PHARMACEUTICAL STATISTICS

Pharmaceut. Statist. 2009; 8: 170–172

Published online 11 May 2009 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/pst.379

Literature Review January–March 2009

S. Krishna Padmanabhan1 and Andrew Stone2,�,y1Wyeth Research and Development, Collegeville, PA, USA2AstraZeneca Pharmaceuticals, Clinical Development, Alderley Park,

Macclesfield, UK

INTRODUCTION

This review covers the following journalsreceived during the period from January to March2009:

� Applied Statistics, volume 58, part 1.� Biometrical Journal, volume 51, issue 1.� Biometrics, volume 65, issue 1.� Biometrika, volume 96, issue 1.� Biostatistics, volume 10, part 1.� Clinical Trials, volume 6, part 1.� Contemporary Clinical Trials, volume 30, parts

1 and 2.� Drug Information Journal, volume 43, parts 1

and 2.� Journal of Biopharmaceutical Statistics,

volume 19, parts 1 and 2.� Journal of the Royal Statistical Society,

Series A, volume 172, part 1.� Journal of the Royal Statistical Society,

Series B, volume 71, part 1.� Statistics in Medicine, volume 28, parts 1–8.� Statistical Methods in Medical Research,

volume 18, part 1.

SELECTED HIGHLIGHTS FROMTHE LITERATURE

Multiplicity

The January issue of the Journal of Biopharmaceu-tical Statistics contains a series of papers related tomultiplicity. The main paper discusses a number ofmultiple testing issues in regulatory applications,which are also discussed in separate articles by anumber of prominent authors in the field.

� Hung HMJ,Wang SJ. Some controversial multipletesting problems in regulatory applications. Journalof Biopharmaceutical Statistics 2009; 19:1–11.

Potential applications in clinical trials are pre-sented when there are co-primary endpoints present.

� Qian H. Li. Evaluating co-primary endpointscollectively in clinical trials. BiometricalJournal 2009; 51:137–145.

Meta-analyses

This paper provides comprehensive review ofapproaches to random-effects meta-analysis. Theauthors provide a clear set of recommendations,describe the possible objectives of a random-effectsmeta-analysis and potential pitfalls in interpretation

� Higgins JPT et al. A re-evaluation of randomeffects meta-analysis. Journal of the RoyalStatistical Society A 2009; 172:137–159.yE-mail: Andrew.stone@astrazeneca.com

*Correspondence to: Andrew Stone, AstraZeneca Pharma-ceuticals, Clinical Development, Alderley Park, Macclesfield,SK10 4TG, UK.

Copyright r 2009 John Wiley & Sons, Ltd.

The following article describes meta-analyses oflongitudinal data and present the advantagesobtained when individual patient data are incor-porated due to the ability to correctly model thecorrelation between repeated observations.

� Jones AP et al. Meta-analysis of individualpatient data versus aggregate data from long-itudinal clinical trials.Clinical Trials 2009; 6:16–27

Missing data

The handling of missing data is a crucial con-sideration in any analysis. The following papercompares two approaches to the handling ofmissing data at a pre-specified timepoint followinga series of repeat observations per patient. LastObservation Carried Forward (LOCF) and aMixed-Effects Regression Model (MMRM) arecompared. The MMRM analysis is a repeatedmeasures approach that allows estimation ofseparate treatment effects for each timepoint bymeans of the inclusion of a treatment-by-timeinteraction. The paper presents a series of simula-tions and re-analyses of 48 trials submitted inNDAs for neurological psychiatric drug productsdatasets.

� Siddiqui O et al. MMRM vs. LOCF: acomprehensive comparison based on simula-tion study and 25 NDA datasets. Journal ofBiopharmaceutical Statistics 2009; 19:227–246.

Adaptive designs

There is an ever expanding body of literature onadaptive designs. An example adaptive doseresponse design is presented for a phase I, dose-escalating study, where, subject to some con-straints, the number of patients per dose level ischosen to optimize estimation of specific para-meters from an Emax model. In doing so theauthors also provide a useful summary of variousversions of the Emax model.

� Leonov S, Miller S. An adaptive optimaldesign for the Emax model and its applicationin clinical trials. Journal of BiopharmaceuticalStatistics 2009; 19:360–385.

In many trials the design goal is to find the doseassociated with a certain target toxicity rate(Phase I) or the dose with a certain mean valueof a continuous response (dose-finding). In thisarticle, a unified dose-finding design for any dose-finding study with a target dose is described.

� Ivanova A, Kim SH. Dose finding for con-tinuous and ordinal outcomes with a mono-tone objective function: a unified approach.Biometrics 2009; 65:307–315.

This paper is presented as a ‘Tutorial inBiostatistics’ with a focus on adaptive designs forconfirmatory clinical trials. The authors reviewadaptive design methodologies for a single nullhypothesis and how to perform adaptive designswith multiple hypotheses using closed test proce-dures. They report the results of an extensivesimulation study evaluating the operating char-acteristics of the various methods.

� Bretz F et al. Adaptive designs for confirma-tory clinical trials. Statistics in Medicine2009;28:1181–1217.

Pharmacoepidemiology

The February edition of Statistical Methods inMedical Research contains a series of articles onPharmacoepidemiology that is becoming increas-ingly relevant to the pharmaceutical statistician.The issue contains a series of articles that serve asan excellent introduction to the area especially thefollowing.

� Hanley JA, Dendukuri N. Efficient samplingapproaches to address confounding in data-base studies. Statistical Methods in MedicalResearch 2009;18:81–105.

Miscellaneous (communication)

The authors recommend a format for commu-nicating an estimate with its standard error orconfidence interval. The format reinforces that the

Copyright r 2009 John Wiley & Sons, Ltd. Pharmaceut. Statist. 2009; 8: 170–172DOI: 10.1002/pst

Literature Review 171

associated variability is an inseparable componentof the estimate and it substantially improvesclarity in tabular displays.

� Louis TA, Zeger SL. Effective communicationof standard errors and confidence intervals.Biostatistics 2009; 10:1–2.

Data analysis

Analytical data are often subject to left-censoringwhen the actual values to be quantified fall belowthe limit of detection. The primary interest of thispaper is statistical inference for the two-sampleproblem. The authors develop a nonparametricpoint and interval estimation procedure for thelocation shift model. A large set of simulationscompares 14 methods including naive, parametric,

and nonparametric methods. Additionally, a realdata example is given followed by discussion.

� Zhang D et al. Nonparametric methods formeasurements below detection limit. Statisticsin Medicine 2009; 28:700–715.

Regulatory

The author considers and compares an indirectmethod and a direct method to evaluate thetreatment effect in a bridging study by borrowingthe strength of effect observed in the foreign trial.Issues considered include a prospectively plannedevaluation of global treatment effect along with apre-specified region-specific treatment effect.

� Wang SJ. Bridging study versus prespecifiedregions nested in global trials. Drug Informa-tion Journal 2009;43:27–34.

Copyright r 2009 John Wiley & Sons, Ltd. Pharmaceut. Statist. 2009; 8: 170–172DOI: 10.1002/pst

172 Literature Review