Target trial emulation

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# Target trial emulation for the study of social determinants in dementia research

.pull.right[

L. Paloma Rojas-Saunero MD, PhD
<br>
Postdoctoral scholar
<br>
**Mayeda Research Group, Department of Epidemiology**

]

???

I would like to thank Dr. Caniglia and Dr. Schisterman for the opportunity to talk in this session. Today I will discuss the implementation of the ttf in the study of social determinants in dementia research.
I should note that through out my talk I will make more questions than give answers, I am confident that this is the perfect place to pose them and take this talk as a conversation starter.

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.footnote[Hill et al. _Ethnicity and disease_. 2015]

???

The national institute of aging has outlined this framework including different exposures or risk factors that drive disparities in dementia and other aging-related outcomes. The final goal, and here I quote: to understand the etiologic pathways and ultimately identify effective targets for intervention... which means, asking causal questions.

However there has been a long resistance to connect causal methods to structural factors is because the best study design is a randomized trial.

While we are mostly focusing on RCTs to answer questions for biological mechanisms, we still need the best of these methods to study structural factors.

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## Ideal randomized trial

- No loss to follow-up

- Full adherence through out the study duration

- Double blind assignment

- Time points aligned by design:

+ Eligibility criteria
    + Treatment assignment (randomization)
    + Follow-up starts

???
Before we go into the nuance of social exposures, I would like to highlight some of the features of an ideal randomized trial.

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## Target trial emulation framework

![](figs/tt.png)
???

With this in mind, we can consider the ttf as a method to analyze observational data, that motivates researchers to conceptualize the hypothetical randomized trial that we would like to conduct that answers our question

That way we can start by defining a clear estimand, and outline the protocol elements of that target trial

And then attempt to emulate the target trial with observational data as close as possible. Finally under certain assumptions and with adequate statistical methods, we can estimate a causal effect.

This workflow shows that an essential part of this framework relies on the study design. Sometimes we heavily focus on this last part, but the essence of the ttf is to be more transparent about the study design decisins we face when we use observational data that has not been collected specifically to answer one specific research question.

With that prior, I would like to discuss an ongoing project, lead by my amazing collaborator TM. 
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## Research question

.center[
_What is the effect of early-life racial residencial segregation in 1940, measured by the dissimilarity index, on later-life memory decline in US population, represented by the Health and Retirement Study (HRS)?_]

.footnote[ _Work led by Taylor Mobley (UCLA, Mayeda's research lab)_ ]

???
THe HRS is a national representative data with oversampling of black, latino population. 
Recruitment started in the 90's and collects all kind of measurements, including memory scores every 2 years.

So now I am going to walk you through the outline of the target trial and how we would emulate it... so hold on to your seats because we are going to be creative

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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col"><b> Target trial protocol </b></th>
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<td class="gt_row gt_left">Population who self-identify as white or Black, born in the US by 1940, who resided in a county with Black residents</td>
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???
First, lets imagine we can take a time machine that transport us to 1940.

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---
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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col"><b> Section </b></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col"><b> Target trial protocol </b></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col"><b> Emulation using observational data </b></th>
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<td class="gt_row gt_left">Population who self-identify as white or Black, born in the US by 1940, who resided in a county with Black residents</td>
<td class="gt_row gt_left">Same + participated in HRS, with linked data for 1940 census, living in counties with > 1 enumeration district and with memory assessment measured by 1998</td></tr>
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<td class="gt_row gt_left"> 0. Do nothing (Status quo) <br>
    1. Reduce racial residencial segregation at a county level by certain amount
     </td>
<td class="gt_row gt_left">Same</td></tr>
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<td class="gt_row gt_left">Random assignment to either strategy in 1940</td>
<td class="gt_row gt_left">Random assignment in 1940, within levels of county level covariates</td></tr>
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<td class="gt_row gt_left">1998 (1st memory assessment) until 2018, including every wave of data in between</td>
<td class="gt_row gt_left">Same</td></tr>
    <tr><td class="gt_row gt_left"><b> Outcome  </b></td>
<td class="gt_row gt_left">Composite memory score <b></td>
<td class="gt_row gt_left">Same</td></tr>
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<td class="gt_row gt_left">Intention-to-treat</td>
<td class="gt_row gt_left">Same</td></tr>
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---
## Estimand

`$$E[Y_{1998}^{a = 1}] - E[Y_{1998}^{a = 0}]$$`
--

<br>

`$$E[Y_{2000}^{a = 1, c = 0}] - [Y_{2000}^{a = 0, c = 0}]$$`
.center[....]
<br>

???
So now we can outline our estimand or paratemer of interest.
Since we our outcome of interest is memory decline, we discretized our estimand, by each time point.
So first, we would be interested in evaluating the mean memory score in 1998 had everyone had followed intervention 1) compared to the mean score at baseline had everyone had followed intervention 0)

Next we would look at the mean difference on the memory score, under the two treatment strategies, and on and on we would do this until the last wave of data, and we could plot these point estimates over time, either as by treatment arm or the difference between them.

`$$E[Y_{2018}^{a = 1, c = 0}] - E[Y_{2018}^{a = 0, c = 0}]$$`
---
## Identifiability assumptions

- Consistency

- Exchangeability

- Positivity

- No interference

???

---

## Consistency

- Refers to having a well defined intervention.

-  _What does it mean to intervene on residential segregation in 1940?_

-  _Is the dissimilarity index a good a proxy of all the racist policies that resulted in residential segregation?_

- _What is a relevant intervention on the dissimilarity index, reducing under certain threshold or by certain value?_

---
## Consistency

- The target trial framework can help us expand the narrative and conversation around these complex exposures.

- At best, this hypothetical intervention would be reducing the impact of all the racist policies that influenced residential segregation, determined by the frequency of these in the studied population.

- It requires deep interdisciplinary dialogue, and only with subject matter expertise we can inform how well we feel about satisfying this assumption.

---
## Exchangeability

- Considering what variables to adjust for is more challenging.

- Since there are many factors that can influence residential segregation, whatever we adjust for is not accounted into how we would intervene.

- There is a moral and ethical value in terms of what variables to adjust.  "Non-allowable" sources are considered unfair and contribute to disparities (_Jackson J. Epidemiology. 2021_).

---

## Take aways

- The target trial framework helps us in being more transparent about our questions, assumptions and interpretations.

- We can use the target trial framework even if the intervention of interest is not measured (or exists!).

- Having a better design can already prevent some sources of bias, and help us detect other sources that could be prevented (or quantified) through the analytic strategies.

- It is a dynamic process, since it requires a deep understanding of the data sources and a constant check that the causal contrasts and subsequent results are informative.

---
class: middle, right

#### _To search for all the refutable consequences of a hypothesis demands highly imaginative thinking. Imagination is needed to arrive at the hypothesis in the first place, let alone to suggest rigorous tests for it. (Carol Buck)_

.footnote[
_Buck C. Popper's Philosophy for Epidemiologists, IJE. 1975_
]

---
##           Thank you, Gracias!

.pull-left[

**Mayeda Research Group**

**Study Contributors:**

- Elizabeth Rose Mayeda
- Joan Casey
- Taylor Mobley
- Kara Rudolph
- Nick Williams
- Heather McBrien
- Milo Gordon

**NIA: R01AG074359**
]

.pull-right[

]

---
background-image: url(./figs/logos.jpg)
background-position: 10% 100%
background-size: 95%
class: even-smaller

### Come work with us!

.pull-left[

.smaller[

#### Dementia Epidemiology Equity and Methods Training Program 
- María Corrada, UC Irvine
- Paola Gilsanz, Kaiser Permanente Northern California
- Maria Glymour, Boston University
- Elizabeth Rose Mayeda, UCLA
- Rachel Whitmer, UC Davis]

]

.pull-right[

![](figs/melodem_pic.jpg) 
]

.smaller[
#### Themes:
- Eliminating dementia disparities
- Methods for dementia and cognitive aging research
- Data harmonization and triangulation
- Determinants of health in the oldest-old
- Lifecourse determinants of health
- Neuroimaging
]