Clinical trials with external control, historical control, concurrent control, contemporaneous control, and synthetic control

Three critical features for modern clinical trials are control, randomization, blinding. For the golden standard of RCTs (randomized controlled clinical trials), a concurrent control group is critical. With recent advances in clinical trial designs, non RCTs such as real-world data (RWD)/real-world evidence (RWE), single arm trial, registry studies have been much discussed. The control group is now expanded to include concurrent control, external control, historical control, contemporaneous control. 

Concurrent Control: ICH E10 "Choice of Control Group in Clinical Trials" defined the concurrent control as the following: 

A concurrent control group is one chosen from the same population as the test group and treated in a defined way as part of the same trial that studies the test treatment, and over the same period of time. The test and control groups should be similar with regard to all baseline and on-treatment variables that could influence outcome, except for the study treatment. Failure to achieve this similarity can introduce a bias into the study. Bias here (and as used in ICH E9) means the systematic tendency of any aspects of the design, conduct, analysis, and interpretation of the results of clinical trials to make the estimate of a treatment effect deviate from its true value. Randomization and blinding are the two techniques usually used to minimize the chance of such bias and to ensure that the test treatment and control groups are similar at the start of the study and are treated similarly in the course of the study (see ICH E9). Whether a trial design includes these features is a critical determinant of its quality and persuasiveness.

Concurrent control is the feature of the RCTs and involves the randomization. The subjects are randomized into the test group or control group over the same period of time. 

External Control and Historical Control: ICH E10 "Choice of Control Group in Clinical Trials" defined the external control (including historical control) as the following:

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 non defined (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 (see section 2.5).

 Historical control is also external control. External control may or may not be historical control 

Contemporaneous Control may also be called contemporaneous cohort. In clinical trials with contemporaneous control group, subjects are recruited (not randomized) into the test group and the control group over the same period of time. The key idea is to compare subjects in the same time frame. For example, in a comparison of surgery versus chemotherapy for breast cancer, you wouldn't want to use surgery patients from 20 years ago as a control group to compare against a current chemo group. 

An great example of a clinical trial with a contemporaneous control group is a study assess the EVLP (ex-vivo lung perfusion) lung versus traditional (normal) lung in lung transplantations. In a non-randomized study "Extending Preservation and Assessment Time of Donor Lungs Using the Toronto EVLP System™ at a Dedicated EVLP Facility", according to the the study protocol, a contemporaneous control group was included to provide context for EVLP results and to inform control measures for future research. For every EVLP lung transplantation, a contemporaneous control lung transplantation with matched study center, single and double lung transplantation, lung allocation score. It is possible that for some EVLP lung transplants, the contemporaneous controls may not be identified which results in the large sample size in EVLP group than the contemporaneous control group. 

Once the donor lung is accepted following EVLP, the eligible recipient, who has provided written informed consent, and receives the lung transplant, is enrolled into the study. Patients who consent for the current EVLP , but receive a conventional (i.e., non-EVLP) lung transplant will be considered for a contemporaneous control group matched to the EVLP treatment group (66 subjects each). This matching will take place on a patient-by-patient basis and only after an EVLP subject has been enrolled at that Study Center. Investigators and their team will be notified by the Sponsor on a real-time basis of the specific matching criteria required for a control subject as EVLP subjects are enrolled. In order to be considered for eligibility, the control patient must “match” a priori to at least one EVLP subject who has already been enrolled at that Study Center based on the following criteria: SLT versus DLT and Lung Allocation Score Disease Diagnosis Group (LASDDG).

Contemporaneous control group is external, but concurrent control. Contemporaneous control group is similar to the matched control group in epidemiological case-control and cohort studies - similar statistical analysis approaches (such as conditional logistic regression) may be used for analyses.

Synthetic Control: synthetic control was discussed in a previous post "Synthetic Control Arm (SCA), External Control, Historical Control". Synthetic control includes subjects who are selected from historical clinical trials and who are on standard of case, and whose baseline characteristics match the current-day experiment group. Synthetic control is historical control, not concurrent control, but with matched baseline characteristics with the concurrent experiment treatment group. 

One Extra Point: 

One interesting discussion is about the control group in platform trial where multiple treatment arms are compared to the common control group. Since the different treatment arms may be added to or removed from the platform at different times, for a specific treatment - control group comparison, the control group may be not recruited over the same period of time. This issue was discussed in a NEJM paper "
Platform Trials — Beware the Noncomparable Control Group" and a JAMA paper "How to Use and Interpret the Results of a Platform Trial".

In platform trial, control group from a randomized trial may not be concurrent control.  

The Use of External Controls in FDA Regulatory Decision Making and Bayesian Borrowing

An good article by Jahanshahi et al "The Use of External Controls in FDA Regulatory Decision Making". The authors reviewed and summarized FDA regulatory approval decisions between 2000 and 2019 for drug and biologic products and summarized the pivotal studies that leveraged external controls, with a focus on select therapeutic areas.

The paper is open-access and available at "The Use of External Controls in FDA Regulatory Decision Making". 

The leading author is from BioMarin Pharmaceutical whose biological product Brineura for treatment of the rare Batten disease was approved by FDA based on an single arm study leveraging the external control from a historical registry. 

In the latest issue of the New England Journal of Medicine, Richeldi et al published a paper "Trial of a Preferential Phosphodiesterase 4B Inhibitor for Idiopathic Pulmonary Fibrosis". The trial was designed as a smaller (in sample size) trial with 2:1 randomization ratio by leveraging the data from the Placebo control groups in historical clinical trials. Bayesian borrowing (or Bayesian dynamic borrowing) approach was used for the analyses, as depicted below. While this is an example of successful use of external control in phase 2 study, it is unlikely that the same approach can be employed in their phase 3 pivotal studies mainly because that idiopathic pulmonary hypertension is a rare disease, but not rare enough. 

Power of the statistical graphs - an example of different ways for a Bar Chart

For the same set of data from the clinical trial, it is important to choose the appropriate statistical analysis methods. It is also important to choose the appropriate plot to display and visualize the data. The example below demonstrate the power of the statistical graphs (even the simple bar charts). 

A clinical trial is designed as a randomized, double-blinded, parallel three-arm study to evaluate the effect of high-dose and low-dose of an investigational drug in comparison with the Placebo. The outcome measure is NYHA Functional Class with four grades (I, II, III, and IV). At each post-baseline visit, function class is improved if it is shifted at least one grade toward the lower end, and functional class deteriorates if it is shifted at least one grade toward the high end. Suppose that at the end of the study, the outcome of the last visit can be organized as the following: 

	High-Dose (n = 200)	Low-Dose (n = 198)	Placebo (n = 202)
Improved	49 (24.5%)	37 (18.7%)	26 (12.9%)
No Change	139 (69.5%)	142 (71.7%)	148 (73.3%)
Deteriorated	12 (6.0%)	19 (9.6%)	28 (13.9%)

Both the dose (high-dose, low-dose, and placebo) and outcome (improved, no change, deteriorated) are considered to be the ordinal data. The statistical test with CMH gives a p-value of 0.0010 indicating the association between the dose groups and the outcome measures. 

The interesting thing is the data visualization - there are different ways to plot the data in the table above. What is the best way to plot the results in the table above?

If we are only interested in the proportion of subjects who have 'improved' functional class:

If we are only interested in the proportion of subjects who have 'deteriorated' functional class:

If we are interested in both the proportion of subjects who have 'improved' functional class and the proportion of subjects who have 'deteriorated' functional class: 

The bar chart can be arranged by outcome (improved and deteriorated) and the dose group (high-dose, low-dose, and placebo)

or the bar chart can be arranged by the dose group (high-dose, low-dose, and placebo) and then the outcome (improved and deteriorated). 

A stacked bar chart can be used to include all three outcome categories (improved, no change, and deteriorated):

The best approach is to place the outcome of 'improved' and 'deteriorated' functional class on the same vertical bar, but one above the 0-line and one below the 0-line. This approach assigns the 'improved' category as a positive value and the 'deteriorated' category as a negative value. 

These bar charts can be created using existing software such as SAS/Graphs, Microsoft Excel, and Graphpad Prism. It seems to be easier to use SAS to manipulate the data or obtain the aggregate data and then use Excel or Prism to create the charts. Prism is better and easier to use for creating charts for publications.