+ Strengthening Causal Inference in HIV Studies: Introduction and Practical Examples CAPS Methods Core Presentation, April 18, 2012 Starley Shade, Sheri

+

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.

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+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?


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


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 .


Risk Behavior

Baseline 6 months 12 months0%

5%

10%

15%

20%

25%

30%

Provider-ledSpecialist-ledMixedAssessment


Analysis Inverse probability of treatment weights

)...()()(]|[ 3210 gayawhiteamaleaaLAE

)|(/1 LAPwt


Analysis Inverse probability of censoring weights

Weighted logistic regression

)...()()(

)...()(],|1)([

gaycwhitecmalec

specialistcproviderccLAjCE

)(()(

)]|([log**

3210 mixedbspecialistbproviderbb

AYEitww ct

]...,|)1([/1*],|)([/1 LAjCPLAjCPwc


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




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)


+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)


110 -0.01 (-0.28, 0.26)


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

Documents

+ Strengthening Causal Inference in HIV Studies: Introduction and Practical Examples CAPS Methods Core Presentation, April 18, 2012 Starley Shade, Sheri