- 1 Study Designs: Basics of Research
- 2 Randomization and Allocation
- 3 Biased Coin Randomization
- 4 References
Study Designs: Basics of Research
Conducting a medical study requires precise planning and effective management. One of the crucial aspects experts must consider, revolves around the participants – the main focus of digital health research. Recruitment procedures, sample size, randomization, and allocation are some of the factors that require careful consideration before the actual start of the study.
Since medical professionals cannot test the whole population due to financial, ethical and time limitations, a representative sample is often needed. At the same time, there’s always a chance that the chosen sample won’t represent the population of interest (Peat, 2011). In the end, when only a portion of the population is studied, there’s always a risk that this particular group does not accurately represent the target population.
Randomization and Allocation
To minimize errors and bias, randomization has become a sufficient way to select participants. Random allocation is another paramount method used to assign participants to different research groups (Peat, 2011). In other words, randomization is a practice that’s used to achieve generalizability, while random allocation – to minimize confounders and eliminate systematic bias. Thus, random selection and random allocation are the most efficient methods, each with its challenges and advantages.
For instance, the random allocation can lead to unbalanced and balanced groups (Peat, 2011). When it comes to unbalanced samples, two methods can be employed: simple randomization (via a random number table or a computer-generated sequence) or quasi-randomization (through a random number; e.g., age). When balanced groups are needed, several methods can be employed. Restricted randomization is one of the effective methods, which is achieved by sealed envelopes. Block randomization, on the other hand, is achieved in small blocks. Another technique is replacement allocation which requires from experts to reject sequences when they exceed the pre-specified balance. Dynamic balanced randomization is also vital, and it involves forced allocation to unbalanced groups. Bias coin randomization, which implies that probability is changed in unbalanced groups, can be employed as well. Last but not the least, minimization is a popular method which requires allocation by prognostic factors when there are unbalanced groups.
Random Selection and Random Allocation in Details
As explained above, random selection is the most beneficial way to ensure representativeness and generalizability. Random selection can be made from an ordered list. This list can include vital indicators (e.g., towns) which have a unique number. Consequently, these unique numbers can be randomly selected from the list. In studies with less than 100 participants, tables prove to be the most effective way to select subjects. When using a table, the pattern of reading it is important: the table can be read by row, by column or by block (Peat, 2011). After experts have decided on the pattern, a random start should be chosen, and a random sequence should be used to select numbers. Note that any number repeated should be discarded. For studies with more than 100 participants, computer software to generate random number sequences is suggested. Again, since duplicates should be excluded, a longer list is recommended to ensure enough numbers.
Random selection is vital, so is random allocation. Allocation methods, such as the ones described above, are used to assign participants to two or more study groups (e.g., treatment and control groups). Usually, the allocation is used to remove confounders. Note that experts can control for confounders in the analysis (via post-hoc methods and multivariate data); however, it’s better to do that at the design stage of the study (Peat, 2011). The most effective allocation can be achieved via an unpredictable allocation sequence. To avoid bias, staff should be blinded to the results. Allocation concealment, or when staff does not know what the next intervention allocation will be, is also paramount to diminish selection bias. Balanced groups are the most desired outcome of allocation. Nevertheless, the main aspect to consider is to ensure that each subject has the chance to be allocated to any group and to ensure that differences in groups are due to treatment only. A good practice is to keep the allocation code unavailable until subjects are eligible or until they consent.
Another important difference between studies is their qualitative or quantitate nature (Moffatt, 2006). While quantitative methods rely on conventional data collection and statistical procedures, qualitative studies involve open questions and other in-depth methods, such as interviews with patients.
As mentioned above, quantitative studies help researchers collect data and transform it into statistics. Such studies are well structured and can include large samples. As a result, they are widely used in research and medical practice.
On the other hand, qualitative studies can help researchers gain insights and generate testable hypotheses. They can be used parallel to quantitative studies to explore patients’ feelings, attitudes towards a new treatment, and personal tactics to cope with a disease.
Simple randomization is one of the most popular methods used to select and assign subjects (Peat, 2011). It’s also called complete unbalanced or unrestricted randomization. In fact, simple randomization is the best method to perform the random allocation. It can be achieved by tossing a coin or by selecting random numbers.
Note that when a random number is taken from a table, experts need to decide how to use it. For example, if a team gets the following numbers:
05 10 22
they can use the following combinations:
- 5, 10, 22
- 5, 1, 0, 2, 2
On top of that, researchers can use either the first digits of the selected numbers:
- 0, 1, 2
or only the last ones:
- 5, 0, 2
After the selection of numbers, the subjects are divided into groups.
Simple randomization is a great method which balances prognostic factors. However, it can lead to unbalanced groups. This can become problematic in small studies or across different research centers. Unfortunately, small numbers lack the statistical power to show clinically significant results.
Quasi-randomization is another popular method, which relies on a systematic assignment achieved by essential indicators, such as date of birth or medical number (Kim et al., 2017). Since this method is not exactly random, experts call it quasi-randomization. Note that alternation is another type of quasi-randomization: it’s achieved by following the order via which the subjects were included in the study.
Quasi-randomization may be prone to selection bias and lack of concealment. Another disadvantage of this method is that there’s no balance of confounders or groups.
Block randomization is a method in which selection and allocation are done by small groups, called blocks (Peat, 2011). This method is beneficial in large studies or multi-centered trials, and also during simple and stratified randomization. Block randomization is used to assure balanced groups. Note that to be effective, blocks must generate all possible combinations.
For example, for a block size of four and two treatment groups (A, B), there are six options:
Blocks should be numbered, after which a number will be selected randomly.
On the other hand, for a group of three for three treatments (A, B, C), the following combinations will be valid:
After forming the blocks, experts need to choose a random order. Let’s look at the following random sequence: 6, 2, 3, 6, 1, etc. In this case, the research team will start with blocks CBA, ACB, and so on and on.
However, the block size can ruin concealment as experts may guess the order at the later stages of research. To minimize bias, the block size can be changed during the study (Peat, 2011).
Another challenge is the need for large blocks. In large blocks, experts may face too many combinations. Note, however, that if there are large blocks but only two treatment groups, random randomization can be used to help experts allocate subjects.
When it comes to selection and allocation, unbalanced groups may distort findings. Thus, replacement randomization is an effective method to guarantee balanced groups. The maximum imbalance should be decided before the study, and new sequences will be continuously generated until the planned criteria are met (Peat, 2011).
However, replacement randomization can become unblinded at the later stages of research. Simply because when the group size increases and the sample decreases, more and more replacements will be needed. For this reason, this method is suitable only in small studies with a few groups, including stratified trials. Yet, replacement randomization guarantees an upper limit of imbalance, and it also provides unpredictable sequences, which is an advantage over other methods, such as block randomization.
Biased Coin Randomization
Biased coin randomization is a popular method which is also known as adaptive randomization. This technique follows the probability of assigning subjects to different treatment groups to the point when the groups become unbalanced. This means that the probability of assigning subjects to small groups increases to maintain balance (Peat, 2011).
In particular, biased coin randomization can be used when the imbalance between groups exceeds a specific number, which is specified before the study.
Minimization is another effective method, which as explained above, is used to ensure a balance of prognostic factors (Peat, 2011). This method is critical for balancing numbers over two and more characteristics. In fact, this is crucial in small studies when differences in confounders can occur just by chance. It’s also vital in large studies – when imbalances may reduce the statistical power of small differences. Note that in minimization, the number of subjects is updated continuously.
It’s interesting to mention that the odds of entry in a group can follow the biased coin method described above; as a result, patients can be allocated to the smaller group. If there’s an equal number, then, simple randomization can be employed.
Dynamic Balanced Randomization
Dynamic balanced randomization is a method which is a variation of replacement randomization (Peat, 2011). In other words, when the pre-specified imbalance has been reached, the next subjects are allocated to the smaller group. This method is beneficial in studies in which randomization by strata is needed. A disadvantage of dynamic balanced randomization, though, is that there’s a risk of ruining concealment. Nevertheless, this research technique is less prone to bias compared to block randomization; also, there’s better protection of imbalance when compared to minimization.
In medical research, equal numbers are the most desired and effective way to achieve meaningful results. Thus, as described above, there are many methods that help experts balance groups during randomization and allocation (Peat, 2011).
However, equal numbers are not always the ultimate solution. For instance, when a new and an old treatment must be compared, more subjects must be randomized in the new treatment group in order to show significant differences (both statistical and clinical differences). In this case, the ratio between new and old treatments can be 2:1 or 3:2. Note that the methods to select and allocate subjects include all the techniques described above (Peat, 2011).
Randomization in Clusters
Last but not the least, epidemiological studies also need some special consideration. When experts need to measure the prevalence of a disease or mortality rates within a community, clusters – not individuals – should be the focus of analysis. For instance, when experts should select children from a particular population, schools must be randomized – not individual students.
However, this method does not account for cultural differences between the chosen communities. There will be a difference between rural and city schools, for example. This can lead to a significant loss of efficiency. However, randomization in clusters can be beneficial in cases when the number of practices is substantial and the number of subjects small. In fact, to reach an adequate sample size, the number of practices and interventions must be considered – not the number of individuals.
Randomization and allocation allow experts to exercise control over any clinical trial, eliminate bias, balance groups, and confounders, and give each patient an equal chance of receiving the same treatment (Suresh, 2011). Therefore, randomization is a fundamental part of medical research and sound management practices.
Kim, J., Kim, T., In, J., Lee, D., Lee, S., & Kang, H. (2017). Assessment of risk of bias in quasi-randomized controlled trials and randomized controlled trials reported in the Korean Journal of Anesthesiology between 2010 and 2016. Korean Journal of Anaesthesiology, 70(5), 511-519.
Peat, J. (2011). Conducting the Study. Health Science Research, SAGE Publications, Ltd.
Suresh, K. (2011). An overview of randomization techniques: An unbiased assessment of outcome in clinical research. Journal of Human Reproductive Sciences, 4(1), 8-11.