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