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Part 4: Masking and Randomization—Are They Worth the Trouble?
Statistics in Nutrition
Part 4: Masking and Randomization-Are They Worth the Trouble? Janice Derr, PhD*
I
N THE PREVIOUS installment of this series on statistics in nutrition,1 I described and compared observational studies and clinical trials. In part 4, I will focus on some practical considerations of implementing a clinical trial. A recent experience led me to appreciate the way in which a clinical investigator might view the requirements for randomization and for masking the identity of treatment assignments in a clinical trial. By describing this experience, I hope to illustrate the careful planning and coordination required to produce an unbiased and valid clinical trial. The purpose of a clinical trial is to provide a comparison of the effectiveness of two or more treatments, one of which might serve as a control. There are several requirements for a valid trial: (1) Subjects must be assigned at random to a given treatment. (2) The identity of the treatment assigned to each subject should be "masked." Ideally, neither the subject, the clinical staff, nor the data analyst should know the treatment assignment. This is known as "doublemasking." It used to be known as "doubleblinding," but "masking" has replaced "blinding" as the more accepted term.2 Randomization and double-masking help to ensure that the comparison among treat*Managing Director, Statistical Consulting Center, Pennsylvania State University, University Park, PA. Supported in part through an education grant from Abbott Laboratories, Ross Products Division. Address reprint requests to Janice Derr, PhD, Department of Statistics, 323 Classroom Bldg, Pennsylvania State University, University Park, PA 16802. © 1994 by the National Kidney Foundation, Inc. 1051-2276/94/0404-0005$03.00/0
206
ments is as fair and as unbiased as possible. A good reference about sources of bias that can affect a trial should these conditions not be met is Fundamentals of Clinical Trials by Friedman et al. 3 Statisticians often fall into the trap of lecturing others about these requirements without truly considering the impact they have on how a clinical trial is carried out. This is why I was glad to have the opportunity to work with a team of graduate students in statistics on a clinical trial in nutrition. The investigator permitted this team to develop the randomization protocol for the trial. This article summarizes some of the issues we encountered in our work on implementing a protocol for randomization and masking. The study I am using for an illustration involves a clinical trial of a specific vitamin supplement to be given to lactating mothers. Women who have just given birth will be assigned at random to one of three treatments: (1) a placebo, (2) a vitamin supplement at a low dose, and (3) the same vitamin supplement at a higher dose. The women and their children will be followed up for 12 months. A total of 90 women will be enrolled into the trial over a period of approximately 30 months. After considering several plans for randomization, the statistics team selected one that permitted balanced randomization (equal numbers of women in each of the three groups) for smaller blocks of the enrollment period. A block is a smaller subset of the total enrollment. The first b subjects to enroll in the study would be part of block 1 . Within block 1 , an equal number
Journal of Renal Nutrition, Vol 4, No 4 (October), 1994: pp 206-208
207
MASKING AND RANDOMIZATION
of subjects would then be allocated to all three treatments. The next b subjects to enroll would then be part of block 2, with balanced randomization within this block also. An illustration of a randomization plan with three groups and blocks of six subjects per block is shown in Table 1. Randomization in blocks is recommended when the enrollment period occurs over an extended period of time. 2 It ensures against having long, unbroken runs of assignments to the same treatment, which can sometimes occur quite by chance. Blocked randomization helps to provide balance in treatment assignment and in the baseline characteristics of the treatment groups. Imbalances might occur otherwise if the trial were terminated early or if the nature of the patient population changed over time. Because of the interest in maintaining balance, the investigator suggested that we reassign the same treatment of a subject who had dropped out of the trial (dropout) to the next subject who enrolled in the trial. Unfortunately, although balance is maintained, this practice poses a threat to the TABLE 1. Randomization Lists Given to Clinical Staff Showing Different Levels of Masking Block Design List Treatment Group Block Block 1 1 2 3 4 5 6 Block 2 7 8 9 10 11 12
A Known Known
Treatment
B Not Known Known
List
Placebo Low dose Low dose High dose Placebo High dose
B* A A
Low dose High dose Placebo Placebo High dose Low dose
A
C B
C C B B
C A
C Not Known Not Known Randomization No.
10H 102 103 104 105 106 107 108 109 110 111 112
*A separate list, not seen by clinical staff, associates the group code with treatment. tA separate list, not seen by clinical staff, associates the individual code with treatment.
validity of the statistical tests used to compare the groups. Instead, we encouraged the clinical staff to develop strategies to minimize dropouts. The informed consent procedure should make sure that the subject has a thorough understanding of what would be expected of him or her during the study. Subjects should not be randomized until they will actually receive the treatment. Meeting these conditions should ensure that dropouts, if they occur, will be distributed evenly among groups. Dropouts occurring more frequently in one group than in others could represent a reaction to some aspect of the treatment regimen and should be investigated accordingly. Once the randomization plan was selected, the statistics team faced the more challenging task of developing a workable, acceptable protocol for implementing the plan. The objective was to develop a randomization protocol that would mask the actual treatment assignments from the clinical staff. Our protocol had to be specifically developed to fit into the logistics of enrolling a subject, assigning her to a treatment, and dispensing the supplements. We soon learned that the protocol we envisioned required much more paperwork and interaction than the investigator was accustomed to working with. This increased effort resulted entirely from our intent to mask the clinical staff to the treatment assignment as completely as possible. We discovered that increasing the extent to which the clinical staff is masked to treatment assignment has two consequences: (1) implementing the protocol becomes more complex and (2) fewer people have the knowledge required to verify the accuracy of the treatment assignments. For example, if there were no clinical masking at all, the clinical staff could simply take the randomization list provided by the statistician (Table 1, list A) and dispense the treatment materials accordingly. This protocol is fairly simple, and several people could verify the accuracy of the treatment assignments. A more complex protocol would result from masking the treatment group by a group code (Table 1, list B). In this protocol, somebody is usually desig-
208 nated to match up the group code with the treatment. The treatment materials can then be grouped together according to the group code and dispensed to subjects accordingly. The clinical staff would know only that a certain group of subjects were receiving treatment A, others were receiving treatment B, and so on. Throughout the statistical analysis, the treatment would remain coded. Only after the statistical comparisons had been made would the actual treatment levels be identified. The most complete masking is provided by assigning a unique treatment code to each subject (Table 1, list C). Somebody is usually designated to match the treatment assignments given by the statistician with the code number given to each subject. All treatment materials would be dispensed with the subject's code number on them. This coding task often is given to a pharmacist if medication is involved. The treatment materials must be set up individually for each subject. More complete masking means that the clinical staff do not know how the subjects are grouped. The statistician would analyze the data by unmasking the subject codes to the group codes. The group codes would be unmasked only after the statistical comparisons had been made among groups. This is the level of masking we attempted to promote in our clinical trial. The statistics team developed two separate protocol notebooks, one for the clinical staff and one for the statistics group. They also outlined the flow of communication and paperwork that would be required for the randomization protocol. A trial run of the procedure rapidly demonstrated the level of cooperation that will be necessary between the two groups to implement this protocol successfully. While we wait for the first subjects to enroll in this study, we can feel satisfied that students and scientists alike now have first-hand knowledge of what it
JANICE DERR
takes to mount a clinical trial with high standards for masking and randomization. Masking and randomization are incorporated into a clinical trial to produce a fair evaluation of the treatments in the trial. Both processes complicate the logistics of the trial. Procedures for guaranteeing the accuracy of treatment assignments must be developed to account for the extent to which the clinical staff are masked to the true identity of the treatment. A trial run of the randomization protocol is an especially good means to test whether any details have been overlooked. The benefit of investing time and energy into these procedures is the increased validity of the results of the clinical trial. I encourage more investigators involve a statistician in the logistics of planning for randomization and masking. This involvement can help the statistician understand the implications of randomization and masking and to plan the best possible arrangement for these requirements.
ACKNOWLEDGMENT This article could not have been written without the efforts of Pennsylvania State University graduate student statisticians Brenda Gaydos, Bonnie Ghosh-Dastidar, Kate Meaker, and Ruthanna Norris. Hillary Shallo, a graduate student in nutrition, worked extensively with the statistics team. The permission of Professor Mary Frances Picciano (Department of Nutrition at Pennsylvania State University) to report experiences from her clinical study is gratefully acknowledged.
REFERENCES 1. Derr JA: Statistics in nutrition. Part 3. Which study design is best? J Renal Nutr 4: 149-151 , 1994 2. Meinert CL: Clinical Trials: Design, Conduct, and Analysis. New York, NY, Oxford University Press, 1986 3. Friedman LM, Furberg CD, DeMets DL: Fundamentals of Clinical Trials (ed 2). Littleton, MA, PGS, 1985
Part 4: Masking and Randomization-Are They Worth the Trouble? Janice Derr, PhD*
I
N THE PREVIOUS installment of this series on statistics in nutrition,1 I described and compared observational studies and clinical trials. In part 4, I will focus on some practical considerations of implementing a clinical trial. A recent experience led me to appreciate the way in which a clinical investigator might view the requirements for randomization and for masking the identity of treatment assignments in a clinical trial. By describing this experience, I hope to illustrate the careful planning and coordination required to produce an unbiased and valid clinical trial. The purpose of a clinical trial is to provide a comparison of the effectiveness of two or more treatments, one of which might serve as a control. There are several requirements for a valid trial: (1) Subjects must be assigned at random to a given treatment. (2) The identity of the treatment assigned to each subject should be "masked." Ideally, neither the subject, the clinical staff, nor the data analyst should know the treatment assignment. This is known as "doublemasking." It used to be known as "doubleblinding," but "masking" has replaced "blinding" as the more accepted term.2 Randomization and double-masking help to ensure that the comparison among treat*Managing Director, Statistical Consulting Center, Pennsylvania State University, University Park, PA. Supported in part through an education grant from Abbott Laboratories, Ross Products Division. Address reprint requests to Janice Derr, PhD, Department of Statistics, 323 Classroom Bldg, Pennsylvania State University, University Park, PA 16802. © 1994 by the National Kidney Foundation, Inc. 1051-2276/94/0404-0005$03.00/0
206
ments is as fair and as unbiased as possible. A good reference about sources of bias that can affect a trial should these conditions not be met is Fundamentals of Clinical Trials by Friedman et al. 3 Statisticians often fall into the trap of lecturing others about these requirements without truly considering the impact they have on how a clinical trial is carried out. This is why I was glad to have the opportunity to work with a team of graduate students in statistics on a clinical trial in nutrition. The investigator permitted this team to develop the randomization protocol for the trial. This article summarizes some of the issues we encountered in our work on implementing a protocol for randomization and masking. The study I am using for an illustration involves a clinical trial of a specific vitamin supplement to be given to lactating mothers. Women who have just given birth will be assigned at random to one of three treatments: (1) a placebo, (2) a vitamin supplement at a low dose, and (3) the same vitamin supplement at a higher dose. The women and their children will be followed up for 12 months. A total of 90 women will be enrolled into the trial over a period of approximately 30 months. After considering several plans for randomization, the statistics team selected one that permitted balanced randomization (equal numbers of women in each of the three groups) for smaller blocks of the enrollment period. A block is a smaller subset of the total enrollment. The first b subjects to enroll in the study would be part of block 1 . Within block 1 , an equal number
Journal of Renal Nutrition, Vol 4, No 4 (October), 1994: pp 206-208
207
MASKING AND RANDOMIZATION
of subjects would then be allocated to all three treatments. The next b subjects to enroll would then be part of block 2, with balanced randomization within this block also. An illustration of a randomization plan with three groups and blocks of six subjects per block is shown in Table 1. Randomization in blocks is recommended when the enrollment period occurs over an extended period of time. 2 It ensures against having long, unbroken runs of assignments to the same treatment, which can sometimes occur quite by chance. Blocked randomization helps to provide balance in treatment assignment and in the baseline characteristics of the treatment groups. Imbalances might occur otherwise if the trial were terminated early or if the nature of the patient population changed over time. Because of the interest in maintaining balance, the investigator suggested that we reassign the same treatment of a subject who had dropped out of the trial (dropout) to the next subject who enrolled in the trial. Unfortunately, although balance is maintained, this practice poses a threat to the TABLE 1. Randomization Lists Given to Clinical Staff Showing Different Levels of Masking Block Design List Treatment Group Block Block 1 1 2 3 4 5 6 Block 2 7 8 9 10 11 12
A Known Known
Treatment
B Not Known Known
List
Placebo Low dose Low dose High dose Placebo High dose
B* A A
Low dose High dose Placebo Placebo High dose Low dose
A
C B
C C B B
C A
C Not Known Not Known Randomization No.
10H 102 103 104 105 106 107 108 109 110 111 112
*A separate list, not seen by clinical staff, associates the group code with treatment. tA separate list, not seen by clinical staff, associates the individual code with treatment.
validity of the statistical tests used to compare the groups. Instead, we encouraged the clinical staff to develop strategies to minimize dropouts. The informed consent procedure should make sure that the subject has a thorough understanding of what would be expected of him or her during the study. Subjects should not be randomized until they will actually receive the treatment. Meeting these conditions should ensure that dropouts, if they occur, will be distributed evenly among groups. Dropouts occurring more frequently in one group than in others could represent a reaction to some aspect of the treatment regimen and should be investigated accordingly. Once the randomization plan was selected, the statistics team faced the more challenging task of developing a workable, acceptable protocol for implementing the plan. The objective was to develop a randomization protocol that would mask the actual treatment assignments from the clinical staff. Our protocol had to be specifically developed to fit into the logistics of enrolling a subject, assigning her to a treatment, and dispensing the supplements. We soon learned that the protocol we envisioned required much more paperwork and interaction than the investigator was accustomed to working with. This increased effort resulted entirely from our intent to mask the clinical staff to the treatment assignment as completely as possible. We discovered that increasing the extent to which the clinical staff is masked to treatment assignment has two consequences: (1) implementing the protocol becomes more complex and (2) fewer people have the knowledge required to verify the accuracy of the treatment assignments. For example, if there were no clinical masking at all, the clinical staff could simply take the randomization list provided by the statistician (Table 1, list A) and dispense the treatment materials accordingly. This protocol is fairly simple, and several people could verify the accuracy of the treatment assignments. A more complex protocol would result from masking the treatment group by a group code (Table 1, list B). In this protocol, somebody is usually desig-
208 nated to match up the group code with the treatment. The treatment materials can then be grouped together according to the group code and dispensed to subjects accordingly. The clinical staff would know only that a certain group of subjects were receiving treatment A, others were receiving treatment B, and so on. Throughout the statistical analysis, the treatment would remain coded. Only after the statistical comparisons had been made would the actual treatment levels be identified. The most complete masking is provided by assigning a unique treatment code to each subject (Table 1, list C). Somebody is usually designated to match the treatment assignments given by the statistician with the code number given to each subject. All treatment materials would be dispensed with the subject's code number on them. This coding task often is given to a pharmacist if medication is involved. The treatment materials must be set up individually for each subject. More complete masking means that the clinical staff do not know how the subjects are grouped. The statistician would analyze the data by unmasking the subject codes to the group codes. The group codes would be unmasked only after the statistical comparisons had been made among groups. This is the level of masking we attempted to promote in our clinical trial. The statistics team developed two separate protocol notebooks, one for the clinical staff and one for the statistics group. They also outlined the flow of communication and paperwork that would be required for the randomization protocol. A trial run of the procedure rapidly demonstrated the level of cooperation that will be necessary between the two groups to implement this protocol successfully. While we wait for the first subjects to enroll in this study, we can feel satisfied that students and scientists alike now have first-hand knowledge of what it
JANICE DERR
takes to mount a clinical trial with high standards for masking and randomization. Masking and randomization are incorporated into a clinical trial to produce a fair evaluation of the treatments in the trial. Both processes complicate the logistics of the trial. Procedures for guaranteeing the accuracy of treatment assignments must be developed to account for the extent to which the clinical staff are masked to the true identity of the treatment. A trial run of the randomization protocol is an especially good means to test whether any details have been overlooked. The benefit of investing time and energy into these procedures is the increased validity of the results of the clinical trial. I encourage more investigators involve a statistician in the logistics of planning for randomization and masking. This involvement can help the statistician understand the implications of randomization and masking and to plan the best possible arrangement for these requirements.
ACKNOWLEDGMENT This article could not have been written without the efforts of Pennsylvania State University graduate student statisticians Brenda Gaydos, Bonnie Ghosh-Dastidar, Kate Meaker, and Ruthanna Norris. Hillary Shallo, a graduate student in nutrition, worked extensively with the statistics team. The permission of Professor Mary Frances Picciano (Department of Nutrition at Pennsylvania State University) to report experiences from her clinical study is gratefully acknowledged.
REFERENCES 1. Derr JA: Statistics in nutrition. Part 3. Which study design is best? J Renal Nutr 4: 149-151 , 1994 2. Meinert CL: Clinical Trials: Design, Conduct, and Analysis. New York, NY, Oxford University Press, 1986 3. Friedman LM, Furberg CD, DeMets DL: Fundamentals of Clinical Trials (ed 2). Littleton, MA, PGS, 1985