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Table of Contents
                            dke11_fm.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		SERIES INTRODUCTION
		PREFACE
		CONTENTS
			PART I METHODS FOR EARLY TRIALS
				1. BAYESIAN METHODS FOR CANCER PHASE I CLINICAL TRIALS
				2. DESIGN OF EARLY TRIALS IN STEM CELL TRANSPLANTATION: A HYBRID FREQUENTIST-BAYESIAN APPROACH
			PART II METHODS FOR RANDOMIZED TRIALS
				3. DESIGN AND ANALYSIS OF THERAPEUTIC EQUIVALENCE TRIALS
				4. ADAPTIVE TWO-STAGE CLINICAL TRIALS
				5. DESIGN AND ANALYSIS OF CLUSTER RANDOMIZATION TRIALS
				6. DESIGN AND ANALYSIS OF CLINICAL TRIALS WITH MULTIPLE ENDPOINTS
				7. SUBGROUPS AND INTERACTIONS
				8. A CLASS OF PERMUTATION TESTS FOR SOME TWO-SAMPLE SURVIVAL DATA PROBLEMS
				9. BAYESIAN REPORTING OF CLINICAL TRIALS
			PART III MORE COMPLEX PROBLEMS
				10. METHODS INCORPORATING COMPLIANCE IN TREATMENT EVALUATION
				11. ANALYSIS OF LONGITUDINAL DATA WITH MISSINGNESS
				12. STATISTICAL ISSUES EMERGING FROM CLINICAL TRIALS IN HIV INFECTION
			CONTRIBUTORS
		INDEX OF ABREVIATIONS
dke11_ch1.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 1: BAYESIAN METHODS FOR CANCER PHASE I CLINICAL TRIALS
			1. INTRODUCTION
				1.1. GOAL AND DEFINITIONS
				1.2. DEFINITION OF DOSE
				1.3. CHOICE OF STARTING DOSE
				1.4. EXAMPLES
			2. GENERAL BAYESIAN METHODOLOGY
				2.1. FORMULATION OF THE PROBLEM
				2.2. DOSE-TOXICITY MODEL
				2.3. PRIOR DISTRIBUTION
				2.4. POSTERIOR DISTRIBUTION
				2.5. LOSS FUNCTION
			3. MODIFICATIONS AND EXTENSIONS
				3.1. MAXIMUM LIKELIHOOD
				3.2. DELAYED RESPONSE
				3.3. RAPID INITIAL ESCALATION
				3.4. CONSTRAINED ESCALATION
				3.5. MULTINOMIAL AND CONTINUOUS RESPONSE MEASURES
				3.6. DESIGNS FOR DRUG COMBINATIONS
				3.7. INCORPORATION OF COVARIATE INFORMATION
				3.8. MONITORING SAFETY AND EFFICACY IN PHASE I AND II TRIALS
			4. STATISTICAL CONSIDERATIONS
			5. CONCLUDING REMARKS
			REFERENCES
dke11_ch2.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 2: DESIGN OF EARLY TRIALS IN STEM CELL TRANSPLANTATION: A HYBRID FREQUENTIST-BAYESIAN APPROACH*
			1. INTRODUCTION
			2. A PHASE I/II DESIGN FOR PBSC TRANSPLANTATION TRIALS
			3. BAYESIAN STOPPING RULE FOR SAFETY
			4. A PHASE II TRIAL DESIGN WITH AN INTERMEDIATE ENDPOINT AND BAYESIAN STOPPING RULES FOR EARLY FAILURE
			5. A PHASE II DESIGN WITH MULTIPLE PRIORITIZED ENDPOINTS
			6. DISCUSSION
			REFERENCES
dke11_ch3.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 3: DESIGN AND ANALYSIS OF THERAPEUTIC EQUIVALENCE TRIALS
			1. INTRODUCTION
			2. COMMONLY USED STATISTICAL FORMULATIONS
				2.1. TESTING THE NULL HYPOTHESIS OF EQUIVALENCE
				2.2. TESTING A NONNULL HYPOTHESIS
				2.3. CONFIDENCE INTERVALS
				2.4. SPECIFICATION OF  "DELTA"
			3. BAYESIAN DESIGN AND ANALYSIS OF ACTIVE CONTROL TRIALS
				3.1. ANALYSIS
				3.2. DESIGN
				3.3. EXAMPLE
			4. CONCLUSION
			REFERENCES
dke11_ch4.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 4: ADAPTIVE TWO-STAGE CLINICAL TRIALS*
			1. INTRODUCTION
			2. REESTIMATION BASED ON A NUISANCE PARAMETER
				2.1. CONTINUOUS OUTCOME CASE
				2.2. EXAMPLE: DASH
				2.3. THE DICHOTOMOUS OUTCOME CASE
				2.4. DASH EXAMPLE EMBELLISHED
			3. SAMPLE SIZE REESTIMATION BASED ON TREATMENT EFFECT
				3.1. EXAMPLE
			4. CONCLUSIONS
			REFERENCES
dke11_ch5.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 5: DESIGN AND ANALYSIS OF CLUSTER RANDOMIZATION TRIALS
			1. INTRODUCTION
			2. GENERAL DESIGN CONSIDERATIONS
				2.1. UNIT OF RANDOMIZATION AND ANALYSIS
				2.2. SAMPLING METHODOLOGY AND HANDLING OF PARTICIPANT MIGRATION
				2.3. SOME FURTHER CONSIDERATIONS
			3. STATISTICAL ANALYSIS STRATEGIES
				3.1. UNADJUSTED ANALYSES
				3.2. ADJUSTED ANALYSES
			4. SAMPLE SIZE CALCULATION
			5. EXAMPLE: THE CATCH TRIAL
			6. SUMMARY
			REFERENCES
dke11_ch6.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 6: DESIGN AND ANALYSIS OF CLINICAL TRIALS WITH MULTIPLE ENDPOINTS*
			1. INTRODUCTION AND NOTATION
			2. SOME HYPOTHESIS TESTS FOR MULTIPLE ENDPOINTS
				2.1. BONFERRONI METHODS
				2.2. RESAMPLING METHODS (WESTFALL AND YOUNG, 1993; TROENDLE, 1995, 1996)
				2.3. LINEAR COMBINATIONS OF ENDPOINTS
				2.4. WALD-TYPE TEST FOR MULTIPLE BINARY OUTCOMES
				2.5. LIKELIHOOD RATIO TESTS
				2.6. NONPARAMETRIC TESTS
			3. A STEP-DOWN CLOSED PROCEDURE FOR DETERMINING WHICH ENDPOINTS DIFFER FOLLOWING A GLOBAL TEST
				3.1. LEHMACHER ET AL.’S PROCEDURE
				3.2. A PROCEDURE WITH WEAK CONTROL OF THE OVERALL TYPE I ERROR
			4. GROUP SEQUENTIAL METHODS IN TRIALS WITH MULTIPLE ENDPOINTS
				4.1. ASYMPTOTICALLY NORMALLY DISTRIBUTED TEST STATISTICS WITH KNOWN COVARIANCE MATRIX
				4.2. NONNORMALLY DISTRIBUTED TEST STATISTICS
				4.3. STEP-DOWN PROCEDURES FOR GROUP SEQUENTIAL TRIALS WITH MULTIPLE PRIMARY ENDPOINTS
			5. AN EXAMPLE: THE NINDS STROKE TRIAL
			6. EXTENSIONS TO MORE THAN TWO SAMPLES
			7. DISCUSSION
			ACKNOWLEDGMENTS
			REFERENCES
dke11_ch7.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 7: SUBGROUPS AND INTERACTIONS*
			1. INTRODUCTION
			2. STANDARD APPROACHES
				2.1. POWER OF TESTS OF INTERACTION
				2.2. MULTIPLICITY
			3. OTHER APPROACHES TO INTERACTION
				3.1. TESTS OF QUALITATIVE INTERACTION
				3.2. MULTIVARIATE APPROACHES TO INTERACTION
			4. AVID TRIAL
			5. DISCUSSION
			ACKNOWLEDGMENTS
			REFERENCES
dke11_ch8.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 8: A CLASS OF PERMUTATION TESTS FOR SOME TWO-SAMPLE SURVIVAL DATA PROBLEMS
			1. INTRODUCTION
			2. DPT FRAMEWORK
			3. TWO INDEPENDENT SAMPLES
				3.1. ESTIMATION OF "Y"
				3.2. TESTS
				3.3. EXAMPLES
			4. STRATIFIED DATA
				4.1. ESTIMATION OF "Y"
				4.2. FEATURES
				4.3. EXAMPLE 3: PROSTATE CANCER CLINICAL TRIAL
			5. MATCHED-PAIR DATA
				5.1. RANK TESTS
				5.2. PAIRED T TEST
				5.3. EXAMPLE 4: SKIN GRAFT DATA
			6. ALTERNATIVE METHODS
			ACKNOWLEDGMENT
			REFERENCES
dke11_ch9.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 9: BAYESIAN REPORTING OF CLINICAL TRIALS
			1. INTRODUCTION
			2. RANDOMIZED TRIAL OF TWO REGIMENS OF CHEMOTHERAPY IN OPERABLE OSTEOSARCOMA: A BAYESIAN PERSPECTIVE ON A TRIAL OF THE EUROPEAN OSTEOSARCOMA INTERGROUP
				2.1. BACKGROUND
				2.2. PATIENTS AND METHODS
				2.3. RESULTS
				2.4. INTERPRETATION
				2.5. DISCUSSION*
			3. EPILOGUE
			ACKNOWLEDGMENTS
			REFERENCES
dke11_ch10.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 10: METHODS INCORPORATING COMPLIANCE IN TREATMENT EVALUATION
			1. INTRODUCTION
			2. POTENTIAL OUTCOMES AND ‘‘ CAUSAL’’ ’’ EFFECTS
			2.1. POTENTIAL OUTCOMES
			2.2. LOCAL AVERAGE TREATMENT EFFICACY (LATE)
			3. STRUCTURAL MODELS FOR BINARY RESPONSES
				3.1. ‘‘NULL’’ VERSUS ‘‘FULL’’ COMPLIANCE
				3.2. NULL, PARTIAL, AND FULL COMPLIANCE
			4. STRUCTURAL MEAN MODELS
				4.1. THE MODEL AND SEMIPARAMETRIC ESTIMATION
				4.2. EFFICIENCY
				4.3. IMPLEMENTATION: AN EXAMPLE
			5. STRUCTURAL DISTRIBUTION MODELS
			6. DISCUSSION
			REFERENCES
dke11_ch11.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 11: ANALYSIS OF LONGITUDINAL DATA WITH MISSINGNESS*
			1. INTRODUCTION
			2. METHODOLOGY FOR ANALYZING LONGITUDINAL DATA
				2.1. SIMPLE UNIVARIATE ANALYSES OF SUMMARY MEASURES
				2.2. LONGITUDINAL METHODS FOR GAUSSIAN DATA
				2.3. LONGITUDINAL METHODS FOR DISCRETE DATA
			3. MISSINGNESS IN LONGITUDINAL CLINICAL TRIALS: TERMINOLOGY
			4. IMPLICATIONS OF MISSING DATA ON LONGITUDINAL METHODOLOGY
			5. MISSING DATA IN LONGITUDINAL CLINICAL TRIALS: GENERAL APPROACHES
				5.1. ANALYSES ON SUMMARY MEASURES
				5.2. SELECTION VERSUS PATTERN MIXTURE MODELS
				5.3. SHARED RANDOM EFFECTS MODELS AND INFORMATIVE MISSINGNESS
				5.4. GEE WITH MISSINGNESS
			6. ANALYZING LONGITUDINAL GAUSSIAN DATA WITH MISSINGNESS: IPPB TRIAL
			7. ANALYZING LONGITUDINAL BINARY DATA WITH MISSINGNESS: OPIATE TRIAL
			8. ANALYZING LONGITUDINAL COUNT DATA WITH DROPOUT: EPILEPSY CLINICAL TRIAL
			9. CONCLUSIONS
			REFERENCES
dke11_ch12.pdf
	ADVANCES IN CLINICAL TRIAL B IOSTATISTI CS
		CONTENTS
		CHAPTER 12: STATISTICAL ISSUES EMERGING FROM CLINICAL TRIALS IN HIV INFECTION
			1. INTRODUCTION
			2. ANALYSIS OF MULTIPLE EVENTS
				2.1. MARGINAL MODELS
				2.2. FRAILTY MODELS
				2.3. CONDITIONAL AND MULTISTATE MODELS
			3. BIOLOGICAL MARKERS OF DISEASE PROGRESSION
				3.1. SURROGACY
				3.2. ANALYSIS OF BIOLOGICAL MARKERS AS CONTINUOUS VARIABLES
				3.3. ANALYSIS OF BIOLOGICAL MARKERS AS FAILURE TIME DATA
			4. ADJUSTING FOR CHANGES FROM ALLOCATED TREATMENT
			5. CONCLUSIONS
			REFERENCES
                        
Document Text Contents
Page 1

ADVANCES IN
CLINICAL TRIAL
B IOSTATISTI CS

edited by

NANCY L. GELLER
National Heart, Lung, and Blood Institute

National Institutes of Health
Bethesda, Maryland, U.S.A.

M A R C E L

MARCEL DEKKER, INC.

D E K K E R

NEW YORK - BASEL
Copyright n 2004 by Marcel Dekker, Inc. All Rights Reserved.

Page 2

This book was edited by Nancy L. Geller in her private capacity. The views expressed do not

necessarily represent the views of NIH, DHHS, or the United States.

Although great care has been taken to provide accurate and current information, neither the

author(s) nor the publisher, nor anyone else associatedwith this publication, shall be liable for

any loss, damage, or liability directly or indirectly caused or alleged to be caused by this book.

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for any specific situation.

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and are used only for identification and explanation without intent to infringe.

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ISBN: 0-8247-9032-4

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Page 139

As is common, BARI was monitored by an independent Data Safety
Monitoring Board (DSMB) while the study was ongoing. The DSMB
monitored mortality differences between the two groups, overall and for
the five factors. While tests of interaction could have been used here, it was
decided to monitor within subgroups. In 1992, the DSMB requested that
the treatment effect in diabetics and nondiabetics also be monitored as
additional subgroups. Due to the concern about multiplicity, a p value of
.005 was decided upon as a threshold for the test of treatment effect in the
diabetic subgroup.

Before the study was completed, a striking result was observed in
the diabetic subgroup: the 5 year survival for CABG as 80.6 percent
compared to 65.5 percent for PTCA (nominal p = .0024). This was less
than the threshold of .005 and these results were disseminated before the
completion of followup for the main trial by a Clinical Alert. Because
diabetics were chosen as a subgroup a priori and because of the concern
about multiplicity, the primary results paper suggested that the result in
the diabetic subgroup should be confirmed in other populations. Overall,
the 5 year survival rates were 89.3 (CABG) and 86.3 (PTCA), p = .19.

After BARI was over, a permutation method was was used to pro-
vide an exact p value for the result observed in diabetics, controlling for
the multiple subgroups. Based on the 5 initial variables plus the diabetic
variable, there were a total of 15 overlapping subgroups. Within each
subgroup, a standardized log-rank test statistic was used so that there
were 15 test statistics, T1, . . . ,T15. A Bonferroni correction applied to these
15 tests would require a p value less than .05/15 or .0033 to declare
significance. Since the nominal p value in diabetics was .0024, a Bonferroni
adjusted p value is 15� .0024= .036. However, the Bonferroni correction
is conservative and a simple permutation method was used to provide an
exact adjustment for the multiplicity. Under this method, the treatment
and control labels were permuted a large number of times and for a generic
permutation, say the bth, the vector of test statistics T1

(b)
, , . . . ,TK

(b)
, was

calculated as well as M
b
= max(jT1(b)j , . . . , jTK(b)j). By simulation, Brooks

et al. (1997) estimated a permutation p value for the diabetic subgroup of
.026, i.e. the maximum associated with the original vector of test statistics,
M = max(jT1j , . . . , jTKj), was at the 97.4th percentile of the Mbs. In this
analysis, the Bonferroni correction is not very conservative. Strictly speak-
ing, this permutation procedure tests the strong null that treatment has no
effect whatsoever, i.e. no overall effect, and no effect in any subgroup. See,
e.g., Edgington (1995).

Though we have focused on the impact of multiplicity on testing,
multiplicity also has an impact on estimation. If several variables are

Follmann130

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Page 140

examined for interactions or subgroups and the single variable with the
most extreme value is selected, the estimate of the effect of treatment
associated with this variable is likely to be biased. Suppose that K
subgroups are examined, and the subgroup with the most extreme estimate
of treatment effect singled out. This will provide a biased (away from 0)
estimate of treatment effect in that subgroup. The amount of bias depends
on the configuration of the true treatment effects in the K subgroups, e.g.
d1 , . . . , dK. If dk = d for all k, the bias is most extreme. Harrell, Lee, and
Mark (1996) discuss strategies which produce statistical models with
accurate estimates of treatment effect while allowing for differential effects
across subgroups or covariates.

3. OTHER APPROACHES TO INTERACTION

In terms of statistical methodology, the standard approaches to sub-
groups and interaction are quite straightforward as both can be effected
by fitting model (2). In this section, we discuss some other methods that
may be useful in certain settings.

3.1. Tests of Qualitative Interaction

As argued by Peto and others, quantitative interactions are likely to exist,
but are unlikely to be clinically important. Here, treatment causes no
harm at any value of X, but may be relatively better for certain values of
X compared to other values of X (see Fig. 1b). Unless there were values of
X for which treatment was basically the same as control, and treatment
were associated with some nontrivial side effects, the clinical implication
of a quantitative interaction would be that treatment should be given to
anyone satisfying the inclusion criteria of the trial.

On the other hand, qualitative interaction where treatment causes
harm for certain values of X is quite clinically important (see Fig. 1c). It
seems important therefore to check for qualitative interaction when
reporting the main results of the trial. There are two ways of doing this.
First, one could check for harm in various subgroups. However, if
apparent harm is observed in a specific subgroup (e.g., p< .05) it may
be due to chance, for reasons of multiplicity as well as the arguments given
by Peto (1995). Therefore it seems more logical to perform a formal test of
interaction here.

A standard test of interaction [H0 : b3 = 0 in (2)] does not make a
distinction between quantitative and qualitative interaction. Thus if b3 is

Subgroups and Interactions 131

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Page 277

Neaton, J. D., Wentworth, D. N., Rhame, F., Hogan, C., Abrams, D. I., Deyton,

L. (1994). Considerations in choice of a clinical endpoint for AIDS clinical
trials. Statistics in Medicine 13:2107–2125.

Newcombe, R. G. (1988). Explanatory and pragmatic estimates of the treatment

effect when deviations from allocated treatment occur. Statistics in
Medicine 7:1179–1186.

O’Brien, P. C. (1984). Procedures for comparing samples with multiple endpoints.

Biometrics 40:1079–1087.
Pickles, A., Crouchley, R. (1995). A comparison of frailty models for multivariate

survival data. Statistics in Medicine 14:1447–1461.
Prentice, R. L., Williams, B. J., Peterson, A. V. (1981). On the regression analysis

of multivariate failure time data. Biometrika 68:373–379.
Prentice, R. L. (1989). Surrogate endpoints in clinical trials: Definition and

operational criteria. Statistics in Medicine 8:431–440.

Raab, G. M., Parpia, T. (2001). Random effects models for HIV marker data:
Practical approaches with currently available software. Statistical Methods
in Medical Research 10:101–116.

Robins, J. M., Tsiatis, A. A. (1991). Correcting for non-compliance in ran-
domized trials using rank preserving structural failure time models.
Communications in Statistics—Theory and Methods 20:2609–2631.

Robins, J. M., Greenland, S. (1994). Adjusting for differential rates of
prophylaxis therapy for PCP in high-dose versus low-dose AZT treatment
arms in an AIDS randomized trial. Journal of the American Statistical
Association 89:737–749.

Schoenfeld, D. A. (1995). Issues in the testing of drug combinations. In: Fin-
kelstein, D. M., Schoenfeld, D. A., eds. AIDS Clinical Trials: Guidelines
for Design and Analysis. New York: Wiley.

Schoenfeld, D. A. (1996). Long-term follow-up in AIDS clinical trials. Statistics
in Medicine 15:2366–2539.

Schwartz, D., Lellouch, J. (1967). Explanatory and pragmatic attitudes in thera-

peutic trials. Journal of Chronic Disease 20:637–648.
Shevitz, A., Wanke, C. A., Falutz, J., Kotler, D. P. (2001). Clinical perspectives on

HIV-associated lipodystrophy syndrome: an update. AIDS 15:1917–1930.
Smith, P. J., Thompson, T. J., Jereb, J. A. (1997). A model for interval-censored

tuberculosis outbreak data. Statistics in Medicine 16:485–496.
Sommer, A., Zeger, S. L. (1991). On estimating efficacy from clinical trials. Sta-

tistics in Medicine 10:45–52.

Sun, J. (1997). Regression analysis of interval-censored failure time data. Sta-
tistics in Medicine 16:497–504.

Thall, P. F., Cheng, S. C. (1999). Treatment comparisons based on two-

dimensional safety and efficacy alternatives in oncology. Biometrics 55:746–
753.

Touloumi, G., Pocock, S. J., Babiker, A. G., Darbyshire, J. H. (1999). Estimation

Babiker and Walker270

Copyright n 2004 by Marcel Dekker, Inc. All Rights Reserved.

Page 278

and comparison of rates of change in longitudinal studies with informative

drop-outs. Statistics in Medicine 18:1215–1233.
Touloumi, G., Pocock, S. J., Babiker, A. G., Darbyshire, J. H. (2002). Impact of

missing data due to selective drop-outs in cohort studies and clinical trials.

Epidemiology 13:347–355.
Tubert-Bitter, P., Bloch, D. A., Raynauld, J. P. (1995). Comparing the bivariate

effects of toxicity and efficacy of treatments. Statistics in Medicine 14:1129–

1141.
Volberding, P. A., Lagakos, S. W., Koch, M. A., Pettinelli, C. B., et al. (1990).

Zidovudine in asymptomatic HIV infection: a controlled trial in persons
with fewer than 500 CD4-positive cells per cubic millimeter. New England

Journal of Medicine 322:941–949.
Volberding, P. A., Lagakos, S. W., Grimes, J. M., Stein, D. S., Balfour, H. H.,

Reichman, R. C., Bartlett, J. A., Hirsch, M. S., Phair, J. P., Mitsuyasu, R.

T., Fischl, M. A., Soeiro, R., the AIDS Clinical Trials Group of the Na-
tional Institute of Allergy and Infectious Diseases (1994). The duration of
zidovudine benefit in persons with asymptomatic HIV infection. Journal of

the American Medical Association 272:437–442.
Walker, A. S. (1999).The analysis of multivariate failure time data with applica-

tion to multiple endpoints in trials in HIV infection. Unpublished PhD

thesis, University College, London.
Walker, A. S., Babiker, A. G., Darbyshire, J. H. (2000). Analysis of multivariate

failure-time data from HIV clinical trials. Controlled Clinical Trials 21:75–
93.

Wang, S. T., Klein, J. P., Moeschberger, M. L. (1995). Semi-parametric es-
timation of covariate effects using the positive stable frailty model. Applied
Stochastic Models and Data Analysis 11:121–133.

Wei, L. J., Lin, D. Y., Weissfeld, L. (1989). Regression analysis of multivariate
incomplete failure time data by modelling marginal distributions. Journal
of the American Statistical Association 84:1065–1073.

Wei, L. J., Glidden, D. V. (1997). An overview of statistical methods for multiple
failure time data in clinical trials. Statistics in Medicine 16:833–839.

White, I. P., Walker, A. S., Babiker, A. G., Darbyshire, J. H. (1997). Impact of
treatment changes on the interpretation of the Concorde trial. AIDS 11:999–

1006.
White, I. R., Goetghebeur, E. J. T. (1998). Clinical trials comparing two treat-

ment policies: which aspect of the treatment policies make a difference?

Statistic in Medicine 17:319–339.
White, I. R., Babiker, A. G., Walker, A. S., Darbyshire, J. H. (1999). Random-

ization-based methods for correcting for treatment changes: examples from

the Concorde trial. Statistics in Medicine 18:2617–2634.
White, I. R., Walker, A. S., Babiker, A. G., Darbyshire, J. H. (2002). Strbee:

Randomization-based efficacy estimator. The Stata Journal 2:140–150.

Statistical Issues Emerging from Clinical Trials in HIV Infection 271

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