Monitoring a general class of two-sample survival statistics with applications
Identifieur interne : 002353 ( Istex/Checkpoint ); précédent : 002352; suivant : 002354Monitoring a general class of two-sample survival statistics with applications
Auteurs : M. Gu [Hong Kong] ; D. Follmann [États-Unis] ; Nl Geller [États-Unis]Source :
- Biometrika [ 0006-3444 ] ; 1999-03.
English descriptors
- Teeft :
- Adaptive, Adaptive endpoint, Alternative hypotheses, Asymptotic, Asymptotic distribution, Asymptotic representation, Average duration, Boundary points, Brookmeyer crowley, Calendar time, Clinical trial, Clinical trials, Continuous function, Convergence, Converges, Covariance function, Cumulative hazard, Deterministic function, Discontinuity points, Endpoint, Gaussian, Gaussian process, General class, Gill, Hong kong, Increment, Independent increments, Infant infection status, Information times, Interim analyses, Intravenous immunoglobulin, Joint distribution, Local alternative, Local alternatives, Maximal value, Median test, Months power duration, Murray tsiatis, National institutes, Null hypotheses, Null hypothesis, Optimal statistic, Optimal weight, Pepe fleming, Positive women, Same power, Sample size, Scenario, Sequential, Sequential methods, Sequential monitoring, Statist, Statistic, Survival curves, Survival distributions, Survival function, Survival functions, Survival statistics, Technical report, Test statistic, Test statistics, Total variation, Transmission rate, Usual group sequential techniques, Variance, Weak convergence, Weight function, Weighted statistic, Weighted statistics.
Abstract
This paper considers a general class of statistics for testing the equality of two survival distributions in clinical trials with sequential monitoring. The tests can be expressed as Lebesgue-Stieltjes integrals of a weight function with respect to the difference between two survival distributions. Prominent members of this class include the two-sample difference in Kaplan-Meier estimates, the test of medians (Brookmeyer & Crowley, 1982), a truncated version of Efron's (1967) test and the Pepe-Fleming statistic (Pepe & Fleming, 1989, 1991). Statistics in this class are shown to converge to a Gaussian process, indexed by information time, under both null and local alternatives even if different statistics are used at different information times. Properly standardised, statistics in a subclass converge to Gaussian processes with independent increments so that the usual group sequential techniques for monitoring a clinical trial can be applied. The design of a trial comparing two treatments with respect to mother-to-newborn transmission of HIV is used to illustrate practical aspects of monitoring. Keywords:Clinical trial; Failure time data; Group sequential monitoring; Pepe-Fleming statistic.
Url:
DOI: 10.1093/biomet/86.1.45
Affiliations:
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Follmann</name></author><author><name sortKey="Geller, Nl" sort="Geller, Nl" uniqKey="Geller N" first="Nl" last="Geller">Nl Geller</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:218866003CEA89C78C1DCC81647CD601F6B3E23E</idno><date when="1999" year="1999">1999</date><idno type="doi">10.1093/biomet/86.1.45</idno><idno type="url">https://api.istex.fr/ark:/67375/HXZ-1CKNR6RP-N/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">000756</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">000756</idno><idno type="wicri:Area/Istex/Curation">000751</idno><idno type="wicri:Area/Istex/Checkpoint">002353</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Checkpoint">002353</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Monitoring a general class of two-sample survival statistics with applications</title><author><name sortKey="Gu, M" sort="Gu, M" uniqKey="Gu M" first="M" last="Gu">M. Gu</name><affiliation wicri:level="4"><country wicri:rule="url">Hong Kong</country><wicri:regionArea>Department of Statistics, The Chinese University of Hong Kong, New Territory, Hong Kong</wicri:regionArea><orgName type="university">Université chinoise de Hong Kong</orgName><placeName><settlement type="city">Sha Tin</settlement></placeName></affiliation></author><author><name sortKey="Follmann, D" sort="Follmann, D" uniqKey="Follmann D" first="D" last="Follmann">D. Follmann</name><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Office of Biostatistics Research, National Heart Lung and Blood Institute, 2 Rockledge Center, Bethesda Maryland 20892-7938</wicri:regionArea><wicri:noRegion>Bethesda Maryland 20892-7938</wicri:noRegion></affiliation><affiliation></affiliation></author><author><name sortKey="Geller, Nl" sort="Geller, Nl" uniqKey="Geller N" first="Nl" last="Geller">Nl Geller</name><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Office of Biostatistics Research, National Heart Lung and Blood Institute, 2 Rockledge Center, Bethesda Maryland 20892-7938</wicri:regionArea><wicri:noRegion>Bethesda Maryland 20892-7938</wicri:noRegion></affiliation><affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Biometrika</title><title level="j" type="abbrev">Biometrika</title><idno type="ISSN">0006-3444</idno><idno type="eISSN">1464-3510</idno><imprint><publisher>Oxford University Press</publisher><date type="published" when="1999-03">1999-03</date><biblScope unit="volume">86</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="45">45</biblScope><biblScope unit="page" to="57">57</biblScope></imprint><idno type="ISSN">0006-3444</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0006-3444</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="Teeft" xml:lang="en"><term>Adaptive</term><term>Adaptive endpoint</term><term>Alternative hypotheses</term><term>Asymptotic</term><term>Asymptotic distribution</term><term>Asymptotic representation</term><term>Average duration</term><term>Boundary points</term><term>Brookmeyer crowley</term><term>Calendar time</term><term>Clinical trial</term><term>Clinical trials</term><term>Continuous function</term><term>Convergence</term><term>Converges</term><term>Covariance function</term><term>Cumulative hazard</term><term>Deterministic function</term><term>Discontinuity points</term><term>Endpoint</term><term>Gaussian</term><term>Gaussian process</term><term>General class</term><term>Gill</term><term>Hong kong</term><term>Increment</term><term>Independent increments</term><term>Infant infection status</term><term>Information times</term><term>Interim analyses</term><term>Intravenous immunoglobulin</term><term>Joint distribution</term><term>Local alternative</term><term>Local alternatives</term><term>Maximal value</term><term>Median test</term><term>Months power duration</term><term>Murray tsiatis</term><term>National institutes</term><term>Null hypotheses</term><term>Null hypothesis</term><term>Optimal statistic</term><term>Optimal weight</term><term>Pepe fleming</term><term>Positive women</term><term>Same power</term><term>Sample size</term><term>Scenario</term><term>Sequential</term><term>Sequential methods</term><term>Sequential monitoring</term><term>Statist</term><term>Statistic</term><term>Survival curves</term><term>Survival distributions</term><term>Survival function</term><term>Survival functions</term><term>Survival statistics</term><term>Technical report</term><term>Test statistic</term><term>Test statistics</term><term>Total variation</term><term>Transmission rate</term><term>Usual group sequential techniques</term><term>Variance</term><term>Weak convergence</term><term>Weight function</term><term>Weighted statistic</term><term>Weighted statistics</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper considers a general class of statistics for testing the equality of two survival distributions in clinical trials with sequential monitoring. The tests can be expressed as Lebesgue-Stieltjes integrals of a weight function with respect to the difference between two survival distributions. Prominent members of this class include the two-sample difference in Kaplan-Meier estimates, the test of medians (Brookmeyer & Crowley, 1982), a truncated version of Efron's (1967) test and the Pepe-Fleming statistic (Pepe & Fleming, 1989, 1991). Statistics in this class are shown to converge to a Gaussian process, indexed by information time, under both null and local alternatives even if different statistics are used at different information times. Properly standardised, statistics in a subclass converge to Gaussian processes with independent increments so that the usual group sequential techniques for monitoring a clinical trial can be applied. The design of a trial comparing two treatments with respect to mother-to-newborn transmission of HIV is used to illustrate practical aspects of monitoring. Keywords:Clinical trial; Failure time data; Group sequential monitoring; Pepe-Fleming statistic.</div></front></TEI><affiliations><list><country><li>Hong Kong</li><li>États-Unis</li></country><settlement><li>Sha Tin</li></settlement><orgName><li>Université chinoise de Hong Kong</li></orgName></list><tree><country name="Hong Kong"><noRegion><name sortKey="Gu, M" sort="Gu, M" uniqKey="Gu M" first="M" last="Gu">M. Gu</name></noRegion></country><country name="États-Unis"><noRegion><name sortKey="Follmann, D" sort="Follmann, D" uniqKey="Follmann D" first="D" last="Follmann">D. Follmann</name></noRegion><name sortKey="Geller, Nl" sort="Geller, Nl" uniqKey="Geller N" first="Nl" last="Geller">Nl Geller</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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