Critical Methological Issues in Recent Randomised Trials Paolo Bruzzi Epidemiologia Clinica IRCCS...

Critical Methological Issues in Recent Randomised Trials

Paolo Bruzzi

Epidemiologia Clinica

IRCCS AUO San Martino IST

MSO – ROMEa November 23, 2012

Topics

1. Changes in the methodology of the Phases of cancer trials

2. Efficacy: Do we still need (Large) Randomised Controlled Trials?

3. Perspectives ?

Conventional Methodology

- Phase I: dose increases -> MTD

- Phase II: Uncontrolled -> % Response in metastatic pts with a specific cancer

- Phase III: Large RCT’s in relatively heterogeneous pts: OS (or EFS)



The More the Better?

DOSE –RESPONSE!

PHASE I-II TRIALS



- Response? Direct anticancer effect?

- Metastatic pts? - Activity in pts with less disease burden- Need of repeated biopsies

Modern Methodology

- Phase II trials:

- Biological Endpoints

- Window-of-opportunity studies- Neoadjuvant trials – Locally advanced dis.

- Randomised Controls

Topics



3. Perspectives ?

Reasons for the need of Randomised Control groups

a) Inability to predict individual outcome

b) Inability to predict group outcome

c) Inability to predict effect of treatments based on mechanisms

Reasons for the need of Randomised Control groups

a) Inability to predict individual outcome

b) Inability to predict group outcome

c) Inability to predict effect of treatments based on mechanisms

STILL TRUE!

On the other hand ...

if a new drug,

- with a well-identified molecular target

- which is present in subgroups of cancers in different sites

- produces a strong benefit in one of these cancers,

…it may become ETHICALLY unacceptable to run a randomised trial in other cancers with the same target

Example

Mortality

Tumor X Nil vs A 15% vs 12.5%

N=12000 P = 0.0007

H0 Rejected: A is effective in X

Example

Mortality


N=12000 P = 0.0007

Tumor Y Nil vs A 15% vs 7.5%

N= 240 P=0.066

H0 not rejected: A not shown effective in y

Prior Information:

Mortality


N=12000 P = 0.0007


N= 240 P=0.066

Prior Information: tumors X and Y are BRAF+

Mortality


N=12000 P = 0.0007


N= 240 P=0.066

Prior Information: tumors X and Y are BRAF+A = Anti BRAF Mortality


N=12000 P = 0.0007


N= 240 P=0.066

Interpretation?

Prior Information: tumors X and Y are BRAF+A = Anti BRAF

I have to plan a trial in the rare tumor Z, which is usually BRAF+, and for which there is no effective treatment

Do I need a randomised control group?

Is it ethically acceptable?

GLEEVEC

CML -> Large RCT

GIST -> Large uncontrolled trial

Chordomas -> Case Series

New paths to drug use Large RCT in a frequent cancer with the

target - Proof of principle – Toxicity

Uncontrolled (but formal) trial(s) in other cancers with the target

Off label use in individual cases with the target

New paths to drug useLarge RCT in a frequent cancer with the

target - Proof of principle – Toxicity

Uncontrolled (but formal) trial(s) in other cancers with the target

Off label use in individual cases with the target

Acceptable? Methodology?

Topics



3. Perspectives?

3. Perspectives

a) Adaptive trials

b) ‘Personalised’ Medicine

c) New Statistical Approaches

3. Perspectives

Adaptive trials

‘Personalised’ Medicine

New Statistical Approaches

3. Perspectives

a) Adaptive trials



Radical changes in the way cancer trials are designed and analysed

Three modern revolutions

1950 Randomized Clinical Trial

1985 Evidence Based Medicine

1990 Molecular Medicine

Empirical Approach

Preclinical work + Clinical observations

Clinical rationale

Empirical Approach

Preclinical work + Clinical observations

Clinical rationale

CLINICAL STUDIES

INTERPRETATION

Fondamenti della sperimentazione clinica randomizzata

• Protezione da risultati falsamente positivi:– Randomizzazione– Protocolli rigidi/Piano statistico predeterminato– Intention to treat– (doppio cieco)

• Protezione da risultati falsamente negativi– Dimensioni

RCT -> EBM in Oncology Golden Age

• Rigid protocols – Drugs – Doses – Cycles– Modifications for toxicity or

progression/relapse

• Generic Selection Criteria– Site (e.g. Stomach)– Histology (ADK vs Lymphoma)– Stage (early vs late)

RCT -> EBM in Oncology Golden Age

• Large and Simple Clinical Trials - Systematic Reviews – Meta-analyses

• Clinical Guidelines/Recommendations – Generic

• Flexibility in pt management not considered

Evidence Based Medicine

≈

Cookbook Medicine?

New Century

• Technological advances – Discoveries– Cellular functions

– Molecular mechanisms/pathways

– Genes/Mutations /Gene Functions

– Genomics/Proteomics/(Metabolomics)

– Targeted Drugs

Patients Heterogeneity

New Approaches!

3. Perspectives

a) Adaptive trials



a) Adaptive design

FDA’s draft guidance for industry on adaptive design clinical trials

(http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM201790.pdf).

http://www.fda.gov/downloads/

Adaptive design clinical trial

FDA’s Definition:

“… a study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data (usually interim data) from subjects in the study”

Why adaptive designs are so attractive?

• Early Responses

• Effects in Subgroups of patients

• Toxicity

It is possible to obtain, during the trial,

crucial information to improve some of its design features

Most conventional trials have an adaptive component

Stopping rules based on interim analyses:

• Toxicity

• Rejection of null-hypothesis

• Futility

Conventional Trials

Few, if any, planned interim analyses

1. Change selection criteria ?

2. Change treatment protocol ?

3. NO Change of the Endpoint

4. NO Change of the Statistical Plan

Adaptive designs

Interim analyses critical in study design

1. Change selection criteria

2. Change treatment protocol

3. Change Endpoint

4. Change Statistical Plan

Adaptive designs

Interim analyses

1. Change selection criteria (target)


3. Change Endpoint


3. Perspectives

a) Adaptive trials



Adaptive designs

Interim analyses

1. Change selection criteria (target)


3. Change Endpoint


3. Perspectives

a) Adaptive trials



New Statistical approaches

• Bayesian statistics (rare tumors and subgroups)

• Changing the null hypothesis (Sobrero & Bruzzi, 2009-2012)

• Statistical methods for uncontrolled trials

Metodologia dei trials clinici: Elementi critici

• Primary Aim(s)

• Endpoint(s)

• Selection of patients

• Study Design

• Randomization

• Assessment of outcome

• Statistics (Statistical plan, ITT analysis)

Metodologia dei trials clinici: Prospettive

• Validita’ Interna (Statistica) = Assenza di bias

• Validita’ Esterna= Implicazioni- Generalizzazioni (Proof of Principle)- Applicazioni (pratica clinica, sanita’, ricerca,…)


• Primary Aim(s)• Endpoint(s)• Selection of patients• Study design• Randomization• Assessment of outcome• Statistics (Statistical plan, ITT analysis)

Validita’ Interna (Assenza di Bias)

Industry-Sponsored Trials

• Randomizzazione

• Valutazione outcome

• Statistica

I moderni trial dell’industria, nella stragrande maggioranza, sono studi di altissima qualita’, privi di bias rilevanti

} Tecnicamente perfetti !


1. Primary Aim(s)

2. Endpoint(s)

3. Selection of patients

4. Study Design

5. Randomization

6. Assessment of outcome

7. Statistics (Statistical plan, ITT analysis)


1. Scopo/i Primario/i

2. Endpoint(s)

3. Selezione dei pazienti

4. Disegno di studio

‘Validita’ Esterna’ =

= Rilevanza Clinica e di Sanita’ Pubblica

1. Scopo

Possibili scopi di un Trial Clinico

Valutare:

- (Attivita’ – meccanismi -> Fase II)

- Efficacy (proof of principle)

- Effectiveness (benefici clinici concreti)

Industry-Sponsored Trials

• Scopo Primario:

Rifiutare l’ipotesi nulla H0: P<0.05

H0: Trattamento Sperimentale (con/senza treatment (with/without standard) identico allo Standard

Significativita’ Statistica

• P = Probabilita’ di osservare, per caso, un differenza grande quanto quella osservata o piu’ grande se i due trattamenti sono identici (H0)

Nota: Nei trials di fase III, H0 spesso non plausibile

(precedenti trials di fase II, in altre malattie/stadi)

Significativita’ Statistica

• P = Probabilita’ di osservare, per caso, un differenza grande quanto quella osservata o piu’ grande se i due trattamenti sono identici (H0)

• Rilevanza Clinica: Beneficio per i pazienti sufficiente per far adottare il trattamento sperimentale come standard (considerando costi, tossicita’, rischi)

Trials Sponsorizzati dalle industrie farmaceutiche

1. Scopo: Trovare una differenza statisticamente significativa

2. Endpoint

3. Criteri di Selezione

4. Disegno di Studio a) Sample Size

b) Collocazione temporale delle analisi

2. Endpoints

Endpoints naturali nei trials di efficacia:

= Scopi del trattamento:

Aumentare

Quantita’

e/o di vita

Qualita’

2. Endpoints

Endpoints naturali nei trials di efficacia:

• Overall Survival

• Quality of Life

• (Qualy)

2. Endpoints

Endpoints spesso usati nei trials di efficacia:

• Endpoints di attivita’

• Endpoints Surrogati non sempre validati

Endpoints Surrogati in oncologia

• Relapse-Free Survival, Disease-Free Survival nella malattia operata

• Progression-Free Survival nella malattia metastatica

Perche’ i trials dell’industria si basano cosi’ fortemente su

endpoints surrogati ?

Perche’ i trials dell’industria si basano cosi’ fortemente su

endpoints surrogati ?

- Per abbreviare il tempo alle analisi ad interim e finale (piu’ eventi) (Basterebbe aspettare)

- Effetto piu’ forte! soprattutto nel periodo iniziale del follow-up - Maggiore potenza e ‘rilevanza clinica’

3. Popolazione in studio

• Pazienti selezionati (es. Eta’)– Compliance– Suscettibili agli effetti del trattamento– Meno sensibili alla tossicita’ – Massimizzare gli effetti del trattamento

Possibilita’ di extrapolare a popolazioni di pazienti differenti?

(Trials adiuvanti nel BC - eta’ mediana : 50years)

4. Disegno di Studio

a) Sample Size

b) Timing delle analisi ad interim e finale

4. Study Design

a) Sample Size : Aumentando le dimensioni dello studio,

differenze Clinicamente poco importanti -> Statisticamente

significative

Es. Molti farmaci ‘targeted’ con effetti sul PFS<3 mesi in vari tumori solidi e p<0.001

HR 0.6 0.7 0.8 0.9 1.0 1.1

MCWE, H1

Conventional Trial

No Difference, H0

HR =0.8Power= 80%635 events

HR=0.86 P=0.05

HR 0.6 0.7 0.8 0.9 1.0 1.1

MCWE, H1

Conventional Trial

No Difference, H0

HR =0.8Power= 95%1050 events

HR=0.89 P=0.05

HR 0.6 0.7 0.8 0.9 1.0 1.1

MCWE, H1

Conventional Trials

No Difference, H0

HR =0.8Power= 95%1050 events HR=0.89

Confidence Limits: From No Effect to MCWE

Treatment effects in trials of targeted drugs in advanced solid tumors

All p-values <0.001 (except one)

0100200300400500600700800900

1000

0 1 2 3 4 5 6 7

Increase in median PFS (months)

N. o

f P

atie

nts

P<0.03

4. Study design

b) Timing delle analisi: Interim Analisi precoci– Effetti precoci molto plausibili (Attivita’)– Effetti maggiori e minore focalizzazione sui

fallimenti a lungo termine (tumori avanzati)– Minore focalizzazione sulla tossicita’ a lungo

termine (malattia precoce)

Typical treatment effect in advanced solid tumors

Interim Analysis

Interim analyses• I metodi di correzione della p usati nella analisi ad interim servono a preservare solo l’errore alfa

• Per vari motivi statistici, gli studi interrotti per interim analisi positive tendono a fornire SOVRASTIME dell’efficacia del trattamento sperimentale

• Queste stime quindi non dovrebbero essere usate per valutazioni cliniche e costi/benefici

Case-study: Aromatase Inhibitors in early breast cancer

Three original papers• Anastrozole alone or in combination with tamoxifen

versus tamoxifen alone for adjuvant treatment of postmenopausal women with early breast cancer: first results of the ATAC randomised trial - Lancet 2002

• A Randomized Trial of Letrozole in Postmenopausal Women after Five Years of Tamoxifen Therapy for Early-Stage Breast Cancer – NEJM Nov. 2003

• A Randomized Trial of Exemestane after two to three years of Tamoxifen therapy in postmenopausal women with primary Breast Cancer – NEJM March 2004

Trials results (1st analysis)

Trial Stage Sample Size

F-up Events DFS red. %

Tam vs Anastrovs A+T

Postmenop 61% N-

84% HR+

6200

(2/3 arms)

2.7 years 696 -17%

(-14%)

Letro vs Placebo

5-yrs Tam

Postm ER+

50% N-

5187 2.4 years 207 (73) - 43%

Tam vs

Exem.

2-3yrs Tam

Postm/ER+? 51% N-

4742 2.5 years 449 (199)

-32%

Effetti - a lungo termine?- su mortalita’?

• Cross-over programmato

• Altre terapie

Diluzione effetto?

• Tecniche statistiche non ITT

Ultima Generazione di trials sponsorizzati dall’industria

• Trials sovradimensionati per garantire P significativa ad effetti moderati

• Analisi concentrate su endpoints surrogati e/o nelle analisi ad interim

• Effetti precoci spesso plausibili, ma benefici e tossicita’ a lungo termine?

• Endpoints Surrogati + Interim Analyses: Sovrastima del beneficio?

Trials sponsorizzati dall’ Industria

• I moderni trials: virtualmente privi di difetti dal punto di vista metodologico e statistico

• Disegnati per massimizzare la probabilita’ di osservare un effetto ‘statisticamente signficativo’ del farmaco sperimentale, a prescindere dalla rilevanza clinica dell’effetto ‘reale’

Trials sponsorizzati dall’ Industria (2)

• Questi trials forniscono una prospettiva distorta sulla reale efficacia dei nuovi farmaci

• Ciononostante, sulla base di questi risultati, spesso le agenzie regolatorie approvano questi farmaci, scaricando il problema degli alti costi x benefici limitati sui sistemi sanitari locali

Soluzioni?

• Trials Indipendenti (?)

• Contrattazione ‘forte’ tra agenzie sanitarie-comunita’ scientifica internazionale e Big Pharma

• Nuovi requisiti per l’approvazione dei farmaci

Proposals of new statistical designs for phase III cancer trials

A. Sobrero, P. Bruzzi 2009-2011

Sintesi della proposta

- Trials di efficacia

- Scopo : rifiutare

H0: Delta < MCWE

(Minimal Clinically Worthwhile Effect)

Example

• Metastatic Colorectal Cancer

• Expected Overall Survival: 24 months

• Experimental treatment: Limited toxicity and costs

• MCWE: increase in OS = 3 months (HR= 0.9)

HR 0.5 0.6 0.7 0.8 0.9 1.0

MCWE, H0

New proposal

APPROVE

Discard

LIMBO

H0: No effect

Issues to discuss

1. Choice of the MCWE

2. P value computation

3. Power/required sample size

4. Analysis and interpretation of the results

5. Adaptive designs/Interim analyses

Conclusione

• La metodologia delle sperimentazioni cliniche e dell’intero processo di sviluppo delle terapie oncologiche e’ destinata a modificarsi radicalmente nei prossimi anni per rispondere alle esigenze che derivano dai progressi nella quantita’ e nel tipo delle conoscenze disponibili

Processo di sviluppo delle nuove metodologie

Esperti + Stakeholders

Cost-efficacy

Bevacizumab (BEV) plus chemotherapy (CT) continued beyond first progression in patients with metastatic

colorectal cancer (mCRC) previously treated with BEV + CT: Results of a randomised phase III intergroup study –

TML (ML18147)

D Arnold1, T Andre2, J Bennouna3, J Sastre4, P Österlund5, R Greil6 E Van Cutsem7, R von Moos8, I Reyes-Rivera9, B Bendahmane10, S Kubicka11

on behalf of the AIO, GERCOR, FFCD, UNICANCER GI, TTD, GEMCAD and AGMT groups

1Hamburg, Germany; 2Paris, France; 3Nantes, France; 4Madrid, Spain5Helsinki, Finland; 6Salzburg, Austria; 7Leuven, Belgium; 8Chur, Switzerland 9South San

Francisco, USA; 10Basel, Switzerland; 11Reutlingen, Germany

Aims and objectives

• The efficacy and safety of BEV continued after first PD has not been investigated in a randomised clinical trial

• TML (ML18147) is the first randomised phase III study to evaluate BEV continued with standard second-line CT in patients with mCRC who progressed after BEV plus standard first-line CT

Statistical considerations• Study initiated as AIO KRK 0504 then transferred to Roche

(after enrolment of 261 patients)

– Primary endpoint changed from PFS to OS

– Sample size increased from 572 to 810 patients

• Designed to detect 30% (HR 0.77) improvement in median OS (90% power, 2-sided 5%) 613 events required for analysis

• OS curves estimated using Kaplan–Meier method, differences assessed using unstratified log-rank tests

– Unstratified Cox regression model used to estimate HR for OS

– Stratified log-rank tests and Cox regression analyses used as supportive analyses

Main eligibility criteria

Inclusion

• Patients ≥18 years with histologically confirmed diagnosis of mCRC

• Eastern Cooperative Oncology Group (ECOG) PS 0–2

• PD (≥1 measurable lesion according to RECIST v1 assessed by investigator, documented by CT or MRI), ≤4 weeks prior to start of study treatment

• Previously treated with BEV plus standard first-line CT, not candidates for primary metastasectomy

Exclusion

• Diagnosis of PD >3 months after last BEV administration

• First-line patients with PFS in first-line of <3 months

• Patients receiving <3 consecutive months of BEV in first-line

CharacteristicCT

(n=411)BEV + CT(n=409)

Male, % 63 65

Age, median years 63 63

ECOG performance status, %012

43525

44515

First-line PFS, %≤9 months>9 months

5644

5446

First-line CT, %Irinotecan-basedOxaliplatin-based

5842

5941

Demographic and baseline characteristics: Randomised patients

Patients were accrued between February 2006 and June 2010

Demographic and baseline characteristics: Randomised patients (cont’d)

aPatient population refers to sequential enrolment of patients in original AIO study and subsequent enrolment in ML18147 when study was transferred to Roche

Characteristic CT

(n=411) BEV + CT

(n=409)

Duration from last BEV dose to randomisation, %

≤42 days >42 days

7723

7723

Patient populationa, %AIOML18147

3268

3268

Liver metastasis only, %NoYes

7129

7327

No. of organs with metastasis, %1>1

3961

3664

Second-line chemotherapy during study: Randomised patients

Second-line CT regimen, % CT

(n=407) BEV + CT

(n=407)

Irinotecan-based CT 43 42

FOLFIRI 14 16

LV5FU2 + CPT11 (Douillard regimen1) 7 7

XELIRI 12 12

Other regimens 10 7

Oxaliplatin-based CT 57 58

FOLFOX4 / mFOLFOX4 18 19

FOLFOX6 13 16

FUFOX 9 6

XELOX 11 14

Other regimens 6 4

1. Douillard et al. Lancet 2000;355:1041–7

Primary endpoint:overall survival

OS: ITT populationO

S e

stim

ate

Time (months)

1.0

0.8

0.6

0.4

0.2

00 6 12 18 24 30 36 42 48

No. at riskCT 410 293 162 51 24 7 3 2

0BEV + CT 409 328 188 64 29 13 4 1

0

CT (n=410)BEV + CT (n=409)

9.8 mo 11.2 mo

Unstratifieda HR: 0.81 (95% CI: 0.69–0.94)

p=0.0062 (log-rank test)

Stratifiedb HR: 0.83 (95% CI: 0.71–0.97)


aPrimary analysis method; bStratified by first-line CT (oxaliplatin-based, irinotecan-based), first-line PFS (≤9 months, >9 months), time from last dose of BEV (≤42 days, >42 days), ECOG performance status at baseline (0, ≥1)

Median follow-up: CT, 9.6 months (range 0–45.5); BEV + CT, 11.1 months (range 0.3–44.0)

Subsequent anti-cancer therapies: Safety population

Subsequent therapy, %CT

(n=409)BEV + CT(n=401)

Patients who received ≥1 subsequent anti-cancer therapy

67.7 68.6

Subsequent anti-cancer therapies

BEV 12.2 11.5

Anti-EGFR 41.3 39.2

Other 50.4 54.9

EGFR: epidermal growth factor receptor

Subgroup analysis of OS: ITT population

aPatient population refers to sequential enrolment of patients in original AIO and subsequent enrolment in ML18147 when study was transferred to Roche. All patients listed under AIO were included in primary analysis

Category Subgroup n HR (95% CI)

All All 819 0.81 (0.69–0.94)

Patient populationa AIO 260 0.86 (0.67–1.11)

ML18147 559 0.78 (0.64–0.94)

Gender Female 294 0.99 (0.77–1.28)

Male 525 0.73 (0.60–0.88)

Age <65 years 458 0.79 (0.65–0.98)

≥65 years 361 0.83 (0.66–1.04)

ECOG performance status 0 357 0.74 (0.59–0.94)

≥1 458 0.87 (0.71–1.06)

First-line PFS ≤9 months 449 0.89 (0.73–1.09)

>9 months 369 0.73 (0.58–0.92)

First-line CT Oxaliplatin-based 343 0.79 (0.62–1.00)

Irinotecan-based 476 0.82 (0.67–1.00)

Time from last BEV ≤42 days 630 0.82 (0.69–0.97)

>42 days 189 0.76 (0.55–1.06)

Liver metastasis only No 592 0.81 (0.67–0.97)

Yes 226 0.79 (0.59–1.05)

No. of organswith metastasis

1 307 0.83 (0.64–1.08)

>1 511 0.77 (0.64–0.94)

HR 0 1 2

Subgroup analysis of PFS (ITT population)

aPatient population refers to sequential enrolment of patients in original AIO and subsequent enrolment in ML18147 when study transferred to Roche. All patients listed under AIO were included in primary analysis

Category Subgroup n HR (95% CI)

All All 819 0.68 (0.59–0.78)

Patient populationa AIO 260 0.65 (0.51–0.84)

ML18147 559 0.69 (0.58–0.82)

Gender Female 294 0.85 (0.67–1.07)

Male 525 0.60 (0.50–0.72)

Age <65 years 458 0.66 (0.55–0.80)

≥65 years 361 0.71 (0.57–0.87)

ECOG PS 0 357 0.59 (0.48–0.74)

≥1 458 0.76 (0.63–0.92)

First-line PFS ≤9 months 449 0.75 (0.62–0.90)

>9 months 369 0.58 (0.47–0.72)

First-line CT Oxaliplatin-based 343 0.68 (0.55–0.85)

Irinotecan-based 476 0.67 (0.56–0.81)

Time from last BEV ≤42 days 630 0.72 (0.61–0.85)

>42 days 189 0.56 (0.41–0.75)

Liver metastasis only No 592 0.68 (0.57–0.80)

Yes 226 0.68 (0.52–0.89)

No. of organswith metastasis

1 307 0.74 (0.59–0.94)

>1 511 0.64 (0.53–0.77)

HR 0 1 2

Summary• BEV + standard second-line CT, crossed over from BEV +

standard first-line CT, significantly prolongs OS and PFS– OS

• Median: BEV + CT 11.2 months, CT 9.8 months• HR: 0.81 (95% CI: 0.69–0.94), p=0.0062a

– PFS• Median: BEV + CT 5.7 months, CT 4.1 months• HR: 0.68 (95% CI: 0.59–0.78), p≤0.0001a

• Findings from subgroup analyses for OS generally consistent with overall population– Treatment effect according to gender appeared to be different; however,

treatment-gender interaction test was not statistically significant

• Differences in best overall response rate not statistically significant; low response rate in both treatment groups

• AEs not increased when continuing BEV beyond PD; AE profile consistent with previous findings

Conclusions

• First randomised clinical trial that prospectively investigated the impact of continued VEGF inhibition with BEV beyond first progression

• Study confirms that continuing BEV beyond first progression while modifying CT is beneficial for patients with mCRC and leads to a significant improvement in OS and PFS

• This provides a new second-line treatment option for patients who have been treated with BEV + standard CT in first line while maintaining an acceptable safety profile

• Findings indicate a potential new model for treatment approaches through multiple lines and this is currently being investigated in other tumour types

OS: ITT populationO

S e

stim

ate

Time (months)

1.0

0.8

0.6

0.4

0.2

00 6 12 18 24 30 36 42 48

No. at riskCT 410 293 162 51 24 7 3 2

0BEV + CT 409 328 188 64 29 13 4 1

0

CT (n=410)BEV + CT (n=409)

9.8 mo 11.2 mo

Unstratifieda HR: 0.81 (95% CI: 0.69–0.94)


Stratifiedb HR: 0.83 (95% CI: 0.71–0.97)


aPrimary analysis method; bStratified by first-line CT (oxaliplatin-based, irinotecan-based), first-line PFS (≤9 months, >9 months), time from last dose of BEV (≤42 days, >42 days), ECOG performance status at baseline (0, ≥1)

Median follow-up: CT, 9.6 months (range 0–45.5); BEV + CT, 11.1 months (range 0.3–44.0)

Long term benefit?

Long term benefit?

2 mos

2-3 months 3.5 months

Clinically Worthwhile Effect?

• Median follow-up: 3.8 vs 2.3 months

• HR = 0.37 (0.26-55): Meaningless!

• Long term benefit: None (Mature data?)

• Increase in median survival: ≈ 2 months

• Average benefit:< 2 months– 30% of the pts: 3 months– 30% of the pts: 2 months– 40% of the pts: 0

} Clinically worthwhile?

Conclusions• The medical research community should rethink the

terms of cooperation with industry in clinical trials, taking into account a wider clinical and public health perspective.

Conclusions • The medical research community should rethink the


• Our health systems risk bankruptcy for the

skyrocketing costs of drugs that were developed on their own patients, using strategies that ignore the patients’ needs and priorities.

Conclusions • The medical research community should rethink the


• Our health systems risk bankruptcy for the skyrocketing

costs of drugs that were developed on their own patients, using strategies that ignore the patients’ needs and priorities.

• Governments, health systems, and regulatory agencies must identify new paths for drug development and set new standards for drug approval

Critical Methological Issues in Recent Randomised Trials Paolo Bruzzi Epidemiologia Clinica IRCCS...

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