4 common study designs for clinical trials

Clinical trial design is an important aspect of interventional trials that serves to optimise, ergonomise and economise the clinical trial conduct. The goals of a clinical trial, whether medtech or pharma, can encompass assessment of safety, dosage optimisation, evaluation of efficacy or accuracy and comparison to existing treatments or diagnostics. This of course varies with the phase of the trial. For phase III or IV trials the goal is most often to determine superiority, non-inferiority, or equivalence of the novel therapeutic or device to one in standard use. A well-conducted study that achieves regulatory approval for the asset in an efficient way depends upon the design that informs it. An optimal design, from a statistical and data collection perspective, ensures accurate evaluation efficacy and safety, as well as getting the product to market sooner. Knowing which study designs best suit your research will improve the chances of success, enable the best method for sample size estimation and re-estimation, save time and reduce unnecessary costs (Evans, 2010). While many clinical study designs exist this article focuses on perhaps the most rudimentary and frequently used designs

  • Parallel group design
  • Crossover design
  • Factorial design
  • Randomised withdrawal design

1. Parallel group study design

A commonly used study design is a parallel arm design. When using this as a study design, subjects are randomised and allocated to one or more study arms. In a parallel group study design, each study arm is allocated a different intervention. After study subjects have been randomised and allocated to a study arms they can not be allocated to another arm throughout the study.

Advantages of parallel group trial study design

A key advantage of parallel group trial design is that it can be applied to many different diseases as well as allows for conducting multiple experiments simultaneously between many groups. A further advantage is that these different groups need not be sourced from the same site.

Note: Once patients have been randomised and assigned to a specific arm, these arms are mutually exclusive. This means that unplanned co- interventions or cross-overs between different treatments cannot be introduced.

Steps involved in a parallel arm trial design:

1. Eligibility of study subject assessed

2. Recruitment into study after consent

 3. Randomisation

4. Allocation to either treatment or control arm
 

2. Cross-over study design

There are some ethical limitations to the use of placebo controls that can be partially overcome by using a cross over design. This means that every patient taking part in the clinical trial will receive both treatment and placebo being given in a randomised order (Evans, 2010). Cross-over study design can also be used in the absence of placebo where the intention is to compare the new treatment to the standard one.

Advantages of cross-over design

One of the advantages of cross over design is the fact that each patient acts as their own control results in order to balance the covariates in treatment and control arm.

Another major advantage of cross over design is the fact that it requires a smaller sample size (Nair, 2019).

Note: When cross over design is applicable and chosen for the study, some of the patients will start the trial with using intervention A and then switch to intervention B which is known as a AB sequence, whereas other patients will start with using intervention B and later switch to intervention A which is known as BA sequence.

! There needs to be an adequate washout period before the crossover in order to eliminate the effects from initially assigned and administrated intervention. After all data has been collected the results are then compared within the same subject assessing the effect of intervention A vs. effect of intervention B (Nair, 2019).

Variations of cross-over design

(i) Switch back design (ABA vs BAB arms) –

1. Drug A -> Drug B-> Drug A

2.Drug B -> Drug A -> Drug B

The switch back and multiple switchback designs are of emerging relevance with the advent of biosimilars where switchability and interchangeability of a biosimilar to a bio-originator molecule can only be confirmed by such trial designs.

(ii) N of 1 design – N of 1 trials or “single-subject” or “structured within-patient randomized controlled multi-crossover trial design”

This type of cross over design is used for evaluating all interventions in a single patient. A typical N of 1 design clinical trial consists of repeating experimental/ control treatment periods number of times. The interventions being tested are assigned randomly within each period pair. This design has gained a lot of popularity, because in most cases the aim of using this type of design is to determine which treatment works best for the individual patient.

3. Factorial design

Factorial design is most suited when the study is looking at two or more interventions in various combinations within one study setting. This design helps in the study of interactive effects that have resulted from a combination of different interventions (Nair, 2019).

Advantages of Factorial design

A key advantage of factorial design is that it can help answer multiple research questions in a clinical trial instead of conducting multiple trials.  This helps to optimise resources, thereby reducing costs and speeding up research pipelines.

2 × 2 factorial design with placebo

In a 2 × 2 factorial design with placebo, patients are randomized into four groups:

i) treatment A plus placebo
 ii) treatment B plus placebo
 iii) both treatments A and B
 iv) neither of them, placebo only.

Limitations of the factorial design

The main limitations of using factorial design for clinical trials is the fact that:

○  Increased complexity of the trial overall

○  Makes it more difficult to meet inclusion criteria

○  Inability to combine multiple incompatible interventions

○  The protocols are complex

○  High complexity of statistical analysis

4. Randomised withdrawal design (EERW)

The aim of randomised withdrawal design is to evaluate the optimal duration of the treatment for patients that are responsive to the intervention.

 After the initial enrichment period (open label period) which main purpose is to assign the subjects to intervention, the subjects that are not responding are removed (dropped) from the study and the subjects that did respond are randomised into receiving the intervention or placebo during the second phase of the clinical trial (Nair, 2019).

Note: This means that only subjects that have responded are carried forward to the second stage of the study and randomised.

Statistical analysis of randomised withdrawal design

When using randomised withdrawal design the analysis of the study is conducted using only data from the withdrawal phase. Outcome is usually set to relapse of symptoms. The aim of the enrichment phase is to increase the statistical power for the estimated sample size.

Advantages of EERW

A main advantage of a randomised withdrawal design is that it can reduce the time patients receive placebo. Only patients that are responsive to the intervention are randomised to placebo, hence an increased ethical advantage.
            A further advantage of this study design is that it can help to determine if the treatment should be stopped or continued (Nair,2019).

Conclusion

One of the key stages of planning a clinical trial involves deciding on the appropriate study design to ensure the success of the research and help to choose the right method for sample size estimation and re-estimation, save time and reduce unnecessary costs.

The most commonly used study designs are :

  • Parallel group study design
  • Cross over study design
  • Factorial study design
  • Randomised withdrawal study design (EERW )


            A well-conducted study with optimal design, that encorporates a robust hypothesis evolved from clinical practice, goes a long way in facilitating the regulatory approval process – evaluating efficacy and safety, and getting the product to market. Therefore when undertaking a clinical trial close attention should be paid to ensure that a study design forms a solid foundation upon which to conduct the trial phases.

References
 Evans, S., 2010. Fundamentals of clinical trial design. [online] PubMed Central (PMC). Available at: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3083073/>.
 Expert, T., 2022. Clinical Trial Designs & Clinical Trial Phases | Credevo Articles. [online] Credevo Articles. Available at: <https://credevo.com/articles/2021/02/05/the-phase-of- study-clinical-trial-design/>.
 Nair, B., 2019. Clinical Trial Designs. [online] PubMed Central (PMC). Available at: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434767/>.
 The BMJ | The BMJ: leading general medical journal. Research. Education. Comment. n.d. 13. Study design and choosing a statistical test | The BMJ. [online] Available at: <https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/ 13-study-design-and-choosing-statisti>.

Bayesian approach for sample size estimation and re-adjustment in clinical trials

            Accurate sample size calculation plays an important role in clinical research. Sample size in this context simply refers to the number of human patients, wheather healthy or diseased, taking part in the study. Clinical studies conducted using an insufficient sample size can lack the statistical power to adequately evaluate the treatment of interest, whereas a superfluous sample size can unnecessarily waste limited resources.

          Various methods can be applied for determining the optimal sample size for a specific clinical study. Methods also exist for any re-adjustments throughout the study,  if required. These methods vary widely from straightforward tests and formulas to complex, time-consuming ones, depending on the type of study and available information from which to make the estimate. Most commonly used sample size calculation procedures are developed from a frequentist perspective

Importance of knowing your study parameters

          Accurate sample size calculation requires, information on several key study and research parameters. These parameters usually include an effect size and variability estimate, derived from available sources; a clinically meaningful difference. In practice these parameters are  generally unknown and must be estimated from the existing literature or from pilot studies.

The Bayesian Framework in sample size estimations and re-adjustments

The Bayesian Framework has gradually become one of the most frequently mentioned methods when it comes to randomised clinical trial sample size estimations and re-adjustments.

In practice, sample size calculation is usually treated explicitly as a decision problem and employs a loss or utility function.

The Bayesian approach involves three key stages:

  • 1. Prior estimate

A researcher has a prior estimate about the treatment effect (and other study parameters) that has been derived from meta-analysis of existing research, pilot studies, or  based on expert opinion in absence of these.

  • 2. Likelihood

Data is simulated to derive a likelihood estimate of prior parameters.

  • 3. Posterior estimate

Based on the insights obtained, prior estimates from the first stage are updated to give a more precise final estimate.

         

A challenge of using this approach is knowing when to stop this cycle when enough evidence has been gathered and avoid creating bias (Dreibe,2021). Peaking at the data in order to make a stopping decision is called “optional stopping”. In general an optional stopping rule is cautioned against as it can increase type one error rates (de Heide & Grunewald, 2021).

How to decide when to stop the simulation cycle ?

There are two approaches one could take.

  • 1. Posterior probability

            Calculating the posterior probability that the mean difference between the treatment and control arm is equal or greater than the estimated effect of the intervention. Based on the level of probably calculated (low or high) the cycle could be stopped and without any further need to gather more data.

  • 2. PPOS ( predictive probability of success)

        Calculating the predictive probability of achieving a successful result at the end of the study is a commonly used approach. It is really helpful when it comes to determining the success or failure of a study. Similarly, as with posterior probability based on the level of probability a decision could be made to stop or continue the study.

How to plan a Bayesian sample size calculation for a clinical trial

The key elements to consider when planning a Bayesian clinical trial are the same as for frequentists clinical trial.

Key planning stages:

  • Determine the objective of the clinical study
  • Determine and set endpoints
  • Decide on the appropriate study design
  • Run a meta analysis or review of existing evidence related to your research objective
  • Statistical test and statistical analysis plan (SAP)

Even though the key planning stages are the same for both approaches it does not mean that they can be mixed through out the study. If you have chosen to use one approach you can’t change to another method once the calculations have been generated and research started.

Bayesian approach vs Frequentist approach for sample size calculations

BayesianFrequentist
Prior and posterior( uses probability of hypothesis and data)No prior or posterior( never gives probability of hypothesis)
Sample size depends on the prior and likelihoodSample size depend on the likelihood
Requeres finding/deciding on prior in order to estimate sample sizeDoes not require prior to estimate sample size
Computationally intensive due to integration over many parametersLess computationally intense

          Frequentist measures such as p-values and confidence intervals continue to predominate the methodology across life sciences research, however, the use of the Bayesian approach in sample size estimations and re-estimation for RTCs has been increasing over time.

Bayesian approach for sample size calculations in medical device clinical trial

           In the recent years Bayesian approach has gained more popularity as the method used in clinical trials including medical device studies. One of the reasons being that if good prior information about the use of the specific therapeutic or device is available, the Bayesian approach may allow to include this information into the statistical analysis part of the clinical trial. Sometimes, the available prior information for a device of interest may be used as a justification for smaller sample size and shorten the length of the pivotal trial (Chen et al., 2011).

Computational algorithms and growing popularity of Bayesian approach

          Bayesian statistical analysis can be computationally intense. Despite that there have been multiple breakthroughs with computational algorithms and increased computing speed that have made it much easier to calculate and build more realistic Bayesian models, further contributing to the popularity of Bayesian approach. (FDA, 2010).

Markov Chain Monte Carlo (MCMC) method

          One of the basic computational tools being used is Markov Chain Monte Carlo ( MCMC) method. This method computes large number of simulations from the distributions of random quantities.

Why MCMC?

          MCMC helps to deal with computational difficulties one often can face when using Bayesian approach for needed sample  size estimations. The MCMC is an advanced random variable generation technique which allows one to simulate different samples from more sophistocated probability distributions.

Conclusion

          Sample size calculation plays an important role in clinical research. If underestimated, statistical power for the detection of a clinically meaningful difference will likely be insufficient; if overestimated, resources are wasted unnecessarilly.

          The Bayesian Framework has become quite popular approach for sample size estimation. There are advantages of using the Bayesian method, depite this there has been some criticism of this approach as a sample size estimation and re-adjustment method due to the prior being subjective and possibility of different researchers selecting different priors leading to different posteriors and final conclusions.

In reality, both the Bayesian and frequentist approaches to sample size calculation involve deriving the relevant input parameters from the literature or clinical expertise and could potentially differ due to variations in individual expert opinion as to which studies to include or exclude in this process.

          Bayesian approach is more computationally intensive compared to the traditional frequentist approaches. Therefore, when it comes to selecting a method for sample size estimation, it should be chosen carefully to best fit the particular study design and base-on advice provided by statistical professionals with expertise in cliical trials.

References:

Bokai WANG, C., 2017. Comparisons of Superiority, Non-inferiority, and Equivalence Trials. [online] PubMed Central (PMC). Available at: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5925592/> [Accessed 28 February 2022].

Chen, M., Ibrahim, J., Lam, P., Yu, A. and Zhang, Y., 2011. Bayesian Design of Noninferiority Trials for Medical Devices Using Historical Data. Biometrics, 67(3), pp.1163-1170.

E, L., 2008. Superiority, equivalence, and non-inferiority trials. [online] PubMed. Available at: <https://pubmed.ncbi.nlm.nih.gov/18537788/> [Accessed 28 February 2022].

Gubbiotti, S., 2008. Bayesian Methods for Sample Size Determination and their use in Clinical Trials. [online] Core.ac.uk. Available at: <https://core.ac.uk/download/pdf/74322247.pdf> [Accessed 28 February 2022].

U.S. Food and Drug Administration. 2010. Guidance for the Use of Bayesian Statistics in Medical Device Clinical. [online] Available at: <https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-use-bayesian-statistics-medical-device-clinical-trials> [Accessed 28 February 2022].

van Ravenzwaaij, D., Monden, R., Tendeiro, J. and Ioannidis, J., 2019. Bayes factors for superiority, non-inferiority, and equivalence designs. BMC Medical Research Methodology, 19(1).

de Heide. R, Grunewald, P.D, 2021, Why optional stopping can be a problem for Bayesians; Psychonomic Bulletin & Review, 21(2), 201-208.

Medical Device Clinical Trials vs Pharmaceutical Clinical Trials – What’s the Difference?


Medical devices and drugs share the same goal – to safely improve the health of patients. Despite this, substantial differences can be observed between the two. Principally, drugs interact with biochemical pathways in human bodies while medical devices can encompass a wide range of different actions and reactions, for example, heat, radiation (Taylor and Iglesias, 2009). Additionally, medical devices encompass not only therapeutic devices but diagnostic devices, as well (Stauffer, 2020).

More specifically medical device categories can include therapeutic and surgical devices, patient monitoring, diagnostic and medical imaging devices, among others; making it a very heterogeneous area (Stauffer, 2020). As such, medical device research spills over into many different fields of healthcare services and manufacturing. This research is mostly undertaken by SME’s ( small to medium enterprises) instead of larger well-established companies as is more predominantly the case with pharmaceutical research. SME’s and start-ups undertake the majority of the early stage device development, particularly where any new class of medical device is concerned, whereas the larger firms get involved in later stages of the testing process (Taylor and Iglesias, 2009).

Classification criteria for medical devices

There are strict regulations that researchers and developers need to follow, which includes general device classification criteria. This classification criterion consists of three classes of medical devices, the higher class medical device the stricter regulatory controls are for the medical device. 

  • Class I, typically do not require premarket notifications
  • Class II,  require premarket notifications
  • Class III, require premarket approval

Food and Drug Administration (FDA)

Drug licensing and market access approval by the Food and Drug Administration (FDA) and international equivalents require manufacturers to undertake phase II and III randomised controlled trials in order to provide the regulator with evidence of their drug’s efficacy and safety (Taylor and Iglesias, 2009).

Key stages of medical device clinical trials

In general medical device clinical trials are smaller than drug trials and usually start with feasibility study. This provides a limited clinical evaluation of the device. Next a pivotal trial is conducted to demonstrate the device in question is safe and effective (Stauffer, 2020).

Overall the medical device trials can be considered to have three stages:

  • Feasibility study,
  • Pivotal study to determine if the device is safe and effective,
  • Post-market study to analyse the long-term effectiveness of the device.

Clinical evaluation for medical devices

Clinical evaluation is an ongoing process conducted throughout the life cycle of a medical device. It is first performed during the development of a medical device in order to identify data that need to be generated for regulatory purposes and will inform if a new device clinical investigation is necessary. It is then repeated periodically as new safety, clinical performance and/or effectiveness information about the medical device is obtained during its use.(International Medical Device Regulators Forum, 2019)

During the evaluative process, a distinction must be made between device types – diagnostic or therapeutic. 

The criteria for diagnostic technology evaluations are usually divided into four groups:

  • technical capacity
  • diagnostic accuracy
  • diagnostic and therapeutic impact
  • patient outcome

The importance of evaluation

Evaluations provide important information about a device and can indicate the possible risks and complications. The main measures of diagnostic performance are sensitivity and specificity. Based on the results of the clinical investigation the intervention may be approved for the market. When placing a medical device on the market, the manufacturer must have demonstrated through the use of appropriate conformity assessment procedures that the medical device complies with the Essential Principles of Safety and Performance of Medical Devices(International Medical Device Regulators Forum, 2019).The information on effectiveness can be observed by conducting experimental or observational studies.

Post-market surveillance

Manufacturers are expected to implement and maintain surveillance programs that routinely monitor the safety, clinical performance and/or effectiveness of the medical device as part of their Quality Management System (International Medical Device Regulators Forum, 2019). The scope and nature of such post market surveillance should be appropriate to the medical device and its intended use. Using data generated from such programs (e.g. safety reports, including adverse event reports; results from published literature, any further clinical investigations), a manufacturer should periodically review performance, safety and the benefit-risk assessment for the medical device through a clinical evaluation, and update the clinical evidence accordingly.

The use of databases in medical device clinical trials

The variations in the available evidence-base for devices means that, unlike with drugs, medical devices will typically require the consideration and analysis of data from observational studies in ascertaining their clinical and cost-effectiveness. Using modern observational databases has advantages because these databases represent continuous monitoring of the device in real-life practice, including the outcome (Maresova et al., 2020).

Bayesian methods as an alternative framework for evaluation

Bayesian methods for the analysis of trial data have been proposed as an alternative framework for evaluation within the FDA’s Center for Devices and Radiological Health. These methods provide flexibility and may make them particularly well suited to address many of the issues associated with the assessment of clinical and economic evidence on medical devices, for example, learning effects and lack of head-to-head comparisons between different devices.

Use of placebo in medical vs pharmaceutical trials

An additional key difference between drug and medical device trials are that use of placebo in medical device trials are rare. If placebo is used in a trial for surgical / implanted devices  it would usually be a sham surgery or implantation of a sham device (Taylor and Iglesias, 2009). Sham procedures are high risk and may be considered unethical. Without this kind of control, however, there is in many cases no sure way of knowing whether the device is providing real clinical benefit or if the benefit experienced is due to the placebo effect. 

Conclusion

            In conclusion, there are many similarities between medical device and pharmaceutical clinical trials, but there are also some really important differences that one should not miss:

  1.  In general medical device clinical trials are smaller than drug trials.
  2.  The research is mostly undertaken by SME’s ( small to medium enterprises) instead of big well-known companies
  3. Drugs interact with biochemical pathways in human bodies whereas medical devices use a wide range of different actions and reactions, for example, heat, radiation.
  4. Medical devices can be used for not only diagnostic purposes but therapeutical purposes as well.
  5.  The use of placebo in medical device trials are rare in comparison to pharmaceutical clinical trials.

References:

Bokai WANG, C., 2017. Comparisons of Superiority, Non-inferiority, and Equivalence Trials. [online] PubMed Central (PMC). Available at: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5925592/> [Accessed 28 February 2022].

Chen, M., Ibrahim, J., Lam, P., Yu, A. and Zhang, Y., 2011. Bayesian Design of Noninferiority Trials for Medical Devices Using Historical Data. Biometrics, 67(3), pp.1163-1170.

E, L., 2008. Superiority, equivalence, and non-inferiority trials. [online] PubMed. Available at: <https://pubmed.ncbi.nlm.nih.gov/18537788/> [Accessed 28 February 2022].

Gubbiotti, S., 2008. Bayesian Methods for Sample Size Determination and their use in Clinical Trials. [online] Core.ac.uk. Available at: <https://core.ac.uk/download/pdf/74322247.pdf> [Accessed 28 February 2022].

U.S. Food and Drug Administration. 2010. Guidance for the Use of Bayesian Statistics in Medical Device Clinical. [online] Available at: <https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-use-bayesian-statistics-medical-device-clinical-trials> [Accessed 28 February 2022].

van Ravenzwaaij, D., Monden, R., Tendeiro, J. and Ioannidis, J., 2019. Bayes factors for superiority, non-inferiority, and equivalence designs. BMC Medical Research Methodology, 19(1).