Bayesian sample size for exploratory clinical trials incorporating historical data

This paper presents a simple Bayesian approach to sample size determination in clinical trials. It is required that the trial should be large enough to ensure that the data collected will provide convincing evidence either that an experimental treatment is better than a control or that it fails to improve upon control by some clinically relevant difference. The method resembles standard frequentist formulations of the problem, and indeed in certain circumstances involving 'non-informative' prior information it leads to identical answers. In particular, unlike many Bayesian approaches to sample size determination, use is made of an alternative hypothesis that an experimental treatment is better than a control treatment by some specified magnitude. The approach is introduced in the context of testing whether a single stream of binary observations are consistent with a given success rate p(0). Next the case of comparing two independent streams of normally distributed responses is considered, first under the assumption that their common variance is known and then for unknown variance. Finally, the more general situation in which a large sample is to be collected and analysed according to the asymptotic properties of the score statistic is explored. Copyright (C) 2007 John Wiley & Sons, Ltd.

The Role of Cell Sterilization in Population Based Studies of Radiogenic Second Cancers Following Radiation Therapy

Advances in radiotherapy have generated increased interest in comparative studies of treatment techniques and their effectiveness. In this respect, pediatric patients are of specific interest because of their sensitivity to radiation induced second cancers. However, due to the rarity of childhood cancers and the long latency of second cancers, large sample sizes are unavailable for the epidemiological study of contemporary radiotherapy treatments. Additionally, when specific treatments are considered, such as proton therapy, sample sizes are further reduced due to the rareness of such treatments. We propose a method to improve statistical power in micro clinical trials. Specifically, we use a more biologically relevant quantity, cancer equivalent dose (DCE), to estimate risk instead of mean absorbed dose (DMA). Our objective was to demonstrate that when DCE is used fewer subjects are needed for clinical trials. Thus, we compared the impact of DCE vs. DMA on sample size in a virtual clinical trial that estimated risk for second cancer (SC) in the thyroid following craniospinal irradiation (CSI) of pediatric patients using protons vs. photons. Dose reconstruction, risk models, and statistical analysis were used to evaluate SC risk from therapeutic and stray radiation from CSI for 18 patients. Absorbed dose was calculated in two ways: with (1) traditional DMA and (2) with DCE. DCE and DMA values were used to estimate relative risk of SC incidence (RRCE and RRMA, respectively) after proton vs. photon CSI. Ratios of RR for proton vs. photon CSI (RRRCE and RRRMA) were then used in comparative estimations of sample size to determine the minimal number of patients needed to maintain 80% statistical power when using DCE vs. DMA. For all patients, we found that protons substantially reduced the risk of developing a second thyroid cancer when compared to photon therapy. Mean RRR values were 0.052±0.014 and 0.087±0.021 for RRRMA and RRRCE, respectively. However, we did not find that use of DCE reduced the number of patents needed for acceptable statistical power (i.e, 80%). In fact, when considerations were made for RRR values that met equipoise requirements and the need for descriptive statistics, the minimum number of patients needed for a micro-clinical trial increased from 17 using DMA to 37 using DCE. Subsequent analyses revealed that for our sample, the most influential factor in determining variations in sample size was the experimental standard deviation of estimates for RRR across the patient sample. Additionally, because the relative uncertainty in dose from proton CSI was so much larger (on the order of 2000 times larger) than the other uncertainty terms, it dominated the uncertainty in RRR. Thus, we found that use of corrections for cell sterilization, in the form of DCE, may be an important and underappreciated consideration in the design of clinical trials and radio-epidemiological studies. In addition, the accurate application of cell sterilization to thyroid dose was sensitive to variations in absorbed dose, especially for proton CSI, which may stem from errors in patient positioning, range calculation, and other aspects of treatment planning and delivery.

Genome-wide association study of virologic response with efavirenz-containing or abacavir-containing regimens in AIDS clinical trials group protocols.

BACKGROUND: Efavirenz and abacavir are components of recommended first-line regimens for HIV-1 infection. We used genome-wide genotyping and clinical data to explore genetic associations with virologic failure among patients randomized to efavirenz-containing or abacavir-containing regimens in AIDS Clinical Trials Group (ACTG) protocols. PARTICIPANTS AND METHODS: Virologic response and genome-wide genotype data were available from treatment-naive patients randomized to efavirenz-containing (n=1596) or abacavir-containing (n=786) regimens in ACTG protocols 384, A5142, A5095, and A5202. RESULTS: Meta-analysis of association results across race/ethnic groups showed no genome-wide significant associations (P<5×10) with virologic response for either efavirenz or abacavir. Our sample size provided 80% power to detect a genotype relative risk of 1.8 for efavirenz and 2.4 for abacavir. Analyses focused on CYP2B genotypes that define the lowest plasma efavirenz exposure stratum did not show associations nor did analysis limited to gene sets predicted to be relevant to efavirenz and abacavir disposition. CONCLUSION: No single polymorphism is associated strongly with virologic failure with efavirenz-containing or abacavir-containing regimens. Analyses to better consider context, and that minimize confounding by nongenetic factors, may show associations not apparent here.

Sample size in orthodontic randomized controlled trials: are numbers justified?

Sample size calculations are advocated by the Consolidated Standards of Reporting Trials (CONSORT) group to justify sample sizes in randomized controlled trials (RCTs). This study aimed to analyse the reporting of sample size calculations in trials published as RCTs in orthodontic speciality journals. The performance of sample size calculations was assessed and calculations verified where possible. Related aspects, including number of authors; parallel, split-mouth, or other design; single- or multi-centre study; region of publication; type of data analysis (intention-to-treat or per-protocol basis); and number of participants recruited and lost to follow-up, were considered. Of 139 RCTs identified, complete sample size calculations were reported in 41 studies (29.5 per cent). Parallel designs were typically adopted (n = 113; 81 per cent), with 80 per cent (n = 111) involving two arms and 16 per cent having three arms. Data analysis was conducted on an intention-to-treat (ITT) basis in a small minority of studies (n = 18; 13 per cent). According to the calculations presented, overall, a median of 46 participants were required to demonstrate sufficient power to highlight meaningful differences (typically at a power of 80 per cent). The median number of participants recruited was 60, with a median of 4 participants being lost to follow-up. Our finding indicates good agreement between projected numbers required and those verified (median discrepancy: 5.3 per cent), although only a minority of trials (29.5 per cent) could be examined. Although sample size calculations are often reported in trials published as RCTs in orthodontic speciality journals, presentation is suboptimal and in need of significant improvement.

Standardizing assessment of adverse effects in rheumatology clinical trials. Status of OMERACT Toxicity Working Group March 2000: Towards a common understanding of comparative toxicity/safety profiles for antirheumatic therapies

This paper describes the background and current status of an OMERACT facilitated effort to improve the consistency of adverse event reporting in rheumatology clinical trials, The overall goal is the development of an adverse event assessment tool that would provide a basis for use of common terminology and improve the consistency of reporting severity of side effects within rheumatology clinical trials and during postmarketing surveillance. The resulting Rheumatology Common Toxicity Criteria Index encompassed the following organ systems: allergic/immunologic, cardiac, ENT, gastrointestinal, musculoskeletal, neuropsychiatric, ophthalmologic, pulmonary and skin/integument. Before this tool is widely accepted, its validity, consistency, and feasibility need to be assessed in clinical trials.

A program for the computation of power and determination of sample size in hierarchical experimental designs.

We have devised a program that allows computation of the power of F-test, and hence determination of appropriate sample and subsample sizes, in the context of the one-way hierarchical analysis of variance with fixed effects. The power at a fixed alternative is an increasing function of the sample size and of the subsample size. The program makes it easy to obtain the power of F-test for a range of values of sample and subsample sizes, and therefore the appropriate sizes based on a desired power. The program can be used for the 'ordinary' case of the one-way analysis of variance, as well as for hierarchical analysis of variance with two stages of sampling. Examples are given of the practical use of the program.

Genomewide association study of atazanavir pharmacokinetics and hyperbilirubinemia in AIDS Clinical Trials Group protocol A5202.

BACKGROUND: Atazanavir-associated hyperbilirubinemia can cause premature discontinuation of atazanavir and avoidance of its initial prescription. We used genomewide genotyping and clinical data to characterize determinants of atazanavir pharmacokinetics and hyperbilirubinemia in AIDS Clinical Trials Group protocol A5202. METHODS: Plasma atazanavir pharmacokinetics and indirect bilirubin concentrations were characterized in HIV-1-infected patients randomized to atazanavir/ritonavir-containing regimens. A subset had genomewide genotype data available. RESULTS: Genomewide assay data were available from 542 participants, of whom 475 also had data on estimated atazanavir clearance and relevant covariates available. Peak bilirubin concentration and relevant covariates were available for 443 participants. By multivariate analysis, higher peak on-treatment bilirubin levels were found to be associated with the UGT1A1 rs887829 T allele (P=6.4×10), higher baseline hemoglobin levels (P=4.9×10), higher baseline bilirubin levels (P=6.7×10), and slower plasma atazanavir clearance (P=8.6×10). For peak bilirubin levels greater than 3.0 mg/dl, the positive predictive value of a baseline bilirubin level of 0.5 mg/dl or higher with hemoglobin concentrations of 14 g/dl or higher was 0.51, which increased to 0.85 with rs887829 TT homozygosity. For peak bilirubin levels of 3.0 mg/dl or lower, the positive predictive value of a baseline bilirubin level less than 0.5 mg/dl with a hemoglobin concentration less than 14 g/dl was 0.91, which increased to 0.96 with rs887829 CC homozygosity. No polymorphism predicted atazanavir pharmacokinetics at genomewide significance. CONCLUSION: Atazanavir-associated hyperbilirubinemia is best predicted by considering UGT1A1 genotype, baseline bilirubin level, and baseline hemoglobin level in combination. Use of ritonavir as a pharmacokinetic enhancer may have abrogated genetic associations with atazanavir pharmacokinetics.

Postoperative pain control in cats: clinical trials with pre-emptive lidocaine epidural co-administered with morphine or methadone

To evaluate the effectiveness of epidural lidocaine in combination with either methadone or morphine for postoperative analgesia in cats undergoing ovariohysterectomy. Under general anesthesia, 24 cats that underwent ovariohysterectomy were randomly allocated into three treatments groups of eight each. Treatment 1 included 2% lidocaine (4.0 mg/kg); treatment 2 included lidocaine and methadone (4.0 mg/kg and 0.3 mg/kg, respectively); and treatment 3 included lidocaine and morphine (4.0 mg/kg and 0.1 mg/kg, respectively). All drugs were injected in a total volume of 0.25 ml/kg via the lumbosacral route in all cats. During the anesthetic and surgical periods, the physiological variables (respiratory and heart rate, arterial blood pressure and rectal temperature) were measured at intervals of time zero, 10 mins, 20 mins, 30 mins, 60 mins and 120 mins. After cats had recovered from anesthesia, a multidimensional composite pain scale was used to assess postoperative analgesia at 2, 4, 8, 12, 18, and 24 h after epidural. The time to first rescue analgesic was significantly (P <0.05) prolonged in cats that received both lidocaine and methadone or lidocaine and morphine treatments compared with those that received the lidocaine treatment. All cats that received lidocaine treatment alone required rescue analgesic within 2 h of epidural injections. All treatments had significant cardiovascular and respiratory changes but they were within acceptable range for healthy animals during the surgical period. The two combinations administered via epidural allowed ovariohysterectomy with sufficient analgesia in cats, and both induced prolonged postoperative analgesia.

Addressing missing participant outcome data in dental clinical trials

Missing outcome data are common in clinical trials and despite a well-designed study protocol, some of the randomized participants may leave the trial early without providing any or all of the data, or may be excluded after randomization. Premature discontinuation causes loss of information, potentially resulting in attrition bias leading to problems during interpretation of trial findings. The causes of information loss in a trial, known as mechanisms of missingness, may influence the credibility of the trial results. Analysis of trials with missing outcome data should ideally be handled with intention to treat (ITT) rather than per protocol (PP) analysis. However, true ITT analysis requires appropriate assumptions and imputation of missing data. Using a worked example from a published dental study, we highlight the key issues associated with missing outcome data in clinical trials, describe the most recognized approaches to handling missing outcome data, and explain the principles of ITT and PP analysis.

A study of sample size in making decisions about instructional materials /

Project No. 711151.01.