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Transcript of + Strengthening Causal Inference in HIV Studies: Introduction and Practical Examples CAPS Methods...
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Strengthening Causal Inference in HIV Studies: Introduction and Practical Examples
CAPS Methods Core Presentation, April 18, 2012Starley Shade, Sheri Lippman, Mi-Suk Kang Dufour & Carol Camlin
+Outline
Answering causal questions: common roadblocks in HIV research
Causal Inference Framework and Overview of methods
Concrete example: Using treatment and censoring weighting in Prevention with Positives
Concrete example: G-comp for population level attributable risk in the SHAZ study
Q & A
+Roadblocks in HIV research: selection bias / who gets exposedPopulation surveillance and surveys in
probability-based samples study participants (in testing, in survey
research, etc.) almost always systematically differ from non-participants
Observational studies using ‘comparison’ clinics, communities:
Systematic differences in study arms exist and/or may accrue over time
+Common roadblocks in HIV research: Loss To Follow-up
Cohort studies of HIV+ individuals: highly susceptible to loss to follow-up >20% after 2 years, in resource-poor settings:
medical records don’t capture patient mobility Death registries rarely available & those who
die mistakenly assumed to be lost to follow-up Those who drop out are systematically
different from those who stay engaged in care
+ Roadblocks in HIV research: time dependent confounding
Expos1 STI1C (&U)1
Expos2 STI2C (&U)2
Expos3 STI3C (&U)3
Expos0 STI0C (&U)0
C = group of confounders
U = unmeasured confounders
Time dependent confounding – if C is related to prior exposure & affects sub-sequent exposure
+Common roadblocks in HIV research: Complex, multi-component intervention studiesIncreasing calls for comprehensive HIV
prevention interventions addressing multiple levels and domains of influence on individual behavior
Evaluation of such studies hampered by: Diverse levels of exposure to individual
intervention components Difficult to distinguish relative contributions of
individual intervention components to observed outcomes
+Mending our comparison – the causal /counter factual framework “We may define a cause to be an object
followed by another… where, if the first object had not been, the second never had existed” (Hume 1748)
An association can be considered causal when, if the exposure had been altered, the outcome would have been different
Key part is the counterfactual element – reference to what would have happened if, contrary to fact, the exposure had been something other than what it actually was
8+Counterfactual framework
“Ideal experiment” illustrates the framework a hypothetical study which, if we could actually
conduct it, would allow us to infer causality
Ideal experiment: Person or population experiences one exposure and
observed for outcome over a given time period Roll back the clock Change the exposure but leave everything else the
same, observe for outcome over the same time period
9+
Counterfactual question: how long would Person A have survived had if he/she had not received treatment?
Counterfactual framework
Person A ART Deatht
Time
AIDSOBSERVED:
10+
No ART Deathnt
AIDS
Counterfactual framework
Person AUNOBSERVED:
Person A ART Deatht
Time
AIDSOBSERVED:
11+Counterfactuals – specifying what we really want to know
Thinking about the counterfactual outcome(s) as something we are missing and something we are trying to estimate when we analyze HIV studies or any epidemiologic data is instructive Akin to a missing data problem
When we compare groups of people observed as exposed or unexposed we want to compare groups that best estimate the counterfactual outcomes that are unobserved or missing
+Notation for presentation
A = treatment
Y = outcome
W = confounders (point treatment)
L = confounders (longitudinal)
The Likelihood of Data simplifies to: L(O) = P(Y|A,W,L)P(A|W,L)
A Y
W, L
+Rationale for causal inference approach
Basic regression models produce stratum specific, or conditional, estimates (i.e., “while holding constant a set of covariates”)
Where Y is outcome, A is observed exposure and L is matrix of time-dependent covariates
Therefore, our estimates of effect are also conditional
1),0|(),1|[ bLAYELAYE
)...(),|[ 310 jLbAbbLAYE
+Rationale for causal inference approach
Causal inference approaches help us model our way back to the ideal (counter factual) experiment
Where Y is outcome and a is counterfactual where all individuals are exposed (a=1) or unexposed (a=0)
)]0()1([ aYaYE
+Inverse Probability Weighting
+Inverse Probability of Treatment Weighting (IPTW) Re-create the counter factual data set by
weighting
IPTW assigns a weight for each subject equivalent to the inverse probability of being in their exposure group at each interval.
The treatment model is based on values of past and current covariates (L(j)) and past exposures (A(j-1)).
)](),1(|1)([/1 jLjAjAPwt
)...1()1()(()](),1(|)([ 4320 jLajAajLaajLjAjAE
+Inverse Probability of Treatment Weighting (IPTW) The treatment weights are applied to the
observed population (e.g. weighted logistic regression)
Creates a new pseudo-population in which the distribution of confounders is balanced between the two exposure groups, essentially mimicking a randomized trial.
AbbAYEwt 10)]|([
1)]0()1([ baYaYE
+Inverse Probability of Censoring Weighting (IPCW) Like IPTW, IPCW assigns a weight equivalent to
the inverse probability of remaining in the study at each interval, based on values of observed covariates and past outcomes and exposures.
The censoring weights are applied to the observed population, creating a new pseudo-population in which censored subjects are “replaced” by up-weighting uncensored subjects with the same values of past exposures and covariates.
)](),(|1[/1 jLjACPwc
+Example: Prevention with Positives
Demonstration Projects Fifteen HRSA-funded demonstration projects
implemented prevention with positives in clinical settings
Each site decided whether to randomize patients to: Provider-delivered intervention vs. Assessment Specialist-delivered intervention vs. Assessment Mixed intervention vs. Provider intervention
How do we assess the effectiveness of each intervention type?
+Example: Prevention with Positives
Patient characteristicsStandard of care
Provider
Specialist
Mixed p<
Male 781 (74) 490 (64) 705 (72) 530 (72) .001
White 410 (39) 282 (37) 332 (25) 298 (22) .001
Heterosexual 453 (43) 371 (48) 478 (49) 297 (39) .001
Age 40 or more
720 (68) 423 (55) 704 (72) 431 (57) .001
Education (Less than HS)
540 (51) 377 (49) 524 (54) 371 (49) ns
Employed 411 (39) 355 (46) 324 (33) 279 (37) .001
CD4 < 200 152 (14) 109 (14) 154 (16) 120 (16) ns
VL < 75 381 (36) 216 (28) 418 (43) 219 (29) .001
+Example: Prevention with Positives
Retention At the 12-month follow-up assessment,
58% of patients were retained in the standard of care group,
76% of patients were retained in the provider intervention sites;
62% were retained in the specialist sites; and 44% in the mixed intervention sites.
There were differences in retention by patient characteristics. Older, white, gay males with more than a high school
education but who did not use cocaine or injection drugs were more likely to be retained in the study at 12-months .
+Example: Prevention with Positives
Risk Behavior
Baseline 6 months 12 months0%
5%
10%
15%
20%
25%
30%
Provider-ledSpecialist-ledMixedAssessment
+Example: Prevention with Positives
Analysis Inverse probability of treatment weights
)...()()(]|[ 3210 gayawhiteamaleaaLAE
)|(/1 LAPwt
+Example: Prevention with Positives
Analysis Inverse probability of censoring weights
Weighted logistic regression
)...()()(
)...()(],|1)([
gaycwhitecmalec
specialistcproviderccLAjCE
)(()(
)]|([log**
3210 mixedbspecialistbproviderbb
AYEitww ct
]...,|)1([/1*],|)([/1 LAjCPLAjCPwc
+Example: Prevention with Positives
ResultsIntervention type 6 months
OR (95% CI)12 months
OR (95% CI)
Provider-delivered 0.93 (0.60, 1.20) 0.55 (0.32, 0.94)
Specialist-delivered 0.58 (0.35, 0.96) 0.67 (0.39, 1.14)
Mixed 0.89 (0.53, 1.51;) 0.89 (0.53, 1.51)
Assessment only Reference Reference
+G-computation and Population intervention Models
27
G-computation
Sometimes called substitution estimation approach
G-computation approach is to model the exposure and outcome relationship and then “control” exposure in the population by substituting counterfactual exposures in your model
Population intervention models use this approach to answer practical questions
+Population Intervention Models
Standard regression models give conditional estimate:
Marginal structural models allow total effect estimate:
For interventions what we care about is the population difference when intervention is present or absent:
),0|(),1|( wWAYEwWAYE
)()( 01 YEYE ww
)()( YEYE waw
+Analogous to Attributable Risk
Traditional population Attributable Risk or Attributable Fraction: The proportion of the disease risk in the total population
associated with the exposure
This assumes the exposure causes the outcome and that there are no other causes i.e. in absence of that exposure there would be no outcome
100*expexp
expexp osedproportionIncidence
IncidenceIncidence
osed
osedunosed
+Why PIMS?
Rarely looking at outcomes with only one important predictor/confounder PIMS allow assessment of effect averaged across covariates
Rarely able to completely eliminate a risk factor from population PIMS allow estimation for realistic interventions
+Population Intervention Models: estimation
1) Estimate outcome model
2) Create new dataset setting covariate(s) of interest to intervention levels
3) Predict outcome of interest using model estimated in step 1
4) Calculate the difference between predicted mean outcome and observed mean outcome
+Example: SHAZ! study
SHAZ! (Shaping the Health of Adolescents in Zimbabwe)
Enrolled adolescent orphan girls ages 16 to 19
Overall project was designed as an HIV prevention intervention based on provision of reproductive health services, economic livelihoods training and life-skills education
+Example: SHAZ! study
Using baseline data to look at a secondary outcome
Interested in the potential of interventions to improve mental health for adolescent orphan girls
Several structural factors considered as potentially modifiable with intervention
BaselineMental Health status
SSQ
Socioeconomic statusFood security
Ability to pay for medicationEver homeless
Changes in householdCompleted education
Social environmentFemale caregiver relationship
Social supportExposure to violenceFeeling safe at homeCaring for ill person
Poor physical healthGeneral health status
Viral infection
OrphaningAge at orphaning
Baseline Self efficacyPsychological distress
(Unmeasured)
+PIMS Question:
What is the potential impact of intervening on these factors on this population’s mental health status?
+Domain/variable Prevalence in Population Hypothesized intervention level N % Social environment
Physical violence 18 4.7% no experience of physical violenceSexual violence 29 7.6% no experience of sexual violence
forced sex 28 7.3% no experience of forced sexUnsafe home environment 241 62.9% home environment considered very safe
Household expereince of violence 34 8.9% noone in the house experiencing violence
Caring for ill 115 30.0% not caring for someone ill in the householdLow social support 231 60.3% "enough" people you can count on
Absence of supportive female caregiver 116 30.3% presence of a female caregiver who is "often" or "always" supportive
Socioeconomic status Food security 132 34.5% never going to bed hungry or not eating because
there is no foodUnable to buy medicine 235 61.4% able to buy needed medicine within 2 days
Changes in household location 197 51.4% no changes in household location within the past 5 years
Ever homeless 86 22.5% never homelessLess than form 4 education 99 25.8% at least form 4 (secondary) education
Low baseline self efficacy 335 87.5% Average response of "agree/strongly agree" with positive statements, "disagree/strongly disagree" with negative statements
Poor physical health Less than excellent health 278 72.6% excellent self reported health
Viral infection HIV/HSV-2 42 11.0% no viral infection with HIV or HSV-2
+Traditional regression results
Conditional Effects parameter
(standard regression)
DichotomizedSocial environment OR
Physical violence 3.67Sexual violence 0.61forced sex 2.99Unsafe home environment 1.50Household expereince of violence 1.85Caring for ill 5.19Low social support 1.64Absence of supportive female caregiver 2.57
Socioeconomic status Food security 0.88Unable to buy medicine 1.30Changes in household location 1.11Ever homeless 2.40Less than form 4 education 1.38
Low baseline self efficacy 4.84Poor physical health
Less than excellent health 2.67Viral infection HIV/HSV-2 2.57
Potential Impact of InterventionsDomain/variable
Prevalence in Population
Population Intervention parameter
N %Social environment
Physical violence 18 4.7% -1.1%Sexual violence 29 7.6% 0.0%forced sex 28 7.3% -0.7%Unsafe home environment 241 62.9% -3.5%Household experience of violence 34 8.9% -1.1%Caring for ill 115 30.0% -5.8%Low social support 231 60.3% -4.4%Absence of supportive female caregiver 116 30.3% -3.9%
Socioeconomic status
Food security 132 34.5% 0.4%Unable to buy medicine 235 61.4% -2.7%Changes in household location 197 51.4% -0.9%Ever homeless 86 22.5% -2.8%Less than form 4 education 99 25.8% -0.5%
Low baseline self efficacy 335 87.5% -9.2%Poor physical health
Less than excellent health 278 72.6% -7.4%Viral infection HIV/HSV-2 42 11.0% -1.3%
+
Intervention Participation:Life-skillsRed Cross
Baseline covariates
Intervention Participation:Start vocational training
6 month covariates
BaselineMental Health
12 month covariates
18 month covariates
Mental Health at 6 months
Mental Health at 12 months
Mental Health at 18 months
Mental Health at 24 months
Intervention Participation:finish vocational training
Intervention Participation:Receive grant
Extension of this approach to longitudinal context:
Time
+Question:
Does poor mental health status affect participation in the intervention over time?
+Analytic approach
Interested in effect of exposure (A) on outcome (Y) given covariates and past exposure and outcome
EW[E0(Y|A=1,W)‐E0(Y|A=0,W)]
Where W includes past exposure and outcome and other covariates
+Analytic approach cont.
Fit a series of point treatment models for outcomes at timepoints following exposure(s) of interest
+Example 1:
Intervention Participation:Life-skills (Y)Red Cross (Y)
Baseline covariates (W)
Intervention Participation:Start vocational training
6 month covariates
BaselineMental Health (A)
Mental Health at 6 months
+Example 2:
Intervention Participation:Life-skillsRed Cross (W)
Baseline covariates (W)
Intervention Participation:Start vocational training (Y)
6 month covariates (W)
BaselineMental Health (W)
Mental Health at 6 months(A)
Odds of Completion of Intervention Components by Symptomatic Status for Mental Health Distress at Baseline, Conditional on Completing Previous Intervention Components:
Estimates from Logistic Regression
Lifeskills Red Cross Start vocational trainingCompleted vocational
training Received GrantSample
SizeOR
(95% CI)Sample
SizeOR
(95% CI)Sample
SizeOR
(95% CI)Sample
SizeOR
(95% CI)Sample
SizeOR
(95% CI)
3001.1
(0.35, 3.42) 2820.57
(0.30, 1.11) 1141.30
(0.14, 12.14) 1140.63
(0.26, 1.54) 780.54
(0.05, 6.37)
Difference in Intervention Component Completion by Mental Health Distress Symptoms, Conditional on Completing Previous Intervention Components: Average Treatment Effects
(ATE) using tmle(D/S/A) estimation
Lifeskills Red CrossStart vocational
trainingCompleted vocational
training Sample
SizeATE
(95% CI)Sample
SizeATE
(95% CI)Sample
SizeATE
(95% CI)Sample
SizeATE
(95% CI)
Symptomatic at baseline
300 0.03 (-0.02,0.08)
282 -0.23 (-0.41,-0.05)
119 -0.01 (-0.16, 0.14)
114 -0.18 (-0.43, 0.07)
Symptomatic at 6 months
118 0.05 (0.02,0.10)
113 0.04 (-0.19,0.26)
Symptomatic at 12 months
110 -0.01 (-0.28, 0.26)
Symptomatic at 18 months
bold numbers indicate parameters statistically significant at p<0.05
+Assumptions and Limitations
+Assumptions
No Unmeasured Confounding There is no way to empirically
test for no unmeasured confounding;
collection of data on a complete set of covariates should be incorporated in the design phase
Experimental Treatment Assignment (ETA) or positivity Groups defined by all possible combinations of covariates
must have the potential to be in any (either) treatment groups.
If there are covariate groups that will only be observed in one treatment state, then we cannot estimate the effect of the exposure within that group
Time-ordering (temporality) Need to be certain the
covariates measured were prior to treatment if used in Tx weights/ treatment is prior to outcome.
+Acknowledgements
Thanks to:
Alan Hubbard, UCB
Mark van der Laan , UCB
Jennifer Ahern, UCB