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Literature Review October–December 2010 Andrew Stone, a and Tianhui Zhou b INTRODUCTION This review covers the following journals received during the period from October to December 2010: Applied Statistics, volume 59, issue 5. Biometrical Journal, volume 52, issues 5 and 6. Biometrics, volume 66, issue 4. Biometrika, volume 97, issue 4. Biostatistics, volume 11, issue 4. Clinical Trials, volume 7, issues 5 and 6. Contemporary Clinical Trials, volume 32, issue 1. Journal of Biopharmaceutical Statistics, volume 20, issue 6. Journal of the Royal Statistical Society, Series A, volume 173, issue 4. Journal of the Royal Statistical Society, Series B, volume 72, issue 5. Statistics in Biopharmaceutical Research, volume 2, issue 4. Statistics in Medicine, volume 29, issues 25–30. Statistical Methods in Medical Research, volume 19, issues 5 and 6. SELECTED HIGHLIGHTS FROM THE LITERATURE This quarter features many journals themed to concentrate on very relevant issues; we describe these here along with interesting and relevant individual articles. Non-inferiority The PISC Expert Team has published a white paper on non- inferiority and argues for a synthesis method that allows for a consistent approval standard for placebo and active control trials. This paper is accompanied by rejoinders from statisticians from FDA and MHRA. Peterson P, Carroll K, Chuang-Stein C et al. PISC Expert Team white paper: toward a consistent standard of evidence when evaluating the efficacy of an experimental treatment from a randomized, active-controlled trial. Statistics Biopharm Research 2010; 2:522–531. Adaptive designs Issue 6 of the Journal of Biopharmaceutical Statistics is devoted to adaptive designs and concentrates on various commentaries on the FDA draft guidance, and also includes a report from the Basel Biometric Society Spring Conference held in March 2010. A selection of papers is listed here. Benda D, Brannath W, Bretz F et al. Perspectives on the use of adaptive designs in clinical trials. Part II. Panel discussion. Journal of Biopharmaceutical Statistics 2010; 20:1098–1112. Emerson SS, Fleming TR. Adaptive methods: telling ‘‘the rest of the story ‘‘. Journal of Biopharmaceutical Statistics 2010; 20:1150–1165. Cook T, DeMets DL. Review of draft FDA adaptive design guidance. Journal of Biopharmaceutical Statistics 2010; 20:1132–1142. The PhRMA working group on Adaptive Dose-Ranging Studies have presented an updated white paper. The PhRMA paper is accompanied by a commentary by statisticians from FDA and MHRA, which highlight the potential for bias resulting from random highs resulting from clustering of patients to doses. Pinheiro J, Sax F, Antonijevic Z et al. Adaptive and model- based dose-ranging trials: quantitative evaluation and recommendations. White paper of the PhRMA Working Group on adaptive dose-ranging studies. Statistics Biopharm Research 2010; 2:435–454. Wang SJ. The bias issue under the complete null with response adaptive randomization: commentary on ‘‘adaptive and model- based dose-ranging trials: quantitative evaluation and recom- mendations’’. Statistics Biopharm Research 2010; 2:458–461. The adaptive dose-finding study with two stages is consid- ered in the following paper where the second stage doses are chosen based on the first stage results. Trend tests based on a single contrast or on the maximum of multiple contrasts are performed. The adaptivity is taken into account and a method for type I error control is discussed. This paper illustrates the theory discussed and compares the performance of several tests for the adaptive design in a simulation study. Miller F. Adaptive dose-finding: proof of concept with type I error control. Biometrical Journal 2010; 52:577–589. 183 LITERATURE REVIEW Published online 10 February 2011 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/pst.490 Pharmaceut. Statist. 2011, 10 183–184 Copyright r 2011 John Wiley & Sons, Ltd. a AstraZeneca Pharmaceuticals, Clinical Development, Alderley Park, Macclesfield, SK10 4TG, UK b Pfizer, 500 Arcola Road, Collegeville, PA 19426, USA *Correspondence to: Andrew Stone, AstraZeneca Pharmaceuticals, Clinical Development, Alderley Park, Macclesfield, SK10 4TG, UK. E-mail: [email protected]

Literature Review October–December 2010

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Page 1: Literature Review October–December 2010

Literature Review October–December 2010Andrew Stone,a� and Tianhui Zhoub

INTRODUCTION

This review covers the following journals received during theperiod from October to December 2010:

� Applied Statistics, volume 59, issue 5.� Biometrical Journal, volume 52, issues 5 and 6.� Biometrics, volume 66, issue 4.� Biometrika, volume 97, issue 4.� Biostatistics, volume 11, issue 4.� Clinical Trials, volume 7, issues 5 and 6.� Contemporary Clinical Trials, volume 32, issue 1.� Journal of Biopharmaceutical Statistics, volume 20, issue 6.� Journal of the Royal Statistical Society, Series A, volume 173,

issue 4.� Journal of the Royal Statistical Society, Series B, volume 72,

issue 5.� Statistics in Biopharmaceutical Research, volume 2, issue 4.� Statistics in Medicine, volume 29, issues 25–30.� Statistical Methods in Medical Research, volume 19, issues 5

and 6.

SELECTED HIGHLIGHTS FROM THE LITERATURE

This quarter features many journals themed to concentrate onvery relevant issues; we describe these here along withinteresting and relevant individual articles.

Non-inferiority

The PISC Expert Team has published a white paper on non-inferiority and argues for a synthesis method that allows for aconsistent approval standard for placebo and active controltrials. This paper is accompanied by rejoinders from statisticiansfrom FDA and MHRA.

� Peterson P, Carroll K, Chuang-Stein C et al. PISC Expert Teamwhite paper: toward a consistent standard of evidencewhen evaluating the efficacy of an experimental treatmentfrom a randomized, active-controlled trial. Statistics BiopharmResearch 2010; 2:522–531.

Adaptive designs

Issue 6 of the Journal of Biopharmaceutical Statistics is devotedto adaptive designs and concentrates on various commentarieson the FDA draft guidance, and also includes a report from the

Basel Biometric Society Spring Conference held in March 2010.A selection of papers is listed here.

� Benda D, Brannath W, Bretz F et al. Perspectives on the useof adaptive designs in clinical trials. Part II. Panel discussion.Journal of Biopharmaceutical Statistics 2010; 20:1098–1112.

� Emerson SS, Fleming TR. Adaptive methods: telling ‘‘the restof the story ‘‘. Journal of Biopharmaceutical Statistics 2010;20:1150–1165.

� Cook T, DeMets DL. Review of draft FDA adaptive designguidance. Journal of Biopharmaceutical Statistics 2010;20:1132–1142.

The PhRMA working group on Adaptive Dose-Ranging Studieshave presented an updated white paper. The PhRMA paper isaccompanied by a commentary by statisticians from FDA andMHRA, which highlight the potential for bias resulting fromrandom highs resulting from clustering of patients to doses.

� Pinheiro J, Sax F, Antonijevic Z et al. Adaptive and model-based dose-ranging trials: quantitative evaluation andrecommendations. White paper of the PhRMA WorkingGroup on adaptive dose-ranging studies. Statistics BiopharmResearch 2010; 2:435–454.

� Wang SJ. The bias issue under the complete null with responseadaptive randomization: commentary on ‘‘adaptive and model-based dose-ranging trials: quantitative evaluation and recom-mendations’’. Statistics Biopharm Research 2010; 2:458–461.

The adaptive dose-finding study with two stages is consid-ered in the following paper where the second stage doses arechosen based on the first stage results. Trend tests based on asingle contrast or on the maximum of multiple contrasts areperformed. The adaptivity is taken into account and a methodfor type I error control is discussed. This paper illustrates thetheory discussed and compares the performance of several testsfor the adaptive design in a simulation study.

� Miller F. Adaptive dose-finding: proof of concept with type Ierror control. Biometrical Journal 2010; 52:577–589.

18

3

LITERATURE REVIEW

Published online 10 February 2011 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/pst.490

Pharmaceut. Statist. 2011, 10 183–184 Copyright r 2011 John Wiley & Sons, Ltd.

aAstraZeneca Pharmaceuticals, Clinical Development, Alderley Park, Macclesfield,SK10 4TG, UK

bPfizer, 500 Arcola Road, Collegeville, PA 19426, USA

*Correspondence to: Andrew Stone, AstraZeneca Pharmaceuticals, ClinicalDevelopment, Alderley Park, Macclesfield, SK10 4TG, UK.E-mail: [email protected]

Page 2: Literature Review October–December 2010

Personalised health care

The move towards ‘targeted therapies’ raises the possibility ofincreasing treatment success rates; the October issue of Clinicaltrials features a series of papers that were presented at theUniversity of Pennsylvania annual conference. Highlighted aretwo papers that take different approaches to identifyingpatients who are most likely to benefit – the methods beingequally applicable to side effects.

� Simon R. Clinical trials for predictive medicine: newchallenges and paradigms. Clinical Trials 2010; 7:516–524.

� Ruberg SJ, Chen L, Wang Y. The mean does not mean asmuch anymore: finding sub-groups for tailored therapeutics.Clinical Trials 2010; 7:574–583.

Meta-analysis

A new confidence interval is proposed that has better coveragethan the DerSimonian–Laird method, and that is less sensitive topublication bias. The new method centers confidence intervalson a fixed effects estimate, but allows for heterogeneity byincluding an assessment of the extra uncertainty induced by therandom effects setting. The fixed effects estimates are lesssensitive to such biases than random effects estimates, sincethey put relatively more weight on the larger studies andrelatively less weight on the smaller studies.

� Henmi M, Copas JB. Confidence intervals for random effectsmeta-analysis and robustness to publication bias. Statistics inMedicine 2010; 29:2969–2983.

Multiple binary outcomes

Frequently in clinical studies, a primary outcome is formulatedfrom a vector of binary events. This paper gives a nice overviewand comparison of methods to assess treatment effects onmultiple correlated binary outcomes. Using a flexible method tosimulate multivariate binary data, they show that the relativeefficiencies of the assessed tests depend in a complex way onthe magnitudes and variability of component incidences andtreatment effects, as well as correlations among componentevents. Two clinical trials are discussed and analyzed, andrecommendations for practice are made.

� Maschal EJ, Imrey PB. Factors affecting power of tests formultiple binary outcomes. Statistics in Medicine 2010;29:2890–2904.

Missing data

Using clinical trial data from patients with diffuse systemicsclerosis, the paper reviews some of the basic considerationsfor missing data and surveys a range of statistical techniquesfor the analysis of longitudinal clinical trial data with missing-ness. They show that different approaches to handling missingdata can lead to different conclusions on the efficacy of thetreatment and suggest how such discrepancies might beaddressed.

� Wong WK, Boscardin WJ, Postlethwaite AE et al. Handlingmissing data issues in clinical trials for rheumatic diseases.Contemporary Clinical Trials 2010; 32:1–9.

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Literature Review

Copyright r 2011 John Wiley & Sons, Ltd. Pharmaceut. Statist. 2011, 10 183–184