>> GOOD MORNING, WELCOME TO
THE NIHCD SEMINAR SERIES, I AM
DOCTOR ALANAFORD AND WILL BE
STANDING IN FOR.
[INDISCERNIBLE]
-- BUT BECAUSE I HAVE THE
PLEASURE OF INTRODUCING MY
COLLEAGUE, Dr. STRESS.
Dr. STRESS IS A
EPIDEMIOLOGIST AND STAFF
SCIENTIST AT NIMHCD --
[INDISCERNIBLE]
Dr. STRAPH WAS ALSO THE
REPRESENTATIVE FOR THE GLOBAL
DISEASE COLLABORATION WHICH IS
TASKED WITH MORTALITY RATES
AND QUALITY OF LIFE FOR RACE
AND ETHNICITY.
Dr. STOPH IS INTERESTED IN
EXPLORING IMPACT OF BIAS IN
LARGE COMPUTATIONAL DATASETS
AND MAKING THE INFORMATION
AVAILABLE TO RESEARCHERS.
Dr. STRAPH RECEIVED HER
PH.D. AT UNIVERSITY OF NORTH
CAROLINA AT CHAPEL HILL.
PRIOR TO THAT, SHE WAS
ASSISTANT PROFESSOR AT
UNIVERSITY OF NORTH CAROLINA
AT CHAPEL HILL.
IN 1993, Dr. STRAPH RECEIVED
THE WOMEN'S INNOVATION AWARD
AND LEADER OF THE AWARDS IN
THE DIVISION.
I AM LOOKING FORWARD TO Dr.
STRAPH'S TALK AND Q&A SESSION
AND AS A REMINDER, PLEASE POST
YOUR QUESTION ON THE SCREEN.
>> THANK YOU FOR THE NICE
INTRODUCTION, LET ME SHARE MY
SCREEN.
OKAY, SO AS ALANA MENTIONED,
MY NAME IS PAULA STRASSLE AND
I AM TALKING ABOUT USING
OBSERVATIONAL DATA TO ADDRESS
DISPARITIES IN CLINICAL TRIALS
THAT WE SEE.
HERE ARE MY DISCLAIMERS AND
THEN JUST AS A QUICK AGENDA, I
WILL TALK ABOUT DISPARITIES IN
CLINICAL TRIAL PARTICIPATIONS,
THE OBSERVATIONAL BIASES IN
STUDIES, FOCUSING ON
DISPARITIES AS THE TITLE
SUGGESTS AND THEN TALKING
ABOUT THE OBSERVATIONAL
STUDIES IN A APPROACHABLE WAY
AND HOPEFULLY GETTING MORE
USERS IN THIS I WOULD SAY NEW
METHOD IN EPIDEMIOLOGY.
PARTICIPATION CLINICAL TRIALS
IS LOW BUT ESPECIALLY IN
CANCER CLINICAL TRIALS, IT IS
ESPECIALLY LOW.
ONLY 2 TO 3 PERCENT OF ADULT
CANCER PATIENTS PARTICIPATE IN
A CLINICAL TRIAL DURING THEIR
TREATMENT AND AS YOU CAN SEE
IN THIS FIGURE ON THE RIGHT,
BY THIS REVIEW, THEY IDENTIFY
SEVERAL BARRIERS TO
PARTICIPATE IN CLINICAL TRIALS
FOR CANCER PATIENTS WITH THE
BIGGEST ONE BEING STRUCTURAL
AND THE FACT THAT THE MAJORITY
OF PLACES AND CENTERS FOR
INDIVIDUALS GET TREATED FOR
CANCER, THEY DON'T PARTICIPATE
IN TRIALS, DON'T HAVE TRIALS
AVAILABLE AT THE CENTERS SO
YOU KNOW THERE JUST ISN'T ANY
RESPECTABILITY IN THE TRIALS.
THE SECOND BARRIER IS CLINICAL
IN THAT CLINICAL TRIALS
USUALLY HAVE
INCLUSION/EXCLUSION CRITERIA
WHICH MAKES PATIENTS
INELIGIBLE LIKE PATIENTS WITH
COMORBIDITIES, ADVANCED
CANCER, A LOT OF THESE THINGS
CAN MAKE YOU INELIGIBLE FOR
THESE TRIALS EVEN WHEN YOU
HAVE ACCESS TO THEM.
AND IF YOU CAN IMAGINE, THE
STRUCTURAL AND CLINICAL
OBSERVATIONAL BARRIERS ARE
MORE LIKELY TO OCCUR IN ETHNIC
MINORITIES AND WITH THESE
PATIENTS BEING UNDER
REPRESENTED IN CANCER CLINICAL
TRIALS BUT OVERALL IN ALL
CLINICAL TRIALS AND AS YOU CAN
SEE FROM THIS FIGURE, BLACK
POPULATIONS ARE UNDER
REPRESENTED IN ENROLLMENT TO
CLINICAL TRIALS COMPARED TO
INCIDENTS OF CANCER AND
MORTALITY RELATED TO CANCER.
AND ALL OF THESE FIGURES FOR
OVERALL CANCER, WHEN WE LOOK
ACROSS ALL OF THE TYPES OF
CANCER AND CANCER SITES, WE
SEE A SIMILAR PICTURE.
TO ASSUME IN ON ONE PARTICULAR
KIND OF CANCER USING THAT IN
OUR EXAMPLE LATER, WE CAN SEE
THE MORTALITY BLACK AND
HISPANIC POPULATIONS MAKE
ABOUT 20 PERCENT BUT REPRESENT
ONLY 5 PERCENT OF PARTICIPANTS
IN CLINICAL TRIALS SO THIS CAN
BE A HUGE ISSUE, YOU KNOW, IN
REPRESENTATION.
AND YOU KNOW WHY
REPRESENTATION IN CLINICAL
TRIALS MATTER, WHILE WE REFER
TO IT AS ADVISABILITY, IT IS
CRITICAL ESPECIALLY IF
TREATMENT IS GOING TO BE MADE
ACROSS POPULATIONS.
THERE IS DIFFERENT TYPES OF
CANCER, MECHANISMS WITHIN
CANCER IN THE TRIALS FOR CROSS
TREATMENT AND MAKING SURE
THERE IS EFFECTIVENESS ACROSS
ALL POPULATIONS AND IF THE
TREATMENT IS REALLY HARD TO
ADHERE TO BECAUSE THERE IS A
LOT OF STEPS OR YOU HAVE TO
STAY ON IT FOR A LONG TIME,
YOU KNOW, IN A VERY KIND OF
REGIMENTED CLINICAL TRIAL, YOU
CAN END UP WITH HIGHER
ADHERENCE RATES AND LESS
TREATMENT STRATEGIES DUE TO A
VARIETY OF REASONS THAT YOU
SEE IN THE REAL WORLD THAT END
UP MAKING THE EFFECTIVE
TREATMENTS DIFFER.
NOW WHAT IS ALSO REFERRED TO
AS REAL WORLD DATA CAN END UP
BEING VERY DIFFERENT THAN
TREATMENT EFFECTS.
YOU CAN DO A CROSS ANALYSIS,
DO EFFECTIVE STUDIES WITH
REGARD TO BLACK OR HISPANIC
POPULATIONS OR MORE INCOME
POPULATIONS, POPULATIONS WITH
DISABILITIES, YOU CAN DO ALL
SORTS OF ANALYSIS WITH
TARGETED STUDIES AND AS I
MENTIONED, GET MORE OF A
REPRESENTED POPULATION BECAUSE
YOU DON'T HAVE TO RESTRICT
YOURSELF TO CLINICAL TRIALS
AND CAN USE MORE OF RELAXED
INCLUSION/EXCLUSION CRITERIA.
AND THEN OUTSIDE THE CLINICAL
WORLD BUT ESPECIALLY FOR
HEALTH DISPARITIES, YOU ARE
STILL ABLE TO CONDUCT THESE
CLINICAL STUDIES WHEN CLINICAL
TRIALS WOULD BE UNETHICAL OR
IMPOSSIBLE.
FOR EXAMPLE, WE WOULD NEVER
RANDOMIZE AN INDIVIDUAL IN
POVERTY, AN INDIVIDUAL TO BE
SUBJECT TO STEMMATIC RACISM SO
SOME OF THESE IMPORTANT
QUESTIONS WE ARE ASKING IN
MINORITIES WITH HEALTH
DISPARITIES CAN'T BE ANSWERED
IN CLINICAL TRIALS SO WE'RE
REALLY LEFT WITH TRYING TO
DESIGN SOME OBSERVATIONAL
STUDIES TO ANSWER THESE CAUSAL
QUESTIONS.
SO BECAUSE THOUGH OF THE LACK
OF RANDOMIZATION OF OUR
TREATMENT STRATEGIES, THERE
ARE SEVERAL CHALLENGES USING
REAL WORLD DATA.
HOPEFULLY MOST OF YOU HAVE
HEARD OF CON FOUNDING BEFORE,
MISSING DATA IS OFTEN
SOMETHING THAT CAN OCCUR BUT
THESE THINGS ARE PRETTY
COMMONLY DISCUSSED WHEN
LOOKING AT THE QUALITY OF
STUDIES OR WHEN TRYING TO
DESIGN STUDIES.
SO WHAT WE WILL TALK ABOUT
TODAY IN IMMORTAL TIME BIAS IS
OFTEN OVERLOOKED AND NOT
ADDRESSED AND STAND OUT IN THE
SAMPLES OF OUR WORK.
SO JUST TO MAKE SURE WE ARE
ALL ON THE SAME PAGE, I WILL
BRIEFLY DESCRIBE WHAT IMMORTAL
TIME AND IMMORTAL TIME BIAS
IS.
SO IMMORTAL TIME OCCURS WHEN
THE START OF FOLLOW UP DOES
NOT COINCIDE WITH THE START OF
TREATMENT.
SO YOU GET SOMEONE WHO IS
DIAGNOSED WITH CANCER BUT
WON'T START THEIR TREATMENT
THAT DAY AND MAY END UP
GETTING TREATMENT DAYS, WEEKS
OR EVEN MONTHS AFTER THE
DIAGNOSIS.
SO IN A CLINICAL TRIAL, WE
WOULD HAVE FOUR PATIENTS
DIAGNOSED WITH CANCER AND THEN
RANDOMLY ASSIGN THEM TO
UNDERGO A TREATMENT.
SO FOR THIS EXAMPLE, RED GROUP
IS SURGERY, GREEN GROUP IS NO
SURGERY, SO WE RANDOMLY ASSIGN
THEM TO THEIR TREATMENT GROUPS
AND THEN FOLLOW THEM TO SEE
WHAT HAPPENS.
SO AS WE FOLLOW THEM OVER TIME
WHICH CAN SOMETIMES HAPPEN
WITH PATIENT B HERE, DOESN'T
UNDERGO THE SURGERY THEY WERE
ASSIGNED TO HOWEVER IN MOST
CLINICAL TRIAL STUDIES WHEN
DOING THE ANALYSIS, WE STILL
CONSIDER THAT PERSON IN THE
TREATMENT ARM BECAUSE WE ARE
DOING THE PREANALYSIS SO THE
EFFECTS ARE NOT SEEN IF THEY
DON'T UNDERGO SURGERY.
NOW IN A REAL WORLD
DATASETTING WITH OBSERVATIONAL
DATA, THE PATIENTS HAVE GOT
TEN THE DIAGNOSIS, UNDERGONE
TREATMENT IF THEY WERE GOING
TO AND THEN FOLLOWED FOR A
PRE-SPECIFIED AMOUNT OF TIME
SO IF THEY HAVE 1 YEAR OF
MORTALITY, WE HAVE ONE YEAR OF
DATA ON THEM.
THEN WHAT WE TYPICALLY DO IS
LOOK AT THEM IN THEIR
ASSIGNMENT GROUPS.
SO IF YOU LOOK AT THIS PATIENT
HERE, EVEN THOUGH THEY WOULD
HAVE BEEN BASED ON RANDOMIZED
DESIGN, HERE THE PATIENT 2
DOESN'T UNDERGO SURGERY AND
DOESN'T HAVE PLANNED TREATMENT
BUT ONLY ACTUAL OBSERVED
TREATMENT SO THIS DIFFERENCE
IS WHAT INDUCES THE BIAS.
BUT GETTING BACK TO IMMORTAL
TIME, IT IS THIS TIME HERE
BETWEEN THE ENROLLMENT AND
WHEN THE PERSON GETS TREATED,
THEY ARE CONSIDERED IMMORTAL
BECAUSE IF THEY DIED OR
DROPPED OUT, AND IN THIS CASE,
THIS PERSON DIED BEFORE THEY
COULD RECEIVE TREATMENT, THEY
WOULDN'T BE CONSIDERED IN THE
TREATMENT GROUP AND IT IS THIS
IMMORTAL TIME THAT IS INDUCING
BIAS AND IT CAN BE POTENTIALLY
PROBLEMATIC WHEN IT IS ONLY
PRESENT IN ONE TREATMENT
STRATEGY SO SAY IMMORTAL TIME
WAS PRESENT WHEN YOU UNDERWENT
SURGERY BUT IN ANOTHER GROUP
IT DIDN'T HAPPEN OR IF ONE
PATIENT HAS MORE IMMORTAL TIME
THAN ANOTHER.
SO YOU CAN IMAGINE AFTER THE
DIAGNOSIS AND SURGERY, THEN
THEY HAVE TO GO THROUGH
CHEMOTHERAPY AND MAYBE ANOTHER
SURGERY, THEY CAN HAVE A
LONGER IMMORTAL TIME.
SO THE IMPORTANT QUESTION HERE
IS NOT DOES IT HAPPEN BUT DOES
IT IMPACT OUR RESULTS.
AND THE ANSWER IS ACTUALLY
YES.
SO THIS IS A FUN, I WOULD SAY,
EXAMPLE WHERE IN 2001, SOME
INDIVIDUALS LOOKED AT IF
WINNING AN OSCAR WAS AN IMPACT
IN LONGER SURVIVAL THAN AN
ACTOR WHO DIDN'T.
THIS STUDY SHOWED THAT THERE
WAS A FOUR YEARS' DIFFERENCE
BUT A STUDY CAME OUT
AFTERWARDS WHERE THEY DID A
REANALYSIS OF THE DATA AND
LOOKING FOR IMMORTAL TIME THEY
FOUND THERE WAS NO DIFFERENCE
BETWEEN THE GROUP THAT WON
OSCARS AND THE OTHER THAT
DIDN'T.
AND WHILE THIS EXAMPLE IS A
LITTLE SILLY, IT SHOWS IT CAN
HAVE IMPACT ON CLINICAL
DECISION-MAKING.
HERE IS AN EXAMPLE IN 2019
THAT CAME OUT IN INTERNAL
MEDICINE.
A STUDY CAME OUT WHERE THEY
WERE GIVING PATIENTS
ANTIBIOTICS DURING THE FIRST
TWO YEARS OF THEIR ADMISSION
WHEN THEY WERE ADMITTED WITH
ASTHMA, THAT IT WAS A LONGER
SUSTAINED POTENTIAL OUTCOME
HOWEVER THE OTHER MONITOR ON
THE LEFT CAME OUT AND SAID THE
IMMORTAL TIME ANALYSIS IS
NEEDED AND WHEN THE AUTHOR
WENT BACK AND REANALYZED THEIR
DATA, THEY ENDED UP TRASHING
THE ENTIRE ARTICLE BECAUSE
THEIR FINDINGS FULLY CHANGED
AND AS A MATTER OF FACT, WENT
FROM THE POINT WHERE THE
ANTIBIOTICS FROM ASSOCIATED TO
A 2-DAY LONGER LENGTH OF STAY
TO ACTUALLY BEING NO
DIFFERENCE IN LENGTH OF STAY.
IN THE CANCER WORLD, THIS HAS
ALSO HAPPENED.
THERE WERE SEVERAL
OBSERVATIONAL STUDIES ON
SURVIVAL OF BREAST CANCER
PATIENTS TO THE POINT WHERE IT
WAS GOING TO POTENTIALLY
IMPACT THE CLINICAL
RECOMMENDATIONS THAT THESE
STUDIES ARE CONSISTENTLY
FINDING.
SO THEY ENDED UP RUNNING
SEVERAL RANDOMIZED CONTROL
TRIALS, CLINICAL TRIALS AND
FOUND THAT UNDERGOING SURGERY
HAD NO IMPACT ON THE SURVIVAL
OF THE PATIENT AND RESULTED IN
NOT RECOMMENDING SURGERY BUT
THERE WAS A POTENTIAL THAT
WOMEN WERE UNDERGOING
UNNECESSARY MASTECTOMIES FOR
YEARS.
AND TO MOVE AWAY FROM THESE
EXAMPLES, A WORKING GROUP I
WAS A PART OF SIMULATED DATA
WHERE WE FORCED THE DATA TO
SAY THERE WAS NO EFFECTIVE
TREATMENT ON THE OUTCOME USING
THAT BREAST CANCER EXAMPLE.
SO IN OUR DATASET, WE DESIGNED
IT SO THERE WAS NO EFFECT WITH
SURGERY.
AND WE FOUND WHEN WE ANALYZED
OUR DATA AND ACCOUNTED FOR
MORTAL TIME, THE ACTUAL HAZARD
RATIO WAS .4 AND IN ALL OF THE
ANALYSIS, WE GOT NONE OF THEM
CROSSING NULL WHICH WAS THE
TRUTH IN THE DATA SO THE BIAS
INTRODUCED BY THE BIAS ALONE
IS QUITE SUBSTANTIAL.
SO IN SHORT, THE MORTAL TIME,
DOES IT IMPACT CANCER, THE
OVERWHELMING ANSWER IS YES.
SO NOW WE USE OBSERVATIONAL
DATA FOR A LOT OF OUR STUDIES.
IT IS IMPORTANT WE USE REAL
WORLD DATA BUT WE KNOW THERE
IS A IMMORTAL TIME PROBLEM SO
WE LOOK AT THE BIAS THE SAME
WAY WE ANALYZE STUDIES ABOUT
CONFOUNDING DATA BIAS, HOW CAN
WE ADDRESS THIS IN A WAY THAT
DOESN'T REFLECT AFFORD
ABILITY IN OUR STUDIES.
SO TARGET TRIAL EMULATION IS
REALLY A WAY TO DESIGN
OBSERVATIONAL STUDIES AND THIS
IS PUBLISHED IN 2016 BUT I
REMEMBER DURING MY TRAINING,
YOU KNOW, DURING OBSERVATIONAL
TRIAL, OBSERVATIONAL STUDY IN
A OBSERVATIONAL TRIAL AND THIS
APPROACH IS JUST A WAY TO FARM
MALLIZE HOW WE DESCRIBE IT AND
TO INCLUDE THIS INFORMATION IN
OUR PUBLICATION.
SO ESSENTIALLY WHAT YOU DO IS
YOU GO THROUGH ALL OF THE
PROTOCOL COMPONENTS OF THE
TARGET TRIAL AND THEN YOU
EMULATE EACH OF THOSE
COMPONENTS IN YOUR
OBSERVATIONAL DATA.
SO HERE IS AN EXAMPLE AND
AGAIN, I KNOW THIS IS PROBABLY
SMALL AND I WILL ZOOM IN IN A
SECOND BUT THIS IS AN EXAMPLE
LOOKING AT SURGICAL INFECTION
WITH PATIENTS WITH SECOND
STAGE EARLY LUNG CANCER,
TARGETED IN THIS POTENTIAL
TRIAL, A TARGET TRIAL IN RAN
ATTEMPT TO CONDUCT THE STUDY
INCLUDED AND WHILE ADULTS ARE
IN THE STUDY.
TO WE LOOK AT TWO MONTHS AND
SIX MONTHS AND EVEN THOUGH WE
CAN SEE OH THAT IS GOING TO
INTRODUCE IMMORTAL TIME
BECAUSE WHAT THE STRATEGIES
HAVE OVER TIME AND THEN HERE
WE CAN SEE IN A TREATMENT, IN
THE TRIAL THEY ARE RANDOMLY
ASSIGNED AND THEN THE
TREATMENT STRATEGY,
RANDOMIZATIONS FOR CLONING
WHICH IS THE CLONING PROCESS.
SO THIS IS A WAY TO REALLY
EXPLICITLY DESCRIBE AND COME
UP WITH SOLUTIONS IN ORDER TO
MAKE SURE THAT YOU HAVE A WELL
DESIGNED OBSERVATIONAL STUDY.
SO THERE ARE DIFFERENT METHODS
TO ADDRESS THESE TIME-RELATED
BIASES SO IT IS A WAY TO
INTRODUCE RANDOMIZATION OF
TREATMENT INTO OUR
OBSERVATIONAL DATA AND THE WAY
WE ARE ABLE TO DO THAT IS
BECAUSE YOU KNOW IN BOTH A
CLINICAL TRIAL AND
OBSERVATIONAL STUDY, WHEN WE
INITIALLY ENROLL THOSE
PATIENTS, WE DON'T KNOW WHAT
TREATMENT THEY ARE GOING TO
RECEIVE OR ANY TREATMENT, FOR
THAT MATTER.
IT IS UNKNOWN AT THE START OF
FOLLOW UP.
EVEN IF WE HAVE DATA
COLLECTION ALREADY, TWO YEARS
OF FOLLOW UP DATA ON THEM, ON
THAT DATA ENROLLMENT, WE DON'T
KNOW WHAT THEY ARE GOING TO
SEE AND THAT IS THE SAME FOR
CLINICAL TRIALS BEFORE
RANDOMIZATION.
SO WE USE THIS PATH TO BUILD A
CLINICAL TRIAL IN OUR DATA SO
WE WILL WALK THROUGH WITH TREE
STEPS AND THIS PAPER UNDER
REVIEW, TRYING TO DEVELOPMENT
VISUALIZATION THROUGHOUT AND
HOPEFULLY THIS WILL BE HELPFUL
AND MAKE THIS MESSAGE
UNDERSTAND A LITTLE COMPLEX IN
A WAY THAT IS APPROACHABLE.
SO FOR OUR EXAMPLE HERE,
UNDERGOING SURGERY SIX MONTHS,
182 DAYS TO TIE INTO THE
FIGURE HERE.
AFTER DIAGNOSIS OF EARLY STAGE
LUNG CANCER AND ONE-YEAR
MORTALITY.
SO ON THE LEFT SIDE HERE, WE
JUST HAVE FIVE PATIENTS IN
FOLLOW UP TIME.
WE CAN SEE THE FIRST TWO
PATIENTS ULTIMATELY UNDERGO
SURGERY AND WE CAN SEE IN THE
GREEN THE IMMORTAL TIME IS
PHASED IN IF WE DO A STUDY
WHERE WE FLAG THE THREE WHO
WERE TREATED AND COMPARE THEM
TO THOSE WHO HAD NO TREATMENT.
SO THE TREATMENT DESIGN, YOU
CAN SEE THE FIVE PATIENT WHO
UNDERGO SURGERY, THE FIRST
PATIENT DIES, PATIENT 3 DIES
AFTER SURGERY, PATIENT 4 WE
FOLLOW FOR A FULL YEAR AND
THEY DON'T UNDERGO SURGERY AND
THEN PATIENT 5 WE FOLLOW MOST
OF THE YEAR BUT THEY DIED AT
THE END.
IF YOU ARE VISUALIZING WHAT
THE DATA TABLE LOOKS LIKE, ON
THE RIGHT SIDE YOU CAN SEE
WHAT IT LOOKS LIKE, FIVE ROWS
OF DATA, FIVE PARTICIPANTS, WE
HAVE THE SURGERY DATE THEY
UNDERWENT AND I WOULD LIKE TO
NOTE THAT WE HAVE TO HAVE SOME
KIND OF TIME FRAME OR DATE
ATTACHED TO THE TREATMENT.
IF YOU JUST PUT YES OR NO AND
NOT WHEN THEY DID IT, YOU
CAN'T REALLY USE THIS METHOD.
AND THEN WE HAVE THE OUTCOMES
WHETHER THEY DIED OR, YOU
KNOW, WERE SENSITIVE HERE AND
WHAT THE TOTAL FOLLOW UP TIME
IS.
SO THIS IS A PRETTY STANDARD
HAZARD REDUCTION HERE, COST
REDUCTION, ADDITIONAL
INFORMATION OF POTENTIAL
CONFOUNDERS BUT TO KEEP IT
SIMPLE BECAUSE THESE ARE THE
FIVE VARIABLES --
[INDISCERNIBLE]
SO STEP 1 IS TO CLONE THE
PARTICIPANT AND WHAT WE MEAN
BY CLONE IS WE MAKE COPIES.
SO YOU HAVE FIVE PARTICIPANTS
BUT TEN ROWS OF DATA, DATA
POINTS HERE AND WE RANDOMLY
ASSIGN ONE OF THE CLONES OR
COPIES TO UNDERGO TREATMENT A,
SOME UNDERGO SURGERY AND THE
OTHER IS ASSIGNED TO TREATMENT
B AND NO SURGERY.
IF YOU WANT TO STUDY THREE
ARMS, SURGERY, NO SURGERY AND
THEN CHEMO OR RADIATION AND WE
WOULD MAKE THREE COPIES BUT WE
HAVEN'T TOUCHED THE DATA AT
ALL, WE JUST ADDED PEOPLE TO
THE DATASET AND ADDED WHERE WE
SAY TREATMENT STRATEGY 1
WORKED HERE.
SO THEN THE NEXT STEP IS TO
FOLLOW THE CLONES AND COPIES
AND SENSOR THEM.
SO WHAT DOES THIS MEAN?
FOR PARTICIPANT 1 IN THE LIGHT
BLUE IS THE GROUP THAT
UNDERWENT SURGERY SO THE
QUESTION IS DID THEY UNDERGO
SURGERY IN SIX MONTHS AND YOU
CAN SEE YES, WE CONTINUE TO
FOLLOW THEM AND WE ARE
RECEIVING TREATMENT AND
ASSIGNED TO A GROUP.
SO THE SECOND GROUP WE
DEFINED, THAT PERSON UNDERGOES
SURGERY AT SAY THREE MONTHS
AND SO THERE IS NO INFORMATION
ON THE TREATMENT ARM SO WE
STOPPED FOLLOWING THEM.
FOR PARTICIPANT 2 THEY DON'T
UNDERGO SURGERY SO AT THE SIX
MONTH MARK, WE SENSOR THEM
BECAUSE THE SURGERY IS FLEDDED
FLED-- NEEDED FOR THE
PROTOCOL.
AND THEN THE SECOND GROUP,
THEY DON'T UNDERGO SURGERY, WE
KEEP FOLLOWING THEM AND KEEP
DOING THAT FOR PERSON 3, 4 AND
5 AND LOOK TO SEE WHEN THEY
WERE ASSIGNED, DID THEY
UNDERGO SURGERY AT SIX MONTHS,
IF THEY DO UNDERGO SURGERY IN
THE NONTREATMENT TIME, THEY
GUESS SENSORED AFTER THE
SURGERY.
IF THERE IS A PERSON WHO
UNDERWENT SURGERY AFTER SIX
MONTHS BECAUSE THAT IS NOT
PART OF THE DEFINITION, THEY
FALL INTO THE NONTREATMENT
ARM.
AND THEN THE LAST STEP IS TO
REWEIGHT THE CLONE AND SO
BASICALLY YOU JUST REORGANIZE
THE DATA HERE SO THAT THE FIVE
PATIENTS IN TREATMENT A ARE
TOGETHER, FIVE PATIENTS FROM
TREATMENT B ARE TOGETHER AND
THEN WE SAY OKAY THERE'S THREE
PEOPLE SENSORED AT TIME .4 SO
PERSON NUMBER 1 GETS UPGRADED
TO REPRESENT THOSE INDIVIDUALS
WHICH IS HOW SENSOR WEIGHTING
IS NORMALLY PERFORMED.
YOU CAN SEE THE REGRESSION,
THIS ISN'T ANYTHING NEW.
SENSOR WEIGHTING HAS BEEN
AROUND FOR QUITE A WHILE,
WE'RE JUST APPLYING IT HERE SO
FOR THE SURGERY ARM, USING
CHEMO AND SURGERY ALL OF IT AT
THE SIX MONTH MARK, THAT IS
WHERE EVERYONE IS GOING TO
UNDERGO SURGERY BECAUSE IT HAS
NOT BEEN DESIGNED YET AND THEN
IN THE CHEMO, WE HAD ONE
PERSON WHO HAD SURGERY AND
THEY GET SENSORED AND EVERYONE
ELSE IS UP-WEIGHTED.
SO THIS IS THE SAME PICTURE
AND YOU CAN SEE THE DATASET
HAS A TREATMENT ARM, SEVERAL
ZEROS ASSIGNED WHEN WE
ASSIGNED THE DATA AND THE ONLY
THING WORTH NOTING HERE IS THE
DATA, BECAUSE YOU DON'T WEIGHT
THE DATA BEFORE THE SENSOR
MARK, YOU CAN SEE WE HAVE
OBSERVATIONS HERE ON 182, THE
OUTCOME OF 1 AND THEN THE
SECOND OBSERVATION STARTS AT
183 AND THEN AT 13 WHEN THEY
DIE AND HAVE THE OUTCOME.
AND THIS IS PRETTY STANDARD I
WOULD SAY CLONE SENSOR
WEIGHTING STRATEGY.
THIS IS WHAT THE DATE LOOKS
LIKE WHEN YOU USE SENSOR
WEIGHTING.
THE STRATEGIES OF SENSOR
WAITING HAVE NOT BEEN ADAPTED
HERE AT ALL AND SO YOU SHOULD
BE ABLE TO HANDLE THIS, NO
PROBLEM.
SO HERE ARE THE THREE STEPS,
THAT IS ALL THERE IS TO CLONE
CENSOR WEIGHTING AND AT THE
OUTCOME, YOU CAN PERFORM ANY
ANALYSIS YOU WANT.
YOU CAN USE TREATMENT,
ASSIGNED TREATMENT AND THESE
LAST TWO STEPS ARE ACTUALLY
THE SAME STEPS YOU FACE WHEN
ESTIMATING THE PROTOCOL EFFECT
IN A CLINICAL TRIAL.
SO IF YOU HAVE EVER GONE FROM
THE INTENTION TO TREAT TO THE
PREFERRED PROTOCOL EFFECT, OR
THE ACTUAL TREATMENT EFFECT IN
A CLINICAL TRIAL, THESE ARE
THE SAME THINGS YOU WOULD HAVE
TO DO WITH THE DATA IN ORDER
TO ESTIMATE THAT.
SO THIS REALLY ISN'T
DEVELOPING ANYTHING NEW, IT IS
TAKING THINGS WE ALREADY DO
AND APPLYING THEM TO THIS
TARGET TRIAL AND FRAMEWORK.
THE OTHER THING WE CAN DO WITH
CLONE SENSOR WEIGHTING IS
CREATE FLUID DIAGRAMS TO
INCLUSION, EXCLUSION,
BASICALLY THE SAME YOU WOULD
IN A CLINICAL TRIAL.
THE ONLY DIFFERENCE IS IN THE
MIDDLE HERE YOU SEE THERE IS A
CLONING STEP AND THEN YOUR
DATASET DOUBLES.
SO IN THIS EXAMPLE WE GO FROM
2300 TO 2600 PARTICIPANTS OR
2800 PARTICIPANTS, MY
APOLOGIES AND HALF OF THEM GET
ASSIGNED TO TREATMENT A, HALF
GET ASSIGNED TO TREATMENT B,
YOU CAN SEE WHAT THEIR FOLLOW
UP IS AND HOW MANY ARE
NONADHERENT.
AND WHEN YOU USE THESE
APPROACHES, YOU NOT ONLY
INCLUDE POTENTIALLY THOSE
FIGURES THAT YOU SAW ON THE
LAST SLIDE TO VISUALIZE HOW WE
SET THE DATA BUT ALSO FOR THIS
TABLE WHERE YOU OUTLINE THE
TARGET TRIAL AND OBSERVATIONAL
DATA.
NOW, YOU MAY BE THINKING
AREN'T THERE EASIER WAYS TO
ALLOW FOR IMMORTAL TIME AND
THE ANSWER IS YES AND NO AND I
WILL EXPLAIN WHY.
SO GOING BACK TO THE
ACCUMULATION STUDY FROM THE
RESEARCH TEAM WE PERFORMED, WE
DID FIND TWO CLONE SENSOR
WEIGHTING CASES.
ONE WAS USED A TIME BARRIER
EXPOSURE AND THE OTHER CALLED
A LANDMARK APPROACH.
SO TO GET INTO IT AND THIS IS
A NICE TABLE WE INCLUDED TO
THE ASSIGNED PATIENTS INCLUDED
AND INTERPRETATION BUT WHEN
YOU DO A TIME VARYING
EXPOSURE, YOU FOLLOW A PERSON,
CONSIDER THEM UNEXPOSED TILL
THEY UNDERGO TREATMENT OR
SURGERY AND THEN TOGGLE THEM.
SO YOU END UP WITH TWO ROWS OF
DATA PER PARTICIPATE AND SOME
DATA IS INCLUDED IN THE
NONINCLUDED ARM AND SOME OF IT
ENDS UP IN THE INCLUDED ARM.
SO THEN YOU HAVE PATIENTS WHO
UNDERWENT SURGERY TO PATIENTS
WHO NEVER UNDERWENT SURGERY.
A DIFFERENCE OF SIX MONTHS IS
FINE BUT THE QUESTION HERE IS
ONE TO TWO YEARS THE SAME?
TYPICALLY THE ANSWER IS NO BUT
SOMETIMES THAT IS NOT TRUE AND
CLONE-CENSOR-WEIGHTING IS
SOMETHING WE CAN USE BUT A THE
VARIATION THAT WILL OCCUR IN
THE NITTY-GRITTY, NOT EVERY
PATIENT IS GOING TO UNDERGO
SURGERY BY THE SAME SURGEON,
THEIR SURGERY TIMES WILL VARY,
THAT WILL VARY FROM PATIENT TO
PATIENT BUT SOME PEOPLE MIGHT
UNDERGO SURGERY AT SIX MONTHS,
OTHERS AT TWO OR THREE MONTHS
BUT THE TREATMENT VARIATION IS
WHERE WE'RE SAYING IS IT
RELEVANT AND DOES IT MATTER?
ALL THE PEOPLE WHO UNDERGO
SURGERY ARE ESSENTIALLY THE
SAME AND THERE ARE NO
DIFFERENCE THAT WILL
FUNDAMENTALLY IMPACT THE
TREATMENT ON THE OUTCOME.
HOWEVER WHEN YOU ARE COMPARING
SOMEONE WHO GOES UNDER SURGERY
IN SIX MONTHS VERSUS TWO
YEARS, THAT DIFFERENCE IS NO
LONGER RELEVANT AND THAT IS A
FUNDAMENTAL ASSUMPTION WE MAKE
EVERY TIME WE DO A
OBSERVATIONAL STUDY SO WHETHER
YOU SPELL IT OUT, IT IS THERE.
SO TIME VARYING EXPOSURE
REALLY HIGHLIGHTS A CONCERN
THAT WE ARE MAKING THIS
ASSUMPTION WITH OUR DATA.
AND THEN THE SECOND EXAMPLE IS
TO THE LANDMARK APPROACH.
AND WHAT A LANDMARK APPROACH
DOES, IN THIS CASE THE
SIXTH-MONTH POINT, WE REQUIRE
EVERYONE TO BE ALIVE AND NOT
DROP OUT.
WE CHECK TO SEE WHAT
EVERYONE'S TREATMENT IS DURING
THAT PERIOD AND START FOLLOW
UP AT THAT LANDMARK SO WE
ACTUALLY START OUR SURVIVAL
ANALYSES, START FOLLOW UP AT
SIX MONTHS INSTEAD OF AT DATA
DIAGNOSIS.
AND WHAT THIS DOES IS IT
ESTIMATES THE EFFECTS OF
TREATMENT OF UNDERGOING
SURGERY IN SIX MONTHS BUT OF
ALL THOSE WHO SURVIVE DURING
THE LANDMARK.
SO WE'RE NOW DROPPING ANYONE
WHO DIED WITHIN THE SIX MONTHS
OR DROPPED OUT IN THE FIRST
SIX MONTHS AND THEN WE HAVE TO
ASK OURSELVES ARE THE
INDIVIDUALS WHO SURVIVE AND
STAYING IN THE STUDY FOR
X-NUMBER OF MONTHS, THE SAME
AS THOUGH WHO DO NOT?
AND SIMILAR TO THE CLINICAL
TRIAL WITH THE OBSERVATIONAL
DATA ISSUE, WE HAVE TO ASK
QUESTIONS WHETHER OUR DATA
WILL BE GENERALIZABLE TO THE
LARGER CANCER POPULATION AND
THOSE WHO SURVIVE AT SIX
MONTHS IS THAT REPRESENTATIVE
AS A WHOLE?
AND WITH BREAST CANCER, FOR
EXAMPLE, SURVIVAL RATES ARE
PRETTY HIGH AND YOU MAY HAVE
OR KNOW PEOPLE IN YOUR DATASET
WHO DROP OUT AND DIE IN SIX
MONTHS BUT FOR THOSE WHO HAVE
A LONGER TREATMENT, THIS MAY
HAVE A REALLY LARGE IMPACT.
IT DOESN'T BIAS THE STUDY, IT
JUST IMPACTS WHO THE RESULTS
ARE VIABLE.
SO FOR DOCTORS TO USE THIS
DATA ANALYSIS, IF YOU THINK
ABOUT IT, THEY ARE TRYING TO
MAKE TREATMENT DECISIONS AT
THE TIME OF DIAGNOSIS.
THEY DON'T KNOW IF THEIR
PATIENT WILL DIE IN SIX MONTHS
AND I MEAN IDEALLY THEY WON'T
BUT THE DATA WE'RE GIVING THEM
IS IF YOUR PATIENT LIVES AT
LEAST SIX MONTHS AND UNDERGOES
SURGERY DURING THAT TIME, HERE
IS WHAT THEIR SURVIVAL LOOKS
LIKE COMPARED TO THOSE WHO
LIVE SIX MONTHS AND DON'T
UNDERGO SURGERY AND IF YOU ARE
TRYING TO MAKE A DECISION WITH
TIME POINT ZERO, YOU DON'T
ACTUALLY KNOW IF THEY ARE
GOING TO LIVE THAT SIX MONTHS
SO YOU MAY NOT BE SURE AND CAN
APPLY THE DECISION-MAKING
PRETTY TRICKY.
SO BOTH THESE APPROACHES CAN
ADJUST AND ACCOUNT FOR CENSOR
OR IMMORTAL TIME BIAS, THEY
INTRODUCE THE ISSUES WITH
INTERPRETABILITY OF YOUR
RESULTS.
AS YOU CAN IMAGINE, CLONE
CENSOR WEIGHTING DOES NOT HAVE
THESE LIMITATIONS.
WE HAVE A PROTOCOL WHERE WE'RE
COMPARING THE EFFECTS OF
TREATMENT AND THERE IS NO
COMPARISON AND THEN AS YOU SAW
IN THE FIGURES, WE START AT
SIX MONTHS SO WE DON'T HAVE
THE FORCED SURVIVAL FOR AN
EXPECTED AMOUNT OF TIME IN OUR
ANALYSIS.
SO THIS IS ONE OF THE
STRENGTHS COMPARED TO OTHER
ANALYSES AND WHY I PREFER
USING THE
CLONE-CENSOR-WEIGHTING OVER
THE OTHER APPROACHES.
ANOTHER WAY THAT USING
CLONE-CENSOR-WEIGHTING THAT IS
HELPFUL IS YOU MIGHT BE
THINKING MOST CANCER
TREATMENTS AREN'T USUALLY A
SINGLE ONE-TIME OCCURRENCE.
THEY MIGHT BE HORMONAL
INDEFINITELY OR SOMETHING THEY
ARE ON FOR YEARS AND HOW DO
YOU ANALYZE DATA WHEN YOU HAVE
THESE COMPLEX TREATMENT
PROTOCOLS?
WELL, THE SHORT ANSWER IS TIME
EXPOSURES CAN'T HANDLE IT
UNLESS YOU ARE GOING TO TOGGLE
BUT IT GETS VERY COMPLICATED.
AND THE LANDMARK APPROACH, YOU
CAN'T REQUIRE SOMEONE TO
REMAIN ALIVE AND INCLUDED IN
THE DATASET FOR POTENTIALLY
YEARS IN ORDER TO CAPTURE ALL
OF THE SPECIFIC THINGS THAT
HAVE TO HAPPEN SO THEY CAN BE
COMPLIANT FOR TREATMENT.
SO HOPEFULLY AS I AM ALLUDING
TO, YOU CAN CLONE THESE THINGS
IN THE CLONE-CENSOR-WEIGHTING.
AND I WON'T GO THROUGH ALL
THESE BUT HERE IS AN EXAMPLE
OF THE MULTISTEP OR MULTIMODAL
TREATMENT APPROACH, YOU HAVE
THOSE WHO HAVE BEEN CLONED BUT
SENSORED AND THIS IS IN STEP
2, THE COMPARISON OF CHEMO AND
RADIATION WITH SURGERY VERSUS
CHEMO SHOW AND RAID RADIATION
WITHOUT SURGERY, NOBODY IS
UNDERGOING NOTHING HERE AND
EACH ONE OF THEM HAVE MULTIPLE
STEPS AND AS YOU CAN SEE, THIS
CLONE-CENSOR-WEIGHTING
APPROACH CAN HANDLE THIS
MULTISTEP PROCESS.
ANOTHER THING THAT CAN HELP IS
WITH WHAT WE REFER TO AS
STATIC TREATMENT WHICH
ESSENTIALLY MEANS THE
TREATMENT IS DELIVERED OVER
TIME BUT WHEN YOU DELIVER IT,
IT IS KNOWN.
SO IN THIS EXAMPLE YOU ARE
LOOKING AT ANNUAL MAMMOGRAPHY.
SO WOMEN UNDERGO ANNUAL
MAMMOGRAPHY, THEY ARE GOING TO
UNDERGO IT EVERY 12 MONTHS SO
IF I GET MINE IN MAY, I AM
GOING TO GET IT AROUND NEXT
MAY AND THE NEXT MAY, ET
CETERA.
SO THIS CAN BE PROLONGED
POTENTIALLY INDEFINITELY, THIS
KIND OF STATIC TREATMENT BUT
BECAUSE IT HAPPENS IN 12
MONTHS, WE CAN KIND OF
ANTICIPATE WHEN IT WILL OCCUR
BUT AGAIN BETWEEN THE RAW DATA
AND NEXT FIGURES, WE CAN BUILD
IN CLONE-CENSOR-WEIGHTING TO
ENABLE THESE KIND OF
TREATMENTS.
AND THEN WE CAN USE THEM TO
HANDLE DYNAMIC TREATMENT
STRATEGIES.
SO DYNAMIC TREATMENT
STRATEGIES IS WHEN THE
DECISION TO UNDERGO TREATMENT
IS INFLUENCED BY HEALTH STATUS
OR THE DECISION OF THE PATIENT
WHICH HAPPENS AT FOLLOW UP.
SO THIS IS AN EXAMPLE OF MEN
DIAGNOSED WITH PROSTATE CANCER
AND WE'RE COMPARING WHETHER OR
NOT THEY UNDERGO TREATMENT
IMMEDIATELY SO WITHIN 90 DAYS
OF DIAGNOSIS OR THEY DEFER
TREATMENT UNTIL EITHER THEY
SEE PROGRESSION OF THE
DISEASE, SO THAT IS WHAT THE P
STANDS FOR, THERE IS A CERTAIN
TIME FRAME WHICH YOU WOULD
UNDERGO TREATMENT OR IF THE
DISEASE HAS BEEN PROGRESSED
FOR THREE YEARS, YOU WOULD
RECEIVE TREATMENT AT THE
3-YEAR MARK.
SO THIS IS VERY COMPLEX AND
YOU HAVE TO MONITOR LEVELS BUT
AGAIN AS YOU CAN HOPEFULLY SEE
BY THESE FIGURES AND I AM
HAPPY TO TAKE QUESTIONS ON
THESE SPECIFIC APPROACHES,
THIS IS THE WAY WE USE TO
HANDLE THE MULTISTEP
TREATMENTS AS WELL.
SO NOW YOU CAN SEE THE
APPROACH ON WIDE VARIETY OF
STANDARDS, TREATMENTS, WAYS TO
FOLD IN ENVIRONMENTAL AND
UNINTENDED EXPOSURES AND
ADDITIONALLY IF YOU ARE
INTERESTED IN USING THIS
APPROACH BUT HAVEN'T DONE IT
BEFORE, THERE ARE RESOURCES
AVAILABLE.
TO ME ON, THE PAPER ON THE
LUNG CANCER WE KIND OF TOUCHED
ON EARLIER, ONE OF THE
COAUTHORS OF THAT PAPER, SHE
USED DATA CODES TO DO THE
CLONE-CENSOR-WEIGHTING AND
THEN ON THE OTHER STUDY OUR
RESEARCH GROUP DID, WE USED
SAS CODES TO DO THE
CENSOR-WEIGHTING.
SO THE TWO MOST COMMON CODING
WITH SOFTWARE CAN HELP BUT
EVEN HAVING THE CODING
AVAILABLE CAN HELP YOU CODE IT
IN OTHER PLATFORMS AS WELL.
SO THERE ARE RESOURCES OUT
THERE TO HELP YOU GET STARTED
SO YOU DON'T HAVE TO START
FROM SCRATCH.
NOW TO KIND OF SUMMARIZE AND
WRAP THINGS UP SO WE HAVE TIME
FOR QUESTIONS, HOPEFULLY BY
NOW, WE ARE ALL ON THE SAME
PAGE WITH OBSERVATIONAL DATA
AND IT IS REALLY A CRITICAL
IMPORTANCE TO A POPULATION,
HOW IT ALLOWS US TO ASK
QUESTIONS OF A DIVERSE
POPULATION THAT ARE ALSO
INTERESTED IN HOW MINORITY AND
HEALTH DISPARITIES ARE ACUTELY
INTERESTED IN TO HELP ADDRESS
HEALTH EQUITY.
THERE IS ALSO A LOT OF
QUESTIONS WE'RE ASKING THAT
CAN'T BE DONE IN CLINICAL
TRIALS SO OBSERVATIONAL DATA
IS ALL WE'RE REALLY LEFT WITH.
HOWEVER, AS WE'RE HOPEFULLY
ALL AWARE, APPROPRIATE METHODS
ARE NEEDED TO ADDRESS
POTENTIAL BIASES, IMMORTAL
TIME, ET CETERA AND CAN HAVE A
MAJOR IMPACT ON RESULTS AND
CONCLUSIONS SO REALLY NEEDS TO
BE ADDRESSED AND HANDLED.
SO AS WE WALK THROUGH, THE
IMMORTAL TIME AND
CLONE-CENSOR-WEIGHTING ARE
THINGS WE USE IN THIS
OBSERVATIONAL DATA, CAN IT
ACCOUNT FOR TIME VARYING
CONFOUNDING AND CONTRA
INDICATIONS, IT DEPENDS ON HOW
THE STUDY IS DESIGNED AND I
BELIEVE IT CAN BE JUST AS
STRONG OR STRONGER BECAUSE OF
THE OBSERVATIONS WE CAN
INCLUDE IN THE DATA WHEN THE
CLINICAL TRIAL IS RUN
APPROPRIATELY.
AND WHILE I DIDN'T TOUCH ON IT
HERE, WE CAN ACCOUNT FOR THE
TIME-VARYING CONFOUNDING AND
IS SOME APPROACHES, YOU CAN'T
DO THAT.
YOU CAN BUILD FOR CONTRA
INDICATIONS AND BECAUSE YOU
ARE DRAFTING YOUR PROTOCOL,
YOU CAN INCLUDE THE
CONTRAINDICATIONS TO BE
INCLUDED IN YOUR STUDY, LIKE
IN THE MAMMOGRAPHY STUDY,
THOSE WITH BREAST CANCER WOULD
NO LONGER BE REQUIRED TO
UNDERGO MAMMOGRAPHY BUT STILL
NEED TO BE INCLUDED IN THE
TREATMENT STRATEGY.
SO YOU CAN BUILD THOSE IN ON A
TARGET TRIAL EMULATION WITH
ALL THOSE VARIANCES THAT
OCCUR.
AND SO LIKE AGAIN WITH
MAMMOGRAPHY, WHEN IS THAT
USEFUL, DOES IT OCCUR IN THE
CLINICAL SETTING, SO WE NEED
TO BUILD THOSE THINGS THAT
OCCUR, YOU KNOW, THESE EVENTS
THAT HAPPEN INTO OUR DATA AS
WELL.
YOU KNOW, IT IS NOT USEFUL TO
EXCLUDE THEM IN OUR DIAGNOSIS
FOR CANCER, FOR EXAMPLE, WHEN
LOOKING AT CANCER SCREENING AS
AN EFFECTIVE TOOL TO DETERMINE
WHETHER IT WEIGHED IN
MORTALITY.
AND THEN FINALLY THESE METHODS
USED AS AN ESTIMATE OF REAL
TREATMENT STRATEGIES, YOU
DON'T HAVE THE CAVEATS OF WHAT
DAYS ARE YOU UNDERGOING
SURGERY, WHAT DOES IT MEAN TO
KNOW ONLY THE RESULT IN THE
PATIENT POPULATION THAT
SURVIVED ONLY SIX MONTHS SO
THE QUESTIONS CAN BE DIRECTLY
APPLIED TO THE CLINICAL STUDY
AND HOPEFULLY IMPROVE CLINICAL
DECISION-MAKING ON THE
TREATMENT OF PATIENTS AND
HOPEFULLY WHEN APPLIED WITH
THE HEALTH EQUITY LENS CAN
HELP ADDRESS HEALTH EQUITY.
AND WITH THAT, I AM GOING TO
STOP TALKING.
I WANT TO QUICKLY ACKNOWLEDGE
NIMHCD THAT FUNDS MY WORK AND
THE INTERNATIONAL SOCIETY OF
PHARMACOEPIDEMIOLOGY USING THE
GRAPHICS YOU SAW BASED OFF
CHICKEN SCRATCH DRAWINGS IN
NOTEBOOKS THAT WE STARTED WITH
AND THEN THE ISPE CANCER
SPECIAL INTEREST GROUP THAT
PROVIDED USEFUL FEEDBACK.
AND THEN EVERYONE ON THE LEFT
ARE MY COAUTHORS ON THIS PAPER
THAT I WOULD LIKE TO
ACKNOWLEDGE AND IT IS
CURRENTLY UNDER REVIEW BUT
HOPEFULLY OUR PAPER WILL BE
OUT IN PUBLISHING SOON AND YOU
WILL BE ABLE TO REVIEW THIS IN
MORE DEPTH AND START USING
CLONE-CENSOR-WEIGHTING IN YOUR
OWN RESEARCH.
THESE ARE MY REFERENCES AND
WITH THAT I WILL TAKE
QUESTIONS AND FEEL FREE TO
E-MAIL ME LATER IF YOU WANT TO
DISCUSS FURTHER ANY OF THIS
THAT I HAVE HERE.
>> EXCELLENT, THANK YOU, THAT
WAS AN EXCELLENT PRESENTATION,
PAULA AND I THINK THESE
METHODS COULD BE REALLY USEFUL
FOR OBSERVATIONAL STUDIES.
SO WHILE I WAIT FOR THE
AUDIENCE TO SUBMIT THEIR
QUESTIONS, I WILL PUT THE LINK
ON THE SCREEN AND ASK YOU A
QUESTION OF MY OWN.
WHAT ARE SOME OF THE
LIMITATIONS OF HOW --
[INDISCERNIBLE]
>> I THINK THE BIGGEST
LIMITATIONS WITH OBSERVATIONAL
STUDIES WITH
CLONE-CENSOR-WEIGHTING IS
GOING TO BE THE DATA.
YOU NEED TO KNOW THE TIMING OF
THE TREATMENTS TO BE ABLE TO
FOCUS ON THEM WHEN THEY
DEVIATE AND NOT ALL DATASETS
INCLUDE THAT KIND OF
DELINEATION.
SO FOR EXAMPLE, IN A CANCER
STUDY, IF A PATIENT UNDERGOES
SURGERY AND THEN UNDERGOES
CHEMO AND RADIATION, IF YOU
DON'T SAY WHEN IT HAPPENED,
YOU CAN'T USE THESE APPROACHES
IN YOUR STUDY ALTHOUGH IT MAY
BE PART OF THE DESIGN TO NOT
LOOK AT TREATMENT IN YOUR
DATA.
SO ANY KIND OF DATA, NOT JUST
TREATMENT BUT CLINICAL CENTER
DATA, CLINICAL CENTER AT A
HOSPITAL, KINDS OF
ADMINISTRATIVE, INSURANCE,
MEDICARE, MEDICAID, ALL OF
THESE DATA SOURCES INCLUDE
TREATMENT INFORMATION WE NEED
TO DO THIS BUT YEAH, DATA, I
WOULD SAY, AND HAVING THE
VARIABLES IN DATA ARE REALLY
THE ONE LIMITATION I WOULD SAY
TO APPLYING THIS.
>> YEAH, I THINK THAT'S A
PROBLEM WITH THE DATA.
THANK YOU.
SO WHILE WAITING FOR OTHER
QUESTIONS, I HAVE ANOTHER
QUESTION FOR YOU.
CAN TARGET TRIAL EMULATION
INFLUENCE
CLONE-CENSOR-WEIGHTING OF
RESEARCH?
>> YES, AND I WILL SAY TO
DATE -- AND I TRIED TO FIND
EXAMPLES OF THIS, WAS IN TOUCH
WITH SEVERAL COAUTHORS OF THAT
PAPER, IT DOESN'T LOOK LIKE IT
HAS BEEN USED TO DATE WITH
HEALTH DISPARITIES EXPLICITLY.
SO IN THE ONE PAPER, LUNG
CANCER SURGERY LOOKING AT
OLDER ADULTS WHO WERE EXCLUDED
FROM THE TRIALS WHEN LOOKING
AT THE EFFECTS OF SURGERY ON
SURVIVAL AND HOPEFULLY WHILE
CLINICAL TRIALS ARE NOT
EXCLUDING RACIAL OR ETHNIC
POPULATIONS, THEY MAY BE
EXCLUDING THEM BASED ON ACCESS
TO TRIALS AND THE CLINICAL
TRIALS THAT ARE INCLUDED SO WE
CAN CONDUCT STUDIES LOOKING AT
THIS APPROACH AND
EFFECTIVENESS TO CANCER
TREATMENTS IN THE SMALLER
POPULATION IF YOU ARE
INTERESTED IN ORDER TO MAKE
THE POPULATIONS MORE DIVERSE
AND TO MAKE SURE THAT WE'RE
LOOKING AT THE EFFECTS OF THIS
POPULATION OR EFFECTS OF THESE
TREATMENTS ON THE POPULATION.
>> AND CAN THESE FACTORS ALSO
SHOW DETERMINANTS OF HEALTH?
>> YEAH, I THINK THIS METHOD
IS RELATIVELY NEW BUT THERE IS
A LOT OF THINGS WE LOOK AT.
SOCIAL DETERMINANTS OF HEALTH
WHERE THERE IS A SIGNIFICANT
LINE, FOR EXAMPLE, WHEN WE
COMMONLY LOOK AT PAPERS, WE
LOOK AT EDUCATION ON LIFE
EXPECTANCY OR OVERALL
SURVIVAL, WHAT WE DO IS LOOK
AT LIFE EXPECTANCY AT AGE 25
AND THEN CAPTURE EDUCATION AT
25 WHICH ESSENTIALLY IS DOING
A LANDMARK APPROACH IN THESE
KIND OF ANALYSES.
BUT YOU KNOW THE SURVIVAL
DOESN'T -- NOT EVERYONE LIVES
TILL AGE 25.
UNFORTUNATELY THERE ARE
CHILDHOOD CANCER AND OTHER
DISEASES.
THERE IS ALSO A LOT OF OTHER
THINGS THAT I WOULD SAY HAVE
STRUCTURAL RACISM AND OTHER
INEQUITIES THAT HAVE A HUGE
IMPACT ON THAT CAN CAUSE
MORTALITY IN CHILDHOOD AND
EARLY ADULTHOOD THAT WE DON'T
GET TO INCORPORATE IN THESE
STUDIES BECAUSE WE START AT
25.
YOU KNOW THINGS LIKE
EXPERIENCES WITH STRUCTURAL
RACISM, THEY START AT BIRTH
BUT WE MAY NOT BE CAP THAT
CAPTURING OR APPROACHING THEM.
SO THERE IS A LOT OF SPACE TO
LOOK AT THESE TRADITIONAL
METHODS AND A LOT OF THE
PAPERS AND ALL OF THE PAPERS
REALLY THAT I FOUND THAT USED
THIS METHOD HAVE FOCUSED ON
TREATMENT, SO MEDICATION,
TREATMENT, CHEMO, RADIATION,
THOSE KIND OF THINGS AND
HAVEN'T REALLY LOOKED AT THE
EXPOSURE.
BUT I THINK THAT IS AN
INTERESTING PLACE TO TAKE THIS
NEXT.
>> YEAH, I DEFINITELY AGREE
AND MY WORK HAS SHOWN THAT
ALSO.
SO I WAS GOING TO ASK YOU HOW
I COULD APPLY THAT METHOD BUT
THAT MIGHT BE A LONG
CONVERSATION.
>> YEAH, BUT I THINK ANY TIME
WHERE YOUR EXPOSURE OR WHERE
YOU ARE EMBOLDENING PEOPLE AND
MEASURE THEIR EXPOSURE AT A
SECOND DATE, SO A SECOND
WEIGHTING OF THE STUDY
COLLECTION DATA IF YOU HAVE
SOME OF THESE COPORT STUDIES
AND WHAT NOT, IF YOU ONLY
RESTRICT IT TO PEOPLE WHO
PARTICIPATE IN THE SECOND
WEIGHTING, PAW BE INTRODUCING
IMMORTAL TIME IN YOUR STUDIES.
SO IT IS DEFINITELY A PLACE I
CAN SAY IT IS HELPFUL.
I THINK HAVING THE MEASURES TO
APPROPRIATELY CAPTURE SOME OF
THESE DETERMINANTS AND
DETERMINANTS OF HEALTH WILL BE
AN ISSUE BUT WHERE WE HAVE
THAT DATA, WE CAN START
CONDUCTING THESE QUESTIONS,
WHERE THEY ARE A LITTLE
STRONGER INSTEAD OF JUST
LIMITING WHERE WE ASK THE
QUESTIONS, BECAUSE THERE'S
STRUCTURAL RACISM IN
CARDIOVASCULAR DISEASE FOR
EXAMPLE IN YOUR RESEARCH TO IS
IT ASSOCIATED WITH
CARDIOVASCULAR DISEASE AND THE
WITH A STRONGER EVIDENCE,
HOPEFULLY WE WILL BE ABLE TO
INTERVENE IN THE POLICIES AND
THERE WILL BE LESS PUSHBACK ON
WELL IT COULD BE THIS OR THIS
BUT NOBODY IS PUSHING BACK ON
A CLINICAL TRIAL WHEN WHEN --
WHEN YOU SAY THIS TREATMENT
CAN HELP AS SHOWN IN CLINICAL
TRIALS SO HOPEFULLY WHEN WE
LOOK AT OBSERVATIONAL DATA, IT
CAN DO THE SAME THING.
>> ABSOLUTELY.
AND ANOTHER QUESTION FOR YOU,
I KNOW YOU MENTIONED HOW THESE
METHODS ARE APPLIED BY HOW
ABOUT YOU, HOW DO YOU USE
THESE METHODS IN YOUR
RESEARCH?
>> YEAH, THAT IS ONE THING
THAT I DUG INTO MY RESEARCH
WHICH WAS HEALTHCARE OUTCOMES.
SO LOOKING AT THE CANCER BASE,
THERE ARE PLACES TO GO AND
THEN ALSO LOOKING AT HOW
NEIGHBORHOOD EFFECTS AND
CHILDHOOD EXPOSURES AND KIND
OF THESE LONG EXPOSURES TO
RACISM AND ENVIRONMENTAL
FACTORS THAT OCCUR MORE LIKELY
IN FORMAL NEIGHBORHOODS, HOW
THEY AFFECT BOTH THE TREATMENT
OF INDIVIDUALS AND THEIR
OUTCOMES AS WELL.
SO I THINK MY NEXT STEP AS I
KEEP ALLUDING TO AS WE MOVE
THESE METHODS INTO A FOCUSED
HEALTH DISPARITY --
[INDISCERNIBLE]
>> WHY DO YOU THINK THAT IS,
WHY DO YOU THINK IT HAS NOT
GONE THERE YET, WITH HEALTH
DISPARITIES?
>> YEAH, I THINK -- MY GUESS
IS THAT LIKE WHEN IT COMES TO
A LOT OF THESE CAUSAL METHODS
WE'RE TALKING ABOUT, A LOT OF
THEM POP UP IN
PHARMACOEPIDEMIOLOGY AND JUST
OUT OF NECESSITY, FOR HIV
CARE, YOU GIVE THEM ANTIVIRALS
THAT AFFECT THEIR VIRAL LOAD
AND THEN MODIFY THEIR
TREATMENT BASED ON THEIR
RESPONSE.
IF THEY ARE NOT RESPONDING,
YOU FIND DIFFERENT TREATMENT
STRATEGIES AND IF THEY ARE,
THEN YOU KEEP THEM ON IT.
SO IT BECAME AWARE IN THE
RESEARCH SPACE THAT OUR
TREATMENT IMPACTS FUTURE
MEASURES AND THERE ARE SOME
THINGS YOU CAN'T DO
ANALYTICALLY BECAUSE IT IS
VERY OBVIOUS.
PHARMACOEPIDEMIOLOGY IS VERY
OBVIOUS WHERE EVERYONE HAS TO
LIVE SIX MONTHS TO BE ABLE TO
OBSERVE IT SO IF THERE IS NO
AVERAGE SURVIVAL, WHERE DO WE
SEE IMPROVEMENT.
SO IF YOU TAKE FOUR MONTHS TO
UNDERGO SURGERY, YOU WILL SEE
A FOUR-MONTH SURVIVAL.
SO OUT OF NECESSITY, THEY
DEVELOP THESE METHODS.
I THINK SOME OF THIS CAN BE
HARDER TO CONCEPTUALIZE IN
HEALTH DISPARITY RESEARCH AND
ALSO THE LACK OF ABILITY TO
MEASURE APPROPRIATELY, YOU
KNOW, OVER A TIME POINT.
WE DO A LOT OF CROSS-SECTIONAL
DATA BUT I THINK WITH THIS
PUSH OF REAL WORLD DATA AND
BIG DATA, LOOKS LIKE WE ARE
USING IT ALL THE TIME FOR
RESEARCH SO I THINK THE DATA
LIMITATION FOR THOSE WITH
HEALTH DISPARITIES IS NO
LONGER AN ISSUE AND WE'RE ABLE
TO SEE MORE BROADLY, GLOBALLY
AND CAUSALLY AT OUR RESEARCH
WHEN WE START IMPLEMENTING
CAUSAL METHODS AND THAT HAS
EVOLVED IN OTHER AREAS OF THE
POPULATION INTO OUR WORK.
>> AND YOU WERE THE FIRST,
RIGHT, TO USE THIS METHOD?
>> I DON'T KNOW IF I AM THE
ONLY PERSON DOING THIS.
THERE'S DEFINITELY OTHERS WHO
ARE WORKING IN THIS SPACE AND
DOING THIS BUT, YOU KNOW, I
DEFINITELY THINK THERE'S FEWER
OF US THINKING ABOUT THIS THAN
THERE COULD BE AND, YEAH, I
THINK YOU KNOW THE STRONGER
OUR DATA IS, THE STRONGER
ARGUMENT WE CAN MAKE TO MAKE
STRUCTURAL AND POLICY AND
INTERVENTIONAL CHANGES AND GET
RESOURCES TO SOLVE SOME OF
THESE PROBLEMS AND HOPEFULLY
IMPROVE HEALTH EQUITY.
SO I THINK THE STRENGTH OF OUR
DATA I THINK HAS A REALLY
DIRECT TIE TO THE RESOURCES
THAT EVENTUALLY ARE PUT WITH
RISK.
>> ANY INNOVATIVE RESEARCH AND
NEW THINGS IN THE FIELD,
EXPECTING CHALLENGES?
>> YEAH, I THINK THE FIRST
THING, YOU KNOW, IS GETTING
SOME OF THESE METHODS OUT
THERE.
YOU KNOW, TO THE PEOPLE USING
THEM OR WHO SHOULD BE USING
THEM, I THINK IS A GAP NOT
JUST FOR HEALTH DISPARITIES
BUT LIKE THE PAPER THAT I WAS
DEFENDING ON IS A PRIMER IN
THIS FOR RESEARCH BEING DONE
ALL OVER THE PLACE ON CANCER
TREATMENTS ON OUTCOME AND SOME
OF THEM ARE NOT DESIGNING
THEIR STUDIES CORRECTLY AND
THEN PUBLISHING THE RESULTS
AND THE IMMORTAL TIME BIAS
DOESN'T GET PICKED UP BY THE
RESEARCHERS BECAUSE THEY DON'T
GET TRAINING ON IMMORTAL BIAS.
SO WE DIDN'T CREATE THIS
METHOD, SOME OF MY
CORESEARCHERS ARE MORE VERSED
IN THIS METHOD THAN I AM BUT
TO LOOK AT THE STRATEGIES OF
THE DIFFERENT PAPERS, THERE
HAS TO BE AN ACCEPTANCE TO
GETTING THIS PUBLISHED.
WE HAVE HAD A LITTLE TROUBLE
FINDING A JOURNAL THAT WOULD
BE APPROPRIATE FOR THIS.
AND I THINK THE SAME COULD BE
SAID FOR HEALTH DISPARITY
RESEARCHERS, USING THE
TRAINING TO FOCUS ON A
SPECIFIC TYPE OF METHOD
LOGICAL TRAINING, SOCIAL, HOW
TO MENTOR THESE CONCEPTS AND
HOW TO DESIGN THESE KINDS OF
STUDIES BUT THEN IT MIGHT
POTENTIALLY FOCUS ON LESS OF
THESE AND SOME OF THEM DON'T
HAVE A DIRECT TIE TO WHAT
WE'RE DOING BUT DOESN'T MEAN
THAT WE CAN'T USE THEM.
SO THE ACCESS IS IMPORTANT AND
WHEN YOU ARE DOING HOPEFULLY
SOMETHING LIKE THIS AND BEING
OPEN TO LISTENING TO MORE
METHODS AND ALSO HAVING IT BE
MORE ACCESSIBLE, I THINK, WILL
ONLY HELP TO INCLUDE THAT.
>> ABSOLUTELY.
AND I HAVE LEARNED A LOT FROM
THIS AND I AM AN
EPIDEMIOLOGIST AND I AM
LOOKING FORWARD TO LEARNING
MORE ABOUT THIS METHOD AND
COLLABORATING HOPEFULLY ON
SOME OF THESE PROJECTS WITH
YOU.
>> YEAH, I WOULD BE HAPPY TO.
>> WELL THANK YOU,
PAULA STRASSLE FOR YOUR
PRESENTATION AND I WILL STAY
TUNED FOR THAT PUBLICATION.
>> THANK YOU SO MUCH AND THANK
YOU FOR HAVING ME. . 1494 01:00:07,570 >> TAKE CARE.