Meta-Analysis of PSA Growth

Lurdes Y.T. Inoue, Ph.D.Ruth Etzioni, Ph.D.Elizabeth Slate, Ph.D.Christopher Morrel, Ph.D.

OUTLINE

Background

Description of Studies

Change-Point Models

Some Results

Future Plans

BACKGROUND

Prostate Cancer Prostate Cancer:

most commonly diagnosed cancer in men Risk factors:

Race, family history• Black men: higher incidence and poorer survival

Treatment options: Surgery or radiation for localized disease Hormone ablation for advanced or recurrent disease

Survival: Excellent for localized disease Poor if metastases are present (approx. 30% at 5

years)

PSA Screening Most significant and controversial development in

prostate cancer control over the last 20 years Initial studies:

PSA markedly elevated in men with prostate cancer PSA screening resulted in dramatic shift in stage of

disease PSA screening in US exploded in late 1980s and early

1990s Dissemination was not tracked Heterogeneity in how PSA is used

Conclusive evidence of efficacy is still lacking! In absence of clinical trial results, controversy about

role of PSA in PC mortality declines

SCIENCE TIMES, April 9 2002

0

50

100

150

200

250

300

350

400

1975 1979 1983 1987 1991 1995 1999

White Black

Prostate Cancer Trends

Inci

den

ce p

er

10

0,0

00

0

10

20

30

40

50

60

70

80

90

1975 1979 1983 1987 1991 1995 1999

year

PC

a D

eath

s p

er 1

00,0

00

White Black

Cancer Intervention and Surveillance Network (CISNET) Consortium of NCI-sponsored investigators Main Goal:

understand the impact of cancer control interventions (e.g. screening, treatment and prevention) on population trends in incidence and mortality

Approach: Simulation-based models Requirement

• Estimates of PSA growth in cases and non-cases

Prior Studies of PSA Growth

Five prior studies CARET, BLSA, NPCT, VA, KAISER

Variability in the results e.g. 17% increase in Alice Whittemore’s study

versus 33% in the Baltimore study). Small samples

Summarize important growth rate parameters in a large dataset

Understand the impact of advanced cancers (stage/grade) on growth rates

Bayesian approach provide new ways of looking at these data…

Goals

Description of Studies

Baltimore Longitudinal Study of Aging (BLSA)

Continual longitudinal and multi-disciplinary study of

normal human aging

Beta-Carotene and Retinol Efficacy Trial (CARET)

Chemo-preventive efficacy and safety of beta-

carotene and retinol in a population at risk for lung

cancer

Nutritional Prevention of Cancer Trial (NPCT)

Determine whether a supplement of selenium

decreases the incidence of cancer

Data: Summary Statistics

Change-point Models

Broad literature on change-point models, some applied to PSA data: Pearson, et. al. (1994) Morrell et. al. (1995), Slate and Cronin (1997), Slate and Clark (1999)

ALL BASED ON SINGLE STUDIES.

Age

Log(PSA+1)

1 2

Local

Metastasis

Features:

Estimate a change-point in clinically diagnosed cases

Combining data from different studies

HIERARCHICAL MODELS (RANDOM-EFFECTS)

Meta-Analysis using One-Change Point Models

Restricted to prostate cancer patients Is there a growth rate change?

Stratified by Grade/Gleason’s Score Higher growth rate under poor prognosis?

One-Change Point Model

Priors:

Meta-Analysis using Two-Change Point Models

Restricted to prostate cancer patients Use stage information: worse prognosis inducing a second change point in patient’s FU window.

Two-Change Point Model

+ Interval censored observation for second change-point using stage of disease

-0.2 0.0 0.2 0.4

02

46

81

0

OVERALLCARETBALTINPCT

Slopes after the change point

ln(P

SA

+1

)

42 44 46 48 50

01

23

4

ln(P

SA

+1

)

64 65 66 67 68

01

23

4

ln(P

SA

+1

)

55 60 65 70

01

23

4

ln(P

SA

+1

)

55 60 65 70 75 80

01

23

4

ln(P

SA

+1

)

63 64 65 66 67 68

01

23

4

ln(P

SA

+1

)

70 71 72 73 74 75

01

23

4

ln(P

SA

+1

)

30 40 50 60 70 80 90

02

46 CARET

LOCAL

ln(P

SA

+1

)

30 40 50 60 70 80 90

02

46 CARET

METASTASIS

ln(P

SA

+1

)

30 40 50 60 70 80 90

02

46 BLSA

LOCAL

ln(P

SA

+1

)

30 40 50 60 70 80 90

02

46 BLSA

METASTASIS

ln(P

SA

+1

)

30 40 50 60 70 80 90

02

46 NPCT

LOCAL

ln(P

SA

+1

)

30 40 50 60 70 80 90

02

46 NPCT

METASTASIS

AGE

Pre

dic

tive

Ag

e a

t Ch

an

ge

Po

int

40 50 60 70 80 90

0.0

0.0

10

.02

0.0

30

.04

0.0

5

AGE

Pre

dic

tive

me

dia

n lo

g(P

SA

+1

) T

raje

cto

ry

40 50 60 70 80 90

02

46

CH

AN

GE

PO

INT

(Y

EA

RS

BE

FO

RE

DIA

GN

OS

IS)

01

02

03

04

0

CARETBLSANPCT

DX YEAR < 90DX YEAR >=90

ln(P

SA

+1

) a

t CH

AN

GE

PO

INT

01

23

45 CARET

BLSANPCT

DX YEAR < 90DX YEAR >=90

ln(P

SA

+1

) T

WO

YE

AR

S A

FT

ER

CH

AN

GE

PO

INT

01

23

45 CARET

BLSANPCT

DX YEAR < 90DX YEAR >=90

CH

AN

GE

PO

INT

(Y

EA

RS

BE

FO

RE

DIA

GN

OS

IS)

01

02

03

04

0

LOCALMETASTASIS

SL

OP

E A

FT

ER

CH

AN

GE

PO

INT

0.0

0.2

0.4

0.6

0.8

1.0

LOCALMETASTASIS

Other results:

Stratified Analysis: Slope parameter after change-point:

• Grade 0: CI(95%)= [-0.06,0.23]• Grade 1: CI(95%)= [-0.11,0.47]

Indication of faster PSA growth for grade 1 patients (more variability too).

Two-Change Point Analysis: No evidence for a two-change point: unlikely

to occur during subject’s lifetime. Maybe just different post-change point

slopes depending on tumor stage…

Using Stage Information

-0.2 -0.1 0.0 0.1 0.2 0.3

02

46

81

01

21

4

OVERALLCARETBALTINPCT

-1.0 -0.5 0.0 0.5 1.0 1.5

01

23

4

OVERALLCARETBALTINPCT

Post-Change Point Slope – baseline (LOCAL)

Effect of Advanced Stage on Post-Change Point Slope (METASTASIS)

CH

AN

GE

PO

INT

(Y

EA

RS

BE

FO

RE

DIA

GN

OS

IS)

010

2030

40

LOCALMETASTASIS

CARETBLSANPCT

SLO

PE

AF

TE

R C

HA

NG

E P

OIN

T

0.0

0.5

1.0

1.5

LOCALMETASTASIS

CARETBLSANPCT

30 40 50 60 70 80 90

02

46 CARET

LOCAL

30 40 50 60 70 80 90

02

46 CARET

METASTASIS

30 40 50 60 70 80 90

02

46 BLSA

LOCAL

30 40 50 60 70 80 90

02

46 BLSA

METASTASIS

30 40 50 60 70 80 90

02

46 NPCT

LOCAL

30 40 50 60 70 80 90

02

46 NPCT

METASTASIS

42 44 46 48 50

01

23

4

64 65 66 67 68

01

23

4

55 60 65 70

01

23

4

55 60 65 70 75 80

01

23

4

63 64 65 66 67 68

01

23

4

70 71 72 73 74 75

01

23

4

Conclusions

Change point below threshold in many cases

No evidence for second change point in patients with metastasis

Patients with metastasis have higher post-change point slopes

Post-change point slope effects for high and low grade tumors (greater variability for high grade tumors)

Misclassification of metastasis as localized Many prostate cancers are not pathologically staged

Many clinical cases are upstaged at pathological staging

RESULTS ARE CONSISTENT WITH SCIENTIFIC LITERATURE.

Future Plans:

Obtain a fourth longitudinal data set

Validation of the simulation model (CISNET)

Focus on natural history models Effects of intervention (prostate cancer

prevention)

Including controls Dealing with misclassification

Challenge:

Extremely high latent prevalence relative to clinical incidence Majority of men over 70 harbor a

prostate cancer! Lifetime probability of PC onset:

36% Lifetime probability of a PC

diagnosis pre-PSA: 9%9

27

36

20

Model Components

Inputs M odules Outputs

• all cause mortality• asymptomatic incidence

• stage transition ratesNatural History

• lifetime probability of clinicalpresentation

• distribution of stage at clinicalpresentation

ClinicalPresentation

• PSA growth curve (healthy)• PSA growth curve (cancer)

• length of stage A1PSA • annual PSA levels

• screening strategy• PSA growth curve

Screening• age at screen detection

• stage at screen detection

• age and stage specificcancer survival curves

Survival

• age at onset• age at stage transition

• age at other cause death

• age at clinical presentation• stage at clinical presentation

• age at death with screening• age at death without screening