- Email: [email protected]
“Skewed” incomplete block designs for phase I studies
Abstracts
221
cross-sectional percentages at a given time in the trial. These results may be difficult to interpret as participants may be withdrawn from the study prior to its conclusion and before they ever complain of an adverse effect; if they remained in the study they might have complained at a later time. Withdrawals are participants who died, were~lost to follow-up, or were never asked about an adverse effect. However, survival analysis methods can be used to compare the distributions of "time to first complaint" in the active and placebo treatment groups, taking into account withdrawals. Particpants in the Beta-Blocker Heart Attack Trial were monitored for possible adverse effects. On each follow-up visit, they were asked whether they experienced any of seven conditions (blacking out, depression, fatigue, nightmares, hallucinations, bronchospasm, and decrease in sexual activity) since their previous visit about 3 months earlier. The patients were followed for up to 38 months. For fatigue and bronchospasm, the complaint-free time was significantly longer in the placebo versus active (propranolol) treatment group (p < .005).
Response Conditional Two-Period Crossover Design B. W h i t e
Otsuka Pharmaceutical Co., Ltd., Rockville, MD (04) To balance the conflicting interests of medicine, ethics, and economics inherent in clinical investigation, this paper presents a design intended for use in prospective, controlled studies on patients in which the best standard is compared with an experimental treatment. Proposed is a two-period design with random assignment to treatment and response conditional crossover to a second treatment. Maximum likelihood estimation procedures are used as a means of deriving inferences. Precision comparisons are made with the single-period parallel groups design. Relative precision is measured by asymptotic variances of the estimated difference in recovery rates for the two treatments. Computer simulations of situations that often occur in clinical practice-at least in trials of chronic neurological disorders--are used to assess small sample accuracy and relative efficiency of parameter estimation. Data are presented from an actual clinical trial utilizing this design, and statistical inferences are derived from a likelihood analysis of the results.
"Skewed" Incomplete Block Designs for Phase I Studies Barrett ScoviUe a n d Takashi Y o k o y a m a
Otsuka Pharmaceutical Co., Ltd., Frankfurt, Federal Republic of Germany (05) Two incomplete block designs are presented for early human studies incorporating randomized treatment assignments to several dosages or placebo, with "skewing" of treatment assignments so that dosage assignments proceed from lower to higher doses as the experiment progresses. In Design I, for pilot work when no previous human data exist, an initial presumed safe dose and a target plasma drug concentration are selected. Subjects are randomized in groups of three treatments--placebo, a low, and a higher dose. In successive groups, the high dose of the previous group becomes the low dose until the target plasma concentration is achieved or toxicity emerges. In Design II, when initial pilot data exist, power calculations are used to estimate the number of subjects desired per dose. An initial group of volunteers receive two treatments, placebo or dose 2D, assumed a mid-range dose. Depending on the outcome of the first treatment assignments, further assignments include 4D or 1D, to achieve the desired study N. Successful applications of these designs are presented. Partial Factorial Designs for Randomized C l i n i c a l Trials D a v i d Byar, S t e v e n Piantadosi, a n d A g n e s H e r z b e r g
Clinical and Diagnostic Trials Section, National Cancer Institute, National Institutes of Health, Bethesda, MD (06) Recently there has been increased interest in considering factorial designs for randomized clinical trials where there is a genuine interest in studying two or more treatments. Such designs may offer impressive gains in efficiency, especially in the absence of interactions between the treatments. When interactions are of interest, factorial designs provide one sensible approach for their study, although larger sample sizes may be required because tests for interactions have lower power than those for main effects. Sometimes it may not be reasonable, interesting, feasible, or ethical to study all treatment combinations in a balanced factorial design, and yet a classical fractional factorial design might be inappropriate because of aliasing. In this paper, a new class
221
cross-sectional percentages at a given time in the trial. These results may be difficult to interpret as participants may be withdrawn from the study prior to its conclusion and before they ever complain of an adverse effect; if they remained in the study they might have complained at a later time. Withdrawals are participants who died, were~lost to follow-up, or were never asked about an adverse effect. However, survival analysis methods can be used to compare the distributions of "time to first complaint" in the active and placebo treatment groups, taking into account withdrawals. Particpants in the Beta-Blocker Heart Attack Trial were monitored for possible adverse effects. On each follow-up visit, they were asked whether they experienced any of seven conditions (blacking out, depression, fatigue, nightmares, hallucinations, bronchospasm, and decrease in sexual activity) since their previous visit about 3 months earlier. The patients were followed for up to 38 months. For fatigue and bronchospasm, the complaint-free time was significantly longer in the placebo versus active (propranolol) treatment group (p < .005).
Response Conditional Two-Period Crossover Design B. W h i t e
Otsuka Pharmaceutical Co., Ltd., Rockville, MD (04) To balance the conflicting interests of medicine, ethics, and economics inherent in clinical investigation, this paper presents a design intended for use in prospective, controlled studies on patients in which the best standard is compared with an experimental treatment. Proposed is a two-period design with random assignment to treatment and response conditional crossover to a second treatment. Maximum likelihood estimation procedures are used as a means of deriving inferences. Precision comparisons are made with the single-period parallel groups design. Relative precision is measured by asymptotic variances of the estimated difference in recovery rates for the two treatments. Computer simulations of situations that often occur in clinical practice-at least in trials of chronic neurological disorders--are used to assess small sample accuracy and relative efficiency of parameter estimation. Data are presented from an actual clinical trial utilizing this design, and statistical inferences are derived from a likelihood analysis of the results.
"Skewed" Incomplete Block Designs for Phase I Studies Barrett ScoviUe a n d Takashi Y o k o y a m a
Otsuka Pharmaceutical Co., Ltd., Frankfurt, Federal Republic of Germany (05) Two incomplete block designs are presented for early human studies incorporating randomized treatment assignments to several dosages or placebo, with "skewing" of treatment assignments so that dosage assignments proceed from lower to higher doses as the experiment progresses. In Design I, for pilot work when no previous human data exist, an initial presumed safe dose and a target plasma drug concentration are selected. Subjects are randomized in groups of three treatments--placebo, a low, and a higher dose. In successive groups, the high dose of the previous group becomes the low dose until the target plasma concentration is achieved or toxicity emerges. In Design II, when initial pilot data exist, power calculations are used to estimate the number of subjects desired per dose. An initial group of volunteers receive two treatments, placebo or dose 2D, assumed a mid-range dose. Depending on the outcome of the first treatment assignments, further assignments include 4D or 1D, to achieve the desired study N. Successful applications of these designs are presented. Partial Factorial Designs for Randomized C l i n i c a l Trials D a v i d Byar, S t e v e n Piantadosi, a n d A g n e s H e r z b e r g
Clinical and Diagnostic Trials Section, National Cancer Institute, National Institutes of Health, Bethesda, MD (06) Recently there has been increased interest in considering factorial designs for randomized clinical trials where there is a genuine interest in studying two or more treatments. Such designs may offer impressive gains in efficiency, especially in the absence of interactions between the treatments. When interactions are of interest, factorial designs provide one sensible approach for their study, although larger sample sizes may be required because tests for interactions have lower power than those for main effects. Sometimes it may not be reasonable, interesting, feasible, or ethical to study all treatment combinations in a balanced factorial design, and yet a classical fractional factorial design might be inappropriate because of aliasing. In this paper, a new class