Changes in Plasma HIV-1 RNA and CD4+ Lymphocyte Counts and the Risk of Progression to AIDS

O’Brien, William A.; Hartigan, Pamela; Martin, David P.; Esinhart, James D.; Hill, Andrew; Benoit, Sharon L.; Rubin, Marc; Simberkoff, Michael S.; Hamilton, John D.

doi:10.1056/nejm199602153340703

Cited by 671 publications

(289 citation statements)

References 29 publications

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“…Their evaluation suggested that only if the unadjusted treatment effect was more than four times its standard error would it be possible to validate that a surrogate was explaining some of the treatment effect. O'Brien et al [7] used P (FGS) to evaluate the proportion of zidovidine's (AZT) effect on progression to AIDS explained by HIV viral RNA copy number and by CD4. The authors used estimates of P (FGS) to conclude that viral RNA explained a larger proportion of AZT's effect than CD4, but using them both as joint surrogates in a Cox model explained the largest proportion.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of a Measure of the Proportion of the Treatment Effect Explained by a Surrogate Marker

Bycott¹,

Taylor²

1998

Controlled Clinical Trials

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Time-dependent markers, such as CD4 and viral load, are potential surrogate markers in AIDS clinical trials. A critical issue with surrogate markers is whether changes in these markers explain the beneficial effect of treatment on the real end point of the clinical trial. A statistic to measure the proportion of the treatment effect explained by the surrogate is P (FGS) ϭ 1 Ϫ ␥/␣, where ␣ is the treatment effect coefficient in a Cox model and ␥ is the treatment effect coefficient from a time-dependent Cox model adjusted for the marker. In this article we evaluate the statistical properties of P (FGS) . Using a Monte Carlo study we show that the statistic is not well calibrated, because it can fall outside the range zero to one, even in very large samples. In the simulation study we consider situations where the time-dependent marker is measured with error at a fixed number of times. We show that a method of fitting a time-dependent Cox model involving smoothing the marker reduces the bias in the estimate of P (FGS) compared with the standard method of using the current or last observed marker value. We also show that the estimate of P (FGS) has considerable variability and can have wide confidence intervals. We conclude that P (FGS) is only likely to be useful in large trials with a strong treatment effect. The methods are illustrated using CD4 counts from an AIDS clinical trial of zidovidine versus placebo. Controlled Clin Trials 1998;19:555-568 © Elsevier Science Inc. 1998

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Section: Introductionmentioning

confidence: 99%

An Evaluation of a Measure of the Proportion of the Treatment Effect Explained by a Surrogate Marker

Bycott¹,

Taylor²

1998

Controlled Clinical Trials

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“…Prediction of the treatment effect at the trial level is undoubtedly central to the problem of surrogate validation, and some approaches are in fact based exclusively on trial-level information [7]. Prediction of the true endpoint at the individual level is only incidental to the validation problem, even though the correlation between the surrogate and the true endpoints is one of the elements to consider in the evaluation process [27,28]. Although a good correlate may not be a good surrogate [9], a poor correlate is even less likely to be one.…”

Section: Discussionmentioning

confidence: 99%

Statistical challenges in the evaluation of surrogate endpoints in randomized trials

Molenberghs

Buyse

Geys

et al. 2002

Controlled Clinical Trials

115

105

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The validation of surrogate endpoints has been studied by Prentice, who presented a definition as well as a set of criteria that are equivalent if the surrogate and true endpoints are binary. Freedman et al. supplemented these criteria with the so-called proportion explained. Buyse and Molenberghs proposed to replace the proportion explained by two quantities: (1) the relative effect, linking the effect of treatment on both endpoints, and (2) the adjusted association, an individual-level measure of agreement between both endpoints. In a multiunit setting, these quantities can be generalized to a trial-level measure of surrogacy and an individual-level measure of surrogacy. In this paper, we argue that such a multiunit approach should be adopted because it overcomes difficulties that necessarily surround validation efforts based on a single trial. These difficulties are highlighted.

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“…Since differences of less than 0.5 log are not considered clinically significant (Saag et al 1996), these results were satisfactory, confirming that the NASBA system is a reliable method for assessing viral load. However, it is noteworthy that the determination of this 0.5 log range has been based mostly on studies using bDNA and PCR assays , O´Brien et al 1996.…”

Section: Discussionmentioning

confidence: 99%

Quantitation of HIV-1 RNA Viral Load Using NASBA Methodology and Comparison with other Surrogate Markers for Disease Progression

Sitnik¹,

Pinho

1998

Mem. Inst. Oswaldo Cruz

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Changes in Plasma HIV-1 RNA and CD4+ Lymphocyte Counts and the Risk of Progression to AIDS

Abstract: Treatment-induced changes in the plasma HIV-1 RNA level and the CD4+ lymphocyte count, taken together, are valid predictors of the clinical progression of HIV-related disease and can be used to assess the efficacy of zidovudine and possibly other antiretroviral drugs as well.

Cited by 671 publications

References 29 publications

An Evaluation of a Measure of the Proportion of the Treatment Effect Explained by a Surrogate Marker

An Evaluation of a Measure of the Proportion of the Treatment Effect Explained by a Surrogate Marker

Statistical challenges in the evaluation of surrogate endpoints in randomized trials

Quantitation of HIV-1 RNA Viral Load Using NASBA Methodology and Comparison with other Surrogate Markers for Disease Progression

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