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```									     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 Sciences
National 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
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)

Pi  Probability of surviving all causes in interval (i) given alive at (i-1)
hci  Crude probability of death from cancer in interval (i) given live at (i-1)
hoi  Crude probability of death from other causes in interval (i) given alive at (i-1)

GcM = Cumulative probability of dying of cancer through time interval M
M
 x 1 
          Pi  hcx
x 1    i 1 

GoM = Cumulative probability of dying of other causes through time interval M
M
 x 1 
          Pi  hox
x 1    i 1 
Two Data Situations

Competing Risks
Analysis

All of the relevant patient
Cancer and other cause of
characteristics for both
death characteristics are in
cancer and other causes are
separate data sets
in the same data set
I. Everything in A Single Data Set

• Example: co-morbidity added to SEER through
– 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

d ci  Net probability of dying of cancer in interval (i) given alive at (i-1)
d oi  Net probability of dying of other causes in interval (i) given alive at (i-1)

GcM = Cumulative probability of dying of cancer through time interval M
 x 1                                 
Pi   d cx    d cx  d ox  
M
1
           
x 1      i 1               2                 

GoM = Cumulative probability of dying of other causes through time interval M

 x 1                                 
Pi   d ox    d cx  d ox  
M
1
            
x 1        i 1               2                 

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

Hakulinen T, Net Probababilities in the Theory of Competing Causes,
Scan Actuarial Journal , (1977)
Using Relative Survival*

Pi
d ci  1  Ri (1- interval relative survival for time interval i, i.e. 1                 )
Ei
d oi  1- Ei (1 - interval expected probability of surviving interval i)

GcM = Cumulative probability of dying of cancer through time inteval M
 x 1                                     
Pi  1  Rx   1  Rx  1  Ex  
M
1
        
x 1    i 1                2                    

GoM = Cumulative probability of dying of other causes through time interval M
 x 1                                    
Pi  1  Ex   1 Rx  1  Ex  
M
1
        
x 1    i 1                2                   

* 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 ( z j ) and other cause characteristics ( w j )

GcM (z j ,w j ) = Cumulative probability of dying of cancer through time interval M for
an individual (j) with cancer characteristics (z j ) and other cause characteristics (w j )
 x 1                                                                               
Ri ( z j ) Ei ( w j )  1  Rx ( z j )   1  Rx ( z j ) 1  Ex ( w j )  
M
1
       
x 1    i 1                                           2                                   

GoM (z j ,w j ) = Cumulative probability of dying of other causes through time interval M for
an individual (j) with cancer characteristics (z j ) and other cause characteristics (w j )
 x 1                                                                               
Ri ( z j ) Ei ( w j )  1  Ex ( w j )   1  Rx ( z j ) 1  Ex ( w j )  
M
1
       
x 1    i 1                                           2                                   
Analogue When We Use
Cause of Death Information

Si ( z j )  net cause-specific cancer survival through interval (i)
for an individual with cancer characteristics (z j ), given alive at start of interval (i)

GcM (z j ,w j ) = Cumulative probability of dying of cancer through time interval M for
individual (j) with cancer characteristics (z j ) and other cause characteristics (w j )
 x 1                                                                                 
Si ( z j ) Ei ( w j )  1  S x ( z j )   1  S x ( z j ) 1  Ex ( w j )  
M
1
       
x 1    i 1                                            2                                    

GoM (z j ,w j ) = Cumulative probability of dying of other causes through time interval M for
individual (j) with cancer characteristics (z j ) and other cause characteristics (w j )
 x 1                                                                                
Si ( z j ) Ei ( w j )  1  Ex ( w j )   1  S x ( z j ) 1  Ex ( w j )  
M
1
       
x 1    i 1                                           2                                    
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

Si ( z j ) or Ri ( z j ) is estimated using discrete time Cox
regression* from SEER, but stratified to accurately capture
baseline survival for appropriate subgroups

 Ei ( w j ) is estimated using the methods described in Angela's talk
(but other co-morbidity calculators could be substituted)

*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.
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

Pre-treatment Clinical               Pure Clinical

Stage        Co-morbidity                     Co-morbidity
All                                All
(Age 66+)                        (Age 66+)
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

Path

Stage        Co-morbidity
All
(Age 66+)
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/
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

32.3 alive in 5 years
55.5 die due to cancer
12.2 die of other causes

32.3 alive in 5 years
13.8 alive due to chemotherapy
39.9 die due to cancer
14.0 die of other causes
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
– Breast cancer
• Adding new comorbidity calculators (NHIS –based)
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
– Dave Penson (Urologist, Vanderbilt)
– Deborah Schrag (CRC Oncologist, Dana Farber)
– (Consultants - User Interface)
• Scott Gilkeson, Bill Killiam
One Dataset                                                  Dataset 1                Dataset 2
Cancer Patients           Non-cancer
Cox                                      Cox
Model 1                                  Model 2                   Cox                                        Cox
Model 1                                    Model 2
Net probability                Net probability
Net probability      Net probability
of dying of                    of dying of
of dying of          of dying of
Cancer                         Other Causes
Cancer               Other Causes
Equations are the same

Crude probabilities dying of Cancer                                  Crude probabilities dying of Cancer
and Other Causes                                                     and Other Causes
 No need for independence assumption
 Needs independence assumption of
 Minjung used a continuous time model where                         competing risk and that populations are
estimates are computed using counting
process*
similar*
 Estimates and SE’s of cumulative incidence                        Can take advantage of the richness of
are identical if independence is assumed or                        alternative different data sources.
not (Nonidentifiability: Tsiatis,1975)                            Use discrete time model – CI’s of cumulative
*Cheng SC, Fine JP, Wei LJ, “Prediction of the Cumulative            incidence computed using delta method
Incidence Function under the Proportional Hazards Model”,
Biometrics, 54, 1998.

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