Cancer Survival Query System (CSQS):Making Survival Estimates from Population-Based

Cancer Registries More Timely and Relevant for Recently Diagnosed Patients

Sept. 20-21, 2010 Methods and Applications for Population-Based Survival Workshop

Fascati, Italy

Eric J. (Rocky) Feuer, Ph.D.Chief, Statistical Methodology and Applications Branch

Division of Cancer Control and Population SciencesNational Cancer Institute

Some Questions

• When someone calls 1-800-4CANCER and asks about the prognosis of a family member who was newly diagnosed, where should the information come from?

• How can physicians get a better understanding of the potential impact of competing risks for newly diagnosed cancer patients with significant comorbidities?

• Can population-based cancer registry data play a role in answering these questions?

Outline

I. Statistical Methodology

II. Application to Prostate Cancer

III. Demonstration

IV. Testing Usefulness in Real World Situations

I. Statistical Methodology

Competing Risks Analysis (Discrete Time)

Crude probability of death from cancer i

Probability of surviving all causes i

n interval (i) given live at (i-1)

n interval (i

Cr

) given alive at

ude probability

(

of

i-1)

death from other c

ci

i

oi

h

h

P

1

1 1

auses in interval (i) given alive at (i-1)

= Cumulative probability of dying of cancer through time interval M

= Cumulative probability of dying of other causes th

cM

xM

i cxx i

oM

G

P h

G

1

1 1

rough time interv M

al

 

xM

i oxx i

P h

Two Data Situations

Competing Risks Analysis

All of the relevant patientcharacteristics for both

cancer and other causes arein the same data set

All of the relevant patientcharacteristics for both

cancer and other causes arein the same data set

Cancer and other cause ofdeath characteristics are in

separate data sets

Cancer and other cause ofdeath characteristics are in

separate data sets

I. Everything in A Single Data Set

• Example: co-morbidity added to SEER through SEER-Medicare linkage – Standard competing risks analysis methods can be used– No assumption of independence of competing risks is

necessary– Some restrictions on the parameterization may be

necessary • (Example: complicated if the time scales for both causes of

death are not the same – e.g. time since dx for cancer and age for other causes)

– Minjung Lee will present

II. Cancer and Other Cause Mortality Derived from Separate Data Sets

• Examples:– Other cause mortality derived from combination of

SEER-Medicare and 5% non-cancer matching patients (Angela’s talk)

– Other-cause mortality derived from mortality follow-up of National Health Interview Surveys (NHIS) as a function of general health status, functional status, and self-reported conditions – (all ages available!)

• Conditional independence is required (conditional on covariates)

• Parameterization for each cause is flexible• Covered in this talk!

Competing Risks Under Independence

Net probability of dying of other causes

Net probability of dying of cancer i

in interval (i) given alive at (i-1)

n inter

val (i) given a

= Cumulative pr

live at

obabili

(i-1)

ty of dying of c

ci

oi

cM

d

d

G

1

1 1

1

1 1

ancer through time interval M

 

= Cumulative probability of dying of other causes through time interval

M

1

2

1

2

cx cx ox

ox cx

xM

ix i

oM

xM

ix i

ox

d d d

d d

P

G

dP

   

Assuming uniform deaths from cancer and other causes in the interval.

Hakulinen T, Net Probababilities in the Theory of Competing Causes,

, (1977) Scan Actuarial Journal

Using Relative Survival*

(1- interval relative survival for time interval i, i.e. )

(1 - interval expected probability of surviving interval i)

= Cumulative probability of dying of cancer throu

1-

1

1

gh

oi

cM

iici

i

iEd

G

Pd R

E

1

1 1

1

1 1

time inteval M

1  

2

= Cumulative probability of dying of other causes through time inte

rval M

1 1

1

1

1

21 1  

xM

ix i

oM

xM

i

x x

xx

xi

x

x

E

E E

G

R RP

P R

* Cronin and Feuer, “Cumulative Cause-Specific Mortality for Cancer Patients in the Presence of Other Causes – A Crude Analogue of Relative Survival”, Statistics in Medicine, 2000.

Moving from Cohort to Individual

• Up to now the equations apply to estimating competing risk survival for a cohort of individuals (e.g. age 60+, Stage II CRC, both genders, all races)

• We are interested in customizing the estimates for individual (j) with

– Cancer characteristics (zj ) • E.g. Gleason’s score, stage, age, race, comorbidity

– Other cause characteristics ( wj ) • E.g. age, race, co-morbidity

Customized for individual ( j ) with cancer characteristics ( ) and other cause characteristics ( )

1

1 1

(z , ) = Cumulative probability of dying of cancer through time interval M for

an individual (j) with cancer characteristics (z ) and other cause

characteristics ( )

( ) (

cM j j

j j

xM

xi

iij

G w

wR E

w

z

  

(z , ) = Cumulative probability of dying of other causes through time interval M for

an individual (j) with cancer characteristics (z ) and o

( ) ( )

ther ca

( )

u

11 1

) 12

se c

x j x j

o

x jj

M j j

j

G w

R z R z E w

1

1 1

1 1 1 1

2

( ) ( ) (

harac

(

terist

)

ics ( )

  

) )(

i j x j x j

j

xM

x ii j x jR E w E

w

z wzw ER

jz jw

Analogue When We UseCause of Death Information

net cause-specific cancer survival through interval (i)

for an individual with cancer characteristics ( ), given alive at sta

(z

rt of interval (i

, ) = Cumulative probab

)

ili

)

(

tc j j

j

M

i j

z

w

S

G

z

1

1 1

y of dying of cancer through time interval M for

individual (j) with cancer characteristics (z ) and other cause characteristics (

(

)

11 1 ( ) ( ) 1 )

2(( ) )

j j

xM

ix

i j x j x j x ji

jS z S z S z

w

wE Ew

1

1 1

  

(z , ) = Cumulative probability of dying of other causes through time interval M for

individual (j) with cancer characteristics (z ) and other cause characterist

ics (

)

oM j j

j j

xM

x i

G w

w

S

1( ) ( )1 1 1  ( ) ( )  

2( )i j x j x jj xi jz SE w w wzE E

II. Application to Prostate Cancer*

*Colorectal cancer also available

Basics

Models fit using SEER 13 + entire state of CA (20.3% of US)

from 1995-2005 to allow consistent modern staging over time

( ) or ( ) is estimated using discrete time Cox

regression* from SEER, bu

i j i jS z R z

t stratified to accurately capture

baseline survival for appropriate subgroups

) is estimated using the methods described in Angela's talk

(but other co-morbidity calculators could be substit e

(

ut d)

i jE w

*Prentice RL and and Glockeler LA "Regression Analysis of Grouped Survival Data with Application to Breast Cancer, Biometrics, 1978.

Hakulinen T and Tenkanen L "Regression Analysis of Relative Survival Rates, Applied Statistics, 1987.

3 Staging Groups

• Pre-Treatment Clinical– For patients who have not yet been treated– Estimable because for prostate cancer SEER maintains

data on both clinical and pathologic staging

• Pure Clinical– For patients who elected not to have surgery

• Pathologic– For patients who had surgery

Prostate Cancer – Extent of Disease

• T1 (Clinical Staging only)– T1a: Tumor incidentally found in 5% or less of resected prostate

tissue (TURP).– T1b: Tumor incidentally found in > 5% of resected prostate tissue

(TURP). – T1c: Tumor found in a needle biopsy performed due to elevated

PSA.

• T2: Tumor confined within prostate.• T3: Tumor extends through prostatic capsule.• T4: Tumor is fixed, or invades adjacent structures other

than seminal vesicles, e.g., bladder neck, external sphincter, rectum, levator muscles, and/or pelvic wall.

Prostate Cancer

• Inclusion Criteria – Age 94 and under– First Cancer

• Staging– Localized (Inapparent) - T1a,T1b,T1c N0 M0 (Clinical only)– Localized (Apparent) - T2 N0 M0– Locally Advanced I – T3 N0 M0 – Locally Advanced II - T4 N0 M0– Nodal Disease I - T1-T3 N1 M0– Nodal Disease II – T4 N1 M0– Distant Mets – Any T, Any N, M1 (Clinical Only)

Strata and Sample Sizes

Stage

Pre-treatment Clinical Pure Clinical

Co-morbidity (Age 66+)

AllCo-morbidity

(Age 66+)All

Localized (Inapparent) 34839 109079 25516 63222

Localized (Apparent) 49706 137518 35714 79418

Locally Adv and Nodal 3649 9455 2757 6669

Distant Metastases 3997 9756 3486 8592

Totals 92191 265808 67473 157901

Stage

Path

Co-morbidity (Age 66+)

All

Localized 11063 60338

Locally Adv and Nodal 5490 27116

Totals 16553 87454

Prostate Covariates

• Substages of Localized (Inapparent)• Substages of Locally Advanced and Nodal Disease • Gleason’s Score (2-7 and 8-10)• Substages x Gleason's Score• Age (cubic spline – flat under age 50 and after age 90)• Race (white, black, other)• Marital Status (married, other)• Co-morbidity – age 66+ (linear – flat at high values )• Calendar year (linear)

– Projected to most recent data year (2005) and then flat to (conservatively) represent prognosis of recently dx patient

– Mariotto AB, Wesley MN, Cronin KA, Johnson KA, Feuer EJ. Estimates of long-term survival for newly diagnosed cancer patients: a projection approach. Cancer. 2006 May 1;106(9):2039-50.

III. Demonstration

Website

http://www16.imsweb.com/Username: imsdevPassword: website

CSQS Home Page

Prostate, Pre-Trt Clinical

T3 N0 M0

Gleasons 8-10

73 White Married

73 Chronologic Age, 67 Health Adjusted Age

Show Diabetes, Congestive Heart Failure

Show Health Adjusted Age at 82,Then Add 3 Years Subjective 85

People Chart for 1, 5, 10 Years

People Chart for 1, 5, 10 Years

Pie Chart for 1, 5, 10 Years

Pie Chart for 1, 5, 10 Years

Summary Chart – Alive

Summary Chart – Death From Other Causes

Summary Chart – Death From Cancer

IV. Testing Usefulness in Real World Situations

Questions

• Should this system be public, or only for use by clinicians?

• How can the results of this system be best used to contribute to health care provider-patient communications?

• Can this system contribute to tumor board discussions?• For what medical specialties is this system best suited?

Oncologist, Surgical Oncologist, Primary Care Physician?

• Can modifiable risk factors (such as treatment) be added to the system?

No Additional Therapy

Additional SlidesWith Selected Additional Therapy

32.3 alive in 5 years55.5 die due to cancer12.2 die of other causes

32.3 alive in 5 years

39.9 die due to cancer13.8 alive due to chemotherapy

14.0 die of other causes

Example of Adjuvant!Online Output (http://www.adjuvantonline.com/)

Future Directions

• Testing in clinical settings (tumor board and patient perceptions)

– Supplemental grant to the Centers for Excellence in Communications (Kaiser HMO setting)

• Validation• Potential new cancer sites

– Head and neck cancers– Breast cancer

• Adding new comorbidity calculators (NHIS –based)• Adding ecologic covariates

Collaborators

• NCI– Angela Mariotto, Minjung Lee, Kathy Cronin, Laurie Cynkin,

Antoinette Percy-Laurry

• IMS– Ben Hankey, Steve Scoppa, Dave Campbell, Ginger

Carter, Mark Hachey, Joe Zou

• Advisory– Dave Penson (Urologist, Vanderbilt)– Deborah Schrag (CRC Oncologist, Dana Farber)– (Consultants - User Interface)

• Scott Gilkeson, Bill Killiam

One Dataset

Net probability of dying of Cancer

Net probability of dying of Cancer

Net probability of dying of Other Causes

Net probability of dying of Other Causes

Cox Model 2

Cox Model 1

Dataset 1 Cancer Patients

Net probability of dying of Cancer

Net probability of dying of Cancer

Net probability of dying of Other Causes

Net probability of dying of Other Causes

Dataset 2 Non-cancer

Cox Model 2

Cox Model 1

Crude probabilities dying of Cancer and Other CausesCrude probabilities dying of Cancer and Other Causes

Crude probabilities dying of Cancer and Other CausesCrude probabilities dying of Cancer and Other Causes

No need for independence assumption Minjung used a continuous time model where

estimates are computed using counting process*

Estimates and SE’s of cumulative incidence are identical if independence is assumed or not (Nonidentifiability: Tsiatis,1975)

*Cheng SC, Fine JP, Wei LJ, “Prediction of the Cumulative Incidence Function under the Proportional Hazards Model”, Biometrics, 54, 1998.

Needs independence assumption of competing risk and that populations are similar*

Can take advantage of the richness of alternative different data sources.

Use discrete time model – CI’s of cumulative incidence computed using delta method

Equations are the same