Utility and pitfalls of some statistical methods in active controlled clinical trials

Wang, Sue‐Jane; Hung, H. M. James; Tsong, Yi

doi:10.1016/s0197-2456(01)00155-6

Cited by 80 publications

(80 citation statements)

References 15 publications

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“…Arguably, the most difficult, often intractable, issues are 'constancy' and 'assay sensitivity' -the latter being the ability of a non-inferiority trial to detect a real difference between the treatments compared. Constancy is inherently an unverifiable assumption and inflation of the type I error when this assumption is not met has been well documented [5,7,25]. In fact 'constancy' and 'assay sensitivity' are closely related concepts, since the presence of 'assay sensitivity' guarantees that the control treatment will have a non-zero effect and the assumption of constancy guarantees the size of that effect.…”

Section: Discussionmentioning

confidence: 98%

Active‐controlled, non‐inferiority trials in oncology: arbitrary limits, infeasible sample sizes and uninformative data analysis. Is there another way?

Carroll

2006

Pharmaceutical Statistics

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In oncology, it may not always be possible to evaluate the efficacy of new medicines in placebo-controlled trials. Furthermore, while some newer, biologically targeted anti-cancer treatments may be expected to deliver therapeutic benefit in terms of better tolerability or improved symptom control, they may not always be expected to provide increased efficacy relative to existing therapies. This naturally leads to the use of active-control, non-inferiority trials to evaluate such treatments. In recent evaluations of anti-cancer treatments, the non-inferiority margin has often been defined in terms of demonstrating that at least 50% of the active control effect has been retained by the new drug using methods such as those described by Rothmann et al., Statistics in Medicine 2003; 22:239-264 and Wang and Hung Controlled Clinical Trials 2003; 24:147-155. However, this approach can lead to prohibitively large clinical trials and results in a tendency to dichotomize trial outcome as either 'success' or 'failure' and thus oversimplifies interpretation. With relatively modest modification, these methods can be used to define a stepwise approach to design and analysis. In the first design step, the trial is sized to show indirectly that the new drug would have beaten placebo; in the second analysis step, the probability that the new drug is superior to placebo is assessed and, if sufficiently high in the third and final step, the relative efficacy of the new drug to control is assessed on a continuum of effect retention via an 'effect retention likelihood plot'. This stepwise approach is likely to provide a more complete assessment of relative efficacy so that the value of new treatments can be better judged.

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Section: Discussionmentioning

confidence: 98%

Active‐controlled, non‐inferiority trials in oncology: arbitrary limits, infeasible sample sizes and uninformative data analysis. Is there another way?

Carroll

2006

Pharmaceutical Statistics

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“…Section 3 gives an example. Notice that the solution c S is independent of the interim data Z 1 as Z 1 has been integrated out in (16). This property is a great advantage over other conditional power methods such as Proschan and Hunsberger [18], as discussed in Li et al [10].…”

Section: Test Superiority With Adaptive Two-stage Designmentioning

confidence: 92%

“…After obtaining c E from (19), n 2E is solved from equation (13) (or (15)). Recall that we also plug c E in (16) to solve c S . The gain of having considered the futility boundary h 1 is clearly seen in (16) and (19): c E and c S would be larger if h 1 = −∞ (i.e.…”

Section: Test Non-inferioritymentioning

confidence: 99%

“…Toward this end, we ÿrst connect the test statistic with the lower conÿdence limit, which is more intuitive thus often used for testing H S or H E . The conÿdence interval (CI) approach using the worst limit of the 95 per cent conÿdence interval of the active control e ect to deÿne the margin is also more robust against the so-called 'constancy assumption' when using historical data for the active control treatment e ect in preserving the alpha-level [13,14,16].…”

Section: Conditional Powermentioning

confidence: 99%

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Two‐stage adaptive strategy for superiority and non‐inferiority hypotheses in active controlled clinical trials

Shih

Quan

Li³

2004

Statistics in Medicine

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In active controlled trials without a placebo arm, there are usually two study objectives: to test a superiority hypothesis that the experimental treatment is more effective than the active control therapy, and to test a non-inferiority hypothesis that the experimental treatment is therapeutically no worse than the active control within a defined margin. For a two-stage adaptive design, it is not necessary to give a fixed sample size calculation at the planning stage of the study when treatment effect information is often insufficient. Instead, decision and estimation of the design specifications can be made more reliably after the first stage when interim results are available. We propose the use of conditional power approach to determine the sample size and critical values for testing the superiority and non-inferiority hypotheses for the second stage based on the observed result of the first stage. The proposed adaptive procedure preserves the overall type I error rate for both superiority and non-inferiority, and has the flexibility of early termination of the study (for futility or efficacy) or extending the study by appropriate sample size.

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“…Searching for such papers was hampered by the lack of recognised terminology for indirect comparisons; other terms used in the papers identified include cross-trial (or cross-study) comparison, 82,99 connected comparative experiment, 54 network meta-analysis, 88 mixed comparison, 76 and virtual comparison. 100 Hardly any of the papers identified cited any of the others. It is thus quite possible that other methodological papers were missed, although it is unlikely that there are important omissions regarding methods of analysis.…”

Section: Commentsmentioning

confidence: 99%

Indirect comparisons of competing interventions

Glenny

Altman²,

Song³

et al. 2005

Health Technol Assess

601

485

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Utility and pitfalls of some statistical methods in active controlled clinical trials

Cited by 80 publications

References 15 publications

Active‐controlled, non‐inferiority trials in oncology: arbitrary limits, infeasible sample sizes and uninformative data analysis. Is there another way?

Active‐controlled, non‐inferiority trials in oncology: arbitrary limits, infeasible sample sizes and uninformative data analysis. Is there another way?

Two‐stage adaptive strategy for superiority and non‐inferiority hypotheses in active controlled clinical trials

Indirect comparisons of competing interventions

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