Clinical and Diagnostic Laboratory Immunology, March 2001, p. 468-469, Vol. 8, No. 2
Effectiveness of Remune
Churdboonchart et al. (2) paint an impressive picture
of the effects of Remune on CD4 cell count in HIV-infected subjects. However, as individuals who were closely involved in the study, we
believe that the paper presents a misleading account of the study
results and a distorted view of the beneficial effects of Remune in
this population.
We became involved in this study during its planning stages as a result
of a request from Thailand's AIDS Subcommittee for HIV Vaccine Trials,
National Commission for the Prevelopment and Control of AIDS, to
provide statistical expertise. We were integrally involved in the study
design, contributed to the development of case report forms, provided
data management training for the sites and for Dr. Churdboonchart's
staff, set up the study randomization and held the blinded treatment
codes during the study, and gave presentations to the study's Data and
Safety Monitoring Board (DSMB) on the role of DSMBs and the planned
interim analysis for this study. One of us (S.K.) served as a member of
the DSMB. We developed an analysis plan for the final study data that
was approved by Dr. Churdboonchart, conducted the interim analysis of
the study and presented this to the DSMB, and prepared a final
statistical report on the study results.
The prespecified primary analysis of CD4 cell count for this study used
the summary statistic approach (3), in which a slope was
computed for each subject by fitting a linear regression to his or her
log-transformed CD4 measurements at weeks 0, 12, 24, 36, and 40 and
where the resulting slopes were then compared between the Remune and
placebo (incomplete Freund's adjuvant) groups using the van der
Waerden nonparametric test (4). This prespecified primary
analysis of CD4 cell count yielded a P value of 0.34, indicating no significant difference between the Remune and placebo groups.
There were also several prespecified secondary analyses of the CD4
endpoint, all based on computing a single summary statistic for each
subject and then comparing the Remune and placebo groups using the van
der Waerden test. These secondary analyses were based on using
untransformed (as opposed to log-transformed) CD4 counts, two
alternative metrics to the CD4 slope (change between baseline and week
40 and normalized area under the CD4 curve [AUC]), and an alternative
method for calculating an individual CD4 count based on the
"averaging" method described by Churdboonchart et al.
(2). The analyses based on the averaging method were added as secondary analyses at the request of Dr. Churdboonchart at the
completion of the study. Table 1 lists
the results of the primary analysis of CD4 counts and the nine
secondary analyses included in our final report. We have not adjusted
any of these P values to control for the inflated
false-positive rate that arises when multiple tests are conducted
(American Statistical Association [ASA] ethical guidelines for
statistical practice [http://www.amstat.org/profession/ethicalstatistics.html]).
Note that the primary analysis and seven of the nine secondary analyses
of CD4 cell count fail to demonstrate a statistically significant
difference between the Remune and placebo groups. Although some of
the secondary analyses suggested a possible difference between the
Remune and Placebo groups, the multiplicity of tests undertaken,
as well as the fact that these were secondary analyses, argues
against much emphasis being placed on them (ASA guidelines [see
above]). Accordingly, the final statistical analysis of the study that
we prepared for Dr. Churdboonchart and the AIDS Subcommittee noted
that while some of the secondary analyses were suggestive of
a possible association between Remune and CD4 count, the
study data overall did not demonstrate a significant difference in CD4 between the Remune and Placebo groups.
In contrast to these results, Churdboonchart et al. present only
a single analysis of CD4 count, corresponding to analysis 10 in
Table 1 but using the Wilcoxon test (4) instead of the van
de Waerden (the Wilcoxon and van der Waerden tests usually give similar results, and in this case the Wilcoxon test gives P = 0.03 as opposed to the value of 0.024 in Table 1).
Churdboonchart et al. do not acknowledge that the analysis of CD4 count
they present was not the prespecified primary analysis, do not report the prespecified primary analysis, and do not acknowledge that their
reported analysis was just one The pitfalls associated with reporting only selective analyses in a
scientific report are well-known (1; ASA guidelines [see
above]). For example, the ASA ethical guidelines state:
1071-412X/01/$04.00+0 DOI: 10.1128/CDLI.8.2.468-469.2001
LETTERS TO THE EDITOR
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LETTER
TABLE 1.
Statistical analyses of CD4 count
the most statistically significant one
of multiple secondary analyses of CD4 count.
Running multiple tests on the same data set at the same stage of
an analysis increases the chances of obtaining at least one invalid
result. Selecting the one "significant" result from a multiplicity
of parallel tests poses a grave risk of an incorrect conclusion.
Failure to disclose the full extent of tests and their results in such
a case would be highly misleading.
In our opinion, the paper by Churdboonchart et al. gives a distorted account of the clinical trial by virtue of its incomplete and selective reporting of the CD4 cell count results.
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REFERENCES |
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| 1. | Bailar, J. C., III, and F. Mosteller (ed.). 1992. Medical uses of statistics, 2nd ed. New England Journal of Medicine Books, Boston, Mass. |
| 2. |
Churdboonchart, V.,
C. Sakondhavat,
S. Kulpradist,
B. Isarangkura Na Ayudthya,
V. Chandeying,
S. Rugpao,
C. Boonshuyar,
W. Sukeepaisarncharoen,
W. Sirawaraporn,
D. J. Carlo, and R. Moss.
2000.
A double-blind, adjuvant-controlled trial of human immunodeficiency virus type 1 (HIV-1) immunogen (Remune) monotherapy in asymptomatic, HIV-1-infected Thai subjects with CD4-cell counts of >300.
Clin. Diagn. Lab. Immunol.
7:728-733 |
| 3. | Dawson, J., and S. Lagakos. 1993. The size and power of two-sample tests of repeated measures. Biometrics 49:1022-1032[CrossRef][Medline]. |
| 4. | Holander, M., and D. A. Wolfe. 1999. Nonparametric statistical methods. Wiley Interscience, New York, N.Y. |
|
David Glidden University of California San Francisco, California | |||||
|
Soyeon Kim Stephen Lagakos Harvard School of Public Health Boston, Massachusetts |
We welcome the opportunity to respond to the letter by Glidden et al.,
coinvestigators for statistical analysis in the Remune clinical trials
in Thailand, regarding phase II trial results. The primary endpoint of
changes in CD4 cell counts and the absolute changes from baseline that
were observed during the trial are clearly described in the article.
The increases in CD4 cell counts observed after immunization were
reviewed by all investigators and presented to the Technical
Subcommittee on AIDS Vaccine Development and the National Ethical
Committee, Thailand Ministry of Health, and at various AIDS conferences
including the International AIDS Conference in Durban, where it was
considered one of the most important clinical presentations by an
independent clinical rapporteur (Brian Gazzard, personal
communication). Furthermore, statistical models which predict the
clinical relevance of absolute changes in CD4 cells counts, comparable
to those observed in this trial, also suggest the clinical relevance of
the increases in CD4 cell counts observed after immunization
(2).
The area under the curve (AUC) metric was chosen because it is, indeed,
the most common metric used to examine changes in CD4 cell counts or
viral load in HIV clinical trials as noted by statisticians from the
Harvard School of Public Health (3, 5) including Dr.
Lagakos, who used AUC as a consultant to the U.S. Remune clinical trial
(4). Our statistician utilized AUC based on a clinical
hypothesis that immunization would result in increments in CD4 cell
counts over time associated with enhanced HIV-specific immunity. The
AUC metric makes no assumptions about the distribution of the data and
provides a good approximation of the biological effects of
immunization. In contrast, the slope metric makes assumptions about
data distribution, which may not be valid, and potentially excludes
important information. Therefore, the most appropriate analysis is the
AUC metric. We had also written to Dr. Lagakos regarding his preference
for the use of slope analysis rather than AUC as the primary analysis
but received no reply. We believe that Dr. Lagakos' preference for the
slope metric as the primary analysis for this study lacks a valid
scientific rationale. Indeed, in their final analysis report (21 October 1999) Drs. Kim and Lagakos state "Had AUCMB been used as the
primary analysis it would have been declared significant."
In summary, our article reports absolute changes in CD4 cell counts,
which we and other clinicians in Thailand have deemed clinically
significant and important from a public health perspective. We have
utilized AUC, one of the most utilized metrics for surrogate markers in
AIDS clinical trials. It is important to realize that one of the first
AIDS trials to utilize AUC was a study comparing dideoxyinosine (ddI)
to zidovudine (AZT), which revealed a small (15-cell maximum)
difference for one of the two doses of ddI and AZT, which was not
significant for the mean or median counts. However, comparison of the
AUCs showed a significant difference (P < 0.03)
between each of the ddI arms and AZT. It was this analysis which
contributed to the approval of ddI (6). We believe the insistence on the slope metric without a valid scientific rationale is
therefore unjustified. Dr. Tukey, a leader of modern statistical theory, warned that statistics should not be "sanctified" to impede scientific progress (1). We are indeed pleased that both
the Technical and the Ethical committees of the Thailand Ministry of
Health have reviewed all of the information from this trial and have
approved further clinical development of Remune in Thailand.
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AUTHORS' REPLY
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Kahn, J.,
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Efficacy and safety of adefovir dipivoxil with antiretroviral therapy: a randomized controlled trial.
JAMA
282:2305-2312 |
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Kahn, J. O.,
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2000.
Evaluation of HIV-1 immunogen, an immunologic modifier, administered to patients infected with HIV having 300 to 549 × 106/L CD4 cell counts: a randomized controlled trial.
JAMA
284:2193-2202 |
| 5. | Kim, S., M. D. Hughes, S. M. Hammer, J. B. Jackson, V. DeGruttola, and D. A. Katzenstein. 2000. Both serum HIV type 1 RNA levels and CD4+ lymphocyte counts predict clinical outcome in HIV type 1-infected subjects with 200 to 500 CD4+ cells per cubic millimeter. AIDS Clinical Trials Group study 175 virology study team. AIDS Res. Hum. Retrovir. 16:645-653[CrossRef][Medline]. |
| 6. | Stein, D. S., J. A. Korvick, and S. H. Vermund. 1992. CD4+ lymphocyte cell enumeration for prediction of clinical course of human immunodeficiency virus disease: a review. J. Infect. Dis. 165:352-363[Medline]. |
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Vina Churdboonchart Worachart Sirawaraporn Chaweewon Boonshuyar Mahidol University Bangkok, Thailand | |||||
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Chuanchom Sakondhavat Wisut Sukeepaisarncharoen Khon Kaen University Hospital Khon Kaen, Thailand | |||||
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Sang-a-roon Kulpradist Vajira Hospital, Bangkok Metropolitan Authority Bangkok, Thailand | |||||
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Boonsri Isarangkura Na Ayudthya Pramongkutklao College of Medicine Bangkok, Thailand | |||||
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Verapol Chandeying Songklanakarindh University Songkla, Thailand | |||||
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Sungwal Rugpao Chiang Mai University Hospital Chiang Mai, Thailand |
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| Antimicrob. Agents Chemother. | Clin. Microbiol. Rev. | Infect. Immun. |
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