Clinical and Health Research Clinic

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Current Notes (2024)

2024 February 29

Jacob Franklin (Allison McCoy), Biomedical Informatics

The project is clinical evaluation of the utility, usability, and impact of a pilot trial using ambient AI documentation vendor solutions. I would like to discuss how to statistically analyze the survey and various clinical electronic health record metrics for each vendor solution and then how best to compare amongst vendors with the different pilots.

2024 February 22

Attendees: Frank Harrell, Cass Johnson, Jackson Resser, Angela Bonino, Eriel Confer, Soha Patel, Niharika Ravichandran

Niharika Ravichandran (Soha Patel), OB/GYN

Influenza, tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis (Tdap) and COVID vaccines are routinely recommended during pregnancy to prevent adverse maternal and neonatal outcomes. It is well known that pregnant individuals infected with influenza or COVID are at increased risk of severe illness and adverse perinatal outcomes compared to non-pregnant individuals. Prior research has shown that global COVID-19 vaccination prevalence in pregnant women is low. Multiple factors are suggested to be associated with vaccine uptake including age, ethnicity and social living conditions.

The purpose of this study is to conduct a preliminary analysis of vaccine uptake before and after the COVID19 pandemic at our institution and understand the determinants associated with decreased uptake.

Population: Pregnant patients who delivered Vanderbilt with at least one pre-natal visit

Questions from investigator: sample size

Compare rates pre-pandemic to post-pandemic

Use highest resolution data: address > zip code

- population density, median family income for area

Initial step: understand who is coming into clinic. Relevant to understand change in participant characteristics over time

- trend in median family income over time

- population density over time

- trend in vaccine receipt over time

Look at trends in raw form and adjusted form

- estimate prevalence of vaccine over time, adjusted for covariates

10 years pre-pandemic sounds good, but subject-matter knowledge should guide that decision

CDC - social vulnerability index

Potential exclusions: allergy to vaccine, fetal anomalies

List variables to adjust for, factors that would alter tendency to receive vaccine

Analysis methods: Logistic regression model (probability of uptake by time trend, age, address, etc)

LR: to estimate prevalence in a single group well, need sample size of at least 400

Think about as prospective cohort study

Potential next steps: VICTR voucher, VICTR studio

Potential second part: vaccines administered to new-born after discharge

Eriel Confer (Angela Bonino), Audiology, Hearing & Speech Sciences

We would like to create a cohort of children who received either received an ASD diagnosis or a speech/language disorder diagnosis by our department\x{2019}s clinic. We would like to be able to pull some data from EHR (diagnoses, demographics) but know that some of the audiological data will not likely be able to be pulled by your system (it\x{2019}s housed on a 3rd party system that then interfaced with eSTAR.)

Population: children with autism

- Look at population being discharged from clinic vs not

Big study question: how many visits did patients have, what information was used to make decision (four potential pieces of info that could be used)

- Clarification: what information was available for them to use

- Laying out things that were important to capture, make sure you can capture them accurately

Most children will have between 1-3 visits

History variables to understand current context

Potential exclusion criteria: facial cranial, certain ages, language, family/family history

Boys more likely to be diagnosed with autism earlier

- Age against something else

Goals sound more descriptive

- Estimate proportions of sample characteristics with confidence intervals

Make study questions as specific as possible (specific enough that it's possible the data may not be able to answer the question)

Combining EHR data with third party data

- Need to figure out what is extractable from third party

- Ask around dept

Sample size: to estimate prevalence of Yes's to +/- 0.05, need sample of at least 400

- Confidence intervals will be self-limiting

Get flexible time trend

- create windows of interest in overall smooth trend

- superimpose discontinuity

Interrupted time series analysis

2024 February 15

Mark Rolfsen (Wes Ely), Pulmonary and Critical Care

With CIBS biostatisticians we have created a prediction model for cognitive impairment following the ICU using logistic regression technique. My question is how we can adjust this based on the initial results (e.g. we have 2 outcome variables but might want to reduce to 1 outcome variable) and how could we turn this into a reasonable clinical tool/calculator? In general looking for an open discussion on CPM\x{2019}s to help guide next steps

Attendees: Frank Harrell, Mark Rolfsen, Rameela Raman, Onur Orun, Wes Ely, Jackson Resser, Cass Johnson

40 \x{2013} 60% of patients may have cognitive impairment, but having a model to help individuals understand their own risk would be new.

Logistic Regression \x{2013} using prespecified baseline and in-hospital characteristics. Outcome variable was either cognitive impairment or functional disabilities. Two models: one three month, one twelve month

541 and 465 patients per model, respectively

Outcome variable occurred in 50% of 3-month patients and 43% of 12-month patients

Calibration curve \x{2013} predicted probability of outcome vs. actual probability.

Clinical context \x{2013} loved one may be at high risk of impairment, inform clinical conditions or potential support options (Bedside tool towards end of hospital stay).

Questions from Frank:
  • External validation \x{2013} separate study of 300 patients. Same variables were collected. Is the development sample big enough to stand on its own, without validation patients?
  • One issue here \x{2013} inclusion / exclusion criteria could vary patient population. You could explore if there is a more narrow range of important predictor variables.
  • Drop off from internally-validated to externally-validated was .04 \x{2013} R squared measures may also be used. Frank likes 90th percentile of absolute differences, as well.
  • Half patients had outcome \x{2013} the way you analyzed, we don\x{2019}t take into account whether a patient was very close to being cognitively impaired but wasn\x{2019}t. An overall scale (Frank will provide FDA talk; https://www.fharrell.com/talk/cos/) could be a path forward.
  • Maximum resolution outcome variable is best. Averaging ranks of two scales, using one scale to predict another to that one scale may be calibrated to another\x{2026} some additional discussion could be done to help here.
Question from Mark: How do we feel about taking these tools and boiling them down to a usable bedside tool?
  • Support study, end of life decision making; if personalized, reliable survival curve is provided, provisions didn\x{2019}t make as big of a difference as one may think. Misinterpretation of results was common, and patients didn\x{2019}t latch on to risk scale.
  • Median life expectancy would maybe have been more effective.
  • The proposed model will give a risk-based estimate, but volume, amount of cognitive challenge, other unit of measurement. may be effective for patient interpretation.
  • Wes\x{2019}s summary: if you take away from this paper that we enable a clinician or team to tell patient that they have X% likelihood of new brain disfunction, and they way we would handle that is support / classes / etc, that would be a win. Calculator may be less important; a distillable statement may be preferable.
  • Mark\x{2019}s concern: Many people will fall into a category that, rehab may be needed \x{2013} maybe a very small low-risk and small high-risk population.
  • Also worth noting that this is a survival-only model. Ordinal longitudinal analysis may be communicated as % of being included in a cognitive level or worse; if you\x{2019}re excluding the people that die after counseling, as in the current analysis, that is likely misleading.
  • That would align with being prospectively defined. Chance of being in good cognitive function + alive, poor function but alive, dead.
  • Having one scale would make the biostatistical problems easier to solve. Can estimate median scale for a person.
  • If the model was to be changed to be just cognitive impairment, but death was included, with an ordinal scale \x{2013} patients would have a median. Challenge would then be in communication to patients.
  • Frank\x{2019}s comments based on similar study; density function with most likely level of disability a year after surgery. Median could be target summary, but 10th and 90thpercentile could be provided as well.
    • Hui Nian may be able to help.
    • 4-8 levels, a stacked bar chart may be effective for discrete / ordinal outcome.
    • Adjusting of independent variable; can one or two variables be subbed in and out while remaining methodologically rigorous?
      • Just don\x{2019}t try a lot of variables that you then discard, and then in the subsequent validation fail to repeat those \x{201c}tries\x{201d}.
      • But yes, adding a few variables at this point (length of stay, for example) is not an issue.
      • If many experts were assembled, and various levels of the two scales was given, and you asked each which one is worse; if you can order those combinations such that they agree, that may be effective. 20 or so experts would be needed, though.
      • Bare minimum would be five ordinal levels (not including death); ten would be ideal.
      • Takeaways: Ordinal scale is best, combination of outcomes or only one, adjustment with independent variables is possible, and communication + interpretation of results will need to be thought about for patients.
      • Multiple observations over time as a longitudinal analysis may be another good option.
        • Ex: If you died after the first time point, that\x{2019}s an absorbing state.
      • Frank may be an author if desired, or perhap acknowledged.
Frank confirms that this project would be a good fit for a VICTR voucher.

2024 February 8

Attendees: Frank, Cass, Jackson, Marissa Khalil, Mikaela Bradley

Marissa Khalil (Rondi Kauffmann), Surgery

We are conducting a retrospective analysis comparing the sociodemographics and outcomes between average onset of diagnosis and young onset of diagnosis breast cancer in Kenya. We have completed the data collection and review and are looking to start univariate analysis and multivariate analysis. We have 3 tables in place:
Table 1: Patient and Tumor characteristics comparison between young onset and average onset patients
Table 2: Survival and Recurrence rates between the 2 populations
Table 3: odds ratios, kaplan meir curves vs logistic regression etc

Two ways of getting into system:

1) oncologist started database for cancer patients (all cancer patients at the hospital)

2) At every follow-up visit, patient added to database

Some women could have failed to enter the study population because the cancer became severe quickly

Want to guard against ill-defined denominator

- Problem: patients who die before entering population (not a random sample)

- Example: cats falling off buildings; cats that died the moment after the fall were excluded

Paucity of data for breast cancer in this area of Africa

Time-oriented outcome like age of onset prone to bias

Could be difference in types of breast cancer in population

Value in determining pieces in the data that don't matter and then confirming that they don't matter

- Negative controls give you more confidence in positive controls

Data exploration: make a model to predict a missing lab value

First: dig into data, build demographic tables

Multivariable analysis of the differences (logistic regression model) to predict age cohort

- Looking for unique differences

Pre-cursor analyses: degree of missingness could limit types of analyses you could run

- Cluster analysis: understand degree of missingness on the same individual

Regression analysis: using R - https://hbiostat.org/rmsc/software

Also resources available to help get data from REDCap into R

Kaplan-Meier vs logistic regression

- LR better when time is not important

In some cases, not confident whether participant died from breast cancer or another cause

Mikaela Bradley (Gillian Hooker), Genetic Counseling

Neurofibromatosis Type 1 (NF1) is a common genetic condition that affects approximately 1 in 2,500-3,000 individuals. The goal of this study is to investigate if a reported family history of NF1 influences perceived levels of stress and coping styles in adults with NF1. To do this, adults with NF1 completed a survey that includes questions about their diagnosis, their family history, the Perceived Stress Scale 10-Item Version, the Brief Coping Orientation to Problems Experienced Inventory, short response questions, and demographics.

We have completed a lot of our bivariate analyses and are working on a hierarchical multiple linear regression to identify other variable that modify people\x{2019}s experience of stress. During this clinic, I would like to review the analyses that I have run to ensure I am reporting things correctly. Within this, I would like to talk through the blocks through which I created to make sure we are teasing out the variables correctly.

Grand question: is there a difference in stress levels and coping styles based on family history?

How do people get into cohort?

- Survey, recruited from three different sources

- Diagnosis of NF1, > 18 years old, US resident who can speak english

- Current age and age of diagnosis available

Stage-wise multi-linear regression in SPSS

- Base model (outcome is stress level):

- M1: demographics

- M2: demographics + NF1 characteristics

"stage-wise" = different models with additional covariates

F Change = overall F for corresponding model

Additional variables adding half as much explained variation (stress level hard to predict)

Spline functions to deal with non-linear relationships

- F statistic for joint influence of age and age^2

Too many covariates to look at each individual -- result = a lot of noise

Can't look at correlation to determine which variables to analyze (double-dipping)

- Wouldn't do any statistical testing (remove p-values), report correlations to two decimal places

Can compare correlations, never p-values

Three coping subscales and have them interacting with family history in stage 3

- F test with six degrees of freedom and R^2

- Do subscales predict stress level for either family history group?

The more chunk tests you use, the more license you have to deal with things without p-value corrections like Bonferroni

Can remove p-values, report correlations as descriptive measures: do better to assume correlations are non-zero

Grouping variables into blocks is a good practice

Adjusted R^2: tells you if added variables are worth the $$

Cass suggests using "nested" terminology

2024 February 1

Andrew DeFilippis, Cardiology

I have the privilege of reporting on a pre-specified subgroup analysis of a RCT (MINT Trial, NEJM).

Briefly, MINT randomized participants with an acute MI and anemia to a liberal versus restrictive transfusion strategy. I am reporting out on a stratified analysis by type of MI (Type 1 vs Type 2 MI). If possible, I would very much like to discuss how to address the fact that the size of MI differs between Type 1 and Type MI in this trial (likely confounding the interpretation of MI type on the outcome).

Attendance: Frank Harrell, Andrew DeFilippis, Jackson Resser, Cass Johnson

\x{201c}Not all heart attacks are the same\x{201d}

Prespecified subgroup analysis \x{2013} differ by index enrollment MI was Type 1 or Type II

MINT \x{2013} 3,500 patients with heart attacks who were also anemic, randomized to liberal transfusion strategy or restricted. Outcome is 30 day death, MI.

Protocol specifies that index hospitalization includes designation of Type 1 or II MI. Very few unknowns.

Primary result: Whether allcomer MI\x{2019}s did better with liberal or restricted transfusion. Death / MI in Type 1 vs. Type 2, liberal vs. restricted

Troponin measurement: Many different assays, but in a heart attack, troponin value can change by 10,000 fold. Size of MIs were categorized (somewhat arbitrarily) into 5 categories -- <1, 1 to <10, 10 to <100, 100 to <1000, greater than or equal to 1000.

Frank: Wouldn\x{2019}t patients who got more troponins drawn have a better possibility of having the peak value found?
  • Troponin stays elevated for 2 weeks \x{2013} peaks 12 -48 hours after MI
  • Dynamic range is very large
  • Possibility of secondary analysis \x{2013} log ratio to upper limit of normal. Relationship between log ratio and outcome, as well as same relationship for number of components drawn, to assess if here\x{2019}s bias that makes interpretation difficult.
    • Help determine if peak troponin should be adjusted for number of draws
  • Andrew: If Frank were reviewing, would he want to see an analysis where size of MI is held constant? Or would he ask for a second stratification (within Type 1, then by size; within Type 2, then by size)?
    • The current display is not that helpful due to heterogeneity between Type 1 and Type 2
    • Graph that shows log ratio vs. outcome; if adjusted for log ratio vs. outcome, does type add anything to predicting the outcome? (In Andrew\x{2019}s words, MI by size vs. outcome, and see if that is impacted by size of MI)
      • May be more useful to see if log ratio interacts with treatment
      • May be fit with spline function
Type 1 / Type 2 variable is low resolution compared to size variable; Frank thinks size may be more important to show in table compared to Type 1 and 2 because of this. You could do it both ways.

Andrew: Are additional analyses irresponsible? This could be a concern of coauthors.
  • Perpetuating clinical trials to give minimal information to the reader.
  • MI\x{2019}s are being treated as \x{201c}equally big\x{201d}. An analysis that looks at relationship between liberal and restricted, and how big of an MI someone got as a second MI, would be encouraged.
    • Andrew notes that this is set for a second paper; Frank thinks it may be best used here.
Andrew\x{2019}s position is that it would be best to be liberal with analyses performed, but conservative with interpretations. Other investigators have taken opposite approach.

Fundamentally flawed design \x{2013} if we know that people have different variables at baseline, especially when they are predictors of the outcome, makes results incredibly difficult to interpret.

Clinical trials have nothing to do to control for within-group variability. We need to know which ones can be defended as big players, not in pursuit of controlling for every single variable.

Back to Table 6 \x{2013} MI size would be very important to relate to the outcome. Push to do analysis looking at if transfusion strategy would impact large vs. small MIs

Figure 2 \x{2013} concerns with confounding, propose analyses for quantifying MI type and treatment strategy. Frank thinks that size variable is likely to be more important, would hesitate to call that confounding.

Relationship between troponin and outcome \x{2013} log ratio

Is the number of troponins reported related to actual outcome?

Of your ability to predict something, how much of it comes from variable x / y/ z. Dot plot in descending order; big prognostic players that can\x{2019}t be learned from what is currently provided.

Regarding Figure 2; age, LVF are not included.

Scatterplot of MI size at study start vs. second MI, with two colors for treatment type, could be good to look at. Then we can include baseline characteristics (hemoglobin)

Andrew suspects that size will be a second paper; Frank thinks best route for improving Figure 2 would be baseline size vs. outcome, stratified by treatment and type (four curves). Could also do it without stratifying by type for larger denominators / greater stability.

These would not be Kaplan-Meier curves; logistic regression models (size on X axis, yes/ no at 30 days). Don\x{2019}t assume that log ratio is linear (Frank Harrell and Magnus Olsen, nonparametric regression on Troponin in NEJM, or spline function)

Also \x{2013} Spearman correlation coefficient between size and LVF

When firm threshold is present for qualification into the study (hemoglobin), which is also the variable being treated to, you may need to verify that there's no boundary artifacts. People at 9.9 hurt by treatment vs. helped, for example.

2024 January 25

Shayan Rakhit (Amelia Maiga), Surgery

This is an already completed analysis of which the abstract is posted below. We would would like to discuss potential methods to account for unmeasured confounders:

Introduction:
Research in animal studies, retrospective cohorts, and secondary clinical trials analyses suggests that plasma may improve outcomes in traumatic brain injury (TBI). We examined the association between plasma administration and mortality in moderate-severe TBI, hypothesizing plasma is associated with decreased mortality after accounting for confounding, including by indication.

Methods:
Patients greater than 18 years with moderate-severe TBI from the 2017-2020 Trauma Quality Improvement Program (TQIP) dataset were included. Patients with anticoagulant/antiplatelet use, specific comorbidities (bleeding disorders, cirrhosis, chronic renal failure, congestive heart failure, chronic obstructive pulmonary disease), outside hospital transfer, and missing hospital mortality were excluded. Multivariable logistic regression examined the association between plasma volume and hospital mortality, adjusting for sociodemographics, severity of injury/illness, neurologic status, and volume of other blood products, including interaction terms of plasma with shock and need for hemorrhage control procedure, respectively (see Table 1 for details). Sensitivity analysis excluded patients with shock and hemorrhage control.

Results:
Of 4,273,914 patients in TQIP, 63,918 met inclusion. Hospital mortality was 37.0%. 82.8% received no plasma. Other cohort characteristics were mean age: 44.9; mean Injury Severity Score: 28.5; percent female: 24.4%; percent severe TBI: 69.4%; percent in shock: 7.4%, percent needing hemorrhage control procedure: 12.2%. Unadjusted, each categorical increase in plasma volume (from 0 to 0-2 to 2-6 to 6-12 to greater than 12 units) is significantly associated with greater odds of mortality. Confounder adjustment attenuates this effect (Table 1): the odds ratio (95% confidence interval) increasing from 0 to 0-2 units is 1.23 (1.09-1.38); from 0-2 to 2-6 units is 0.96 (0.83-1.11); from 2-6 to 6-12 units is 1.19 (0.96-1.47); and 6-12 to greater than 12 units is 1.68 (1.20-2.34). Similar results are seen in sensitivity analysis. Shock and need for hemorrhage control procedure significantly (p less than 0.001) modify the relationship between plasma and mortality.

Conclusions:
Plasma\x{2019}s effect on mortality in TBI remains unclear. Likely due to residual confounding despite adjustment, plasma is associated with increased mortality in moderate-severe TBI in this retrospective cohort. Interaction term analysis suggests this is confounding by indication, specifically because plasma is usually administered for hemorrhage (which in turn, increases mortality). A prospective randomized study of plasma for nonbleeding patients with TBI would better answer this important clinical question.

Discussion notes:

Association of plasma and mortality in severe TBI

Question: other methods ot account for unmeasured confounding? Instrument variable analysis (generally limited to randomized study), e-value sensitivity analysis

Inflection point found between 6-10 units of plasma (categorized exposure at clinically relevant threshold)

- Frank: categorization approach counts all values in a category as the same. Categorizing at inflection point does NOT respect the form of the data

Survival bias (patients that die early don't receive as much plasma)

- Higher resolution data needed to address

We want to adjust for confounding of bleeding for the relationship between plasma and severe TBI

- Difficult to disentangle bleeding from plasma -- so intimately intertwined (hard to do without randomized design)

- Question that can be answered: investigate relationship between bleeding and plasma, characterize by other variables

- Could analyze quality of clinical practice, variation in how much plasma was given

- Could inform later analysis when you bring in mortality

Frank R package (rms) to perform instrument analysis

High proportion of participants who did not receive plasma at all

- Need to choose knots in spline function. Placing knots difficult when lots of zeroes

- Manual override places knots using non-zeroes

Retrospective data -- feedback loop

2024 January 18

Alvin Jeffery, Nursing (12:30 - 1 pm)

Follow-up from 1/11/24 to meet with Frank

Attendees: Frank Harrell, Alvin Jeffrey, Marianna LaNoue, Dagmawi Negesse, Jackson Resser, Cass Johnson

Summary of last week:
  • Implementation of complex, quantitative risk information
  • And, how should results of predictive models and other tools be applied?
  • SPECTACULAR \x{2013} rapidly & empirically look at design elements
  • Primary test: four different ways (across 10 nurses, 12 timepoints) to display content
    • Measured preference, what action would be taken from that (Contact RRT, Contact MD, Contact Charge, Contact Peer, Increase monitoring, Continue Same)
    • Many other things could be modified
    • Can we take a factorial design and merge it with a Bayesian adaptive trial to start eliminating design elements that are not preferred / do not lead to the outcome we want?
    • Can this be done on a voucher? Is this project too broad for 90 hours of work?
Discussion:
  • 90 hours of work for pre-award work; so, this may not be a good fit for a voucher
  • Factorial design may be performed where some factors get dropped
    • Ensures balance; gets best average power
    • Marianna: From modelling perspective, how should this be done iteratively?
      • Possible messy part could be a factorial design where some factors interact (if one thing is red, another doesn\x{2019}t work entirely)
      • If these factors can be thought of as independent, that is best for sample size calculation
      • Alvin: Similar paper w/ 72 possibilities. Bayesian (non-adaptive), so comparable framework has been done, just not in the field of interest
      • Statistical simulation study may be another possibility
      • Response-surface design may be comparable. Breakfast cereal industry is notable here.
        • Polynomial regression estimating optimum combination of factors (optimize response surface)
        • Outcome is average taste test rating
        • By solving for optimum, that\x{2019}s how they decide what to market. Could be comparable.
        • Per Alvin: Different \x{201c}types\x{201d} of users (field, facility type, etc) may have different preferences, which would be great to parse out
        • Thermometer plot may be recommended. \x{201c}People\x{201d} plot (X out of 100) may be preferred by patients, but not preferred by Frank / Alvin / previous sample of nurses.
        • Would there be biostatisticians able to work on this?
          • May depend on timing. Frank may discuss with other department members.
          • If design can be nailed down, that may help determine what statistical support is required.
        • Fractional Factorial Design: By not having balance in every possible cell, occasionally of use

2024 January 11

Serena Fleming (Sarah Stallings), Genetic Counseling

I am working on a master\x{2019}s thesis project to conduct a retrospective chart review for individuals undergoing testing for Huntington Disease at VUMC across two decades to assess whether there are differences between asymptomatic and symptomatic individuals. The question I would like to address is \x{201c}How do asymptomatic and symptomatic individuals who decide to pursue genetic testing for Huntington Disease differ?\x{201d} I need assistance with descriptive and comparative statistics.

Clinic attendees: Dandan Liu, Cass Johnson, Jackson Resser

Years: 2001-2022; Huntington = neurodegenerative

Population of interest: Tested for huntington's disease (by ICD) initially pulled from pathology

- Within this, those symptomatic or asymptomatic

Symp/asymp: motor symptoms at initial visit or at test

Descriptive study with subgroup comparison

Dates: initial visit date, blood draw date, results disclosure date

Criteria for neurologist used to assess symptomatic/asymptomatic

406/415 have complete symp/asymp: need to really think about how to handle missingness for symp/asymp

Age = continuous variable; if normally distributed -> two sample t-test

If non-normal -> non-parametric Wilcoxon rank sum test (preferred because it makes less assumptions)

Chi-square test to compare categorical variables

Use test statistic and p-value to assess whether results are significant; report raw differences

Alvin Jeffery, Nursing

We have received a small foundation grant to build a clinical decision support tool evaluation system that we hope can randomize design elements (like a factorial research design) within an adaptive Bayesian analysis (where we can eliminate design features that we no longer need to evaluate). We have software developers who can build the front-end system, but we are looking for assistance with creating the conceptual analysis framework and helping to write the python (PyMC3) code to conduct the analysis.

Clinic attendees: Bryan Blette, Cass Johnson, Jackson Resser

Prior work: tested four risk formats for same underlying info (latin square randomization -- three scenarios)

SPECTACULAR

Phase 1: build your own adventure (pick which do you want to see)

Phase 2: keep chosen design from P1 on one screen, randomize pieces on the other

Can you merge bayesian design with factorial framework?

Could embed rules such that a given participant, based on their info, is more to be randomized a certain way

6-8 factors, about 1000 total combinations

Proposed: drop conditions after person has completed one hour of data collection

Aim: formalize framework then operationalize bayesian-adaptive design

VICTR voucher could be a good fit but Bryan thinks 90 hours might not be enough

- voucher might get desired deliverable

Idea for paper: simulations & power calculations

- Would help to assess feasibility and whether you want to drop factors
Topic revision: r1053 - 22 Feb 2024, JacksonResser
 

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