Statistical quality control in clinical trials

We propose to apply statistical methods of industrial process and quality control to accumulating data in a statistical monitoring center of a clinical trial. We discuss some specific issues connected with the application of these methods over calendar time to patients' characteristics (at a particular individual patient time) or to more formal monitoring characteristics (like the number of queries per case record form). The tools used are Shewart charts, plots, breakpoint regression, and recursive residuals with cusums and V-charts, applied to measurement and event data. A software program based on SAS macros allows easy application of the methods. Some examples, with graphical outputs, from an ongoing trial on patients with primary malignant melanoma, illustrate the methods.     

Statistical properties of randomization in clinical trials

This is the first of five articles on the properties of different randomization procedures used in clinical trials. This paper presents definitions and discussions of the statistical properties of randomization procedures as they relate to both the design of a clinical trial and the statistical analysis of trial results. The subsequent papers consider, respectively, the properties of simple (complete), permuted-block (i.e., blocked), and urn (adaptive biased-coin) randomization. The properties described herein are the probabilities of treatment imbalances and the potential effects on the power of statistical tests; the permutational basis for statistical tests; and the potential for experimental biases in the assessment of treatment effects due either to the predictability of the random allocations (selection bias) or the susceptibility of the randomization procedure to covariate imbalances (accidental bias). For most randomization procedures, the probabilities of overall treatment imbalances are readily computed, even when a stratified randomization is used. This is important because treatment imbalance may affect statistical power. It is shown, however, that treatment imbalance must be substantial before power is more than trivially affected. The differences between a population versus a permutation model as a basis for a statistical test are reviewed. It is argued that a population model can only be invoked in clinical trials as an untestable assumption, rather than being formally based on sampling at random from a population. On the other hand, a permutational analysis based on the randomization actually employed requires no assumptions regarding the origin of the samples of patients studied. The large sample permutational distribution of the family of linear rank tests is described as a basis for easily conducting a variety of permutation tests. Subgroup (stratified) analyses, analyses when some data are missing, and regression model analyses are also discussed. The Blackwell-Hodges model for selection bias in the composition of the study groups is described. The expected selection bias associated with a randomization procedure is a function of the predictability of the treatment allocations and is readily evaluated for any sequence of treatment assignments. In an unmasked study, the potential for selection bias may be substantial with highly predictable sequences. Finally, the Efron model for accidental bias in the estimate of treatment effect in a linear model is described. This is important because the potential for accidental bias is equivalent to the potential for a covariate imbalance.(ABSTRACT TRUNCATED AT 400 WORDS)     

Statistical reasoning in clinical trials: hypothesis testing

Hypothesis testing is based on certain statistical and mathematical principles that allow investigators to evaluate data by making decisions based on the probability or implausibility of observing the results obtained. However, classic hypothesis testing has its limitations, and probabilities mathematically calculated are inextricably linked to sample size. Furthermore, the meaning of the p value frequently is misconstrued as indicating that the findings are also of clinical significance. Finally, hypothesis testing allows for four possible outcomes, two of which are errors that can lead to erroneous adoption of certain hypotheses: 1. The null hypothesis is rejected when, in fact, it is false. 2. The null hypothesis is rejected when, in fact, it is true (type I or alpha error). 3. The null hypothesis is conceded when, in fact, it is true. 4. The null hypothesis is conceded when, in fact, it is false (type II or beta error). The implications of these errors, their relation to sample size, the interpretation of negative trials, and strategies related to the planning of clinical trials will be explored in a future article in this journal.     

Statistical evaluation of HIV vaccines in early clinical trials

The HIV pandemic is a pressing threat to global public health; HIV vaccine development is critical. Clinical evaluation of HIV vaccine candidates differs from the standard therapeutics trial framework primarily due to the fact that healthy individuals are studied. We present an early stage evaluation program developed for the HIV Vaccine Trials Network (HVTN) motivated by characteristics unique to the vaccine setting. The program consists of 3 prototypical stages (Phase I, Ib, II) that provide a unified yet flexible approach to the safety and immunogenicity evaluation of diverse vaccine regimens. The goal of these early trials is to narrow the number of candidate vaccines to the most promising candidates worthy of further study in efficacy trials.     

Statistical issues for design and analysis of single-arm multi-stage phase II cancer clinical trials

BackgroundPhase II trials have been very widely conducted and published every year for cancer clinical research. In spite of the fast progress in design and analysis methods, single-arm two-stage design is still the most popular for phase II cancer clinical trials. Because of their small sample sizes, statistical methods based on large sample approximation are not appropriate for design and analysis of phase II trials.MethodsAs a prospective clinical research, the analysis method of a phase II trial is predetermined at the design stage and it is analyzed during and at the end of the trial as planned by the design. The analysis method of a trial should be matched with the design method. For two-stage single arm phase II trials, Simon's method has been the standards for choosing an optimal design, but the resulting data have been analyzed and published ignoring the two-stage design aspect with small sample sizes.ConclusionsIn this article, we review analysis methods that exactly get along with the exact two-stage design method. We also discuss some statistical methods to improve the existing design and analysis methods for single-arm two-stage phase II trials.     

Statistical problems in comparative clinical chemistry analysis methods

The previous workshop on this subject put forward recommendations for the use of statistical methods in the comparison of clinical chemical analytical procedures. In particular, standardized principal components analysis was recommended as a replacement for classical regression analysis. In the present workshop, two non-parametric methods were described, and their advantages and disadvantages, compared with standardized principal components analysis, were discussed.     

Hospice patients in clinical cancer treatment trials

Patients with cancer and practitioners face a conflict when no known curative treatments exist because Medicare and other insurers do not allow clinical trial participation and hospice care to occur concurrently. Patients may not fully benefit from hospice care because of late enrollment. Barriers to hospice referrals exist in the form of clinician attitudes and insurance limitations. Patients with advanced cancer may be prevented from enrolling in clinical trials, and trial participation may limit hospice care. Factors related to this situation are discussed.     

Sequential balancing: a simple method for treatment allocation in clinical trials

Although minimisation methods have frequently been advocated for treatment allocation in clinical trials, they are not widely used. As this may partly be due to the complexity of the methods, we devised a new and simple minimisation method to balance for prognostic factors, called sequential balancing. Each factor is dealt with sequentially and when a new subject enters the trial, he or she is allocated the treatment that leads to improved balance of the first factor over the treatments. If the balance of the first factor was already satisfactory, then the treatment is allocated that leads to improved balance of the second factor and so on. The algorithm requires no calculations. We simulated a realistic trial and compared the performance of this method to the performance of alternative allocation strategies: the variance minimisation method, simple randomisation and stratification. The sequential balancing method led to better balance than randomisation and stratification. In the case of four factors or less, the performance of the sequential balancing method and the variance minimisation method were comparable and the sequence of the factors was not very relevant. When more factors were introduced, the balance of the sequential method remained comparable with the balance achieved with the variance minimisation method for the first four factors, but it started to decrease from the fifth factor onwards. We conclude that the ease and simplicity of the new method make it an attractive option when balance is required for four factors or less. If there are more than four factors, the sequential balancing method may still be an acceptable option, but the advantage of simplicity has to be weighed against the loss of performance compared to other minimisation methods.     

Some statistical methods for multiple endpoints in clinical trials

This paper summarizes, defines, and discusses multiple endpoints comparison procedures, concepts, and methodologies for applications to clinical trials. We address the more widely used methods of α-level, p-value, and critical value adjustments. We examine global assessment measures such as O'Brien's test and Simes' procedure and contrast them with the α-adjustment procedures of Bonferroni and Holm. We propose a global assessment procedure based on categorization of the individual endpoints to form an overall composite endpoint. Additionally, we discuss a new weighting scheme for Holm's sequentially rejective α-adjustment procedure. Investigation of the correlation between endpoints is examined in relation to adjustment of the α-level. In the context of a clinical trial, the above multiplicity procedures are applied and compared. Finally, some comments concerning ease of use and relevance are summarized for the above methods.     

Statistical methods for assessing agreement between two methods of clinical measurement

In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.     

Obstacles to implementing cancer clinical trials

There are numerous obstacles to implementing and conducting clinical trials. Patient accrual and the costs of clinical trials are difficult problems for researchers. Additional obstacles to implementing clinical trials are patient-related, physician-related, and nurse-related barriers.     

The method of minimization for allocation to clinical trials. a review

Minimization is a largely nonrandom method of treatment allocation for clinical trials. We conducted a systematic literature search to determine its advantages and disadvantages compared with other allocation methods. Minimization was originally proposed by Taves and by Pocock and Simon. The latter paper introduces a family of allocation methods of which Taves' method is the simplest example. Minimization aims to ensure treatment arms are balanced with respect to predefined patient factors as well as for the number of patients in each group. Further extensions of the method have also been proposed by other authors. Simulation studies show that minimization provides better balanced treatment groups when compared with restricted or unrestricted randomization and that it can incorporate more prognostic factors than stratified randomization methods such as permuted blocks within strata. Some more computationally complex methods may give an even better performance. Concerns over the use of minimization have centered on the fact that treatment assignments may be predicted with certainty in some situations and on the implications for the analysis methods used. It has been suggested that adjustment should always be made for minimization factors when analyzing trials where minimization is the allocation method used. The use of minimization may sometimes result in added organizational complexity compared with other methods. Minimization has been recommended by many commentators for use in clinical trials. Despite this it is still rarely used in practice. From the evidence presented in this review, we believe minimization to be a highly effective allocation method and recommend its wider adoption in the conduct of randomized controlled trials.