Coordinating data management for multiple ongoing clinical trials and registries

The Epidemiology Data Center at the University of Pittsburgh has developed a standard set of data management procedures, reports, and computing configurations for use on multicenter research projects. Based on budget restrictions and study design considerations, a project-specific data management system can be quickly constructed by utilizing appropriate components from the EDC tool kit: the PoP software system for the computerization of the database from paper forms to data entry screens; program shells for telecommunication and backup procedures; and procedural documents for providing the necessary training materials for centralized or decentralized processing environments. The EDC data flow specification provides quality control assurances from entry through statistical analysis.     

The impact of breakthrough clinical trials on survival in population based tumor registries.

Three statistical models are developed to study the impact that two breakthrough clinical trials (MOPP for Hodgkin's disease and PVB for disseminated testicular cancer) had on survival in the Connecticut tumor registry and the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) registry program. A segmented regression model is used in conjunction with the Cox semi-parametric proportional hazards model, as well as the parametric Weibull and exponential cure models. These models allow us to determine approximately when survival first began to improve dramatically, indicating that improved treatments had become available, and how long it took for survival to level off again indicating that the full population survival impact had been realized. In addition, the degree to which the parametric models fit allows us to determine if the survival improvements occur within a parametric family. Results of the modelling indicate that dissemination took approximately 11 years in Hodgkin's disease while only 3 years in disseminated testicular cancer. In both disease sites survival first broke with prior trends between the time that the breakthrough trial started and its publication, indicating that earlier moderately successful 'precursor' trials with combination chemotherapy may have initiated the improved population survival trends. Reasons for the difference in dissemination time in the two cancer sites are examined in order to understand what factors may be responsible for the speed of dissemination and effective utilization of new therapies.     

Reporting of noninferiority trials was incomplete in trial registries

Objective

To examine the registration of noninferiority trials, with a focus on the reporting of study design and noninferiority margins.

Study Design and Setting

Cross-sectional study of registry records of noninferiority trials published from 2005 to 2009 and records of noninferiority trials in the International Standard Randomized Controlled Trial Number (ISRCTN) or ClinicalTrials.gov trial registries. The main outcome was the proportion of records that reported the noninferiority design and margin.

Results

We analyzed 87 registry records of published noninferiority trials and 149 registry records describing noninferiority trials. Thirty-five (40%) of 87 records from published trials described the trial as a noninferiority trial; only two (2%) reported the noninferiority margin. Reporting of the noninferiority design was more frequent in the ISRCTN registry (13 of 18 records, 72%) compared with ClinicalTrials.gov (22 of 69 records, 32%; P = 0.002). Among the 149 records identified in the registries, 13 (9%) reported the noninferiority margin. Only one of the industry-sponsored trial compared with 11 of the publicly funded trials reported the margin (P = 0.001).

Conclusion

Most registry records of noninferiority trials do not mention the noninferiority design and do not include the noninferiority margin. The registration of noninferiority trials is unsatisfactory and must be improved.     

Falsificationism and clinical trials.

The relevance of the philosophy of Sir Karl Popper to the planning, conduct and analysis of clinical trials is examined. It is shown that blinding and randomization can only be regarded as valuable for the purpose of refuting universal hypotheses. The purpose of inclusion criteria is also examined. It is concluded that a misplaced belief in induction is responsible for many false notions regarding clinical trials.     

Randomized clinical trials in surgery.

When it is well conducted, a randomized clinical trial provides the strongest evidence available for evaluating the comparative effectiveness of the interventions tested. Over the last two generations, we have learned much about various devices for strengthening them and about methods of avoiding pitfalls in their design, execution, analysis, and reporting. In a trial, we seek evidence for a causal link between treatments and observed outcomes. Because the controlled trial depends on an argument based on exclusion (i.e., no other causes or differences affected the experimental groups), we strengthen its inference by taking steps to exclude any such differences. This article discusses a number of issues that deserve consideration: problems and generalizability, devices for strengthening trials, issues of power and sample size, the relationship between study design and reported gains, when to undertake a trial, the role of collaborative trials, and ways to make trials more feasible in clinical settings.     

Multiple testing in clinical trials.

The basic ideas of multiple testing are outlined and the problem of how to control the probability of erroneous decisions is discussed. The main emphasis is on the concept of the multiple level of significance (controlling the experiment, or family error in the strong sense) which can be achieved by applying the principle of closed tests. Various practical situations encountered in multiple testing in clinical trials are considered: more than one end point; more than two treatments, such as comparisons with a single control, comparisons using ordered alternatives, all pairwise comparisons and contrast methods; and more than one trial. Tests based on global statistics, the union intersection principle and other criteria are discussed. The application of the multiple test concept in sequential sampling is investigated. Finally some comments are made on multiple power, multiple confidence intervals and directed decisions.     

Oncology clinical trials

In 1971, President Nixon declared a "war on cancer" and initiated substantial funding for the National Cancer Program, which has been sustained through the years with a significant return on investment. Recently released 1998 statistics from the National Cancer Institute (NCI), the Centers for Disease Control and Prevention, and the American Cancer Society show the first real decline in cancer since the 1930s. Still, more than 1.2 million Americans will be diagnosed with cancer this year. New developments in biomedicine and advances in science and technology likely will lead to greater declines in cancer incidence and mortality.     

Self-designing clinical trials

I present a method of sequential analysis for randomized clinical trials that allows use of all prior data in a trial to determine the use and weighting of subsequent observations. One continues to assign subjects until one has ‘used up’ all the variance of the test statistic. There are many strategies to determine the weights including Bayesian methods (though the proposal is a frequentist design). I explore further the self-designing aspect of the randomized trial to note that in some cases it makes good sense (i) to change the weighting on components of a multivariate endpoint, (ii) to add or drop treatment arms (especially in a parallel group dose ranging/efficacy/safety trial), (iii) to select sites to use as the trial goes on, (iv) to change the test statistic and (v) even to rethink the whole drug development paradigm to shorten drug development time while keeping current standards for the level of evidence necessary for approval. © 1998 John Wiley & Sons, Ltd.     

Assessment of quality of life in clinical trials.

This paper is intended as an overview of developments in the assessment of quality of life (QOL) in clinical trials over the last decade from the viewpoint of clinical biostatistics. In the first part we deal with aspects of obtaining adequate measurements of quality of life. A literature survey shows that a large number of quite heterogeneous measurement approaches for use in clinical trials exist, a substantial percentage of which cannot be regarded as sufficient for their actual measuring purpose. In the second part we review statistical methods applied to and adapted for the analysis of QOL data. Underlying the analysis should be the assumption of QOL as a stochastic process. Applied analysis procedures are again investigated in a literature survey. Finally, critical conclusions are outlined and suggestions for further research are given.     

Predicting analysis times in randomized clinical trials.

Randomized clinical trial designs commonly include one or more planned interim analyses. At these times an external monitoring committee reviews the accumulated data and determines whether it is scientifically and ethically appropriate for the study to continue. With failure-time endpoints, it is common to schedule analyses at the times of occurrence of specified landmark events, such as the 50th event, the 100th event, and so on. Because interim analyses can impose considerable logistical burdens, it is worthwhile predicting their timing as accurately as possible. We describe two model-based methods for making such predictions during the course of a trial. First, we obtain a point prediction by extrapolating the cumulative mortality into the future and selecting the date when the expected number of deaths is equal to the landmark number. Second, we use a Bayesian simulation scheme to generate a predictive distribution of milestone times; prediction intervals are quantiles of this distribution. We illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease.