Typical Clinical Trial Research Articles

Comorbidities in heart failure: a key issue

Comorbidities in heart failure: a key issue

A Dunnett–Bonferroni‐based parallel gatekeeping procedure for dose–response clinical trials with multiple endpoints

This paper proposes a Dunnett-Bonferroni-based parallel gatekeeping procedure in a setting of dose-response clinical trials with multiple endpoints. It follows the Dunnett-based parallel gatekeeping strategy of Dmitrienko et al. (Pharm. Stat. 2006; 5:19-28), but differs in the calculation of the critical values. The implementation of the Dunnett-based parallel gatekeeping procedure relies on assumptions difficult to justify in typical clinical trials, namely (a) that the joint distribution of the test statistics from different endpoints can be approximated by a multivariate-t distribution and (b) that the true correlation between multiple endpoints can be well estimated using observed data. The proposed Dunnett-Bonferroni-based parallel gatekeeping procedure relaxes the preceding assumptions by splitting type I error rate among families using the Bonferroni inequality. While it is potentially less powerful than a Dunnett-based procedure when both procedures are applicable, the power loss is very minimal. Our proposed method avoids assumptions that might be challenged by regulatory agencies and does so with virtually no cost. Moreover, in most cases this method is easier to implement compared with the Dunnett-based procedure.

Gatekeeping Testing via Adaptive Alpha Allocation

In a typical clinical trial, there are one or two primary endpoints, and a few secondary endpoints. When at least one primary endpoint achieves statistical significance, there is considerable interest in using results for the secondary endpoints to enhance characterization of the treatment effect. Because multiple endpoints are involved, regulators may require that the familywise type I error rate be controlled at a pre-set level. This requirement can be achieved by using "gatekeeping" methods. However, existing methods suffer from logical oddities such as allowing results for secondary endpoint(s) to impact the likelihood of success for the primary endpoint(s). We propose a novel and easy-to-implement gatekeeping procedure that is devoid of such deficiencies. A real data example and simulation results are used to illustrate efficiency gains of our method relative to existing methods.

Identifying subpopulations for subgroup analysis in a longitudinal clinical trial

Background In a typical clinical trial treatment effects will not be expected to be the same on all of the study participants. As a result, investigators are often tempted to look at the effects of a given treatment in subgroups of patients in order to determine who will benefit the most or the least, especially when the treatment effect in the total sample is insignificant or borderline. This paper aims at demonstrating the application of random effect models as one approach to identify subpopulations suitable for subgroup analysis in a longitudinal study. Methods Data collected from a double-blind randomized controlled trial were used to demonstrate how multilevel modeling using random effects can be used to identify subgroups of postmenopausal women who benefit the most from nonhormonal treatment (gabapentin) of their hot flashes. Results We estimated subject-specific treatment effects and correlated these effects with patient characteristics at baseline. We found that women with a higher severity of hot flashes score at baseline were more likely to have the greatest reduction in hot flashes score from the treatment. Also, women who had a serum creatinine level higher than the median level at baseline demonstrated a greater response to gabapentin compared to the placebo group. Conclusion Our proposed method can help researchers identify patient factors that are associated with differential effect. Those factors are potential areas for further clinical investigation or for constructing subgroups for sub-analysis.

Generalizability of clinical trials for alcohol dependence to community samples

There is a growing concern that results of tightly controlled clinical trials of individuals with alcohol use disorders may not generalize to broader community samples. To assess the proportion of community-dwelling adults with alcohol dependence who would have been eligible for a typical alcohol dependence treatment study, we developed a new, simple method: we applied a standard set of eligibility criteria commonly used in alcohol outcome studies to a large ( n = 43,093) representative US adult sample interviewed face-to-face, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). We found that approximately one-half (50.5%) of all individuals with a DSM-IV diagnosis of alcohol dependence ( n = 1484) and 79.4% of those who sought treatment ( n = 185) were excluded by one or more study criteria. Individual study criteria excluded from 0.9% to 48.2% of the overall sample and 0.8% to 43.7% of the treatment-seeking sample. For the overall sample, the lack of motivation/compliance and financial situation criteria excluded the largest percentage of individuals. In the treatment-seeking subsample, comorbid medical conditions and legal problems excluded the largest proportions of individuals. Our study provides a new method to assess the generalizability of clinical trials, and gives further evidence that typical clinical trials for alcohol dependence likely exclude most adults with the disorder in the community and under care, and support the notion that clinical trials recruit “pure” rather than “typical” patients. Clinical trials should carefully evaluate the effects of the selected eligibility criteria on the generalizability of their results.

Serial Whole-Brain N-Acetylaspartate Concentration in Healthy Young Adults

Although the concentration of N-acetylaspartate (NAA) is often used as a neuronal integrity marker, its normal temporal variations are not well documented. To assess them over the 1-2 year periods of typical clinical trials, the whole-brain NAA concentration was measured longitudinally, over 4 years, in a cohort of healthy young adults. No significant change (adjusted for both sex and age) was measured either interpersonally or intrapersonally over the entire duration of the study.

Design of Randomized Controlled Trials

A major factor in the rapid advance of medical science over the past 50 years has been the development and refinement of the clinical research method known as the randomized controlled trial (RCT). A clinical trial is defined as a prospective scientific experiment that involves human subjects in whom treatment is initiated for the evaluation of a therapeutic intervention. In an RCT, each patient is assigned to receive a specific treatment intervention by a chance mechanism. Nothing more clearly indicates the key role of an RCT in modern clinical research than the placement of this specific research method at the top of the list of levels of evidence in evidence-based medicine.1 According to this classification, significant results of an RCT are more definitive than any other type of clinical research information. The purpose of this article is to present an overview of the design of RCTs. Some of the principles of a high-quality study, such as the use of randomization, placebos, and double-blind designs are well known. Other principles such as stratification, use of a decision-making structure, and statistical power are known by many investigators but are not universally recognized or fully understood. These features plus others that indicate the design of a high-quality RCT are discussed. A companion article on the conduct and evaluation of RCTs will appear in a future issue of this journal. One of the most easily recognized aspects of a well-designed and conducted clinical trial is the apparent clarity of the research mechanism evident in its published report. Alternatively, one of the more common problems with a published clinical trial is the apparent “design by committee” in which different members of a protocol team have different goals. Because a clinical trial is a resource-intensive undertaking, many investigators feel that the study should attempt to …

A meta-analysis of cognitive change with haloperidol in clinical trials of atypical antipsychotics: Dose effects and comparison to practice effects

Prospective, double-blind, randomized trials comparing atypical antipsychotic drugs (APDs) to typical APDs, such as haloperidol, indicate that atypical APDs provide a modest benefit to cognitive function in schizophrenia. However, the validity of this inference has been contested by suggestions that the cognitive improvements observed with atypical APDs reflect practice effects associated with repeated testing on the same neuropsychological instruments, or an avoidance of a deleterious effect of haloperidol on cognitive function that might be dose related. These alternate hypotheses were assessed by meta-analyses that 1) examined the relationship between cognitive change and dose of haloperidol within the control arms of prospective atypical vs. typical APD clinical trials; and 2) compared the magnitude of change observed within the haloperidol arms of these studies to estimated practice effects for several commonly used neuropsychological measures. The results indicate that overall cognitive performance improves while on haloperidol. Studies that used a low dose of haloperidol (< 10 mg) did not yield larger effect sizes for overall cognitive function or specific neuropsychological measures than studies that used a high dose (> 10 mg), although doses greater than 24 mg appear to have deleterious effects. For two of the six neuropsychological tests examined (digit symbol substitution and verbal fluency) the magnitude of change observed was significantly less than practice effects. The results indicate that although haloperidol may cause deleterious effects at very high doses, or in specific cognitive domains, these effects are not likely to explain the broader range of cognitive improvements observed with atypical APDs.

On estimating treatment effects under non‐compliance in randomized clinical trials: are intent‐to‐treat or instrumental variables analyses perfect solutions?

In this report, we compared four estimators (intent-to-treat, as treated, per protocol, and instrumental variables estimators) that are conventionally considered for treatment effect estimation by simulation under different non-compliance scenarios in typical clinical trial settings. We found that intent-to-treat and instrumental variables estimators are not perfect and can be problematic in some situations although these two estimators carry desirable properties as we assume.

Collection of Health-Economic Data Alongside Clinical Trials: Is There a Future for Piggyback Evaluations?

Objective The objective of this article is to discuss issues surrounding the conduct of “piggyback evaluations,” in which health-economic data are collected within an otherwise typical clinical trial. Methods We review the methodologic literature on piggyback economic evaluations, as well as selected empiric studies. We summarize the challenges encountered in the conduct of these studies, alternative ways of addressing these challenges, and their future role in pharmacoeconomic research. Results Piggyback evaluations have certain advantages over other types of pharmacoeconomic studies. An economic evaluation can benefit from the experimental design that maximizes the trial's internal validity, and it is often more practical to collect economic data alongside a trial rather than to fund a stand-alone economic study. However, piggyback evaluations are subject to problems deriving from the competing nature of clinical versus economic study objectives, which can give rise to tension in such fundamental aspects of study design as the selection of study subjects and sites; the extent of protocol-mandated health-care services; and the determination of sample size, length of follow-up, and the study comparator(s). Many solutions have been put forth in the literature to address these challenges. Conclusions Piggyback evaluations can be an appropriate means to measure the economic impact of medical interventions, provided that the methodologic challenges are acknowledged and addressed within the context of each individual study. As long as a desire for patient-level data from clinical trials exists, there will be a need for piggyback economic evaluations in the future.

Maximizing power and minimizing treatment failures in clinical trials

Response-adaptive randomization procedures have a long history in the theoretical statistics literature over the past four decades. The main idea historically was to develop randomization procedures that place fewer patients on the inferior treatment. More recent research has changed the main focus to that of usual considerations in typical clinical trials: power, sample size, expected treatment failures, maintaining randomization, among others. We describe response-adaptive randomization procedures for simple clinical trials comparing two binomial success probabilities, including the randomized play-the-winner rule, the drop-the-loser rule, and a modification of the doubly-adaptive biased coin design. We treat as our principal goal minimizing expected treatment failures while preserving power and randomization. Based on some recent theoretical literature, the basic guidelines for selecting an appropriate procedure include targeting optimal allocation, having small variability, and preserving randomization. We use simulation to compare power and expected treatment failures according to these guidelines. When the two treatments had high probabilities (> 0.5) of success, the randomized play-the-winner rule was less powerful than complete randomization and the drop-the-loser rule by 1-3 percent with slightly larger expected number of treatment failures than the drop-the-loser rule. For all the success probabilities we examined, the drop-the-loser rule was within 1 percent of the power of complete randomization with a modest reduction of treatment failures. The doubly-adaptive biased coin design was as powerful or slightly more powerful than complete randomization in every case and expected treatment failures were always less, with modest reductions of the order of 0.3 percent to 8.3 percent. We conclude that the drop-the-loser rule and a modification of the doubly-adaptive biased coin design are the preferred procedures, and simulations show that these procedures yield a modest reduction in expected treatment failures while preserving power over complete randomization.

Gamma failure‐time mixture models: yet another way to establish efficacy

AbstractUsing a Yamaguchi‐type generalized gamma failure‐time mixture model, we analyse the data from a study of autologous and allogeneic bone marrow transplantation in the treatment of high‐risk refractory acute lymphoblastic leukaemia, focusing on the time to recurrence of disease. We develop maximum likelihood techniques for the joint estimation of the surviving fractions and the survivor functions. This includes an approximation to the derivative of the survivor function with respect to the shape parameter. We obtain the maximum likelihood estimates of the model parameters. We also compute the variance‐covariance matrix of the parameter estimators.The extended family of generalized gamma failure‐time mixture models is flexible enough to include many commonly used failure‐time distributions as special cases. Yet these models are not used in practice because of computational difficulties. We claim that we have overcome this problem. The proposed approximation to the derivative of the survivor function with respect to the shape parameter can be used in any statistical package. We also address the issue of lack of identifiability. We point out that there can be a substantial advantage to using the gamma failure‐time mixture models over nonparametric methods. Copyright © 2003 John Wiley &amp; Sons, Ltd.

The PRIME North America randomized double-blind clinical trial of olanzapine versus placebo in patients at risk of being prodromally symptomatic for psychosis: I. Study rationale and design

The first double-blind placebo-controlled clinical trial of an atypical neuroleptic medication is being conducted in symptomatic treatment-seeking patients meeting new diagnostic criteria for a putative prodromal syndrome. This identifies them as being at high risk for developing psychosis in the near future. The study aims include prevention of psychosis onset and disability, as well as palliation of ongoing symptomatology. This report presents the study rationale and design. Recent studies will be reviewed that have advanced our knowledge about the early course of schizophrenia and our ability to predict onset prospectively, advances that have rendered prodromal intervention research feasible and ethical. The study design has many novel features. It tests for prevention versus delay in psychosis onset, as well as for efficacy and safety in a newly defined clinical population. This has required the development of innovative clinical research assessment instruments and a new operational definition of psychosis onset. The integration of these novel elements into an otherwise typical clinical trial design is detailed. The companion report will address sample recruitment and the clinical phenomenology at baseline of this putative “prodromal” entity.

Industry, Government, and Academic Panel Discussion on Multiple Comparisons in a “Real” Phase Three Clinical Trial

A Food and Drug Administration (FDA)/Industry/Academic Panel Discussion on multiplicity aspects of a real Phase III clinical trial was held at the Third International Conference on Multiple Comparisons, August 6, 2002, in Bethesda, Maryland. The goal was to develop some consensus among industry, government, and academic statisticians concerning requirements and methods for multiplicity management in typical clinical trials. The session was tape-recorded; this article mostly comes from an edited transcript.

The effect of latanoprost compared with timolol in African-American, Asian, European, Mexican and US glaucoma or ocular hypertensive patients

Two different principles were studied. 1st - statistical analysis techniques were used to obtain medical results from a patient population. 2nd - the patient population was used to study the statistical analysis techniques. Medical conclusions: latanoprost and timolol treatment showed a statistically significant and clinically useful mean IOP-reduction in a typical worldwide clinical trial population. Latanoprost reduced the IOP 1.6 mm Hg more than timolol. The IOP-reduction was maintained with timolol and slightly enforced with latanoprost up to 6 months of treatment. The mean IOP-reduction was maintained during 2 years of latanoprost treatment. The overall risk of withdrawal due to insufficient IOP-reduction with latanoprost was 8%. The statistical methodological issues are of a general and reoccurring character in trial design of the IOP-reduction: should the statistical hypothesis testing be based on the mean intraocular pressure (IOP) or the proportion of patients who reach a specific IOP level, should the estimate of the IOP or IOP-reduction be based on single eyes, mean of bilaterally eligible and identically treated eyes or the difference between an eye with active treatment and a placebo treated contralateral eye, and is mean of replicated recordings useful? Statistical methodological conclusions: the most effective response variable varies with the selected patient population. Therefore, the trial design process should include a comparison of the variability, test power and required sample size for the possible response variables in a sample of the target population. At minimum a statistical consideration should be done.

Implications of peginterferon use in special populations infected with HCV.

Rapid progress in the treatment of hepatitis C virus (HCV) infection has led to highly successful therapies that lead to viral eradication and sustained viral response in more than 50% of patients. Despite these advances, certain identifiable subpopulations appear to have reduced rates of treatment response. These include patients co-infected with HCV/HIV, alcohol abusers, and African Americans infected with HCV. Using research tools including viral kinetic modeling, factor analysis, and laboratory evaluation of viral and host genomic characteristics, a number of key elements of response have been identified. Examination of special populations reveals that these groups comprise complex mixes of poor prognostic markers. These include both host and viral factors. Use of peginterferons with ribavirin improves response rates in these groups, but does not fully ameliorate the response deficit relative to patients enrolled in typical drug registration clinical trials.

Nutritional papers in ICU patients: what lies between the lines?

The abundance of literature related to nutritional support reflects its recently recognised role in preventing metabolic complications and gut dysfunction during critical illness. However, some published studies lack relevance to critically ill patients, as a result of the selection of subjects and outcome variables, or flaws in the study design, as well as in the type, composition, timing, route of administration and amount of nutritional support given. This review will highlight these confounding factors by describing two imaginary (but typical) clinical trials and by analysing some studies published. The point at issue is that basic quality requirements, such as the formulation of a prospective hypothesis and the delineation of the effects of the reference treatment, are often lacking in many studies published. Data analysis was often found to be biased by the absence of statistical power calculation and intention-to-treat analysis. Globally, studies designed to assess the effects of nutritional support on the outcome of critically ill patients, rarely fulfil basic quality requirements and should therefore be interpreted cautiously. We suggest simple strategies or study design that will allow important questions to be answered by future clinical trials.

Failure-Time Mixture Models: Yet Another Way to Establish Efficacy

We propose to use mixture survival models to establish the efficacy of the trial treatment. In particular, we consider the lognormal distribution to model the right-censored event time and a logistic regression for the incidence part of the model. The model attempts to estimate simultaneously the effects of treatments on the acceleration/deceleration of the timing of a given event and the surviving fraction-the proportion of the population for which the event may never occur. We use the SAS/IML subroutine NLPTR to obtain the maximum likelihood estimates of the model parameters. The estimates of the standard errors of the parameter estimates are computed from the inverse of the observed information matrix. We use the Cox-Snell residual plot based on the unconditional survivor function for evaluating goodness-of-fit of the model. The principal research hypothesis will be that under the trial treatment, the time-to-event will be more decelerated/accelerated compared to the control, given that the event occurs. We suggest that this methodology could be considered as a means to establish efficacy. We emphasize that there can be a substantial advantage to using mixture models even when the log-rank test is valid and significant. Data on overall survival time from a typical colorectal cancer clinical trial are used to illustrate the procedure.

The Number of Centers in a Multicenter Clinical Study: Effects on Statistical Power

In the pharmaceutical industry it is often important to ensure fast patient enrollment. A common practice to speed up patient enrollment is to use more clinical centers. One of the concerns regarding this practice is that increasing the total number of centers to a certain degree may decrease the statistical efficiency of treatment comparisons. This is mainly because a study with too many clinical centers usually includes quite a few small centers and very often they do not have enough patients to represent all treatment groups. These small centers often carry little information on treatment differences. This paper utilizes a statistical model to quantify the relationship between statistical efficiency and the number of centers under typical clinical trial settings. Results in this paper provide useful statistical knowledge in choosing the number of clinical centers when planning multicenter studies.

Threats to the Validity of Clinical Trials Employing Enrichment Strategies for Sample Selection

Subject selection and exclusion criteria employed in typical clinical effectiveness trials of investigational new drugs have two fundamental aims: (1) to ensure that patients entering a study are truly suffering from the condition the drug is intended to treat and (2) to maximize the likelihood that the study will detect an effect of the drug if, in fact, one exists. Typical protocol selection criteria not only specify exacting procedures for establishing and documenting the diagnosis of those recruited for a study but also seek to increase, relative to the prevalence in the general population, the proportion of individuals in the sample likely to respond to pharmacological treatment. Because it is ordinarily impossible to learn prior to extensive clinical experience with a new drug which, if any, patient characteristics reliably predict a consistent treatment response, strategies for sample “enrichment” typically operate by excluding patients (for example, those with very advanced and/or complicated illness, those with serious concomitant illness, those at the extremes of age, those with very mild illness, and so forth) in whom a dependable response to treatment seems unlikely on logical and/or generic grounds.Some studies use positive strategies for sample “enrichment.” In studies evaluating drugs intended to treat recurrent episodes of psychiatric illnesses, many protocols recommend selective recruitment of patients with a history of meaningful positive responses to antipsychotic treatment during prior episodes. Sample selection procedures of these kinds impose limits on the generalizability of a study's results (i.e., external validity), but the use of nonrandom patient samples is ordinarily held to have no effect on the internal validity of the results. In short, studies employing highly selected patient samples are, despite their limited external validity, regularly accepted as valid sources of evidence bearing on a drug's effectiveness.There are exceptions, however; this paper describes one in which the use of a seemingly innocuous sample enrichment maneuver proved highly damaging to the ultimate credibility of an important multicenter trial. In particular, exposure to an experimental treatment during an open qualification phase may invalidate drug-placebo comparisons made during a later randomized, blinded, controlled phase. Our review of the trial also reveals that the enrichment maneuver employed probably failed to accomplish its intended aims, selecting patients whose improvements on the outcome variable may be as reasonably ascribed to chance as to drug effect. This is all the more surprising because the method of sample enrichment employed has much in common with those long recommended in the clinical trial literature.