Differences In Clinical Trials Research Articles

Capturing economic differences in clinical trials

Capturing economic differences in clinical trials

The hope and the hazards of using compliance data in randomized controlled trials

This paper aims to elucidate both the advantages and limitations of using compliance data in the reporting of treatment differences in clinical trials, illustrating the issues with some recent examples. While analysis by intention-to-treat should remain the principal reporting approach for most major clinical trials, arguments are put forward as to why supplementary analyses taking account of compliance can be of value. However, continued recognition of the potential biases inherent in all such selective analysis is of key importance. Some of the possible analytical approaches are presented along with suggestions on interpretation. Particular emphasis is on one case study, a large European trial in obesity incorporating repeated measures of weight loss, drug plasma level and pill count data. In working on compliance data in clinical trials, the statistician's main responsibilities may be to undertake a cohesive analysis strategy not influenced by data dredging, to achieve clarity of exposition without undue complexity or oversimplification, and to provide appropriately cautious interpretations which take account of selection biases and data limitations.

The hope and the hazards of using compliance data in randomized controlled trials

This paper aims to elucidate both the advantages and limitations of using compliance data in the reporting of treatment differences in clinical trials, illustrating the issues with some recent examples. While analysis by intention-to-treat should remain the principal reporting approach for most major clinical trials, arguments are put forward as to why supplementary analyses taking account of compliance can be of value. However, continued recognition of the potential biases inherent in all such selective analysis is of key importance. Some of the possible analytical approaches are presented along with suggestions on interpretation. Particular emphasis is on one case study, a large European trial in obesity incorporating repeated measures of weight loss, drug plasma level and pill count data. In working on compliance data in clinical trials, the statistician's main responsibilities may be to undertake a cohesive analysis strategy not influenced by data dredging, to achieve clarity of exposition without undue complexity or oversimplification, and to provide appropriately cautious interpretations which take account of selection biases and data limitations. © 1998 John Wiley & Sons, Ltd.

Assessing Differences in Clinical Trials Comparing Surgical vs Nonsurgical Therapy

The statement of hypotheses and choice of statistical tests in clinical trials that compare surgical with nonsurgical treatment are complicated by the likelihood of excess risk in the surgical group during the perioperative period but lower risk after that compared with the more uniform risk in the nonsurgical group. Commonly used statistical survival analyses implicitly assume a constant ratio of risks in the 2 groups during the follow-up period. However, the changing pattern of risk for one treatment but not the other implies that the assessment of the relative efficacy of the treatments varies with the length of the follow-up. As such, determining whether survival curves for the 2 groups are different may not translate easily into selecting the best treatment. Alternative statements of the hypothesis based on consideration of the time horizon of patients and on clinical judgment may be more consistent with the goals of the study. Regardless of the choice of a statistical test, the choice of treatment is a decision specific to the individual patient and should be influenced by the patient's life expectancy, attitude toward taking risks, quality of life, and cost considerations. When the survival curves cross, there is a trade-off between the risk of surgery and the increase in life expectancy among the survivors of surgery. Accordingly, assessment of differences in outcomes in clinical trials comparing surgical vs nonsurgical therapy should provide both a conclusion about whether 1 treatment can reasonably be considered best for most patients and should provide information to the individual patient and physician on the expected outcome to aid in the decision-making process.

Assessing differences in clinical trials comparing surgical vs nonsurgical therapy: using common (statistical) sense

Assessing differences in clinical trials comparing surgical vs nonsurgical therapy: using common (statistical) sense

Gender differences in clinical trials in coronary heart disease: response to drug therapy.

Gender differences in clinical trials in coronary heart disease: response to drug therapy.

Predicting the Duration of Mechanical Ventilation: The Importance of Disease and Patient Characteristics

To analyze the determinants of an individual patient's duration of mechanical ventilation and assess interhospital variations for average durations of ventilation. Prospective, multicenter, inception, cohort study. Forty-two ICUs at 40 US hospitals. A total of 5,915 patients undergoing mechanical ventilation on ICU day 1 selected from the acute physiology and chronic health evaluation (APACHE) III database of 17,440 admissions. None. Utilizing APACHE III data collected on the 5,915 patients, multivariate regression analysis was performed on selected patients and disease characteristics to determine which variables were significantly associated with the duration of mechanical ventilation. An equation predicting duration of ventilation was then developed using the significant predictor variables and its accuracy was evaluated. Variables significantly associated with duration of ventilation included primary reason for ICU admission, day 1 acute physiology score (APS) of APACHE III, age, prior patient location and hospital length of stay, activity limits due to respiratory disease, serum albumin, respiratory rate, and PaO2/FIo2 measurements. Using an equation derived from these variables, predicted durations of ventilation were then calculated and compared with actual observed durations for each of the 42 ICUs. Average duration of ventilation for the 42 ICUs ranged from 2.6 to 7.9 days, but 60% of this variation was accounted for by differences in patient characteristics. For patients admitted to the ICU and ventilated on day 1, total duration of ventilation is primarily determined by admitting diagnosis and degree of physiologic derangement as measured by APS. An equation developed using multivariate regression techniques can accurately predict average duration of ventilation for groups of ICU patients, and we believe this equation will be useful for comparing ventilator practices between ICUs, controlling for patient differences in clinical trials of new therapies or weaning techniques, and as a quality improvement mechanism.

Improving Efficiency of Estimation and Testing for Censored Data Using Auxiliary Covariates

This research improves upon standard nonparametric survival estimation and testing in the two sample censored data problem. First, a survival estimator which uses prognostic covariates to more precisely get at the underlying survival curves in the presence of censoring is defined. This estimator has a smaller asymptotic variance than the usual Kaplan-Meier estimator and reduces to the Kaplan-Meier when the covariate is not prognostic or no censoring occurs. Also, this estimate remains consistent when the incorporated covariate contains information about both the censoring process and survival. Since the Kaplan-Meier is biased in this situation the author's estimate is recommended. Using this more efficient survival estimator, a more powerful test statistic for detecting differences in clinical trials is then defined. Sequential methods for using this test are also discussed. This research allows researchers to study primary endpoints rather than taking the risk of substituting a surrogate marker which may...

95P On the likelihood of finding subgroup differences in clinical trials

95P On the likelihood of finding subgroup differences in clinical trials

Practical problems in interim analyses, with particular regard to estimation

This article considers some of the practical problems inherent in interim analyses and stopping rules for randomized clinical trials. Topics covered include group sequential designs, trials with unplanned interim analyses, estimation problems in clinical trials with planned interim analyses, and the balance between individual and collective ethics. Particular attention is paid to the fact that clinical trials that stop early are prone to exaggerate the magnitude of treatment effect. Accordingly, a Bayesian “shrinkage” method of analysis is proposed to help quantify the extent to which surprisingly large point and interval estimates of treatment difference in clinical trials that stop early should be moderated.