Transcript Introduction to Outcomes Research Methods and Data Resources

Introduction to Outcomes Research Methods and
Data Resources
David C. Chang, PhD, MPH, MBA
Director of Outcomes Research
UCSD Department of Surgery
Surgery and public health
Problem in surgical clinical research
• Unregulated
• FDA regulation applies only to “devices” (whether a real
device, or a molecular device in the form of a drug)
• Procedural medicine are not regulated
• Many reasons: complexity, difficulty in standardizing, difficulty of
enforcement (“surgeons know best” attitude)
• Self-regulation
Erroneous literature
RCTs often too late
EVAR-1, DREAM
“Tipping Point”
OVER
Social responsibility
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It is our responsibility in
academic medicine, to shoulder
the responsibility that, in other
fields of medicine, has been
assumed by the FDA
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To ensure that only good
treatment modalities are applied
to patients
Biggest barrier to good research?
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Not having a correctly constructed hypothesis
Incorrect design
Don’t know how to get data
Fear of statistics
Typical questions
• Components
• What/why/when/how
• Verb
• Condition
• “Why is the sky blue?”
• “What is the typical presentation of appendicitis?”
• Open-ended
Open-ended questions
• Descriptive analysis
• Observational study = no comparison = no statistical test
• Only one denominator
• May have more than one numerator, generating more
than one ratio
• All ratios are calculated with the same denominator
Descriptive statistics
43%
57%
P value not applicable to compare different parts of the same
population
Value and pitfall
• To explore the unknown
• When you know nothing, the first step is to explore and document
the numbers
• Risk of over-generalizing
Inferential statistics
43%
57%
45%
55%
P value applicable for comparing parts of two populations
What is a hypothesis?
• Question ≠ hypothesis
• Questions: usually open-ended
• Hypothesis: usually is closed-ended, asking for a yes/no
answer
• Statistical testing can only give yes/no answers
The process – study design
Study design phase
Data preparation
Analysis phase
Question development
Select database
Univariate
Define population
Link database
Bivariate
Define subset
Select data elements
Multivariable
Define outcome
Generate new data elements
Sensitivity
Define primary comparison
Define covariates
Subset analysis
Steps in constructing a hypothesis
• Specify the outcomes (O in PICO)
• Common oversight: Often focus on the P, but vague
about O (a typical question, “What is the outcome (?) of
xyz patients?”)
• Specify the comparisons (C in PICO)
• Not done in open-ended questions
• Specify covariates (control variables, adjustment)
Hypothesis statement
• y = b1X1 + b2X2 + b3X3
• Death = age + race + gender + insurance…
Inclusion/exclusion criteria
• Just like a clinical trials (“eligibility criteria”)
• Diagnosis and/or procedure codes?
• Common mistake
Comparison
43%
57%
55%
45%
Outcome
• Mortality?
• Rare
• Complications
• Length of stay
• Charges
• Be judicious
Covariates / independent variables
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Patient demographcis
Patient comorbidity
Surgeon volume
Hospital volume
Hospital type (teaching vs non-teaching)
Area (rural vs urban)
Hierarchy of influence on surgical outcomes
Nation
Outcomes
research
Region
Hospital
Surgeon
Clinical
trials
Technique and Management
Patient
The process – data preparation
Study design phase
Data preparation
Analysis phase
Question development
Select database
Univariate
Define population
Link database
Bivariate
Define subset
Select data elements
Multivariable
Define outcome
Generate new data elements
Sensitivity
Define primary comparison
Define covariates
Subset analysis
Overview of public and semi-public databases
Multi-specialty
Specialty-specific
• Administrative Databases
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• Nationwide Inpatient Sample
(NIS)
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• Medicare, Medicaid
• California OSHPD
• Clinical Databases
• National Surgical Quality
Improvement Program
(NSQIP)
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Trauma
• National Trauma Databank
(NTDB)
Oncology
• Surveillance, Epidemiology, and
End Results (SEER)
• National Cancer Databank
(NCDB)
Transplant
• United Network for Organ
Sharing (UNOS)
Administrative databases
Advantages
Disadvantages
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Large patient numbers
Less selection bias
Can be linked to other
databases containing other nonmedical information
Limited clinical course
information
• Limited surgical procedure
information
Non-NSQIP
NSQIP
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
Odds Ratio
NSQIP/non-NSQIP in-hospital mortality
In-Hospital Mortality: Single Reference Group
1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Cumulative Incidence of Adhesive Small Bowel Obstruction After
an Isolated Abdominal Surgery
Partial Colectomy
Gastric Bypass
Hysterectomy
Cholecystectomy
Appendectomy
C-Section
Number at Risk
Partial Colectomy
Gastric Bypass
Cholecystectomy
Hysterectomy
Appendectomy
C-Section
150,782
72,404
488,387
431,380
413,557
822,811
116,131
59,894
411,674
382,672
353,208
672,777
92,750
47,221
346,067
332,072
296,143
546,099
72,608
33,005
285,565
279,197
240,536
442,431
54,877
17,556
225,971
223,313
184,135
351,351
39,231
8,071
166,769
162,445
130,948
269,203
24,848
3,320
111,778
106,503
83,744
189,146
12,419
1,039
56,541
53,150
40,897
102,206
576
30
2,896
2,349
1,973
5,045
Select data elements
Generate new data elements
• Most time consuming step of outcomes analysis
• Not every component of your research question is readily
available in the database
• For example, comorbidity
• Charlson Index, Elixhauser Index
• Some common concepts actually undefined
• Readmission?
What is a “re-admission”?
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Not all “admissions” are “re-admissions”
30-day?
Elective?
Transfers?
Diagnosis-specific?
Preventable?
The process – analysis
Study design phase
Data preparation
Analysis phase
Question development
Select database
Univariate
Define population
Link database
Bivariate
Define subset
Select data elements
Multivariable
Define outcome
Generate new data elements
Sensitivity
Define primary comparison
Define covariates
Subset analysis
Hypothesis statement
• y = b1X1 + b2X2 + b3X3
• Death = age + race + gender + insurance…
Table 1: Descriptive analysis
Table 2: Bi-variate analysis
(unadjusted comparison)
Table 3: Multivariable analysis
(adjusted analysis)
Analysis for Table 1
Analysis for Table 1
43%
57%
P value not applicable to compare different parts of the same
population
Analysis for Table 1
• % for categorical data
• Mean/median/SD for continuous data
• For exploratory studies, descriptive studies, case series,
etc., this would be the end of the process
• Reminder, avoid overgeneralizing
Analysis for Table 2
Analysis for Table 2
• Think about data types…
• Continuous data
• Categorical data
• (Ordinal data)
Analysis for Table 2
• Two questions to think about when picking a stats test…
• What is my outcome/dependent variable? What is my
independent/input variable?
• What type of data do I have for each?
• 4 possible combinations:
• 2 variables
• 2 data types
Analysis for Table 2
Cat.
Cat.
c2
Cont.
ROC
T-test
Cont.
Correlation
Rank sum
Analysis for Table 3
Analysis for table 3
Cat.
Cat.
c2
Cont.
ROC
Logistic
regression
Correlation
Linear
regression
T-test
Cont.
Rank sum
Subset analysis
• Consistency of findings
• Generalizability
Generalizability
“This is not research anymore”
“That guy”