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Comparability of randomized groups
297
Abstracts
class with respect to eye discomfort. This simple single-dose model provides a new, valid system to compare eye irritation induced by ophthalmic drugs.
Comparability of Randomized Groups D o u g l a s G. A l t m a n
Clinical Research Centre, Harrow, Middlesex, England (21) Although inappropriate, significance tests are frequently used for assessing the importance of covariate imbalance between randomized groups. This paper explores the effect, on the results of a trial, of nonsignificant imbalance in a dichotomous prognostic factor. The range of magnitude of such an effect is considered in relation to the prevalence of the factor and its prognostic importance. Results are presented from a clinical trial in which the adjustment for a continuous covariate (not significantly different in the two groups) had a profound influence, revealing a previously hidden treatment benefit. Using the same example it is shown that post stratification is an inadequate means of controlling for imbalance, and some general results are presented to explain this. The handling of the comparison of baseline characteristics in published trials is discussed.
A Covariate Model for Repeated Binary Responses Larry R. M u e n z
National Cancer Institute, Bethesda, MD (22) In a therapy trial for a recurrent disease, we might monitor patients at T time points, classifying each patient's state at each point as "good" or "bad" according to the occurrence of an event. In this paper, the state sequence is assumed to follow a binary Markov chain. We model the transition probabilities for the 0 to 0 and I to 0 transitions by two logistic regressions thus showing how p covariates, including treatment, relate to changes in state. The 2(p + 1) parameters are estimated by maximum likelihood. Transition probability estimates are used to test hypotheses about the probability of occupying state 0 at time i, i = 2. . . . . T, and the equilibrium probability of state 0, all depending on the covariates. An example is presented in which women with breast cancer and controls were rated as showing or not showing distress at four time points in the year following surgery. A n a l y s e s of Morbidity with Death as a Competing Risk Daniel Seigel, M a r v i n P o d g o r , a n d Frederick Ferris National Institutes of Health, Bethesda, MD (23) Life table analyses of morbidity in the presence of death are problematic. The investigator needs to be quite careful that a meaningful clinical question is being addressed. Three approaches to the analysis of visual acuity data (morbidity) in the presence of the competing risk of death are explored, using the Diabetic Retinopathy Study (DRS) data base as an example. The three approaches yield different results because of (a) the presence of death as a competing risk, (b) recovery from the poor vision endpoint, and (c) the dependence of mortality on visual acuity. The choice of the appropriate statistic in such circumstances is discussed. Use of the Logistic Model in the Design of Intervention Studies J a m e s D. N e a t o n a n d G l e n n E. Bartsch University of Minnesota, Minneapolis, MN (24) The logistic model has been utilized for estimating expected event rates for control and experimental groups and for selecting risk eligible participants for a number of large primary intervention trials. For a single risk variable let p~ = {[1 + exp(-~x~)] - 1, a < x~ ~ b} denote the probability of an event in fixed time period for participant i with risk factor x~, which lies between a and b, prespecified limits for trial eligibility. ~ is the logistic parameter, which is assumed known. Let p, and pc denote the event rates for the control and experimental groups, respectively. Factors that are considered in estimating pc are the reliability coefficient for the risk factor, the density of x, and the choice of a and b. The extent to which pC is influenced by estimating the effect of risk factor reduction using "true" risk levels rather than observed or screening levels is also considered. In certain situations each of these factors can markedly influence the estimates of p, and pC.
Abstracts
class with respect to eye discomfort. This simple single-dose model provides a new, valid system to compare eye irritation induced by ophthalmic drugs.
Comparability of Randomized Groups D o u g l a s G. A l t m a n
Clinical Research Centre, Harrow, Middlesex, England (21) Although inappropriate, significance tests are frequently used for assessing the importance of covariate imbalance between randomized groups. This paper explores the effect, on the results of a trial, of nonsignificant imbalance in a dichotomous prognostic factor. The range of magnitude of such an effect is considered in relation to the prevalence of the factor and its prognostic importance. Results are presented from a clinical trial in which the adjustment for a continuous covariate (not significantly different in the two groups) had a profound influence, revealing a previously hidden treatment benefit. Using the same example it is shown that post stratification is an inadequate means of controlling for imbalance, and some general results are presented to explain this. The handling of the comparison of baseline characteristics in published trials is discussed.
A Covariate Model for Repeated Binary Responses Larry R. M u e n z
National Cancer Institute, Bethesda, MD (22) In a therapy trial for a recurrent disease, we might monitor patients at T time points, classifying each patient's state at each point as "good" or "bad" according to the occurrence of an event. In this paper, the state sequence is assumed to follow a binary Markov chain. We model the transition probabilities for the 0 to 0 and I to 0 transitions by two logistic regressions thus showing how p covariates, including treatment, relate to changes in state. The 2(p + 1) parameters are estimated by maximum likelihood. Transition probability estimates are used to test hypotheses about the probability of occupying state 0 at time i, i = 2. . . . . T, and the equilibrium probability of state 0, all depending on the covariates. An example is presented in which women with breast cancer and controls were rated as showing or not showing distress at four time points in the year following surgery. A n a l y s e s of Morbidity with Death as a Competing Risk Daniel Seigel, M a r v i n P o d g o r , a n d Frederick Ferris National Institutes of Health, Bethesda, MD (23) Life table analyses of morbidity in the presence of death are problematic. The investigator needs to be quite careful that a meaningful clinical question is being addressed. Three approaches to the analysis of visual acuity data (morbidity) in the presence of the competing risk of death are explored, using the Diabetic Retinopathy Study (DRS) data base as an example. The three approaches yield different results because of (a) the presence of death as a competing risk, (b) recovery from the poor vision endpoint, and (c) the dependence of mortality on visual acuity. The choice of the appropriate statistic in such circumstances is discussed. Use of the Logistic Model in the Design of Intervention Studies J a m e s D. N e a t o n a n d G l e n n E. Bartsch University of Minnesota, Minneapolis, MN (24) The logistic model has been utilized for estimating expected event rates for control and experimental groups and for selecting risk eligible participants for a number of large primary intervention trials. For a single risk variable let p~ = {[1 + exp(-~x~)] - 1, a < x~ ~ b} denote the probability of an event in fixed time period for participant i with risk factor x~, which lies between a and b, prespecified limits for trial eligibility. ~ is the logistic parameter, which is assumed known. Let p, and pc denote the event rates for the control and experimental groups, respectively. Factors that are considered in estimating pc are the reliability coefficient for the risk factor, the density of x, and the choice of a and b. The extent to which pC is influenced by estimating the effect of risk factor reduction using "true" risk levels rather than observed or screening levels is also considered. In certain situations each of these factors can markedly influence the estimates of p, and pC.