon (the investigator) to log into the IRT system to obtain the treatment assignment information. The better approach would be for the surgeon or surgeon’s assistant to open the randomization envelope to obtain the treatment assignment information in the operation room. The randomization procedure was described as the following in the paper:

Randomization

In the Primary Study, patients were randomized 2:1to treatment with FS Grifols or MC after the identification of the TBS during the procedure. Treatment group assignments were generated by the randomization function of the statistics software and communicated using sealed opaque envelopes. Due to the obvious differences between the 2 treatments, blinding of investigators was not possible following randomization

Additional Reads:

Monday, February 01, 2021

BLQs (below limit of quantification) and LLOQ (Lower Limit of Quantification): how to handle them in analyses?

In data analyses of the clinical trial, one type of data is the laboratory data containing the results measured by the central laboratory or specialty laboratory on the specimen (blood sample, plasma or serum sample, urine sample, bronchoalveolar lavage,...) collected from clinical trial participants. The laboratory results are usually reported as quantitative measures in numeric format. However, sometimes, we will see the results reported as '<xxx' or 'BLQ'.

The laboratory measures rely on the assay and the assay has its limit and can only accurately measure the level or concentration to a certain degree - the limit is called the Lower Limit of Quantification (LLOQ) or the Limit of Quantification (LOQ) or the Limit of Detection (LOD). 

In FDA's guidance (2018) "Bioanalytical Method Validation", they defined the Quantification range, LLOQ and ULOQ: 

The quantification range is the range of concentrations, including the ULOQ and the LLOQ that can be reliably and reproducibly quantified with accuracy and precision with a concentration-response relationship.

Lower limit of quantification (LLOQ): The LLOQ is the lowest amount of an analyte that can be quantitatively determined with acceptable precision and accuracy.

Upper limit of quantification (ULOQ): The ULOQ is the highest amount of an analyte in a sample that can be quantitatively determined with precision and accuracy.

According to the article by Vashist and Luong "Bioanalytical Requirements and Regulatory Guidelines for Immunoassays". The LLOQ and LOQ are different. In practice, the LLOQ and LOQ may be used interchangeably. 

The LOQ is the lowest analyte concentration that can be quantitatively detected with a stated accuracy and precision [24]. However, the determination of LOQ depends on the predefined acceptance criteria and performance requirements set by the IA developers. Although such criteria and performances are not internationally adopted, it is of importance to consider the clinical utility of the IA to define such performance requirements.

The LLOQ is the lowest calibration standard on the calibration curve where the detection response for the analyte should be at least five times over the blank. The detection response should be discrete, identifiable, and reproducible. The precision of the determined concentration should be within 20% of the CV while its accuracy should be within 20% of the nominal concentration.

In FDA's guidance "Studies to Evaluate the Metabolism and ResidueKinetics of Veterinary Drugs in Food-ProducingAnimals: Validation of Analytical Methods Used in Residue Depletion Studies", the LOD and LOQ are differentiated a little bit. 

3.4. Limit of Detection
The limit of detection (LOD) is the smallest measured concentration of an analyte from which it is possible to deduce the presence of the analyte in the test sample with acceptable certainty. There are several scientifically valid ways to determine LOD and any of these could be used as long as a scientific justification is provided for their use. 
3.5. Limit of Quantitation
The LOQ is the smallest measured content of an analyte above which the determination can be made with the specified degree of accuracy and precision. As with the LOD, there are several scientifically valid ways to determine LOQ and any of these could be used as long as scientific justification is provided. 

If the level or concentration is below the range that the assay can detect, it will be reported as the BLQ (Below the Limit of Quantification), BQL (Below Quantification Level), BLOQ (Below the Limit Of Quantification), or <xxx where xxx is the LLOQ. The results are seldom reported as 0 or missing since the result is only undetectable using the corresponding assay. It is usually agreed that the BLQ values are not missing values - they are measured, but not measurable. 

In clinical laboratory data with the purpose of safety assessment, the BLQ or <xxx is reported in the character variable. When converting the character variable to the numerical variable, the BLQ or <xxx will be automatically treated as missing unless we do something. The following four approaches may be seen in handling the BLQ values (with an example assuming LLOQ 0.01 ng/mL). 

Reported Value

Converted Value

Explanation

< 0.01 ng/mL

missing

The specific measure will be set to missing and will not be included in summary and analysis.

< 0.01 ng/mL

0

The specific measure will be set to 0 in summary and analysis

< 0.01 ng/mL

0.005 ng/mL

Half of the LLOQ – commonly used in clinical pharmacology studies (Bioavailability and Bioequivalence studies)

<0.01 ng/mL

0.01 ng/mL

Ignore the less than the ‘<’ sign and take the LLOQ as the value for summary and analysis. This approach can also handle the values beyond the ULOQ (upper limit of quantification), for example, '>1000 ng/mL' by removing the greater than '>' sign.

In clinical pharmacology studies (bioavailability and bioequivalence studies), series pharmacokinetic (PK) samples will be drawn and analyzed to get a PK profile for a specific compound or formulation. The series samples will include a pre-dose sample (the sample drawn before the dosing) and multiple time points after the dosing. It is entirely possible to have results reported as BLQ especially for the pre-dose sample and the late time points. BLQ values can also be possible for samples in the middle of the PK profile (i.e., between two samples with non-BLQ values). The rules for handling these BLQs are different depending on the samples at pre-dose, at the middle of the profile, and at the end of the PK profile (with an example assuming LLOQ 0.01 ng/mL)

 Timepoint

Reported Value

Converted Value

Explanation

Pre-dose sample for a compound with no endogenous level

< 0.01 ng/mL

0

The BLQ(s) occurring before the first quantifiable concentration will be set to zero. 

Pre-dose sample for a compound with endogenous level or pre-dose at the steady-state

< 0.01 ng/mL

0.005 ng/mL

The endogenous pre-dose level will be set to half of the LLOQ. 

In multiple-dose situation, the pre-dose sample (trough or Cmin) is set to half of the LLOQ

At middle of the PK profile or between two non-BLQ time points

< 0.01 ng/mL

missing

The BLQ values between the two reported concentrations will be set to missing in the analysis – essentially the linear interpolation rule will be used in AUC calculation.

The last time point(s) of the PK profile

< 0.01 ng/mL

or

0.005 ng/mL

It is common to set the last BLQ(s) to 0 to be consistent with the rule for pre-dose BLQ handling. According to FDA's "Bioequivalence Guidance", "For a single dose bioequivalence study, AUC should be calculated from time 0 (predose) to the last sampling time associated with quantifiable drug concentration AUC(0-LOQ)."

In some situations, the BLQ values after the last non-BLQ measure can also be set to half of the LLOQ.

There are some discussions that these single imputation methods will generate biased estimates. In a presentation by Helen Barnett et al "Non-compartmental methods for BelowLimit of Quantification (BLOQ)responses", they concluded:

It is clear that the method of kernel density imputation is the best performing out of all the methods considered and is hence is the preferred method for dealing with BLOQ responses in NCA. 

For the specific study, rules for handling the BLQs may be different depending on the time point in the PK profile, the measured compound (with or without endogenous concentrations), the single dose or multiple doses, study design (single dose, parallel, crossover). No matter what the rules are, they need to be specified (preferably pre-specified before the study unblinding if it is pivotal study and the PK analysis results are the basis for regulatory approval) in the statistical analysis plan (SAP) or PK analysis plan (PKAP).   

Here are two examples with descriptions of the BLQ handling rules. In a phase I study by Shire, the BLQ handling rules are specified as the following: 


In a phase I study by Emergent Product Development, the BLQ rules are described as the following:


REFERENCES:

Sunday, January 17, 2021

Arithmetic mean, geometric mean, harmonic mean, least square mean, and trimmed mean

In statistics, a central tendency is a central or typical value for data distribution. Mean (or average) is commonly used to measure the central tendency. However, depending on the data distribution or the special situation, different types of Mean may be used: arithmetic mean, geometric mean, least-squares mean, harmonic mean, and trimmed mean.

The most common Mean is the arithmetic mean. If we say ‘Mean’, it is the default for arithmetic mean.

Arithmetic Mean is calculated as the sum of all measurements (all observations) divided by the number of observations in the data set.


Geometric Mean is the nth root of the product of the data values, where there are n of these. This measure is valid only for data that are measured absolutely on a strictly positive scale. Geometric mean is often used in the data that follows the log-normal distribution (for example, the pharmacokinetics drug concentration data, the antibody titer data...). 

In practice, geometric mean is usually calculated with the following three steps:
  • log-transform the original data
  • calculate the arithmetic mean of the log-transformed data
  • back transform the calculated value to the original scale
Harmonic Mean is the reciprocal the arithmetic mean of the reciprocals of the data values. This measure too is valid only for data that are measured absolutely on a strictly positive scale.

The harmonic mean are calculated with the following steps:

  • Add the reciprocals of the numbers in the set. To find a reciprocal, flip the fraction so that the numerator becomes the denominator and the denominator becomes the numerator. For example, the reciprocal of 6/1 is 1/6.
  • Divide the answer by the number of items in the set.
  • Take the reciprocal of the result.
The harmonic mean is not often used in day-to-day statistics but is quite often used in some statistical formula. For example, for two-group t-statistics with unequal sample size in two groups, the t value can be calculated using the following formula with harmonic mean to measure the average sample size.


Least Squares Mean is a mean estimated from a linear model. Least squares means are adjusted for other terms in the model (like covariates), and are less sensitive to missing data. Theoretically, they are better estimates of the true population mean.

In a previous post "Least squares means (marginal means) vs. means", the calculation of least squares mean is compared with the arithmetic mean.
In analyses of clinical trial data, the least-squares mean is more frequently used than the arithmetic mean since it is calculated from the analysis model (for example, analysis of variance, analysis of covariance,...). The difference between two least-squares means is called the ratio of geometric least-squares means (or geometric least-squares mean ratio) - along with its 90% confidence intervals - is the common approach for assessing the bioequivalence. 

Trimmed Mean may also be called truncated mean and is the arithmetic mean of data values after a certain number or proportion of the highest and/or lowest data values have been discarded. The data values to be discarded can be one-sided or two-sided. 

The key for trimmed mean calculation is to determine the percentage of data to be discarded and whether or not the data to be discarded is one-sided or two-sided. The percentage of data to be discarded may be tied to the percentage of missing data. 

Trimmed mean can be calculated and then used to fill in the missing data - a single imputation method for handling the missing data. Trimmed mean as a single imputation method for missing data has its limitations, but it is still used in analyses of clinical trials - usually for sensitivity analyses.

In ICH E9-R1 "Addendum on Estimands and Sensitivity Analysis in Clinical Trials" training material, about the composite strategy to handle the intercurrent event, trimmed mean is mentioned to be an approach in handling the intercurrent event. 
 


Monday, January 11, 2021

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:

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:

Monday, January 04, 2021

Synthetic Control Arm (SCA), External Control, Historical Control

Lately, the term 'synthetic control' or 'synthetic control arm' or SCA, in short, is becoming popular - it is mainly driven by the desire to design more efficient clinical trials that are not traditional, the golden standard RCT (randomized controlled trials) with a concurrent control group. 

In a previous post, I compared historical control versus external control in clinical trials. The subtle difference is mainly in the time element. Historical control is one type of external control, but the reverse is not true. External control can be historical control or contemporaneous control. For example, in a clinical trial to assess the efficacy and safety of the donor lung preserved using ex-vivo lung perfusion (EVLP) technique, the EVLP lung transplantation cohort was compared to a contemporaneous (not concurrent) control cohort that was formed through the matched control from the traditional lung transplantation patients.   

Then what is 'synthetic control' or 'synthetic control arm'?

Synthetic control arm is the use of synthetic data as a control arm in clinical trials. According to an article "Synthetic data in the civil service" in the latest issue of SIGNIFICANCE, synthetic data is defined as "artificially generated data that are modelled on real data, with the same structure and properties as the original data, except that they do not contain any real or specific information about individuals. The goal of synthetic data generation is to create a realistic copy of the real data set, carefully maintaining the nuances of the original data, but without compromising important pieces of personal information."

Synthetic control arm is a control arm generated through existing data resources representing normal patient statistics. Synthetic control arm can serve as a comparator for a single-arm clinical trial or augment the smaller concurrent control group (for example with active:control ratio of 3:1 or 4:1) in RCTs. 

In a presentation by at Harvard Medical School Executive Education Webinar Series,  Mr. Chatterjee presented "Synthetic Control Arms in Clinical Trials and Regulatory Applications" and he defined the 'synthetic control arm' as the following:

In a paper by Thorlund et al "Synthetic and External Controls in Clinical Trials – A Primer for Researchers", they stated that synthetic control arms are external control arms - two terms can be used interchangeably:
External control arms are also called “synthetic” control arms as they are not part of the original concurrent patient sample that would have been randomized into the experimental or the control treatment arms as in a traditional RCT. External controls can take many forms. For example, external control arms can be established using aggregated or pooled data from placebo/control arms in completed RCTs or using RWD (Real World Data) and pharmacoepidemiological methods. Pooled data from historical RCTs can serve as external controls depending on the availability of selected “must have” data, similarity of patients, recency and relevancy of experimental treatments that were tested, availability and similarity of relevant endpoints (eg, operational definitions and assessments), and similarity of other important study procedures that were conducted in these historical trials. It is important to note that using control data from historical RCTs still results in a nonrandomized comparison but has the advantage of standardized data collection in a trial setting and patients who enroll in clinical trials may have more similar characteristics than those who do not.

However, I think that there are subtle differences between these two terms. With 'synthetic' control arms, the term 'synthetic' implies there are some selection, manipulation, derivation, matching, pooling, borrowing from the source data. Just like the meta-analysis is also called research synthesis and requires the statistical approaches to combine the results from multiple scientific studies, the 'synthetic' control also requires the use of statistical approaches to process the data from multiple sources to form a control group to replace the concurrent control in traditional RCT clinical trials. 

The source data for constructing synthetic control can be the data from previous RCT clinical trials, real-world data, registry data, data from natural history studies, electronic health records, ... The source data must be the subject-level data, not the summary or aggregate data. 

ICH E10 "CHOICE OF CONTROL GROUP AND RELATED ISSUES IN CLINICAL TRIALS" included "External Control (including Historical Control)" as one of the options as the control groups in clinical trials. The external control here is not the same as synthetic control. 

1.3.5 External Control (Including Historical Control)
An externally controlled trial compares a group of subjects receiving the test treatment with a group of patients external to the study, rather than to an internal control group consisting of patients from the same population assigned to a different treatment. The external control can be a group of patients treated at an earlier time (historical control) or a group treated during the same time period but in another setting. The external control may be defined (a specific group of patients) or nondefined (a comparator group based on general medical knowledge of outcome). Use of this latter comparator is particularly treacherous (such trials are usually considered uncontrolled) because general impressions are so often inaccurate. So-called baseline controlled studies, in which subjects' status on therapy is compared with status before therapy (e.g., blood pressure, tumor size), have no internal control and are thus uncontrolled or externally controlled.  

How to Create a Synthetic Control Arm? 

The first step of creating a synthetic control arm is to harmonize the source data. The data from different sources or from different clinical trials should be standardized so that they can be used for the synthesis process. 

Various statistical approaches can be used to create a synthetic control arm. In an audiobook on synthetic control arms by Cytel, propensity scoring and Bayesian Dynamic Borrowing methods were discussed. 

The synthetic control arm can be considered as an approach of 'borrowing control' - i.e., some controls are borrowed from historical data. There are numerous options for borrowing controls: 

  • Pooling: adds historical controls to randomized controls 
  • Performance criterion: uses historical data to define performance criterion for current, treated-only trial to beat 
  • Test then pool: test if controls sufficiently similar for pooling 
  • Power priors: historical control discounted when added to randomized controls
  • Hierarchical modeling: variation between current vs. historical data is modeled in Bayesian fashion 

In the article by Thorlund et al, the pros and cons of different methods for generating synthetic control arms were discussed. 


In Mr Chatterjee presentation, "Synthetic Control Arms in Clinical Trials and Regulatory Applications", there is a diagram to describe the process for creating a synthetic control arm. 


Even though the synthetic control arms, the use of real-world data, conducting the single-arm clinical trials are very appealing, the challenges are ahead and the regulatory acceptance is uncertain. There may be limited use in special cases (such as ultra-rare diseases, pediatric clinical trials) and for post-marketing activities (such as label expansion, label modification, post-marketing studies), but not in prime time to replace the concurrent control in traditional RCTs. 

In an article at Statnews.com "Synthetic control arms can save time and money in clinical trials", 

Even with the FDA making the use of real-world data a strategic priority, synthetic control arms can’t be used across the board to replace control arms. Synthetic control arms require that the disease is predictable (think idiopathic pulmonary fibrosis) and that its standard of care is well-defined and stable. That certainly isn’t the case for every disease.

It’s also important to consider that even when information is available from real-world data sources, it may be difficult to extract or of low quality. Routinely captured health care data, such as electronic health records, are typically siloed, fragmented, and unstructured. They are also often incomplete and difficult to access. New tools and methodologies are needed to consolidate, organize, and structure real-world data to generate research-grade evidence and ensure that confounding variables are accounted for in analyses. Analytic techniques such as natural language processing and machine learning will be needed to extract relevant information from structured and unstructured data.

The same view is also expressed in a Pink Sheet article "External Control Arms: Better Than Single-Arm Studies But No Replacement For Randomization".

Synthetic control group derived from historical clinical trial data could augment smaller randomized trials and yield better information than single-arm studies, but this approach should not be viewed as a substitute for randomized trials where feasible

ADDITIONAL REFERENCES:

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, and ObsRO

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
REFERENCES: