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How many repeated estimates of response are needed for reliable assessment of efficacy in clinical trials?
Abstracts
633
A39 HOW MANY REPEATED ESTIMATES OF RESPONSE ARE NEEDED FOR RELIABLE ASSESSMENT OF EFFICACY IN CLINICAL TRIALS?
John Paul Szalal, Richard A. Reeves, and Marko Katlc
Sunnybrook Health Science Centre University of Toronto Toronto, Ontario, Canada Physiological functions often fluctuate around an individual's characteristic value. If such a function is a therapeutic response variable in a clinical trial, one must develop a measurement schedule (replicate measurements within hours within days) to detect important changes in the function. Projections assuming perfect reliability (R) of such measures will overestimate power and underestimate sample size for clinical trials. Ambulatory blood pressure (BP) monitoring provided data for systolic and diastolic BP (SBP, DBP) and HR on 2 days 48 hours apart with 4 readings/hour, 10 hours/day in 25 normal volunteers. Generalizability intraclass reliability coefficients G" and G were determined from a double-hasted random effects model yielding variance component estimates. To each R = 0.80 for an individual requires at least 7 (e.g., 7 days x 1 reading/day) to 18 (e.g., 1 day x 3 hours x 6 readings/hour) reading for SBP, and 14 (e.g., 7 days x 1 hour x 2 readings/hour) to 28 (e.g., 1 day x 4 hours x 7 readings/hour) readings for DBP. Assessing short-term BP changes after interventions requires replicate readings to achieve even modest R. For SBP, DBP & HR, we have developed algorithms yielding vadance component estimates, tables of G" and G, and measurement schedules for R = 0.80, 0.90 and 0.95 from the double-nested design analysis of variance table. The procedures developed are directly applicable to any measure on an interval or ratio scale that is assumed to fluctuate around a central characteristic value for an individual. A40 THE COST EFFECTIVENESS OF A RUN-IN STRATEGY IN A RANOOMIZED CLINICAL TRIAL
Kenneth B. Schechtman end Mae E. Gordon
Washington University St. Louis, Missouri Run-in strategies are pre-randomizstion periods which identify and exclude non-compliant subjects. In general, a run-in reduces the required number of randomized subjects and increase the required number of eligible and screened subjects in a clinical trial. Thus, a run-in reduces post randomization and increase prerandomization costs. Because of these offsetting effects, the cost-effectivanass of a run-in strategy can only be evaluated if the impact of the run-in on the cost of each phase of a clinical trial is quantified. The costeffectiveness of a run-in depends on per-patient costs dudng each phase of the tdal; the compliance rate and the response rates among compliant and noncompliant subjects; and the percent of screened subjects who are protocol eligible. Based on these factors, we derive a necessary and sufficient condition for the costeffectiveness of a run-in period. A run-in is likely to be cost-effective when: (1) per patient costs dudng the post-randomization as compared to the screening period are high; (2) non-compliant patients respond poorly to the treatment when compared to compliant patients; (3) the number of screened patients needed to identify a single eligible patient is small; and (4) the runin period is inexpensive. In general, run-in strategies are costeffective when the ratio of treatment efficacy in non-compilers as compared to compilers is less than 40% (i.e., when non-compilers do poorly in comparison to compilers), and cost-ineffective when that ratio exceeds 80% (i.e., when non-compilers do almost as well as compilers). A41 PROBLEMS IN ESTIMATING COST EFFECTIVENESS IN CLINICAL TRIALS: EXPERIMENTAL VERSUS IMPLEMENTATION COSTS L.A. DeNIno and C.D. Mulrow
VA Hospital San Antonio, Texas Estimating the cost of ambulatory care interventions using clinical tdal data presents difficulties. Often the staffing, protocol, space, and follow-up in a research tdal are not what would occur if the intervention were implemented in the community. This paper uses cost effectiveness analyses (CEA) of a headng aid (HA) screening and intervention tdal as well as a physical therapy (PT) intervention trial to demonstrate the problems of going from experimental to generalizable economic and policy conclusions.
633
A39 HOW MANY REPEATED ESTIMATES OF RESPONSE ARE NEEDED FOR RELIABLE ASSESSMENT OF EFFICACY IN CLINICAL TRIALS?
John Paul Szalal, Richard A. Reeves, and Marko Katlc
Sunnybrook Health Science Centre University of Toronto Toronto, Ontario, Canada Physiological functions often fluctuate around an individual's characteristic value. If such a function is a therapeutic response variable in a clinical trial, one must develop a measurement schedule (replicate measurements within hours within days) to detect important changes in the function. Projections assuming perfect reliability (R) of such measures will overestimate power and underestimate sample size for clinical trials. Ambulatory blood pressure (BP) monitoring provided data for systolic and diastolic BP (SBP, DBP) and HR on 2 days 48 hours apart with 4 readings/hour, 10 hours/day in 25 normal volunteers. Generalizability intraclass reliability coefficients G" and G were determined from a double-hasted random effects model yielding variance component estimates. To each R = 0.80 for an individual requires at least 7 (e.g., 7 days x 1 reading/day) to 18 (e.g., 1 day x 3 hours x 6 readings/hour) reading for SBP, and 14 (e.g., 7 days x 1 hour x 2 readings/hour) to 28 (e.g., 1 day x 4 hours x 7 readings/hour) readings for DBP. Assessing short-term BP changes after interventions requires replicate readings to achieve even modest R. For SBP, DBP & HR, we have developed algorithms yielding vadance component estimates, tables of G" and G, and measurement schedules for R = 0.80, 0.90 and 0.95 from the double-nested design analysis of variance table. The procedures developed are directly applicable to any measure on an interval or ratio scale that is assumed to fluctuate around a central characteristic value for an individual. A40 THE COST EFFECTIVENESS OF A RUN-IN STRATEGY IN A RANOOMIZED CLINICAL TRIAL
Kenneth B. Schechtman end Mae E. Gordon
Washington University St. Louis, Missouri Run-in strategies are pre-randomizstion periods which identify and exclude non-compliant subjects. In general, a run-in reduces the required number of randomized subjects and increase the required number of eligible and screened subjects in a clinical trial. Thus, a run-in reduces post randomization and increase prerandomization costs. Because of these offsetting effects, the cost-effectivanass of a run-in strategy can only be evaluated if the impact of the run-in on the cost of each phase of a clinical trial is quantified. The costeffectiveness of a run-in depends on per-patient costs dudng each phase of the tdal; the compliance rate and the response rates among compliant and noncompliant subjects; and the percent of screened subjects who are protocol eligible. Based on these factors, we derive a necessary and sufficient condition for the costeffectiveness of a run-in period. A run-in is likely to be cost-effective when: (1) per patient costs dudng the post-randomization as compared to the screening period are high; (2) non-compliant patients respond poorly to the treatment when compared to compliant patients; (3) the number of screened patients needed to identify a single eligible patient is small; and (4) the runin period is inexpensive. In general, run-in strategies are costeffective when the ratio of treatment efficacy in non-compilers as compared to compilers is less than 40% (i.e., when non-compilers do poorly in comparison to compilers), and cost-ineffective when that ratio exceeds 80% (i.e., when non-compilers do almost as well as compilers). A41 PROBLEMS IN ESTIMATING COST EFFECTIVENESS IN CLINICAL TRIALS: EXPERIMENTAL VERSUS IMPLEMENTATION COSTS L.A. DeNIno and C.D. Mulrow
VA Hospital San Antonio, Texas Estimating the cost of ambulatory care interventions using clinical tdal data presents difficulties. Often the staffing, protocol, space, and follow-up in a research tdal are not what would occur if the intervention were implemented in the community. This paper uses cost effectiveness analyses (CEA) of a headng aid (HA) screening and intervention tdal as well as a physical therapy (PT) intervention trial to demonstrate the problems of going from experimental to generalizable economic and policy conclusions.