There are two components to randomization: the generation of a random sequence and the implementation of that random sequence, ideally in a way that keeps participants unaware of the sequence allocation concealment.
Randomization removes potential for systematic error or bias. The biggest upside of an RCT is the balancing of both the known and unknown confounding factors which leads to wrong conclusions. Stratified randomization — This refers to the situation in which strata are constructed based on values of prognostic variables and a randomization scheme is implemented separately within each stratum.
The objective of stratified randomization is to ensure balance of the treatment groups with respect to the various combinations of the prognostic variables. This method can be used to achieve balance among groups in terms of subjects' baseline characteristics covariates.
Specific covariates must be identified by the researcher who understands the potential influence each covariate has on the dependent variable. To avoid strata with very less patients, the number of strata should be kept minimum. After all the subjects have been identified and assigned into strata, simple randomization is performed within each stratum to assign subjects to either case or control groups.
Block randomization — Blocking is the arranging of experimental units in groups blocks that are similar to one another. Typically, a blocking factor is a source of variability that is not of primary interest to the experimenter.
An example of a blocking factor might be the sex of a patient; by blocking on sex, this source of variability is controlled for, thus leading to greater accuracy. The block randomization method is designed to randomize subjects into groups that result in equal sample sizes.
This method is used to ensure a balance in sample size across groups over time. Blocks are small and balanced with predetermined group assignments, which keeps the numbers of subjects in each group similar at all times. Randomization by body halves or paired organs Split Body trials — This is a scenario most often used in dermatology and ophthalmic practice where one intervention is administered to one half of the body and the comparator intervention is assigned to other half of the body.
This can be implemented only if experimental treatment acts locally. Randomization is used to select which side of the body receives which drug. The upside is the elimination of confounding factors between trial arms, as the baseline characteristics of both arms are the same. The downside is difficulty in blinding the investigator, statistical analysis, and influence of therapy administered in one half of the body influencing disease on the other side as the halves of the human body is a continuum and not entirely independent entities carryover of the experimental treatment to control half.
Paired data statistical analytic tests need to be done in this scenario. Cluster randomization — Study patients and treating interventionists do not exist in isolation. Sometimes interventions need to be applied at ward level, village level, hospital level, or group practice level. Hence intervention is administered to clusters by randomization to prevent contamination.
Each cluster forms a unit of the trial and either active or comparator intervention is administered for each cluster. Allocation by randomized consent Zelen trials — Eligible patients are allocated to one of the two trial arms prior to informed consent. This is utilized when informed consent process acts as an impediment to study subject accrual. However, this design raises serious ethical uncertainties and must only be used in severely flagging trials in terms of insufficient sample size of great public health importance and is not recommended in routine clinical trial design.
Minimization — Stratification based on multiple co-variates age, sex, gender, baseline severity of disease, personal habits, co-morbidities, treatment naivety, etc. Hence, an alternate strategy to control for prognostic variables to avoid such small strata is minimization. After identification of these variables, they are dichotomized at some break point in case of continuous variables or based on presence or absence of a categorical variable.
Then each dichotomized half is given a value of 0 or 1 e. For example, patient number 1 with score 2 is randomized to control arm. Patient no. So now the control arm total score is 2 and case arm score is 1.
Patient 3 is a female with score 1 and will be allocated to case arm and thus the cumulative score in both groups will be balanced at 2 points. Once the running scores in both arms are tied, the next recruited subject is again randomly allocated and the whole cycle repeats. Thus, minimization is a viable alternative to randomization for known prognostic factors, but does not factor in the unknown prognostic confounding variables.
Hence, it can be considered a platinum standard to the gold standard of random allocation. Parallel arm design is the most commonly used study design. In this design, subjects are randomized to one or more study arms and each study arm will be allocated a different intervention. After randomization each participant will stay in their assigned treatment arm for the duration of the study [ Figure 5 ]. Parallel group design can be applied to many diseases and allows running experiments simultaneously in a number of groups, and groups can be in separate locations.
The randomized patients in parallel groups should not inadvertently contaminate the other group by unplanned co-interventions or cross-overs. Illustrative example — A comparative trial of Acitretin and Apremilast in palmoplantar psoriasis, where there exits clinical equipoise as regarding efficacy can be conducted as a randomized controlled acitretin as active control parallel arm trial design. Another advantage is requirement of a smaller sample size [ Figure 6 ].
The ethical limitations of a placebo control are partially overcome by a cross over design in which each patient receives both interventions but in a different order.
The order in which patient receives interventions is randomized. Another advantage is requirement of a smaller sample size. In this design, some participants start with drug A and then switch to drug B AB sequence in one trial arm, while subjects in other trial arm start with drug B and then switch to drug A BA sequence.
Adequate washout period must be given before crossover to eliminate the effects of the initially administered intervention. Outcomes are then compared within the same subject effect of A vs. The requirements are two fold. Bioequivalence and biosimilar equivalence studies usually utilize a cross over design.
The duration of follow-up for the patient is longer than for a parallel design, and there is a risk that a significant number of patients do not complete the study and drop out leading to compromised study power. Salient points regarding cross over trials are summarized in Table 1. 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.
The order of treatment is randomly assigned within each treatment period pair. Usually, the primary objective of such a trial is to determine the treatment preference for the individual patient and this design is gaining popularity in recent times.
The advantage of this design is its flexibility such that it can be continued until a definitive conclusion can be reached for the particular subject being studied. The utility also rests in analyzing treatments that elicit heterogenous responses in different subjects.
Data from many N-of 1 subjects can be even combined to derive population effect sizes by meta-analysis or Bayesian methods.
This is a design suited for the study of two or more interventions in various combinations in one study setting and helps in the study of interactive effects resulting from combination of interventions. Both use versus non-use of agent and different dose intensities of one agent can be studied as illustrated in Figure 7.
Outcomes are analyzed using two-way analysis of variance ANOVA comparing all patients who receive treatment A groups 1 and 3 with those not treated with A groups 2 and 4 , and all patients who receive treatment B groups 2 and 3 with those not treated with B groups 1 and 4. However, a prerequisite requirement is that there is no interaction between treatments A and B. If interaction exits, then loss of power is possible in case of separate analyses of the four different combinations.
If an interaction is anticipated, then that has to be factored into the sample size in addition to estimated sample size. Hence, it is not suited for rare diseases where interaction between A and B are likely.
The limitations of this trial design are complexity of trial, difficulty in meeting inclusion criteria of both drugs during study subject recruitment, inability to combine two incompatible interventions, complex protocols, and statistical analytical complexities. Incomplete factorial designs are used when it is deemed unethical to exercise a non-intervention option and here the placebo only arm is eliminated. In this design, after an initial open label period enrichment period during which all subjects are assigned to receive intervention, the non-responders are dropped from the trial and the responders the enriched population are randomized to receive intervention or placebo in the second phase of the trial.
Thereby only responders are carried forward and randomized. Study analysis is conducted using only data from the withdrawal phase and outcome is usually relapse of symptoms. The enrichment phase increases statistical power for a given sample size.
The randomized withdrawal design aims to evaluate the optimal duration of a treatment in patients who respond to the treatment. The advantage is reduction in the time on placebo since only responders are randomized to placebo thereby giving an ethical advantage.
This ensures acceptability to trial subjects and hence facilitates recruitment. This design can assess if treatment needs to be continued or can be stopped. This design allows subjects who have therapy withdrawn control arm subjects to re-start effective therapy after they have reached the study end-point e. Illustrative example. Subjects of psoriasis vulgaris are initiated on a biological and a group of patients attain PASI 75 response at 16 weeks.
Subsequently the non-PASI 75 achievers are dropped from the trial due to lack of efficacy. PASI 75 responders are continued on the drug or are assigned to placebo and retention of PASI 75 response at 1 year after randomization can be compared between two arms. If there is no statistically significant difference in outcomes between the arms, an expensive biological can be administered till PASI 75 is achieved and then omitted, thereby reducing cost of therapy. The disadvantages of this design are 1 Missing data due to withdrawals that can be countered by imputation or using time to event analysis.
This paradigm is useful only for studies with binary outcomes and are most useful when the anticipated effect size being evaluated is large. The play-the-winner and the drop-the-loser designs aim to favor the group with the best chance of success by increasing the probability of patients being randomized to that group.
The probability of being randomized to one group or another is modified according to the results obtained with previous patients. The response of each patient after treatment plays an essential role in the determination of subsequent compositions of the study population. The advantage is enhanced exposure of subjects to an effective intervention and increased chances of recruitment and this can also result in unequal group sizes, which can adversely affect statistical power.
The adaptation can be based on the following methods, which can be combined in select trial situations [ Table 3 ]. Response adaptive — This design reduces patient recruitment to ineffective intervention arms. It requires rapidly available measurable responses. It is infeasible in diseases and therapies with a prolonged time to outcome. This design can compromise allocation concealment and result in selection bias as trial progresses. It can also be affected by changes in patient or treatment characteristics over time that are associated with the treatment received resulting from inherent chances of prolonged recruitment schedule temporal drift.
Ranking and selection —First phase of this adaptive design has subjects randomized to many interventions and placebo. The best therapy from Phase 1 is compared with placebo in a randomized parallel or adaptive design in Phase II.
The final comparison is between all subjects receiving the selected intervention versus all the subjects receiving placebo in both phases combined. It is best suited for multiple intervention comparison in low sample size scenarios. However, there is a chance that wrong selection of the most efficacious therapy in phase I will vitiate the trial results. Sequential adaptive design — This design allows repeated interim analysis and stoppage once end point of efficacy, safety or futility is achieved.
In contrast to traditional trials, the final number of participants needed for a sequential trial is unknown at initiation. The trial ends at the first interim analysis which meets pre-set stopping criteria thereby potentially limiting the number of subjects exposed to an inferior, unsafe, or futile treatment or one that is already proven efficacious.
Analysis can be performed after each patient continuous sequential or after a fixed or variable number of patients group sequential. This design is only effective when study enrolment is expected to be prolonged and treatment outcomes occur relatively soon after recruitment, so that outcomes can be measured before the next patient or group of patients is likely to be recruited. Challenges include the complexity of analyzing multiple treatments, Power calculation complexities, and appropriate selection of timing and number of interim analyses.
An interim analysis is then performed to determine which active arm should be dropped. In the confirmatory stage of Phase III study, the treatment groups with the residual effective active arm and control arm will be investigated. In the inference seamless approach, the subjects will carry their treatment arm from learning phase to confirmatory phase, and the data in both phases will be analyzed together.
For the operational seamless variant, the data in two phases are analyzed separately. 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 ; Stanley K. Design of randomized controlled trials. Randomized trial of thymectomy in myasthenia gravis. Randomized controlled trials.
J Clin Oncol ; 24 Clinical trial of adjuvant chemotherapy after surgical resection of colorectal cancer metastatic to the liver. Mayo Clin Proc ; 60 Multicenter international randomized comparison of objective and subjective outcomes between electronic and traditional chest drainage systems.
Ann Thorac Surg ; 98 ; discussion Adjuvant chemotherapy after potentially curative resection of metastases from colorectal cancer: a pooled analysis of two randomized trials. J Clin Oncol ; 26 Methods in health services research. Interpreting the evidence: choosing between randomised and non-randomised studies. 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 , When the groups that have been randomly selected from a population do not know whether they are in the control group or the experimental group. Being able to show that an independent variable directly causes the dependent variable.
This is generally very difficult to demonstrate in most study designs.
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