Thus, to preserve rater blinding almost in outcome, patients were evaluated 4 months after surgical procedure by a neurologist who was not aware of the trial-group assignments. The intention to treat analysis ITT analysis is a solid method to avoid analytical bias The patients that did not performed the planned intervention will be not excluded from RCTs, and this prevent the possible bias of patient withdraw or crossover.
For example, Corris et al. RCTs conducted in perfect and ideal conditions often are not easy to be applied in routine clinical contest. If we aim to obtain RCT strengthening evidence for clinical practice, we have to build them on strong hinges that allow us to influence the scientific literature and change the clinical decision-making activity of physicians involved in thoracic disease management.
Conflicts of Interest: The authors have no conflicts of interest to declare. National Center for Biotechnology Information , U. Journal List J Thorac Dis v. J Thorac Dis. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Correspondence to: Prof. Pier Luigi Filosso, MD. Email: ti. Received Apr 16; Accepted Jun 4.
Copyright Journal of Thoracic Disease. All rights reserved. This article has been cited by other articles in PMC. Abstract Randomized controlled trials RCTs are considered one of the highest level of evidence in clinical practice, due to their strong confidence and robustness in producing data.
Keywords: Randomized controlled trial RCT , clinical research, study design, thoracic disease. Introduction Nowadays, medical decisions such as which type of surgical approach, whether to treat or not a patient and with which pharmacological intervention are evaluated considering the evidence-based medicine 1. Road map and documents Tips: prepare in advance a detailed study protocol, a realistic timeline and proper data collection forms.
Hypothesis and outcome Tips: formulate a single, simple and clear main hypothesis, accompanied by limited number of secondary ones. Selection criteria and sample size Tips: find an equilibrium between very strict and selective criteria standardized patient group and more heterogeneous conditions external validity of the results.
Randomization, stratification, blind and intention to treat analysis Tips: choose and report the methods of Randomization correctly. Acknowledgements None. Footnotes Conflicts of Interest: The authors have no conflicts of interest to declare. References 1. Riegelman R. Studying a study and testing a test: how to read the medical evidence. United States. Prevention Services Task Force.
Guide to clinical preventive services. Centre for Evidence-Based Medicine. Retrieved 25 March Jaillon P. Controlled randomized clinical trials. Bull Acad Natl Med ; Challenging issues in randomised controlled trials. Injury ;S20e3. Albert RK. Pragmatic trials--guides to better patient care? N Engl J Med ; Meinert CL.
Clinical trials: design, conduct, and analysis. The Coronary Drug Project. Design of data forms. Control Clin Trials ; 4 Beyond the randomized clinical trial: the role of effectiveness studies in evaluating cardiovascular therapies.
Circulation ; A study design that randomly assigns participants into an experimental group or a control group. As the study is conducted, the only expected difference between the control and experimental groups in a randomized controlled trial RCT is the outcome variable being studied. The variables being studied should be the only variables between the experimental group and the control group.
To determine how a new type of short wave UVA-blocking sunscreen affects the general health of skin in comparison to a regular long wave UVA-blocking sunscreen, 40 trial participants were randomly separated into equal groups of an experimental group and a control group. All participants' skin health was then initially evaluated. The experimental group wore the short wave UVA-blocking sunscreen daily, and the control group wore the long wave UVA-blocking sunscreen daily.
After one year, the general health of the skin was measured in both groups and statistically analyzed. The preventive effect of the nordic hamstring exercise on hamstring injuries in amateur soccer players: a randomized controlled trial. The American Journal of Sports Medicine, 43 6 , Natour, J.
Pilates improves pain, function and quality of life in patients with chronic low back pain: a randomized controlled trial. Clinical Rehabilitation, 29 1 , Subjective endpoints are more susceptible to individual interpretation. For example, neuropathy trials employ pain as a subjective endpoint.
Other examples of subjective endpoints include depression, anxiety, or sleep quality. Objective endpoints are generally preferred to subjective endpoints since they are less subject to bias. An intervention can have effects on several important endpoints. Composite endpoints combine a number of endpoints into a single measure. The advantages of composite endpoints are that they may result in a more completed characterization of intervention effects as there may be interest in a variety of outcomes.
Composite endpoints may also result in higher power and resulting smaller sample sizes in event-driven trials since more events will be observed assuming that the effect size is unchanged.
Composite endpoints may also reduce the bias due to competing risks and informative censoring. This is because one event can censor other events and if data were only analyzed on a single component then informative censoring can occur. Composite endpoints may also help avoid the multiplicity issue of evaluating many endpoints individually.
Composite endpoints have several limitations. Firstly, significance of the composite does not necessarily imply significance of the components nor does significance of the components necessarily imply significance of the composite. For example one intervention could be better on one component but worse on another and thus result in a non-significant composite. Another concern with composite endpoints is that the interpretation can be challenging particularly when the relative importance of the components differs and the intervention effects on the components also differ.
For example, how do we interpret a study in which the overall event rate in one arm is lower but the types of events occurring in that arm are more serious? Higher event rates and larger effects for less important components could lead to a misinterpretation of intervention impact. It is also possible that intervention effects for different components can go in different directions.
Power can be reduced if there is little effect on some of the components i. When designing trials with composite endpoints, it is advisable to consider including events that are more severe e. It is also advisable to collect data and evaluate each of the components as secondary analyses. This means that study participants should continue to be followed for other components after experiencing a component event. When utilizing a composite endpoint, there are several considerations including: i whether the components are of similar importance, ii whether the components occur with similar frequency, and iii whether the treatment effect is similar across the components.
In the treatment of some diseases, it may take a very long time to observe the definitive endpoint e. A surrogate endpoint is a measure that is predictive of the clinical event but takes a shorter time to observe.
The definitive endpoint often measures clinical benefit whereas the surrogate endpoint tracks the progress or extent of disease. Surrogate endpoints could also be used when the clinical end-point is too expensive or difficult to measure, or not ethical to measure.
Surrogate markers must be validated. Ideally evaluation of the surrogate endpoint would result in the same conclusions if the definitive endpoint had been used. The criteria for a surrogate marker are: 1 the marker is predictive of the clinical event, and 2 the intervention effect on the clinical outcome manifests itself entirely through its effect on the marker.
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.
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