18 Mar 2022
By Sabine Dreibe
Bayesian approach for sample size estimation and re-adjustment in clinical trials

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

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            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 sophisticated 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 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).

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

18 Mar 2022
By Henry Budds

Clinical Trial Phases in Drug Development

Clinical Trials, Pharma & Drug Development


The development of new drugs starts far before they are even seen in clinical trials. The discovery of multiple candidate drugs occur early on in the development process, often as a result of new information about how a disease functions, large-scale screening of small molecules, or the release of a new technology.

After a promising drug has been found, pre-clinical studies can be performed. A pre-clinical study for a new drug is used to determine important information about toxicity and suitable dosage amounts. These studies can be in vitro (in cell culture) and/or in vivo (in animal models) and determine whether a treatment will continue to the clinical trials stage.

Clinical trials test whether these experimental treatments are safe for use in humans, and whether they are more effective in treating or preventing a disease when compared to existing treatments. Clinical trials consist of several stages, called phases, where each phase is focused on answering a different clinical question: Progression of a treatment to the next phase requires the study to meet several parameters to ensure a treatment’s safety or efficacy.

  • Phase 0: Is the new treatment safe to use in humans in small doses?
  • Phase I: Is the new treatment safe to use in humans in therapeutic doses?
  • Phase II: Is the new treatment effective in humans?
  • Phase III: Is the new treatment more effective than existing treatments?
  • Phase IV: Does the new treatment remain safe and effective post-market?
Key phases of a pharmaceutical clinical trial

Phase 0: Small dose safety

Phase 0 studies can help to streamline the other clinical trial phases. Phase 0 consists of giving a few patients small, sub-therapeutic doses of the new treatment. This is to make sure that the new treatment behaves as expected by researchers and isn’t harmful to humans prior to using higher doses in phase I trials.

Phase I: Therapeutic dose safety

Phase I studies evaluate the safety of various doses of the new treatment in humans. This takes several months with typically around 20-80 healthy volunteers. In some cases, such as in anti-cancer drug trials, the study participants are patients with the targeted cancer type. A treatment may not pass phase I if the treatment leads to any serious adverse events.

Initial dosages in phase I studies can be informed based on data obtained during pre-clinical animal studies, and adjustments can be made to investigate the treatment’s side effect profile and develop an optimal dosing program. This could also include comparing different methods of giving a drug to patients (e.g., oral, intravenous etc.).

Phase II: Treatment efficacy

After passing phase I trials and having proven safety in humans, a new treatment advances to phase II studies designed to assess whether it may prevent or treat a disease. This phase can take between several months to 2 years, testing the new treatment in up to several hundred patients with the disease. Using a larger number of patients over a longer time period provides researchers with additional safety and effectiveness data, which is essential for the design of phase III trials.

To further test safety and efficacy, it is common to have a control group that receives either a placebo (a harmless pill or injection without the new treatment) or other current treatment (in trials where the disease is fatal unless treated e.g., cancer).

Phase III: Comparing to current treatments

Phase III studies are the last stage of a clinical trial before a new treatment can be approved for market use. The primary focus of a phase III study is to compare the safety and efficacy of a new treatment with current, existing treatments in patients with the target disease. Anywhere from several hundred to 3,000 patients may be included in a phase III study for between 1 to 4 years. Due to the scale of this phase, long-term or rare side effects are more likely to be uncovered.

Phase III studies are often randomised control trials, where patients will be randomly designated to different treatment groups. These groups may receive placebo, a current treatment (control group), the new treatment, or variations of the new treatment (e.g., different drug combinations). Randomised control trials are often double-blinded, where both the patient and the clinician administering their treatment do not know which treatment group they are assigned to.

A new treatment may continue to market and phase IV trials if the results prove it is as safe and effective as an existing treatment.

Phase IV: Post-market surveillance

If a new treatment passes phase III and is approved by the MHRA, FDA, or other national regulatory agency, it can be put to market. Phase IV is carried out in the post-market surveillance of the new treatment to keep updated on any emerging or long-term safety and efficacy concerns. This may include rare or long-term adverse side effects that were not yet discovered, or long-term analyses to see if the new treatment improves the life expectancy of a patient after recovery from disease.

Summary

Clinical trials are ultimately designed to mitigate risk. This includes the risk to the safety of trial participants by limiting the use of potentially unsafe treatments to small doses in a small number of patients before scaling up to testing therapeutic dose safety. Risk mitigation is not only for patient safety but also for preventing financial misspending as a treatment that is deemed unsafe in phase 0 would not proceed to the later, more costly clinical trial phases.

Not all clinical trials are the same, however, as each trial will have a different disease and treatment context. Trials for medical devices are somewhat different from pharmaceutical trials (for more information about the differences between medical device and pharma trials, click here). In addition, while sample sizes expand with phase progression, the required sample size for each trial and each phase is dependent on several factors including disease context (a rare disease may require lower sample sizes), patient availability (location of trial), trial budget and effect size. The sample size values mentioned earlier in this blog are purely indications of what each phase may use (for more information on how a biostatistician determines a suitable sample size, click here).

References

https://www.fda.gov/patients/drug-development-process/step-3-clinical-research

https://www.healthline.com/health/clinical-trial-phases

12 Mar 2022
By Sabine Dreibe

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

Clinical Trials, Med-Tech & Medical Device, Pharma & Drug Development

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).