Background

Most phase II clinical trials utilize a single primary endpoint to determine the promise of a regimen for future study. However, many disorders manifest themselves in complex ways. For example, migraine headaches can cause pain, auras, photophobia, and emesis. Investigators may believe a drug is effective at reducing migraine pain and the severity of emesis during an attack. Nevertheless, they could still be interested in proceeding with development of the drug if it is effective against only one of these symptoms. Such a study would be a candidate for a clinical trial with co-primary endpoints.

Purpose

The purpose of the article is to provide a method for designing a 2-stage clinical trial with dichotomous co-primary endpoints of efficacy that has the ability to detect activity on either response measure with high probability when the drug is active on one or both measures, while at the same time rejecting the drug with high probability when there is little activity on both dimensions. The design enables early closure for futility and is flexible with regard to attained accrual.

Methods

The design is proposed in the context of cancer clinical trials where tumor response is used to assess a drug's ability to kill tumor cells and progression-free survival (PFS) status after a certain period is used to evaluate the drug's ability to stabilize tumor growth. Both endpoints are assumed to be distributed as binomial random variables, and uninteresting probabilities of success are determined from historical controls. Given the necessity of accrual flexibility, exhaustive searching algorithms to find optimum designs do not seem feasible at this time. Instead, critical values are determined for realized sample sizes using specific procedures. Then accrual windows are found to achieve a design's desired level of significance, probability of early termination (PET), and power.

Results

The design is illustrated with a clinical trial that examined bevacizumab in patients with recurrent endometrial cancer. This study was negative by tumor response but positive by 6-month PFS. The procedure was compared to modified procedures in the literature, indicating that the method is competitive.

Limitations

Although the procedure allows investigators to construct designs with desired levels of significance and power, the PET under the null is smaller than single endpoint studies.

Conclusions

The impact of adding an additional endpoint on the sample size is often minimal, but the study gains sensitivity to activity on another dimension of treatment response. The operating characteristics are fairly robust to the level of association between the two endpoints. Software is available for implementing the methods.

Background

Intention-to-treat analysis requires all randomised individuals to be included in the analysis in the groups to which they were randomised. However, there is confusion about how intention-to-treat analysis should be performed in the presence of missing outcome data.

Purpose

To explain, justify and illustrate an intention-to-treat analysis strategy for randomised trials with incomplete outcome data.

Methods

We consider several methods of analysis and compare their underlying assumptions, plausibility, and numbers of individuals included. We illustrate the intention-to-treat analysis strategy using data from the UK700 trial in the management of severe mental illness.

Results

Depending on the assumptions made about the missing data, some methods of analysis that include all randomised individuals may be less valid than methods that do not include all randomised individuals. Further, some methods of analysis that include all randomised individuals are essentially equivalent to methods that do not include all randomised individuals.

Limitations

This work assumes that the aim of analysis is to obtain an accurate estimate of the difference in outcome between randomised groups, not to obtain a conservative estimate with bias against the experimental intervention.

Conclusions

Clinical trials should employ an intention-to-treat analysis strategy, comprising a design that attempts to follow up all randomised individuals, a main analysis that is valid under a stated plausible assumption about the missing data, and sensitivity analyses which include all randomised individuals in order to explore the impact of departures from the assumption underlying the main analysis. Following this strategy recognises the extra uncertainty arising from missing outcomes and increases the incentive for researchers to minimise the extent of missing data.

Background

The Institute of Medicine (IOM) has recommended that each person with cancer should have access to clinical trials, which have been associated with improving care quality and disparities. With no effective enrollment monitoring system, patterns of trial enrollment remain unclear.

Purpose

We developed a population-based, statewide system designed to facilitate monitoring of cancer trial enrollment and targeting of future interventions to improve it.

Methods

Person-level cancer incidence data from the North Carolina Central Cancer Registry (NCCCR), person-level treatment trial accrual data from the National Cancer Institute (NCI), and county-level Area Resource Files (ARF) measures for 12 years, 1996–2007, were studied. De-identified person-level data necessitated county-level analysis. Enrollment rates were estimated as the ratio of trial enrollment to cancer incidence for each race, gender, year, and county combination. Multivariable analysis examined factors associated with trial accrual. Sensitivity analyses examined spurious fluctuations and temporal discordance of incidence and enrollment.

Results

The NCI treatment trial enrollment rate was 2.39% for whites and 2.20% for minorities from 1996 to 2007, and 2.88% and 2.47%, respectively, for 2005–2007. Numerous counties had no minority enrollment. The 2005–2007 enrollment rates for white and minority females was 4.04% and 3.59%, respectively, and for white and minority males was 1.74% and 1.36%, respectively. Counties with a medical school or NCI Community Clinical Oncology Program (CCOP)-affiliated practice had higher trial enrollment.

Limitations

We examined NCI trial accrual only – industry-sponsored and investigator-initiated trials were excluded; however, NCI studies comprise the majority of all clinical trial participants. Delays in data availability may hinder immediacy of population-based analyses.

Conclusions

Model stability and consistency suggest this system is effective for population-based enrollment surveillance. For North Carolina, it suggests a worsening disparity in minority trial enrollment, though our analyses elucidate targets for intervention. Regional enrollment variation suggests the importance of access to clinical research networks and infrastructure. Substantial gender differences merit further examination.

Background

Low compliance to randomized non-drug interventions can affect treatment estimates of clinical trials. Cluster randomized crossover may be appropriate for increasing compliance in the out-of-hospital cardiac arrest setting.

Purpose

The purpose was to determine whether the elapsed time from start of a non-blinded treatment period to episode enrollment date in a cluster randomized crossover trial is associated with compliance to either a period of brief CPR with electrocardiogram ECG rhythm analysis or a period of longer CPR with delayed ECG rhythm analysis in patients with out-of-hospital cardiac arrest.

Methods

The Resuscitation Outcomes Consortium PRIMED Analyze Late vs Analyze Early trial was a cluster randomized crossover trial at 10 North American regional sites. Clusters were created based on local service preference with treatment periods varying from 3 to 12 months depending on the expected enrollment rate of each randomizing unit. Episodes on the AL arm had a target of 180 seconds from CPR start to Shock Assessment and were deemed compliant if total time was between 150 and 210 seconds. Episodes on the AE arm had a target of <30 seconds from CPR start to Shock Assessment and were deemed compliant if total time was <60 seconds. We used logistic regression to examine the association between compliance (Yes/No) and the elapsed number of days from the start of the treatment period to the episode in the framework of generalized estimating equations, controlling for randomized treatment (Late, ref=Early) and treatment period length (ref=3, 4-5, 6, 7-11, 12 months).

Results

We had 8769 episodes in our analysis population. Overall compliance to the randomized arm was 63.5%. After adjusting for treatment arm and treatment period length, the odds of compliance for episodes occurring >300 days from treatment period start were 33% lower (OR: 0.67, 95% CI: 0.52, 0.86) than for those <60 days from treatment period start. There was no significant difference in compliance between episodes before and immediately after a cluster crossed over to the opposite arm (OR: 0.81, 95% CI: 0.57, 1.17).

Limitations

A major challenge was the lack of synchronicity between training cycles and agency crossover dates.

Conclusion

We found a significant decrease in compliance to the AL vs AE cardiac arrest intervention as the elapsed time from start of treatment period increased. We did not find a difference in compliance immediately before and after a crossover. While these results suggest that future cluster with crossover trials in the out-of-hospital setting be designed with short treatment periods and frequent crossovers, provider logistical concerns must also be considered.

Background and Purpose

Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences.

Methods

We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC.

Results

Our dependence parameter, R, may be less influenced by outcome prevalence, and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required.

Limitations

The methods are developed for equal-sized clusters, whereas cluster size may vary in practice.

Conclusions

The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.

Background

Randomized clinical trials are blinded to prevent knowledge of treatment assignment from influencing outcomes and their assessments, thus protecting the trial’s scientific integrity. Trials involving a warfarin treatment arm are difficult to blind due to the need to continuously adjust dose.

Purpose

We sought to examine the effectiveness of blinding secondary stroke prevention trials with a warfarin treatment arm in which the blinding system incorporates use of placebo warfarin dose modification schedules for patients in the placebo warfarin arm.

Methods

We examined treatment assignment guesses of 569 patients or their next of kin as well as study coordinators and principal neurologists at the clinical sites in a multicenter, randomized, double-dummy, double-blinded clinical trial of warfarin and aspirin using dose adjustment schedules for management of placebo warfarin.

Results

Overall, the crude rates of correct responses are 60% for patient/proxy, 66% for study coordinator, and 56% for principal neurologist. Several indices were used to assess the consistency of guesses with what would be expected if the guessing were done completely at random, and all measures indicate adequate blinding.

Limitations

Comparison to other trials using warfarin is difficult due to limited data and differences in assessment of blinding. However, results compared favorably to one existing trial.

Conclusions

Placebo warfarin dose adjustment schedules can protect blinding adequately in trials involving warfarin.

Background

Case report forms (CRFs) are used to collect data in clinical research. Case report form development represents a significant part of the clinical trial process and can impact study success. Libraries of CRFs can preserve the organizational knowledge and expertise invested in CRF development and expedite the sharing of such knowledge. Although CRF libraries have been advocated, there have been no published accounts reporting institutional experiences with creating and using them.

Purpose

We sought to enhance an existing institutional CRF library by improving information indexing and accessibility. We describe this CRF library and discuss challenges encountered in its development and implementation, as well as future directions for continued work in this area.

Methods

We transformed an existing but underused and poorly accessible CRF library into a resource capable of supporting and expediting clinical and translational investigation at our institution by (1) expanding access to the entire institution; (2) adding more form attributes for improved information retrieval; and (3) creating a formal information curation and maintenance process. An open-source content management system, Plone (Plone.org), served as the platform for our CRF library.

Results

We report results from these three processes. Over the course of this project, the size of the CRF library increased from 160 CRFs comprising an estimated total of 17,000 pages, to 177 CRFs totaling 1.5 gigabytes. Eighty-two of these CRFs are now available to researchers across our institution; 95 CRFs remain within a contractual confidentiality window (usually 5 years from database lock) and are not available to users outside of the Duke Clinical Research Institute (DCRI). Conservative estimates suggest that the library supports an average of 37 investigators per month. The resources needed to curate and maintain the CRF library require less than 10% of the effort of one full-time equivalent employee.

Limitations

Although we succeeded in expanding use of the CRF library, creating awareness of such institutional resources among investigators and research teams remains challenging, and requires additional efforts to overcome. Institutions that have not achieved a critical mass of attractive research resources or effective dissemination mechanisms may encounter persistent difficulty attracting researchers to use institutional resources. Further, a useful CRF library requires both an initial investment of resources for development, as well as ongoing maintenance once it is established.

Conclusions

CRF libraries can be established and made broadly available to institutional researchers. Curation—i.e., indexing newly added forms—is required. Such a resource provides knowledge management capacity for institutions until standards and software are available to support widespread exchange of data and form definitions.

Background

Despite a pressing and well-documented need for better sharing of information on clinical trials data quality assurance methods, many research organizations remain reluctant to publish descriptions of and results from their internal auditing and quality assessment methods.

Purpose

We present findings from a review of a decade of internal data quality audits performed at the Duke Clinical Research Institute, a large academic research organization that conducts data management for a diverse array of clinical studies, both academic and industry-sponsored. In so doing, we hope to stimulate discussions that could benefit the wider clinical research enterprise by providing insight into methods of optimizing data collection and cleaning, ultimately helping patients and furthering essential research.

Methods

We present our audit methodologies, including sampling methods, audit logistics, sample sizes, counting rules used for error rate calculations, and characteristics of audited trials. We also present database error rates as computed according to two analytical methods, which we address in detail, and discuss the advantages and drawbacks of two auditing methods used during this ten-year period.

Results

Our review of the DCRI audit program indicates that higher data quality may be achieved from a series of small audits throughout the trial rather than through a single large database audit at database lock. We found that error rates trended upward from year to year in the period characterized by traditional audits performed at database lock (1997–2000), but consistently trended downward after periodic statistical process control type audits were instituted (2001–2006). These increases in data quality were also associated with cost savings in auditing, estimated at 1000 hours per year, or the efforts of one-half of a full time equivalent (FTE).

Limitations

Our findings are drawn from retrospective analyses and are not the result of controlled experiments, and may therefore be subject to unanticipated confounding. In addition, the scope and type of audits we examine here are specific to our institution, and our results may not be broadly generalizable.

Conclusions

Use of statistical process control methodologies may afford advantages over more traditional auditing methods, and further research will be necessary to confirm the reliability and usability of such techniques. We believe that open and candid discussion of data quality assurance issues among academic and clinical research organizations will ultimately benefit the entire research community in the coming era of increased data sharing and re-use.

Background

Obtaining the definitive data necessary to determine the safety and efficacy of using antiretroviral treatment (ART) to reduce the sexual transmission of HIV in heterosexual couples encountered an array of ethical challenges that threatened to compromise HPTN 052, the multinational clinical trial addressing this issue that has profound public health implications.

Purpose

To describe and analyze the major ethical challenges faced in HPTN 052.

Methods

The ethical issues and modifications of HPTN 052 in response to these issues were catalogued by the principal investigator, the lead coordinator, and the ethicist working on the trial. The major ethical issues that were unique to the trial were then described and analyzed, referring as appropriate to published literature and emerging guidance and policies. Ethical challenges that must be addressed in many clinical trials, such as those related to obtaining informed consent and making provisions for ancillary care, are not described.

Results

When HPTN 052 was being designed, ethical questions emerged related to the relevance of the research question itself given data from observational research and a range of beliefs about the appropriate means of preventing and treating HIV-infection and AIDS. Further, ethical challenges were faced regarding site selection since there was a scientific need to conduct the research in settings where HIV incidence was high, but alternatives to study participation should be available. As in most HIV prevention research, ethical questions surrounded the determination of the appropriate prevention package for all of those enrolled. During the course of the trial, guidance documents and policies emerged that were of direct relevance to the research questions, calling for a balancing of concerns for the research subjects and trial integrity. When the study results were made public, there was a need to ensure access to the treatment shown to be effective that in some cases differed from the guidelines used at the sites where the research was being conducted. In addition, questions were raised about whether there was an obligation to notify subjects about “unlinked’ transmissions of HIV, that is, infections acquired outside of the designated sexual partners enrolled in the study.

Limitations

The ethical issues described are limited to those discerned by the authors and not those of other stakeholders who may have identified additional issues or had a different perspective in analyzing them.

Conclusions

Understanding the ethical challenges faced in HPTN 052 promises to inform the design and conduct of future complex, long-term clinical trials aimed at addressing critical scientific and public health questions, where data and practice patterns emerge over the course of the trial.

Background

Most of the current designs used for Phase I dose finding trials in oncology will either involve only a single cytotoxic agent or will impose some implicit ordering among the doses. The goal of the studies is to estimate the maximum tolerated dose (MTD), the highest dose that can be administered with an acceptable level of toxicity. A key working assumption of these methods is the monotonicity of the dose–toxicity curve.

Purpose

Here we consider situations in which the monotonicity assumption may fail. These studies are becoming increasingly common in practice, most notably, in phase I trials that involve combinations of agents. Our focus is on studies where there exist pairs of treatment combinations for which the ordering of the probabilities of a dose-limiting toxicity cannot be known a priori.

Methods

We describe a new dose-finding design which can be used for multiple-drug trials and can be applied to this kind of problem. Our methods proceed by laying out all possible orderings of toxicity probabilities that are consistent with the known orderings among treatment combinations and allowing the continual reassessment method (CRM) to provide efficient estimates of the MTD within these orders. The design can be seen to simplify to the CRM when the full ordering is known.

Results

We study the properties of the design via simulations that provide comparisons to the Bayesian approach to partial orders (POCRM) of Wages, Conaway, and O'Quigley. The POCRM was shown to perform well when compared to other suggested methods for partial orders. Therefore, we comapre our approach to it in order to assess the performance of the new design.

Limitations

A limitation concerns the number of possible orders. There are dose-finding studies with combinations of agents that can lead to a large number of possible orders. In this case, it may not be feasible to work with all possible orders.

Conclusions

The proposed design demonstrates the ability to effectively estimate MTD combinations in partially ordered dosefinding studies. Because it relaxes the monotonicity assumption, it can be considered a multivariate generalization of the CRM. Hence, it can serve as a link between single and multiple-agent dosefinding trials.

Background

Most trials of interventions are designed to address the traditional null hypothesis of no benefit. VOICE, a phase 2B HIV prevention trial funded by NIH and conducted in Africa, is designed to assess if the intervention will prevent a substantial fraction of infections. Planned interim analysis may provide conclusive evidence against the traditional null hypothesis without establishing substantial benefit. At this interim point, the Data and Safety Monitoring Board would then face the dilemma of knowing the product has some positive effect, but perhaps not as great an effect as the protocol has declared necessary.

Purpose

In March 2008, NIH program staff recommended that the VOICE protocol team discuss the stopping rules with stakeholders prior to initiating the protocol. The goals of the workshop were to inform community representatives about the potential ethical dilemma associated with stopping rules and engage in dialogue about these issues. We describe the resulting community consultation and summarize the outcomes.

Methods

A 2-day workshop was convened with the goal of having a clear and transparent consultation with the stakeholders around the question, ‘Given emerging evidence that a product could prevent some infections, would the community support a decision to continue accruing to the trial?’ Participants included research staff and community stakeholders. Lectures with visual aids, discussions, and exercises using interactive learning tasks were used, with a focus on statistics and interpreting data from trials, particularly interim data.

Results

Results of oral and written evaluations by participants were reviewed. The feedback was mostly positive, with some residual confusion regarding statistical concepts. However, discussions with attendees later revealed that not all felt prepared to engage fully in the workshop.

Limitations

This was the presenters’ first experience facilitating a formal discussion with an audience that had no advanced science, research, or mathematics training. Community representatives’ concern regarding speaking for their communities without consulting them also created a challenge for the workshop.

Conclusions

Open discussion around trial stopping rules requires that all discussants have an understanding of trial design concepts and feel a sense of empowerment to ask and answer questions. The VOICE CWG workshop was a first step toward the goal of open discussion regarding trial stopping rules and interim results for the study; however, ongoing education and dialogue must occur to ensure that all stakeholders fully participate in the process.

Background

Many clinical trials are designed to test an intervention arm against a control arm wherein all subjects are equally eligible for all interventional components. Factorial designs have extended this to test multiple intervention components and their interactions. A newer design referred to as a ‘standardly-tailored’ design, is a multicomponent interventional trial that applies individual interventional components to modify risk factors identified a priori and tests whether health outcomes differ between treatment arms. Standardly-tailored designs do not require that all subjects be eligible for every interventional component. Although standardly-tailored designs yield an estimate for the net effect of the multicomponent intervention, it has not yet been shown if they permit separate, unbiased estimation of individual component effects. The ability to estimate the most potent interventional components has direct bearing on conducting second stage translational research.

Purpose

We present statistical issues related to the estimation of individual component effects in trials of geriatric conditions using factorial and standardly-tailored designs. The medical community is interested in second stage translational research involving the transfer of results from a randomized clinical trial to a community setting. Before such research is undertaken, main effects and synergistic and or antagonistic interactions between them should be identified. Knowledge of the relative strength and direction of the effects of the individual components and their interactions facilitates the successful transfer of clinically significant findings and may potentially reduce the number of interventional components needed. Therefore the current inability of the standardly-tailored design to provide unbiased estimates of individual interventional components is a serious limitation in their applicability to second stage translational research.

Methods

We discuss estimation of individual component effects from the family of factorial designs and this limitation for standardly-tailored designs. We use the phrase ‘factorial designs’ to describe full-factorial designs and their derivatives including the fractional factorial, partial factorial, incomplete factorial and modified reciprocal designs. We suggest two potential directions for designing multicomponent interventions to facilitate unbiased estimates of individual interventional components.

Results

Full factorial designs and their variants are the most common multicomponent trial design described in the literature and differ meaningfully from standardly-tailored designs. Factorial and standardly-tailored designs result in similar estimates of net effect with different levels of precision. Unbiased estimation of individual component effects from a standardly-tailored design will require new methodology.

Limitations

Although clinically relevant in geriatrics, previous applications of standardly-tailored designs have not provided unbiased estimates of the effects of individual interventional components.

Discussion

Future directions to estimate individual component effects from standardly-tailored designs include applying D-optimal designs and creating independent linear combinations of risk factors analogous to factor analysis.

Conclusion

Methods are needed to extract unbiased estimates of the effects of individual interventional components from standardly-tailored designs.

Background

Intermediate outcome variables can often be used as auxiliary variables for the true outcome of interest in randomized clinical trials. For many cancers, time to recurrence is an informative marker in predicting a patient’s overall survival outcome, and could provide auxiliary information for the analysis of survival times.

Purpose

To investigate whether models linking recurrence and death combined with a multiple imputation procedure for censored observations can result in efficiency gains in the estimation of treatment effects, and be used to shorten trial lengths.

Methods

Recurrence and death times are modeled using data from 12 trials in colorectal cancer. Multiple imputation is used as a strategy for handling missing values arising from censoring. The imputation procedure uses a cure model for time to recurrence and a time-dependent Weibull proportional hazards model for time to death. Recurrence times are imputed, and then death times are imputed conditionally on recurrence times. To illustrate these methods, trials are artificially censored 2-years after the last accrual, the imputation procedure is implemented, and a log-rank test and Cox model are used to analyze and compare these new data with the original data.

Results

The results show modest, but consistent gains in efficiency in the analysis by using the auxiliary information in recurrence times. Comparison of analyses show the treatment effect estimates and log rank test results from the 2-year censored imputed data to be in between the estimates from the original data and the artificially censored data, indicating that the procedure was able to recover some of the lost information due to censoring.

Limitations

The models used are all fully parametric, requiring distributional assumptions of the data.

Conclusions

The proposed models may be useful to improve the efficiency in estimation of treatment effects in cancer trials and shortening trial length.

Background

Collaborative care interventions to treat depression have begun to be tested in settings outside of primary care. However, few studies have expanded the collaborative care model to other settings and targeted comorbid physical symptoms of depression.

Purpose

The aims of this report were to: (1) describe the design and methods of a trial testing the efficacy of a stepped collaborative care intervention designed to manage cancer-related symptoms and improve overall quality of life in patients diagnosed with hepatobiliary carcinoma; and (2) share the lessons learned during the design, implementation, and evaluation of the trial.

Methods

The trial was a phase III randomized controlled trial testing the efficacy of a stepped collaborative care intervention to reduce depression, pain, and fatigue in patients diagnosed with advanced cancer. The intervention was compared to an enhanced usual care arm. The primary outcomes included the Center for Epidemiological Studies-Depression scale, Brief Pain Inventory, and Functional Assessment of Cancer Therapy (FACT)-Fatigue, and the FACT-Hepatobiliary. Sociodemographic and disease-specific characteristics were recorded from the medical record; Natural Killer cells and cytokines that are associated with these symptoms and with disease progression were assayed from serum.

Results and Discussion

The issues addressed include: (1) development of collaborative care in the context of oncology (e.g., timing of the intervention, tailoring of the intervention, ethical issues regarding randomization of patients, and changes in medical treatment over the course of the study); (2) use of a website by chronically ill populations (e.g., design and access to the website, development of the website and intervention, ethical issues associated with website development, website usage, and unanticipated costs associated with website development); (3) evaluation of the efficacy of intervention (e.g., patient preferences, proxy raters, changes in medical treatment, and inclusion of biomarkers as endpoints); and (4) analyses and interpretation of the intervention (e.g., confounding factors, dose and active ingredients, and risks and benefits of collaborative care interventions in chronically ill patients).

Limitations

The limitations to the study, although not fully realized at this time as the trial is ongoing, include: (1) heterogeneity of the diagnoses and treatments of participants; and (2) inclusion of caregivers as proxy raters but not as participants in the intervention.

Conclusions

Collaborative care interventions to manage multiple symptoms in a tertiary cancer center are feasible. However, researchers designing and implementing interventions that are web-based, target multiple symptoms, and for oncology patients may benefit from previous experiences.

Background

To date, no cross-national RCT has addressed the mechanisms underlying the relative success of pharmacological and psychotherapeutic interventions for depression. A multi-site clinical trial that includes psychotherapy as one of the treatments presents numerous challenges related to cross-site consistency and communication.

Purpose

This report describes how those challenges were met in the study “Depression: The Search for Treatment Relevant Phenotypes”, being carried out at the University of Pittsburgh and the University of Pisa, Italy.

Methods

Implementing the study required the investigators to address methodological and practical challenges related to the different requirements of the two Institutional Review Boards (IRBs), psychotherapy training, independent evaluator training, patient recruitment, development of common tools for data entry, quality control and generation of weekly reports of patient progress as well as establishing a similar clinical and research framework in two countries with substantially different health care systems.

Results

By having bilingual investigators and staff members who spent time at one another’s sites, making use of frequent conference-call staff meetings and being flexible within the bounds of the sometimes contradictory requirements of the IRBs, the investigators were able to meet the human subjects protection requirements of both institutions, surmount language barriers to consistent therapist and evaluator training and develop common tools for study management. As a result, recruitment goals were met at both sites and retention rates were high. One instance of inconsistent implementation of the protocol was corrected within the first year.

Limitations

This study was conducted in two Western cultures by researchers with long-standing collaboration. Our findings may not be generalizable to other countries or research settings.

Conclusions

The implementation of a cross-national protocol and the adoption and maintenance of common procedures is possible when investigators are aware of the challenges this may present and are proactive in trying to address them.

Background

The Urban Environment and Childhood Asthma (URECA) study is a multicenter prospective birth cohort study designed to examine factors related to the development of childhood asthma and allergies in an inner-city population. The retention of these participants has been challenging due to high mobility, inconsistent phone service, custody issues, and stressful life situations.

Purpose

In this article, we describe the specific retention challenges we encountered during the first 2 years of follow-up in URECA and the strategies we utilized to address them. We also examine how selected maternal characteristics and other factors are related to retention and missed study visits.

Methods

Strategies implemented to engage participants included: collecting updated and alternative contact information, after-hours phone calls to participants, culturally competent staff, flexible study event scheduling, clinic visit transportation, quarterly newsletters, retention events, drop-in home visits, and cell phone reimbursements. An internally developed web-based data management system enabled close monitoring by site teams and the coordinating center. The rate of deactivations was calculated using survival analysis. Characteristics of active and deactivated participants were compared using the chi-squared test with a Cochran–Mantel – Haenszel adjustment for study site. The proportion of missed visits of the total expected in the first 2 years was calculated and compared by family characteristics using an ANOVA model or a trend test controlling for study site. All analyses were performed using SAS version 9.1 (Cary, NC).

Results

The 2-year retention rate was 89%. Participation in the first study event predicted subsequent engagement in study activities. Mothers who did not complete the first visit were more likely to miss future events (46.1% vs. 8.9%, p < 0.0001) and to be deactivated (38.5% vs. 4.5%, p < 0.0001). Mothers under 18 years of age were more likely to leave the study compared to older mothers (22.7% vs. 10.1%, p = 0.02). Also, mothers who were married missed fewer events than those not married (8.8% vs. 15.6%, p = 0.01). In addition, deactivations were more common when the child had entered daycare by 3 months of age (10.9% vs. 3.6%, p = 0.05).

Limitations

The URECA population is predominantly minority, thus our findings might not be generalizable to other populations. Furthermore, we may not be able to observe the effects that might exist in a more diverse population. For example, 86% of the mothers are unmarried, making it difficult to reliably examine the effect of marital status.

Conclusion

In research, successfully engaging and retaining participants is essential for achieving the study objectives. Identifying factors related to missed visits and deactivations are the initial step in recognizing the potential at-risk participants and can enable the design of targeted strategies to retain participants.

Background

It can be difficult to conduct pediatric clinical trials because there is often a low incidence of the disease in children, making accrual slow or infeasible. In addition, low mortality and morbidity in this population make it impractical to achieve adequate power. In this case, the only evidence for treatment efficacy comes from adult trials. Since pediatric care providers are accustomed to relying on evidence from adult studies, it is natural to consider borrowing information from adult trials.

Purpose

The goal of this article is to propose a Bayesian approach to the design and analysis of pediatric trials to allow borrowing strength from previous or simultaneous adult trials.

Methods

We apply a hierarchical model for which the efficacy parameter from the adult trial and that of the pediatric trail are considered to be draws from a normal distribution. The choice of (the variance of) this distribution is guided by discussion with medical experts. We show that with this information, one can calculate the sample size required for the pediatric trial. We discuss how inference of these studies in pediatric populations depends on the parameter that captures the similarity of the treatment efficacy in adults compared to children.

Results

The Bayesian approach can substantially increase the power of a pediatric clinical trial (or equivalently decrease the number of subjects required) by formally leveraging the data from the adult trial.

Limitations

Our method relies on obtaining a value for the inter-study variability,, which may be difficult to describe to a clinical investigator.

Conclusions

The Bayesian approach has the potential of making pediatric clinical trials feasible because it has the effect of borrowing strength from adult trials, thus requiring a smaller pediatric trial to show efficacy of a drug in children.

Background

The identification and enlistment of suitable participants into clinical studies is often challenging, requiring a large commitment of time and staff resources. The recruitment and retention of populations typically underrepresented in research present additional challenges to enrollment of sufficient numbers of participants in clinical studies. Inadequate participation may undermine the pace and direction of new treatment discoveries.

Purpose

Registries of potential research participants are powerful tools to support research by providing a framework to streamline screening and recruitment and to maintain a communication history with potential research participants. The authors present a model for the development and implementation of a web-based database system to support recruitment, enrollment, and retention of potential study participants in close alignment with the goals of the Wisconsin Alzheimer’s Disease Research Center (ADRC).

Methods

The required data elements and major information domains for the registry were identified using a structured problem-solving and system design approach and the collaboration of a multidisciplinary team of stakeholders. The system performance, utility, and usability were assessed through multiple iterations with the users.

Results

The process-oriented approach culminated in a multifaceted tool that combined contact management and potential research participant registration to assist with the challenges of recruitment and retention in clinical research. A unique feature of the registry design model was its contact management capabilities for efficient tracking of all contacts with registrants.

Limitations

We have focused on the development and implementation of a system for the recruitment of older adults with specific cognitive and medical characteristics. However, our procedures for identifying data needs and database system utility and functionality can be transferred easily to other populations and settings. As with any multipurpose registry database system, careful management and training are essential to optimize efficiency.

Conclusion

Adding a contact management element to the registry design significantly improved the efficiency of communication between clinical study coordinators and potential research participants, as well as the communication among coordinators.

Background

Objective measures are needed to quantify dietary adherence during caloric restriction (CR) while participants are free-living. One method to monitor adherence is to compare observed weight loss to the expected weight loss during a prescribed level of CR. Normograms (graphs) of expected weight loss can be created from mathematical modeling of weight change to a given level of CR, conditional on the individual's set of baseline characteristics. These normograms can then be used by counselors to help the participant adhere to their caloric target.

Purpose

(1) To develop models of weight loss over a year of caloric restriction given demographics (age and sex), and well defined measurements of of Body Mass Index, total daily energy expenditure (TDEE) and %CR. (2) To utilize these models to develop normograms given level of caloric restriction, and measures of these variables.

Methods

Seventy-seven individuals completing a 6-12 month CR intervention (CALERIE) had body weight and body composition measured frequently. Energy intake (and %CR) was estimated from TDEE (by doubly labeled water) and body composition (by DXA) at baseline and months 1, 3, 6 and 12. Body weight was modeled to determine the predictors and distribution of the expected trajectory of percent weight change over 12 months of caloric restriction.

Results

As expected, CR was related to change in body weight. Controlling for time-varying measures, initially simple models of the functional form indicated that the trajectory of percent weight change was predicted by a non-linear function of initial age, TDEE, %CR, and sex. Using these estimates, normograms for the weight change expected during a 25%CR were developed. Our model estimates that the mean weight loss (% change from baseline weight) for an individual adherent to a 25% CR regimen is -10.9±6.3% for females and -13.9±6.4% for men after 12 months.

Limitations

There are several limitations. Sample sizes are small (n=77), and, by design, the protocols, including prescribed CR, for the interventions differed by site, and not all subjects completed a year of follow-up. In addition, the inclusion of subjects by age and initial BMI was constricted so that these results may no generalize to other older, obese subjects.

Conclusions

The trajectory of percent weight change during CR interventions in the presence of well measured covariates can be modeled using simple non-linear functions, and is related level of CR, the percent change in TDEE, gender, and age. Displayed on a normogram, individually tailored trajectories can be used by counselors and participants to monitor weight loss and adherence to a CR regimen.

Background

Suppose a standard therapy (Standard) has been established to provide a clinically important reduction in risk of irreversible morbidity or mortality. In that setting, the safety and efficacy of an experimental intervention likely would be assessed in a clinical trial providing a comparison with Standard rather than a placebo arm. Such a trial often is designed to assess whether the efficacy of the experimental intervention is not unacceptably worse than that of Standard, and is called a non-inferiority trial. Formally, the non-inferiority trial usually is designed to rule out a non-inferiority margin, defined as the minimum threshold for what would constitute an unacceptable loss of efficacy.

Purpose

Even though the literature has many important articles identifying various approaches to the design and conduct of non-inferiority trials, confusion remains especially regarding key considerations for selecting the non-inferiority margin. The purpose of this article is to provide improved clarity regarding these considerations.

Methods

We present scientific insights into many factors that should be addressed in the design and conduct of non-inferiority trials to enhance their integrity and reliability, and provide motivation for key considerations that guide the selection of non-inferiority margins. We also provide illustrations and insights from recent experiences.

Results

Two considerations are essential, and should be addressed in separate steps, in the formulation of the non-inferiority margin. First, the margin should be formulated using adjustments to account for bias or lack of reliability in the estimate of the effect of Standard in the non-inferiority trial setting. Second, the non-inferiority margin should be formulated to achieve preservation of an appropriate percentage of the effect of Standard.

Limitations

The considerations, in particular regarding the importance of preservation of effect, might not apply to settings where it would be ethical as well as clinically relevant to include both Standard and placebo arms in the trial for direct comparisons with the experimental intervention arm.

Conclusions

Non-inferiority trials with non-rigorous margins allow substantial risk for accepting inadequately effective experimental regimens, leading to the risk of erosion in quality of health care. The design and conduct of non-inferiority trials, including selection of non-inferiority margins, should account for many factors that can induce bias in the estimated effect of Standard in the non-inferiority trial and thus lead to bias in the estimated effect of the experimental treatment, for the need to ensure the experimental treatment preserves a clinically acceptable fraction of Standard's effect, and for the particular vulnerability of the integrity of a non-inferiority trial to the irregularities in trial conduct. Due to the inherent uncertainties in non-inferiority trials, alternative designs should be pursued whenever possible.

Background

Toxicity grades underlie the definition of a dose limiting toxicity (DLT) but in the majority of phase I designs, the information contained in the individual grades is not used. Some authors have argued that it may be more appropriate to consider a polytomous rather than dichotomous response.

Purpose

We investigate whether the added information on individual grades can improve the operating characteristics of the Continual Reassessment Method (CRM).

Methods

We compare the original CRM design for a binary response with two stage CRM designs which make di erent use of lower-grade toxicity information via simulations. Specifically we study; a two-stage design that utilizes lower-grade toxicities in the first stage only, during the initial non model-based escalation, and two-stage designs where lower grades are used throughout the trial via explicit models. We postulate a model relating the rates of lower grade toxicities to the rate of DLTs, or assume the relative rates of low to high grade toxicities is unknown. The designs were compared in terms of accuracy, patient allocation and precision.

Results

Significant gains can be achieved when using grades in the first stage of a two-stage design. Otherwise, only modest improvements are seen when the information on grades is exploited via the use of explicit models, where the parameters are known precisely. CRM with some use of grade information, increases the number of patients treated at the MTD by approximately 5%. The additional information from lower grades can lead to a small increase in the precision of our estimate of the MTD.

Limitations

Our comparisons are not exhaustive and it would be worth studying other models and situations.

Conclusions

Although, the gains in performance were not as great as we had hoped, we observed no cases where the performance of CRM was poorer. Our recommendation is that investigators might consider using graded toxicities at the design stage.

Background

The National Heart, Lung, and Blood Institute (NHLBI) funded three institutions to conduct effectiveness trials of weight loss interventions in primary care settings. Unlike traditional multi-center clinical trials, each study was established as an independent trial with a distinct protocol. Still, efforts were made to coordinate and standardize several aspects of the trials. The three trials formed a collaborative group, the “Practice Based Opportunities for Weight Reduction (POWER) Trials Collaborative Research Group.”

Purpose

We describe the common and distinct features of the three trials, the key characteristics of the collaborative group, and the lessons learned from this novel organizational approach.

Methods

The Collaborative Research Group consists of three individual studies: “Be Fit, Be Well“(Washington University in St. Louis/Harvard University), “POWER Hopkins” (Johns Hopkins), and “POWER-UP” (University of Pennsylvania). There are a total of 15 participating clinics with ~1,100 participants. The common primary outcome is change in weight at 24 months of follow-up, but each protocol has trial-specific elements including different interventions and different secondary outcomes. A Resource Coordinating Unit at Johns Hopkins provides administrative support.

Results

The Collaborative Research Group established common components to facilitate potential cross-site comparisons. The main advantage of this approach is to develop and evaluate several interventions, when there is insufficient evidence to test one or two approaches, as would be done in a traditional multi-center trial.

Limitations

The challenges of the organizational design include the complex decision making process, the extent of potential data pooling, time intensive efforts to standardize reports, and the additional responsibilities of the DSMB to monitor three distinct protocols.

Conclusions

The POWER Trials Collaborative Research Group is a case study of an alternative organizational model to conduct independent, yet coordinated trials. Such a model is increasingly being used in NHLBI supported trials , especially given the interest in comparative effectiveness research. Nevertheless, the ultimate utility of this model will not be fully understood until the trials are completed.