literature review january-march 2006

PHARMACEUTICAL STATISTICS

Pharmaceut. Statist. 2006; 5: 145–148

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/pst.229

Literature Review January–March 2006

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

INTRODUCTION

This review covers the following journals received during the

period from the beginning of January to the middle of March

2006:

* Applied Statistics, volume 55, parts 1, 2.

* Biometrical Journal, volume 47, part 6 and volume 48,

part 1.

* Biometrics, volume 61, part 4.

* Biostatistics, volume 7, part 1.

* Clinical Trials, volume 2, part 6 and volume 3, part 1.

* Computational Statistics and Data Analysis, volume 50,

parts 5–8.

* Drug Information Journal, volume 40, part 1.

* Journal of Biopharmaceutical Statistics, volume 16,

parts 1, 2.

* Journal of the Royal Statistical Society, Series A, volume

169, parts 1, 2.

* Statistics and Probability Letters, volume 74, parts 1–4.

* Statistics in Medicine, volume 24, part 24 and volume 25,

parts 1–7.

* Statistical Methods in Medical Research, volume 15, part 1.

* Special inclusion this quarter: The AAPS Journal, volume 7,

part 2.

SELECTED HIGHLIGHTS FROM THE LITERATURE

The following special issue of Statistics in Medicine has been

published:

* Lawson A, Gangnon R, Wartenberh D. Preface: develop-

ments in disease cluster detection. Statistics in Medicine

2006; 24:721.

It is worth noting that Statistics in Medicine is now in its 25th

year of publication. This is pretty young compared to some of

the journals we cover, but still a significant event – it having

grown from a quarterly publication of just under 400 pages per

year, to a twice monthly publication with nearly 4100 pages last

year. The editors have commissioned many articles to be

co-authored by members of the past and present editorial

boards. We will, no doubt, highlight many of these papers

throughout the coming year.

Whilst not a specially commissioned special issue but as part

of the 25th anniversary ‘special year’, issue 7 of Statistics in

Medicine contains eight papers looking at different aspects of

non-inferiority studies including specifying the margin, binary

data, choice of analysis populations and effects of non-

compliance, non-parametric methods, and interim analyses.

As an overview, see:

* D’Agostino RB, Campbell M, Greenhouse J. Editorial.

Non-inferiority trials: continued advancements in concepts

and methodology (special papers for the 25th anniversary of

Statistics in Medicine 25(7)). Statistics in Medicine 2006;

25:1097–1099.

One tutorial has appeared in Clinical Trials:

* Garrett-Mayer E. The continual reassessment method for

dose-finding: a tutorial. Clinical Trials 2006; 3:57–71.

A paper, eight brief discussion comments, and a rejoiner

concerning a variety of aspects of the Women’s Health

Initiative have been published in Biometrics. This will be of

interest to those working in hormone therapy but many clinical

trial aspects are covered to interest the more general reader:

study design, measurement error, trial monitoring of multiple

endpoints, analysis with time-dependent hazards, etc.

* Prentice RL, Pettinger M, Anderson GL. Statistical issues

arising in the women’s health initiative. Biometrics 2005;

61:899–941.

The Discussion papers (all within the range of pages given

above are by Carroll; Day; DeMets; Freedman and Petitti;

Greenland; Hernan, Robins and Garcıa Rodrıguez; Thomas;

and Tsiatis and Davidian.

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

yE-mail: [email protected]

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

The last issue of the Biometrical Journal in 2005 features an

extensive discussion of a paper on statistical methods when

using a positive control.

* Hauschke D, Pigeot I. Establishing the efficacy of a new

experimental treatment in the ‘gold standard’ design.

Biometrical Journal 2005; 47(6):782–786. With Discussions

by Lewis J (787–789); Rohmel J (790–791); Koch A (792–

793); Mehrotra D (794); Hung H (795–796). Response by

Hauschke D, Pigeot I (797–798).* Rohmel J. On confidence bounds for the ratio of net

differences in the ‘gold standard’ design with reference,

experimental, and placebo treatment. Biometrical Journal

2005; 47(6):799–806.

Volume 7(2) of the 2005 AAPS Journal is dedicated to

Lewis Sheiner in memorium. Articles of interest to our

readers are:

* Bonate P. Recommended reading in population pharmaco-

kinetic pharmacodynamics. The AAPS Journal 2005;

7:E363–E373.* Nedelman J. On some disadvantages of the population

approach. The AAPS Journal 2005; 7:E374–E382.* Piotrovsky V. Pharmacokinetic-pharmacodynamic model-

ing in the data analysis and interpretation of drug-induced

QT/QTc prolongation. The AAPS Journal 2005; 7:

E609–E624.* Roy A, Ette E. A pragmatic approach to the design of

population pharmacokinetic studies. The AAPS Journal

2005; 7:E408–E420.* Smith B. It’s time. The AAPS Journal 2005; 7:E655–E658.

Phase I

Two papers address dose finding when trading off benefits and

adverse effects. Both adopt a Bayesian approach:

* Loke Y-C, Tan S-B, Cai Y, Machin D. A Bayesian dose

finding design for dual endpoint phase I trials. Statistics in

Medicine 2006; 25:3–22.* Whitehead J, Zhou Y, Stevens J, Blakey G, Price J,

Leadbetter J. Bayesian decision procedures for dose-

escalation based on evidence of undesirable events and

therapeutic benefit. Statistics in Medicine 2006; 25:37–53.

This paper considers Bayesian dose escalation in Phase 1

studies:

* Whitehead J, Zhou Y, Mander A, Ritchie S, Sabin A,

Wright A. An evaluation of Bayesian designs for dose-

escalation studies in normal healthy volunteers. Statistics in

Medicine 2006; 25:433–445.

Phase II

Dose–response testing in Phase II can be a complicated topic.

The following paper discusses ways to look at dose–response

when data are unbalanced and/or covariates are present:

* Bretz F. An extension to the William’s trend test to general

unbalanced linear models. Computational Statistics & Data

Analysis 2006; 50:1735–1748.

Surrogate endpoints

Weir and Walley review some of the literature on evaluation of

biomarkers as surrogate markers and present what they

consider are the best approaches. It is interesting to note that

this paper stops short of validation of surrogate endpoints as

clinical endpoints – it is looking at a stage earlier than that.

* Weir CJ, Walley RJ. Statistical evaluation of biomarkers as

surrogate endpoints: a literature review. Statistics in

Medicine 2006; 25:183–203.

In contrast to the above, the following look at the validation

of surrogate endpoints for the true clinical endpoint. The first

takes a further look at Prentice’s criteria and shows how they

can be incorporated into studies with non-normal outcomes

and studies with repeated measurements. The second looks at

ways of quantifying the effect of different surrogate markers on

each other.

* Alonso A, Molenberghs G, Geys H, Buyse M, Vangeneug-

den T. A unifying approach for surrogate marker validation

based on Prentice’s criteria. Statistics in Medicine 2006;

25:205–221.* Qu Y, Case M. Quantifying the indirect treatment effect via

surrogate markers. Statistics in Medicine 2006; 25:223–231.

A new approach to validation is discussed in:

* Baker S. A simple meta-analytic approach for using a

binary surrogate endpoint to predict the effect of interven-

tion on a true endpoint. Biostatistics 2006; 7:58–70.

Multiplicity

Consider the following scenario: a new treatment unexpectedly

fails to show superiority to the old in survival time in a clinical

trial. There is now pressure from the sponsor, investigators,

patient advocates, regulators, and the marketplace for the

statistician to determine whether there is any subgroup of

patients observed who can benefit from the new treatment. One

such approach to such a hypothesis generating exercise is

discussed in:

* Kehl V, Ulm K. Responder identification in clinical trials

with censored data. Computational Statistics & Data

Analysis 2006; 50:1338–1355.

Copyright # 2006 John Wiley & Sons, Ltd. Pharmaceut. Statist. 2006; 5: 145–148

Literature Review146

Focus on only one primary endpoint may not make sense in

some trials. The following paper discusses the simultaneous use

of multiple statistics important in the oncology clinical setting.

* Yang P, Fleming T. Simultaneous use of weighted log-rank

and standardized Kaplan–Meier statistics. Journal of

Biopharmaceutical Statistics 2006; 16:241–252.

Interim analyses, flexible designs and Data

Monitoring Committees

Flexible designs and interim analyses are common topics for

papers. The following paper looks at estimation problems (so

often displaced in favour of significance testing). It reviews

various approached and uses a planned trial as a case study:

* Posch M, Koeng F, Branson M, Brannath W, Dunger-

Baldauf C, Bauer P. Testing and estimation in flexible group

sequential designs with adaptive treatment selection. Statis-

tics in Medicine 2005; 24:3697–3714.

Bauer and Koenig consider the calculation of conditional

power at interim analyses and problems associated with not

knowing the true effect size. They start from the perspective of

either using the effect size presumed at the planning stage, or

using the effect size observed at the interim analysis, or a

combination (a weighted average) of both. They investigate the

density of the random variable – conditional power.

* Bauer P, Koenig F. The reassessment of trial perspectives

from interim data – a critical view. Statistics in Medicine

2006; 25:23–36.

An excellent overview/review of the inter-related areas of

adaptive designs, group sequential designs and sample size re-

estimation has been written by Joe Shih. As he notes, much of

the literature in this area is written in a very ‘enthusiastic’ way.

Enthusiasm is certainly a good thing – but can sometimes result

in tunnel vision. Shih looks at some of the controversies and

teases out the different purposes and operational aspects of

these different approaches.

* Shih WJ. Group sequential, sample size re-estimation and

two-stage adaptive designs in clinical trials: a comparison.

Statistics in Medicine 2006; 25:993–941.

Study design

Two papers concerned with adaptive randomization

(‘minimization’, etc.) have appeared. They address different

topics. The first looks at the problem of reducing predictability

when ‘centre’ is used as a factor in the balancing algorithm.

Predictability and balance need to be weighed against each

other; the authors arrive at a compromise for both:

* Brown S, Thorpe H, Hawkins K, Brown J. Minimization –

reducing predictability for multi-centre trials whilst retain-

ing balance within centre. Statistics in Medicine 2005;

24:3715–3727.

The second paper compares minimisation and biased coin

methods to one which imposes different weights to balance

different factors (some being considered more important than

others):

* Heritier S, Gebski V, Pillai A. Dynamic balancing

randomization in controlled clinical trials. Statistics in

Medicine 2005; 24:3729–3741.

A different aspect of study design is that of when to

take measurements (particularly in the context of repeated

measurements). Usually they are taken at equally spaced

time intervals but that may not always be most efficient.

Of course, it depends on the precise objectives of the study but

when there is some flexibility in timing possible, this should be

capitalised on.

* Winkens B, Schouten HJA, van Breukelen GJP, Berger

MPF. Optimal time-points in clinical trials with linearly

divergent treatment effects. Statistics in Medicine 2005;

24:3743–3756.

Data analysis issues

There has been some debate in Pharmaceutical Statistics (as

well as elsewhere) about dichotomizing continuous outcome

variables. Clearly, there is a loss of statistical power – there may

be other statistical downfalls but there may be some justifica-

tions too. Royston, Altman and Sauerbrei look at dictotomiz-

ing the covariates. Some might argue for this (or, at least,

categorising the covariates) if the functional form between

covariate and outcome is not known. These authors conclude

that the practice is not necessary and should not be used. Data-

driven searches for ‘optimal’ cut-points come in for particular

criticism.

* Royston P, Altman DG, Sauerbrei W. Dichotomizing

continuous predictors in multiple regression: a bad idea.


Censoring in survival studies is usually considered to be

uniform across all the enrolled patients. It is true that fewer of

the earliest recruited patients will have censored event times

than those patients recruited towards the end of the trial – but

the process is assumed uniform. If there is a trend toward better

(or worse) survival in later patients, then the censoring and

survival times are correlated. Zhang and Heitjan propose a

graphical method to investigate sensitivity of inferences to such

possible non-uniform censoring.

* Zhang J, Heitjan DF. Nonignorable censoring in rando-

mized clinical trials. Clinical Trials 2005; 2:488–496.

Literature Review 147


Missing values and imputation procedures make for common

reading. These authors start from a presumption that multiple

imputation methods are statistically valid in large samples and

investigate their statistical properties in ‘small’ samples.

* Barnes SA, Lindborg SR, Seaman JW Jr. Multiple

imputation techniques in small sample clinical trials.


Group sequential study design is a technique being applied

more and more often in drug development. This paper discusses

how to compute exact confidence intervals when using this

approach:

* Fan X, DeMets D. Conditional and unconditional con-

fidence intervals following a group sequential test. Journal

of Biopharmaceutical Statistics 2006; 16:107–122.

Pharmacovigilance

Safety in drug development is a developing topic. A multi-

variate approach for analysis of adverse event data is

discussed in:

* Goldberg-Alberts R, Page S. Multivariate analysis of

adverse events. Drug Information Journal 2006; 40:99–110.

Miscellaneous

Measuring the practical size of treatment benefit is a different

problem to simply estimating differences between means (for

example). That is what sometimes leads to the use of

‘responder’ analyses. Acion et al. consider an alternative

measure of treatment benefit: the ‘probabilistic index’. This is

a measure of the probability that a patient will benefit more on

one treatment than the other (comparator). This seems to have

some appeal but may still hide the problem that, for example, a

very large proportion of patients will benefit from a ‘new’

treatment – but by hardly any amount at all. Quantifying

treatment benefit in both clinically and statistically useful/

efficient ways does not seem a trivial problem.

* Acion L, Peterson JJ, Temple S, Arndt S. Probabilistic

index: an intuitive non-parametric approach to measuring

the size of treatment effects. Statistics in Medicine 2006;

25:591–602.


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