Literature review September-December 2005

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    Pharmaceut. Statist. 2006; 5: 6769

    Published online in Wiley InterScience ( DOI: 10.1002/pst.204

    Literature Review:

    SeptemberDecember 2005

    Simon Day1,*,y and Scott D. Patterson2

    1Medicines and Healthcare Products Regulatory Agency, Room 13-205, Market Towers,

    1 Nine Elms Lane, London SW8 5NQ, UK2GlaxoSmithKline Pharmaceuticals, 1250 South Collegeville Road, Collegeville, PA

    19426, USA


    We bring two changes to the Literature Reviews section of the

    journal, beginning with this issue. Firstly, we are introducing a

    separate non-clinical review, written by non-clinical experts,

    headed by Professor Ludwig Hothorn. As there is rather less

    non-clinical statistical material published, that review will

    appear biannually, in issues 1 and 3 of the journal. Secondly,

    the clinical review (as we might now call it) has a slight change in

    authorship and, with that, a very slight change in potential

    coverage. But we hope its usefulness will remain at a steady level.

    This review covers the following journals received during the

    period from the middle of September 2005 to end of December


    * Applied Statistics, volume 54, part 5.* Biometrical Journal, volume 47, part 5.* Biometrics, volume 61, parts 2, 3.* Biometrika, volume 92, part 4.* Biostatistics, volume 6, part 4.* Clinical Trials, volume 2, part 5.* Computational Statistics and Data Analysis, volume 50,

    parts 13.* Drug Information Journal, volume 39, part 4.* Journal of Biopharmaceutical Statistics, volume 15, part 6.* Journal of the Royal Statistical Society, Series A, volume

    168, part 4.* Statistics and Probability Letters, volume 74, parts 14.* Statistics in Medicine, volume 24, parts 2023.* Statistical Methods in Medical Research, volume 14, parts 5, 6.


    The theme of Statistical Methods in Medical Research was:

    * Part 6: Statistics in oral health research (pp. 537602).

    One tutorial has appeared in Statistics in Medicine:

    * Gurrin LC, Scurrah KJ, Hazelton ML. Tutorial in

    biostatistics: spline smoothing with linear mixed models.

    Statistics in Medicine 2005; 24:33613381.

    There is also a tutorial in Clinical Trials: this one on an

    important topic of missing data. It compares various ap-

    proaches for imputing missing values. . . from likelihood basedmethods to simple (their term) methods:

    * Beunckens C, Molenberghs G, Kenward MG. Direct

    likelihood analysis versus simple forms of imputation for

    missing data in randomized clinical trials. Clinical Trials

    2005; 2:379386.

    Phase I

    Determining the maximum tolerated dose is the primary goal of

    phase I research, and increasingly adaptive methods are being

    used in clinical trials where the dose given next is dependent on

    previous responses. In oncology trials for example, if a toxicity

    is observed, the next patient may receive a lower dose. If no

    such event is observed, the next patient receives a higher dose.

    This paper reviews coherence principles relating primarily to

    modied continual-reassessment methods:

    * Cheung Y. Coherence principles in dose-nding studies.

    Biometrika 2005; 92:863873.

    Copyright # 2006 John Wiley & Sons, Ltd.Received \60\re /teci


    *Correspondence to: Simon Day, Medicines and HealthcareProducts Regulatory Agency, Room 13-205, Market Towers, 1Nine Elms Lane, London SW8 5NQ, UK.

  • Nonlinear modelling of pharmacokinetic and, increasingly,

    pharmacodynamic and safety repeated-measures data is becom-

    ing more and more common in phase I and clinical

    pharmacology research. This paper describes alternatives to

    the multivariate normal distribution, most often used for this


    * Lindsey J, Lindsey P. Multivariate distributions with

    correlation matrices for nonlinear repeated measurements.

    Computational Statistics and Data Analysis 2005; 50:


    Phase II

    In a (regulatory) trials framework, Bayesian methods certainly

    do not come to prominence reasons may be quite varied.

    Perhaps phase II might be where some deviation from the usual

    might nd a place and this is where Wang et al. review using

    Bayesian methods to consider the posterior probability that a

    treatments effect is of a given magnitude. For the true, die-hard

    Bayesians, however, justifying such approaches on the grounds

    that they control frequentist error rates must be anathema!

    * Wang Y-G, Leung DH-Y, Li M, Tan S-B. Bayesian designs

    with frequentist and Bayesian error rate considerations.

    Statistical Methods in Medical Research 2005; 14:445456.

    A completely different phase II problem is addressed by Lu

    and colleagues who combine total response rate and rate of

    complete response as the endpoint. They develop a two-stage

    design and give guidance on stopping rules, etc.

    * Lu Y, Jin H, Lamborn KR. A design of phase II cancer

    trials using total and complete response endpoints. Statistics

    in Medicine 2005; 24:31553170.

    Surrogate endpoints

    The following paper perhaps has little to take away and use

    and its mathematical content is a bit heavier than the general

    reading we usually highlight but all the same is worth a quick

    look for those interested in the ongoing discussion of how to

    dene surrogate endpoints:

    * Baker SG, Izmirlian G, Kipnis V. Resolving paradoxes

    involving surrogate end points. Journal of the Royal

    Statistical Society, Series A 2005; 168:753762.


    Multiplicity ought (perhaps) to be a problem that is designed

    out of a trial but if that is not done, then clear, up-front, rules

    on how to handle it are essential. Whether statistics should be

    an art, or more rule-based might be a debatable point . . .although pre-specication may need some art, once methods/

    approaches are pre-specied, then inevitably some element of

    following set rules necessarily follows. This paper gives an

    example of setting out rules for families of related endpoints:

    * Chen X, Capizzi T, Binkowitz B, Quan H, Wei L, Luo X.

    Decision rule based multiplicity adjustment strategy.

    Clinical Trials 2005; 2:394399.

    Procedures to adjust for multiple hypotheses are of similar

    importance. This paper describes a procedure for evaluating a

    family of hypotheses:

    * Wiens B, Dmitrienko A. The fallback procedure for

    evaluating a single family of hypotheses. Journal of

    Biopharmaceutical Statistics 2005; 15:929942.

    Sample size calculation and recalculation

    Following from multiplicity (above), designing studies when

    effects are necessary to be seen on more than one endpoint is

    not simple. Xiong et al. use Alzheimers disease as an example

    and show how to calculate power and sample size for an

    appropriate intersectionunion test.

    * Xiong C, Yu K, Gao F, Yan Y, Zhang Z. Power and sample

    size for clinical trials when efcacy is required in multiple

    endpoints: application to an Alzheimers treatment trial.

    Clinical Trials 2005; 2:387393.

    When many correlated outcomes or endpoints are involved,

    simulation-based procedures may be useful.

    * Bang H, Jung S, George S. Sample size calculation for

    simulation-based multiple testing procedures. Journal of

    Biopharmaceutical Statistics 2005; 15:957967.

    Interim analyses and Data Monitoring Committees

    There seem to be two distinct types of interim analysis: those in

    which there is (virtually) complete follow-up on all the patients

    who have been recruited so far (but not all the pre-planned

    patients have been recruited); and those where all patients have

    been recruited but many of them do not have complete follow-

    up. The rst case is about curtailing recruitment; the second case

    is about curtailing follow-up. In most cases, standard (group)

    sequential methods (designed for case one) are used even in case

    two. Troendle et al. look more closely at this problem. It is

    important to understand that, in this second case, different

    hypotheses are being tested at different follow-up times.

    * Troendle JF, Liu A, Wu C, Yu KF. Sequential testing for

    efcacy in clinical trials with non-transient effects. Statistics

    in Medicine 2005; 24:32393250.

    Study design

    Designing studies to compare population pharmacokinetic

    response is not a setting where statisticians have traditionally

    Copyright # 2006 John Wiley & Sons, Ltd. Pharmaceut. Statist. 2006; 5: 6769

    Literature Review68

  • been involved. The need for involvement in design to support

    such studies is discussed in:

    * Narukawa M, Yafune A. A note on power and sampling

    schedule in population pharmacokinetic studies. Drug

    Information Journal 2005; 39:353359.

    Data analysis issues

    Handling missing data can be a complex problem depending on

    the mechanism (typically, missing at random, or not). These

    papers reviewed analysis methods when missing data are


    * Ali M, Talukder E. Analysis of longitudinal binary data

    with missing data due to dropouts. Journal of Biopharma-

    ceutical Statistics 2005; 15:9931007.* Sheng X, Carrie`re K. Strategies for analysing missing item

    response data with an application to lung cancer. Biome-

    trical Journal 2005; 47:605615.

    When data are not necessarily missing but are just sparse, it

    may be of interest to test for homogeneity of treatment response

    across different strata. This paper considers the topic of

    difference in risk:

    * Lui K. A simple test of the homogeneity of risk difference in

    sparse data: an application to a multi-centre study.

    Biometrical Journal 2005; 47:654661.

    Analysis of multivariate data such as that encountered in

    microarrays and imaging is a similarly complex topic with the

    potential for false positives inherent to such large data sets with

    correlated responses being of most concern to drug developers.

    * DeCook R, Nettleton D, Foster C, Wurtele E. Identifying

    differentially expressed genes in unreplicated multiple-

    treatment microarray timecourse experiments. Computa-

    tional Statistics and Data Analysis 2005; 50:518532.* Bowman F. Spatio-temporal modelling of localised brain

    activity. Biostatistics 2005; 6:558575.


    Some have suggested methods for combining data across trials

    to assess non-inferiority rather than predening a xed margin.

    This paper discusses several aspects of such proposals:

    * Lawrence J. Some remarks about the analysis of active

    control trials. Biometrical Journal 2005; 47:616622.


    Modelling of doseresponse is a complex topic, complicated

    further when the response is an unintended side-effect. This

    paper discusses a Bayesian approach to the topic:

    * Johnson T, Taylor J, Ten Haken R, Eisbruch A. A Bayesian

    mixture model relating dose to critical organs and functional

    complication in 3D conformal radiation therapy. Biostatistics

    2005; 6:615632.

    An issue we do not often think of when taking a pill is

    whether the expiry date is in the past, present, or future.

    Different conditions of light, temperature, etc. can have a

    dramatic effect on pharmaceutical products. Please check those

    expiry dates, and to nd out more on how they are derived

    (post-marketing), see:

    * Verbon F, van den Heuvel E, Vermaat C. The cluster design

    for the postmarketing surveillance program. Drug Informa-

    tion Journal 2005; 39:369371.

    Regulatory issues

    A special section of the Drug Information Journal is dedicated

    to the topic of ICH E14, the clinical evaluation of QT/QTc

    interval prolongation and proarrythmic potential for non-

    antiarrythmic drugs. Several of the papers contained in the

    special section are statistical in nature:

    * Dmitrienko A, Sides G, Winters K, Kovacs R, Rebhun D,

    Bloom J, Groh W, Eisenberg P. Electrocardiogram refer-

    ence ranges derived from a standardised clinical trial

    population. Drug Information Journal 2005; 39:395405.* Patterson S, Jones B, Zariffa N. Modelling and interpreting

    QTc prolongation in clinical pharmacology studies. Drug

    Information Journal 2005; 39:437445.* Hosmane B, Locke C. A simulation study of power in

    thorough QT/QTc studies and a normal approximation for

    planning purposes. Drug Information Journal 2005; 39:447



    Finally, if you are proud to be a pharmaceutical statistician and

    not just a general, medical statistician (apologies to readers

    who may quite legitimately be pharmaceutical but denitely not

    medical), then Grieves outgoing Presidential address to the

    Royal Statistical Society will inspire you. And to readers who

    are not proud to be pharmaceutical statisticians, it is still worth

    a read for a better understanding of how our profession is


    * Grieve AP. The professionalization of the shoe clerk.

    Journal of the Royal Statistical Society Series A 2005;


    Literature Review 69

    Copyright # 2006 John Wiley & Sons, Ltd. Pharmaceut. Statist. 2006; 5: 6769