Interpersonal Psychotherapy for Depression - Myrna m Weissman Phd
Patricia Cohen, Ph.D. Henian Chen, M.D., Ph. D. Teaching Assistants Julie KranickSylvia Taylor...
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Transcript of Patricia Cohen, Ph.D. Henian Chen, M.D., Ph. D. Teaching Assistants Julie KranickSylvia Taylor...
Patricia Cohen, Ph.D.
Henian Chen, M.D., Ph. D.
Teaching Assistants
Julie Kranick Sylvia TaylorChelsea Morroni Judith Weissman
Applied Epidemiologic Analysis
Outline Lecture 2
1) Measures of effect/measures of association
2) Review of Study Design
3) Introduction to Data Handling
Learning Objectives
• To understand the relationship between and among absolute and relative measures.
• To understand the relationship between measures of effect and measures of association
• To understand some of the features of measures of effect including effect measure modification and noncollapsibility
Measures of effect
• Effect– endpoint of a causal mechanism– amount of change in a population disease frequency
caused by a specific factor
• Absolute effects– differences in incidence rates, proportions, prevalences
or incidence times
• Relative effect– ratios of these measures
Causal rate difference
Absolute measures
Causal risk difference
0
0
1
1
T
A
TA
NA
NA 01
Causal Rate Ratio
Causal Risk Ratio
Relative measures
0
1
0
0
1
1
II
TAT
A
0
1
0
1
0
1
RR
AA
NAN
A
Relationship between Ratio Measures
0 0
1 1
0
1
0
1
0
1
TI
TI
A
A
N R
N R
R
R
Risk Ratio
because R = A/N and I=A/T
Risk Ratio = Rate Ratio * Ratio of Persontime
0
0
1
1
SRS
R
0
0
1
1
1
1
RR
RR
= =
=
0
0
1
1
ANA
ANA
Odds Ratio
NA
NA
NA
NA
0
0
1
1
1
1
Scenarios when relative measures approximate relative risk
Time period sufficiently small that average T for exposed population is only slightly smaller than T for unexposed
–means T1 and T0 are approximately equal and rate ratio approximates risk ratio
0
0
1
1
0
1
00
11
0
1
SRS
R
I
I
TI
TI
R
R
Sufficiently small proportion of onsets– means R1 and R0 are small, S1 and S0 are close to 1 (odds ratio
approximates risk ratio)
Odds Ratio will overestimate the risk ratio
Scenario 1 where factor increases risk R1 > R0
0011 11 SRRS
11
0 SS
0
0
1
1
10
01
0
11
SRS
R
SR
SR
RR
Scenario 2 where factor decreases risk R1 < R0
0011 11 SRRS
11
0 S
S
0
0
1
1
10
01
0
11
SRS
R
SR
SR
RR
Odds Ratio will underestimate the risk ratio
Scenario 1 where factor increases risk
Rate Ratio with respect to Risk Ratio
01 TT
10
1 TT
0
1
00
11
0
1
0
1
0
11II
TITI
AA
NAN
A
RR
The risk ratio will be closer to null than the rate ratio.
If R1 > R0 then
Scenario 2 where factor decreases risk
Rate Ratio with respect to Risk Ratio
10
1 TT
0
1
00
11
0
1
0
1
0
11II
TITI
AA
NAN
A
RR
The risk ratio will be closer to null than the rate ratio.
If R1 < R0 then
10
1 TT
Magnitude of effect ratios
• If exposure increases disease:
1.0 < Risk ratio < Rate ratio < Odds ratio
• If exposure prevents disease:
1.0 > Risk ratio > Rate ratio > Odds ratio
Because, given equal N, disease occurance shortens cumulative time of exposed subjects.
Cohort dataCases Noncases Total
Exposed 200 99,800 100,000Unexposed 100 99,900 100,000
Risk ratio = (200/100,000)/(100/100,000) = 2.0
Case-controlCases Noncases Total
Exposed 200 99.8 299.8Unexposed 100 99.9 199.9
Odds ratio = (200*99.9)/(100*99.8) = 2.0
Rate = ln (1.0-risk)/time assume time = 1 yearRate (unexposed) = .001Rate (exposed) = .002Rate ratio = 2.0
Example 1: Rare disease
Case-controlCases Noncases Total
Exposed 40,000 60 40,060Unexposed 20,000 80 20,080
Risk ratio = (40,000*80)/(20,000*60) = 2.67
Cohort dataCases Noncases Total
Exposed 40,000 60,000 100,000Unexposed 20,000 80,000 100,000
Risk ratio = (40,000/100,000)/(20,000/100,000) = 2.0
Rate = ln (1.0-risk)/time assume time = 1 yearRate (unexposed) = .22Rate (exposed) = .51Rate ratio = 2.29
Example 2: Non-rare disease
Effect measure modification
If exposure has any effect on an occurrence measure, at most one of the ratio or difference measures of effect can be uniform across strata
Two Examples: Effect Measure Modification
Example 1
Here you see that the risk ratio is constant but therisk difference varies by strata.
Strata 1 Strata 2
Exposed .2 .3
Unexposed .1 .15
Risk Ratio 2 2
Risk difference
.1 .15
Two Examples: Effect Measure Modification
Example 2
Risk Ratio 2 1.67
Risk difference
.1 .1
Strata 1 Strata 2
Exposed .2 .25
Unexposed .1 .15
Here you see that the risk ratio varies by stratabut the risk difference remains constant.
Relation of Stratum-specific measures to overall
• Risk differences and ratio– entire cohort measure must fall in the midst of stratum
specific measures
• Causal odds ratio and rate ratio– entire cohort measure can be closer to null than any of
the causal odds ratios for the strata
•noncollapsibility of the causal odds ratio
•odds ratio not a weighted average
Men Women
% of population 50% 50%
Risk (exposed) .5 .08
Risk (unexposed) .2 .02
Odds ratio 4
(.5/.5)/(.2/.8) 4.3
(.08/.92)/(.02/.98)
Risk ratio 2.5 4
CombinedRisk (exp) = .5*.5 + .5*.08 = .29
Risk (unexp) = .5*.2 +.5*02 = .11
Example of Non-collapsability
Odds ratio = (.29/71)/(.11/89) = 3.3
Risk ratio = .29/.11 = 2.6
– Non-collapsibility can also occur for rate ratio– Only a problem if the outcome is common in a
particular strata
Combining Strata for Rate Ratios
Causation and Causal Attributable Fraction
Assume 2 sufficient causes
Without exposure, can get disease only through C’.
If exposed, can get disease through either sufficient cause (whichever acts first).
Rate difference (I1- I0) does not necessarily equal the proportion of onsets attributable to exposure.
C’EC
Excess fraction:
RRRR
A
AA 1
1
01
Causation and Causal Attributable Fraction
Preventable fraction:
RRA
AA 1
0
10
Generalizing Exposure in Definition of Effect
A sample of three people smoke one pack of cigarettes daily at the start of a 5 year period.
What is the effect of different patterns of mailing anti-smoking literature to them?
Generalizing Exposure in Definition of Effect
Person Pattern 0 A0 T0 Pattern 1 A1 T1
1
Quarterly Dies at year 4 of lung cancer
1 4 No mailing Same
1 4
2
Yearly Dies at year 1 of heart attack
1 1 Yearly Same 1 1
3
No mailing Dies of stroke at year 1
1 3
Quarterly Quits smoking and survives all 5 years
0 5
3 8 2 10
I 3/8 yr-1 3/10 yr-1
R 3/3 =1 2/3 = .67
Causal rate difference 3/8 yr-1 - 2/10 yr-1= .175
Causal rate ratio (3/8)/(2/10) = 1.875
Causal risk difference 1-2/3 = 1/3 = .33
Causal risk ratio 1/(2/3) = 1.5
Example cont.
Key points:Even though same overall portion of the sample are exposed to the three types of mailing in the two patterns,
• effects on I are not same as effect on R
• not all individuals respond alike to exposures or
treatments
Therefore• effects are defined for populations, not
individuals.• Individual characteristics affecting exposure
response are used to stratify analyses
Measures of Association
Given separate exposed and unexposed populations:
Confounding is defined by observed rate differences not equal to the causal rate difference.
– true for ratio measures, average risks, incidence times, or prevalences
– odds are risk-based measures, odds ratios are confounded under same circumstances as risk ratio
A Counterfactual Approach to Causal Reasoning
• Considers the experience of an exposed cohort if, contrary to fact, was unexposed
• and/or the experience of an unexposed cohort if, contrary to fact, was exposed.
Disease under Proportion in
ExposureNo
exposureDescription
Cohort 1(exposed)
Cohort 0(unexposed)
D D Doomed(other causes sufficient)
p1 q1
D 0 Exposure is causal p2 q2
0 DExposure is preventive
p3 q3
0 0 Immune p4 q4
Sum 1.0 1.0
)()(
)()(
ratio Odds Causal
)()(
differencerisk Causal
42
31
43
21
323121
pppp
pppp
pppppp
Note: null value no effect, unless no preventiveeffect is possible, rather null means balancebetween causal and preventive effects
If Cohort 1 represented the population:
)()(
)()(
observed ratio Odds
42
31
43
21
qqqq
pppp
Associational measure = causal counterparts if andonly if q1 + q3 = p1 + p3
Confounder explains a discrepancy between the desired(but unobservable) counterfactual risk or rate and theunexposed risk or rate that was its substitute
In an actual study:
A Counterfactual Approach to Causal Reasoning, cont.
Consider the effect of child abuse on young adult depression:
Counterfactual:
a) abused children had not been abused
b) not abused children had been abused
What are barriers to assuming observed association
Is causal?
Types of Epi Studies
• Experimental
• Non-experimental
• The ideal design, where only one factor varies, is unrealistic (because of biologic variation).
• Settle for amount of variation in key factors that might affect outcome small in comparison to the variation of the key factor under study
Experimental Studies
• Classifications– Clinical treatment or prevention trials
• patients as subjects– Field trial
• non-patient subjects– Community intervention trials
• interventions assigned to the whole community
• Characteristics– Investigator assigns exposure based only on study
protocol, not needs of patient
– ethical only when adherence to protocol does not conflict with subject’s best interest
– all treatment alternatives should be equally
acceptable under present knowledge
Non-experimental studies
• Cohort– classified (& possibly selected on) exposure– direct analog of the experiment but investigator does
not assign exposure
• Case-control– can be more efficient (sample on outcome)– introduces avenues for bias not present in cohort
studies– the critical issue is defining a source population
Non-experimental studies (cont)
• Control group
– main purpose is to determine relative (not absolute) size of exposed and unexposed denominators within the source population
– to do so, controls must be sampled independently of exposure status: do not select on exposure or potential confounders
• Use of prospective & retrospective– use these terms for timing of the disease
occurrence with respect to exposure measurement
– in cohort studies, usually involves follow-up for disease occurance
– in case-control studies, prospective exposures are usually measured via pre-existing records
Non-experimental studies (cont)
Non-experimental studies (cont)
• Cross-sectional
– can classify under case-control
– main problem is with assessing sequencing and timing
– consequently, emphasis is often on prevalence
– but, current exposure may be too recent to be etiologically relevant
• Proportional Mortality Rate – best to think of as a type of case-control study
– main problem is that cannot distinguish whether exposure causes the index causes of death or prevents the reference causes of death
– cannot distinguish between the extent to which exposure causes disease or worsens prognosis
Non-experimental studies (cont)
Introduction to Data Handling
1) Collection
2) Coding
3) Entry
4) Repeat entry
5) Checking & Editing (logic checks, outliers, fix where possible)
6) Reduction (creation of global variables)
7) Analysis
Types of variables
• Quantitative– e.g., height, weight, body mass index, age
• Qualitative– e.g., gender, race, ICD codes, case/control status,
smoker/non-smoker status
• Ordinal– age in discrete years, low/medium/high consumption
Data coding
1) Make enough categories to avoid non-responses
2) Code all responses (even for don’t know or refusal or not applicable responses)
3) Avoid open-ended questions
4) Record exact values rather than categories
5) Record exact date (of birth, death, diagnosis) rather than ages
Data editing
1) Check for illegal or unusual values for each variable
2) Check codes for unknown or missing values
3) Check distribution of variables
- expected proportion of subjects in each category
- check ranges of values
4) Check consistency of variable distribution (e.g., whether nonsmokers have recorded values for the number of cigarettes per day of duration of smoking etc.)
Learning Objectives
• To understand the relationship between and among absolute and relative measures.
• To understand the relationship between measures of effect and measures of association
• To understand some of the features of measures of effect including effect measure modification and noncollapsibility