It is important to note that significant correlation does not necessarily imply that a marker will be an acceptable surrogate. Missing data is one of the biggest threats to the integrity of a clinical trial. Missing data can create biased estimates of treatment effects. Thus it is important when designing a trial to consider methods that can prevent missing data.

Researchers can prevent missing data by designing simple clinical trials e. Similarly it is important to consider adherence to protocol e. Envision a trial comparing two treatments in which the trial participants in both groups do not adhere to the assigned intervention. Then when evaluating the trial endpoints, the two interventions will appear to have similar effects regardless of any differences in the biological effects of the two interventions. Note however that the fact that trial participants in neither intervention arm adhere to therapy may indicate that the two interventions do not differ with respect to the strategy of applying the intervention i.

Researchers need to be careful about influencing participant adherence since the goal of the trial may be to evaluate the strategy of how the interventions will work in practice which may not include incentives to motivate patients similar to that used in the trial. Sample size is an important element of trial design because too large of a sample size is wasteful of resources but too small of a sample size could result in inconclusive results.

Calculation of the sample size requires a clearly defined objective. The analyses to address the objective must then be envisioned via a hypothesis to be tested or a quantity to be estimated. The sample size is then based on the planned analyses. A typical conceptual strategy based on hypothesis testing is as follows:. Formulate null and alternative hypotheses. Select the Type I error rate. Type I error is the probability of incorrectly rejecting the null hypothesis when the null hypothesis is true.

In the example above, a Type I error often implies that you incorrectly conclude that an intervention is effective since the alternative hypothesis is that the response rate in the intervention is greater than in the placebo arm. For example, when evaluating a new intervention, an investigator may consider using a smaller Type I error e. Alternatively a larger Type I error e.

Select the Type II error rate. Type II error is the probability of incorrectly failing to reject the null hypothesis when the null hypothesis should be rejected. The implication of a Type II error in the example above is that an effective intervention is not identified as effective. Type II error and power are not generally regulated and thus investigators can evaluate the Type II error that is acceptable.

For example, when evaluating a new intervention for a serious disease that has no effective treatment, the investigator may opt for a lower Type II error e. Obtain estimates of quantities that may be needed e. This may require searching the literature for prior data or running pilot studies.

Select the minimum sample size such that two conditions hold: 1 if the hull hypothesis is true then the probability of incorrectly rejecting is no more than the selected Type I error rate, and 2 if the alternative hypothesis is true then the probability of incorrectly failing to reject is no more than the selected Type II error or equivalently that the probability of correctly rejecting the null hypothesis is the selected power.

Since assumptions are made when sizing the trial e. Interim analyses can be used to evaluate the accuracy of these assumptions and potentially make sample size adjustments should the assumptions not hold. Sample size calculations may also need to be adjusted for the possibility of a lack of adherence or participant drop-out. In general, the following increases the required sample size: lower Type I error, lower Type II error, larger variation, and the desire to detect a smaller effect size or have greater precision.

An alternative method for calculating the sample size is to identify a primary quantity to be estimated and then estimate it with acceptable precision. For example, the quantity to be estimated may be the between-group difference in the mean response. A sample size is then calculated to ensure that there is a high probability that this quantity is estimated with acceptable precision as measured by say the width of the confidence interval for the between-group difference in means.

Interim analysis should be considered during trial design since it can affect the sample size and planning of the trial. When trials are very large or long in duration, when the interventions have associated serious safety concerns, or when the disease being studied is very serious, then interim data monitoring should be considered.

Typically a group of independent experts i. The DSMB meets regularly to review data from the trial to ensure participant safety and efficacy, that trial objectives can be met, to assess trial design assumptions, and assess the overall risk-benefit of the intervention. The project team typically remains blinded to these data if applicable.

The DSMB then makes recommendations to the trial sponsor regarding whether the trial should continue as planned or whether modifications to the trial design are needed. Careful planning of interim analyses is prudent in trial design. Care must be taken to avoid inflation of statistical error rates associated with multiple testing to avoid other biases that can arise by examining data prior to trial completion, and to maintain the trial blind. Many structural designs can be considered when planning a clinical trial.

Common clinical trial designs include single-arm trials, placebo-controlled trials, crossover trials, factorial trials, noninferiority trials, and designs for validating a diagnostic device. The choice of the structural design depends on the specific research questions of interest, characteristics of the disease and therapy, the endpoints, the availability of a control group, and on the availability of funding.

Structural designs are discussed in an accompanying article in this special issue. This manuscript summarizes and discusses fundamental issues in clinical trial design. A clear understanding of the research question is a most important first step in designing a clinical trial. Minimizing variation in trial design will help to elucidate treatment effects. Randomization helps to eliminate bias associated with treatment selection.

Stratified randomization can be used to help ensure that treatment groups are balanced with respect to potentially confounding variables. Blinding participants and trial investigators helps to prevent and reduce bias. Placebos are utilized so that blinding can be accomplished.

Control groups help to discriminate between intervention effects and natural history. The selection of a control group depends on the research question, ethical constraints, the feasibility of blinding, the availability of quality data, and the ability to recruit participants. The selection of entry criteria is guided by the desire to generalize the results, concerns for participant safety, and minimizing bias associated with confounding conditions.

Endpoints are selected to address the objectives of the trial and should be clinically relevant, interpretable, sensitive to the effects of an intervention, practical and affordable to obtain, and measured in an unbiased manner. Composite endpoints combine a number of component endpoints into a single measure. Surrogate endpoints are measures that are predictive of a clinical event but take a shorter time to observe than the clinical endpoint of interest.

Interim analyses should be considered for larger trials of long duration or trials of serious disease or trials that evaluate potentially harmful interventions. Sample size should be considered carefully so as not to be wasteful of resources and to ensure that a trial reaches conclusive results.

There are many issues to consider during the design of a clinical trial. Researchers should understand these issues when designing clinical trials. The author would like to thank Dr. Justin McArthur and Dr. The author thanks the students and faculty in the course for their helpful feedback.

National Center for Biotechnology Information , U. J Exp Stroke Transl Med. Author manuscript; available in PMC Apr Scott R. Evans , Ph. Author information Copyright and License information Disclaimer. Evans, Ph. Phone: Copyright notice. See other articles in PMC that cite the published article. Abstract Most errors in clinical trials are a result of poor planning.

Keywords: p-value, confidence intervals, intent-to-treat, missing data, multiplicity, subgroup analyses, causation. Introduction The objective of clinical trials is to establish the effect of an intervention. Design Issues There are many issues that must be considered when designing clinical trials.

Table 1 Terms in clinical trial design. Alternative Hypothesis Claim that would like to be made at the end of the trial. The scientific method states that to prove something, assume the compliment is true and then look for contradictory evidence.

If sufficient contradictory evidence is observed, then the desired claim has been proven. Typically the alternative hypothesis is something that the investigator desires to prove e. The investigator thus assumes that the compliment called the null hypothesis is true and then looks for evidence to disprove the null hypothesis and hence claim the alternative hypothesis to be true.

Intent-to-treat ITT Strategy for conducting a trial and analyzing data. Null Hypothesis Claim that an investigator desires to disprove. Phase I The first studies conducted in humans using an experimental intervention.

These trials often have small sample sizes e. Phase II Trials typically conducted to investigate a dose response relationship, identify an optimal dose, and to investigate safety issues.

Phase III Generally large trials i. Phase IV Trials carried out after registration of an intervention. Power The probability of rejecting a null hypothesis when it should be rejected. In superiority trials e. Type I Error The probability if rejecting the null hypothesis when it should not be rejected i. In superiority trials this means the probability of incorrectly identifying a treatment effect when indeed a true treatment effect does not exist.

Type II Error The probability of failing to reject the null hypothesis when it should be rejected i. In superiority trials this means the probability of failing to identifying a treatment effect when indeed a true treatment effect exists. Open in a separate window. Composite endpoints An intervention can have effects on several important endpoints. Surrogate Endpoints In the treatment of some diseases, it may take a very long time to observe the definitive endpoint e.

An example of a surrogate endpoint is blood pressure for hemorrhagic stroke. If you have any questions or concerns regarding any content published here, feel free to contact us using the Contact link below. Login Register. Resources School Projects Maths Projects. Related Articles. How to improve teaching mathematics in secondary schools Some secondary school students have a fear of mathematics. This can be changed by teachers using teaching aids and appropriate textbooks and other revision materials.

Schools should ensure that they only employ learned and experienced teachers of mathematics who should frequently go for refresher courses during school vacations. Further research in this field is going on in various parts of the world. Meanwhile Director Roger Spottiswoode is finalising his project of making a film on the life of Indian mathematical genius Srinivasa Ramanujan. The year happens to be his th birth year.

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