An alternative approach to deal with the absence of clinical trials: a proportional meta-analysis of case series studies

PURPOSE: Systematic reviews are criticized for frequently offering inconsistent evidences and absence of straightforward recommendations. Their value seems to be depreciated when the conclusions are uncertain. To describe an alternative approach of evaluating case series studies in health care when there is absence of clinical trials. METHODS: We provide illustrations from recent experiences. Proportional meta-analysis was performed on surgical outcomes: (a) case series studies, (b) use of cryoablation or radiofrequency ablation, and (c) patients with small renal cell carcinoma. The statistically significant difference between both interventions studied was defined if their combined 95% confidential interval (CI) did not overlap. RESULTS: As demonstrated by the example, this analysis is an alternative approach to provide some evidence of the intervention´s effects under evaluation and plotting all available case series in the absence of clinical trials for the health field. CONCLUSIONS: Although we are leading to a low level of evidence to determine efficacy, effectiveness and safety of interventions this alternative approach can help surgeons, physicians and health professionals for a provisionally decision in health care along with their clinical expertise and the patient´s wishes and circumstances in the absence of high-quality primary studies. It´s not a replacement for the gold standard randomized clinical trial, but an alternative analysis for clinical research.

Phase I/IIa study of cilengitide and temozolomide with concomitant radiotherapy followed by cilengitide and temozolomide maintenance therapy in patients with newly diagnosed glioblastoma

Invasion and migration are key processes of glioblastoma and are tightly linked to tumor recurrence. Integrin inhibition using cilengitide has shown synergy with chemotherapy and radiotherapy in vitro and promising activity in recurrent glioblastoma. This multicenter, phase I/IIa study investigated the efficacy and safety of cilengitide in combination with standard chemoradiotherapy in newly diagnosed glioblastoma.

Randomization in clinical trials in orthodontics: its significance in research design and methods to achieve it

Randomization is a key step in reducing selection bias during the treatment allocation phase in randomized clinical trials. The process of randomization follows specific steps, which include generation of the randomization list, allocation concealment, and implementation of randomization. The phenomenon in the dental and orthodontic literature of characterizing treatment allocation as random is frequent; however, often the randomization procedures followed are not appropriate. Randomization methods assign, at random, treatment to the trial arms without foreknowledge of allocation by either the participants or the investigators thus reducing selection bias. Randomization entails generation of random allocation, allocation concealment, and the actual methodology of implementing treatment allocation randomly and unpredictably. Most popular randomization methods include some form of restricted and/or stratified randomization. This article introduces the reasons, which make randomization an integral part of solid clinical trial methodology, and presents the main randomization schemes applicable to clinical trials in orthodontics.

Communication about standard treatment options and clinical trials: can we teach doctors new skills to improve patient outcomes?

BACKGROUND: The International Breast Cancer Study Group conducted a phase III trial in Australian/New Zealand (ANZ) and Swiss/German/Austrian (SGA) centres on training doctors in clear and ethical information delivery about treatment options and strategies to encourage shared decision making. METHODS: Medical, surgical, gynaecological and radiation oncologists, and their patients for whom adjuvant breast cancer therapy was indicated, were eligible. Doctors were randomised to participate in a workshop with standardised teaching material and role playing. Patients were recruited in the experimental and control groups before and after the workshop. RESULTS: In ANZ centres, 21 eligible doctors recruited a total of 304 assessable patients. In SGA centres, 41 doctors recruited 390 patients. The training was well accepted. There was no overall effect on patient decisional conflict (primary endpoint) 2 weeks after the consultation. Overall, patients were satisfied with their treatment decision, their consultation and their doctors' consultation skills. Considerable variation was observed in patient outcomes between SGA and ANZ centres; the effect sizes of the intervention were marginal (<0.2). CONCLUSIONS: Shared decision making remains a challenge. A sustained training effect may require more intensive training tailored to the local setting. Cross-cultural differences need attention in conducting trials on communication interventions.

Rituximab, bendamustine, and lenalidomide in patients with aggressive B cell lymphoma not eligible for high-dose chemotherapy or anthracycline-based therapy: phase I results of the SAKK 38/08 trial

This phase I trial was designed to develop a new effective and well-tolerated regimen for patients with aggressive B cell lymphoma not eligible for front-line anthracycline-based chemotherapy or aggressive second-line treatment strategies. The combination of rituximab (375 mg/m(2) on day 1), bendamustine (70 mg/m(2) on days 1 and 2), and lenalidomide was tested with a dose escalation of lenalidomide at three dose levels (10, 15, or 20 mg/day) using a 3 + 3 design. Courses were repeated every 4 weeks. The recommended dose was defined as one level below the dose level identifying ≥2/6 patients with a dose-limiting toxicity (DLT) during the first cycle. Thirteen patients were eligible for analysis. Median age was 77 years. WHO performance status was 0 or 1 in 12 patients. The Charlson Comorbidity Index showed relevant comorbidities in all patients. Two DLTs occurred at the second dose level (15 mg/day) within the first cycle: one patient had prolonged grade 3 neutropenia, and one patient experienced grade 4 cardiac adverse event (myocardial infarction). Additional grade 3 and 4 toxicities were as follows: neutropenia (31 %), thrombocytopenia (23 %), cardiac toxicity (31 %), fatigue (15 %), and rash (15 %). The dose of lenalidomide of 10 mg/day was recommended for a subsequent phase II in combination with rituximab 375 mg/m(2) on day 1 and bendamustine 70 mg/m(2) on days 1 and 2.

Phase I trial of combretastatin A4 phosphate (CA4P) in combination with bevacizumab in patients with advanced cancer

The vascular disrupting agent (VDA) combretastatin A4 phosphate (CA4P) induces significant tumor necrosis as a single agent. Preclinical models have shown that the addition of an anti-VEGF antibody to a VDA attenuates the revascularization of the surviving tumor rim and thus significantly increases antitumor activity.

Somatostatin-based radiotherapy with [90Y-DOTA]-TOC in neuroendocrine tumors: long-term outcome of a phase I dose escalation study

BACKGROUND We describe the long-term outcome after clinical introduction and dose escalation of somatostatin receptor targeted therapy with [90Y-DOTA]-TOC in patients with metastasized neuroendocrine tumors. METHODS In a clinical phase I dose escalation study we treated patients with increasing [90Y-DOTA]-TOC activities. Multivariable Cox regression and competing risk regression were used to compare efficacy and toxicities of the different dosage protocols. RESULTS Overall, 359 patients were recruited; 60 patients were enrolled for low dose (median: 2.4 GBq/cycle, range 0.9-7.8 GBq/cycle), 77 patients were enrolled for intermediate dose (median: 3.3 GBq/cycle, range: 2.0-7.4 GBq/cycle) and 222 patients were enrolled for high dose (median: 6.7 GBq/cycle, range: 3.7-8.1 GBq/cycle) [90Y-DOTA]-TOC treatment. The incidences of hematotoxicities grade 1-4 were 65.0%, 64.9% and 74.8%; the incidences of grade 4/5 kidney toxicities were 8.4%, 6.5% and 14.0%, and the median survival was 39 (range: 1-158) months, 34 (range: 1-118) months and 29 (range: 1-113) months. The high dose protocol was associated with an increased risk of kidney toxicity (Hazard Ratio: 3.12 (1.13-8.59) vs. intermediate dose, p = 0.03) and a shorter overall survival (Hazard Ratio: 2.50 (1.08-5.79) vs. low dose, p = 0.03). CONCLUSIONS Increasing [90Y-DOTA]-TOC activities may be associated with increasing hematological toxicities. The dose related hematotoxicity profile of [90Y-DOTA]-TOC could facilitate tailoring [90Y-DOTA]-TOC in patients with preexisting hematotoxicities. The results of the long-term outcome suggest that fractionated [90Y-DOTA]-TOC treatment might allow to reduce renal toxicity and to improve overall survival. (ClinicalTrials.gov number NCT00978211).

Robust meta-analytic-predictive priors in clinical trials with historical control information.

Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

Hyperthermia-related clinical trials on cancer treatment within the ClinicalTrials.gov registry

PURPOSE Hyperthermia has been shown to improve the effectiveness of chemotherapy and radiotherapy in the treatment of cancer. This paper summarises all recent clinical trials registered in the ClinicalTrials.gov registry. MATERIALS AND METHODS The records of 175,538 clinical trials registered at ClinicalTrials.gov were downloaded on 29 September 2014 and a database was established. We searched this database for hyperthermia or equivalent words. RESULTS A total of 109 trials were identified in which hyperthermia was part of the treatment regimen. Of these, 49 trials (45%) had hyperthermic intraperitoneal chemotherapy after cytoreductive surgery (HIPEC) as the primary intervention, and 14 other trials (13%) were also testing some form of intraperitoneal hyperthermic chemoperfusion. Seven trials (6%) were testing perfusion attempts to other locations (thoracic/pleural n = 4, limb n = 2, hepatic n = 1). Sixteen trials (15%) were testing regional hyperthermia, 13 trials (12%) whole body hyperthermia, seven trials (6%) superficial hyperthermia and two trials (2%) interstitial hyperthermia. One remaining trial tested laser hyperthermia. CONCLUSIONS In contrast to the general opinion, this analysis shows continuous interest and ongoing clinical research in the field of hyperthermia. Interestingly, the majority of trials focused on some form of intraperitoneal hyperthermic chemoperfusion. Despite the high number of active clinical studies, HIPEC is a topic with limited attention at the annual meetings of the European Society for Hyperthermic Oncology and the Society of Thermal Medicine. The registration of on-going clinical trials is of paramount importance for the achievement of a comprehensive overview of available clinical research activities involving hyperthermia.

Bayesian doubly optimal group sequential designs for randomized clinical trials

When conducting a randomized comparative clinical trial, ethical, scientific or economic considerations often motivate the use of interim decision rules after successive groups of patients have been treated. These decisions may pertain to the comparative efficacy or safety of the treatments under study, cost considerations, the desire to accelerate the drug evaluation process, or the likelihood of therapeutic benefit for future patients. At the time of each interim decision, an important question is whether patient enrollment should continue or be terminated; either due to a high probability that one treatment is superior to the other, or a low probability that the experimental treatment will ultimately prove to be superior. The use of frequentist group sequential decision rules has become routine in the conduct of phase III clinical trials. In this dissertation, we will present a new Bayesian decision-theoretic approach to the problem of designing a randomized group sequential clinical trial, focusing on two-arm trials with time-to-failure outcomes. Forward simulation is used to obtain optimal decision boundaries for each of a set of possible models. At each interim analysis, we use Bayesian model selection to adaptively choose the model having the largest posterior probability of being correct, and we then make the interim decision based on the boundaries that are optimal under the chosen model. We provide a simulation study to compare this method, which we call Bayesian Doubly Optimal Group Sequential (BDOGS), to corresponding frequentist designs using either O'Brien-Fleming (OF) or Pocock boundaries, as obtained from EaSt 2000. Our simulation results show that, over a wide variety of different cases, BDOGS either performs at least as well as both OF and Pocock, or on average provides a much smaller trial. ^

Outcome-based adaptive randomization for binary data in clinical trials---Compare and contrast

Monte Carlo simulation has been conducted to investigate parameter estimation and hypothesis testing in some well known adaptive randomization procedures. The four urn models studied are Randomized Play-the-Winner (RPW), Randomized Pôlya Urn (RPU), Birth and Death Urn with Immigration (BDUI), and Drop-the-Loses Urn (DL). Two sequential estimation methods, the sequential maximum likelihood estimation (SMLE) and the doubly adaptive biased coin design (DABC), are simulated at three optimal allocation targets that minimize the expected number of failures under the assumption of constant variance of simple difference (RSIHR), relative risk (ORR), and odds ratio (OOR) respectively. Log likelihood ratio test and three Wald-type tests (simple difference, log of relative risk, log of odds ratio) are compared in different adaptive procedures. ^ Simulation results indicates that although RPW is slightly better in assigning more patients to the superior treatment, the DL method is considerably less variable and the test statistics have better normality. When compared with SMLE, DABC has slightly higher overall response rate with lower variance, but has larger bias and variance in parameter estimation. Additionally, the test statistics in SMLE have better normality and lower type I error rate, and the power of hypothesis testing is more comparable with the equal randomization. Usually, RSIHR has the highest power among the 3 optimal allocation ratios. However, the ORR allocation has better power and lower type I error rate when the log of relative risk is the test statistics. The number of expected failures in ORR is smaller than RSIHR. It is also shown that the simple difference of response rates has the worst normality among all 4 test statistics. The power of hypothesis test is always inflated when simple difference is used. On the other hand, the normality of the log likelihood ratio test statistics is robust against the change of adaptive randomization procedures. ^

Interim analysis of clinical trials: Simulation studies of fractional Brownian motion

Interim clinical trial monitoring procedures were motivated by ethical and economic considerations. Classical Brownian motion (Bm) techniques for statistical monitoring of clinical trials were widely used. Conditional power argument and α-spending function based boundary crossing probabilities are popular statistical hypothesis testing procedures under the assumption of Brownian motion. However, it is not rare that the assumptions of Brownian motion are only partially met for trial data. Therefore, I used a more generalized form of stochastic process, called fractional Brownian motion (fBm), to model the test statistics. Fractional Brownian motion does not hold Markov property and future observations depend not only on the present observations but also on the past ones. In this dissertation, we simulated a wide range of fBm data, e.g., H = 0.5 (that is, classical Bm) vs. 0.5< H <1, with treatment effects vs. without treatment effects. Then the performance of conditional power and boundary-crossing based interim analyses were compared by assuming that the data follow Bm or fBm. Our simulation study suggested that the conditional power or boundaries under fBm assumptions are generally higher than those under Bm assumptions when H > 0.5 and also matches better with the empirical results. ^

A comparison of adherence with minocycline treatment in Parkinson's disease and rheumatoid arthritis clinical trials

Background: Little is known about the effects on patient adherence when the same study drug is administered in the same dose in two populations with two different diseases in two different clinical trials. The Minocycline in Rheumatoid Arthritis (MIRA) trial and the NIH Exploratory Trials in Parkinson's disease (NET-PD) Futility Study I provide a unique opportunity to do the above and to compare methods measuring adherence. This study may increase understanding of the influence of disease and adverse events on patient adherence and will provide insights to investigators selecting adherence assessment methods in clinical trials of minocycline and other drugs in future.^ Methods: Minocycline adherence by pill count and the effect of adverse events was compared in the MIRA and NET-PD FS1 trials using multivariable linear regression. Within the MIRA trial, agreement between assay and pill count was compared. The association of adverse events with assay adherence was examined using multivariable logistic regression.^ Results: Adherence derived from pill count in the MIRA and NET-PD FS1 trials did not differ significantly. Adverse events potentially related to minocycline did not appear useful to predict minocycline adherence. In the MIRA trial, adherence measured by pill count appears higher than adherence measured by assay. Agreement between pill count and assay was poor (kappa statistic = 0.25).^ Limitations: Trial and disease are completely confounded and hence the independent effect of disease on adherence to minocycline treatment cannot be studied.^ Conclusion: Simple pill count may be preferred over assay in the minocycline clinical trials to measure adherence. Assays may be less sensitive in a clinical setting where appointments are not scheduled in relation to medication administration time, given assays depend on many pharmacokinetic and instrument-related factors. However, pill count can be manipulated by the patient. Another study suggested that self-report method is more sensitive than pill count method in differentiating adherence from non-adherence. An effect of medication-related adverse events on adherence could not be detected.^

A simulation study of the effects of small center enrollment in multi -center clinical trials

Multi-center clinical trials are very common in the development of new drugs and devices. One concern in such trials, is the effect of individual investigational sites enrolling small numbers of patients on the overall result. Can the presence of small centers cause an ineffective treatment to appear effective when treatment-by-center interaction is not statistically significant?^ In this research, simulations are used to study the effect that centers enrolling few patients may have on the analysis of clinical trial data. A multi-center clinical trial with 20 sites is simulated to investigate the effect of a new treatment in comparison to a placebo treatment. Twelve of these 20 investigational sites are considered small, each enrolling less than four patients per treatment group. Three clinical trials are simulated with sample sizes of 100, 170 and 300. The simulated data is generated with various characteristics, one in which treatment should be considered effective and another where treatment is not effective. Qualitative interactions are also produced within the small sites to further investigate the effect of small centers under various conditions.^ Standard analysis of variance methods and the "sometimes-pool" testing procedure are applied to the simulated data. One model investigates treatment and center effect and treatment-by-center interaction. Another model investigates treatment effect alone. These analyses are used to determine the power to detect treatment-by-center interactions, and the probability of type I error.^ We find it is difficult to detect treatment-by-center interactions when only a few investigational sites enrolling a limited number of patients participate in the interaction. However, we find no increased risk of type I error in these situations. In a pooled analysis, when the treatment is not effective, the probability of finding a significant treatment effect in the absence of significant treatment-by-center interaction is well within standard limits of type I error. ^

Iterative determination of clinical beam phase space from dose measurements

Monte Carlo (MC) method can accurately compute the dose produced by medical linear accelerators. However, these calculations require a reliable description of the electron and/or photon beams delivering the dose, the phase space (PHSP), which is not usually available. A method to derive a phase space model from reference measurements that does not heavily rely on a detailed model of the accelerator head is presented. The iterative optimization process extracts the characteristics of the particle beams which best explains the reference dose measurements in water and air, given a set of constrains