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    string(26) "danielnevo@tauex.tau.ac.il"
    ["state_or_province"]=>
    string(6) "Isreal"
    ["country"]=>
    string(6) "Isreal"
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      ["p_pers_f_name"]=>
      string(6) "Rachel"
      ["p_pers_l_name"]=>
      string(7) "Axelrod"
      ["p_pers_degree"]=>
      string(4) "M.A."
      ["p_pers_pr_affil"]=>
      string(77) "Department of Statistics and Operations Research, Tel Aviv University, Israel"
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      ["requires_data_access"]=>
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  ["project_ext_grants"]=>
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    ["label"]=>
    string(65) "External grants or funds are being used to support this research."
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  ["project_funding_source"]=>
  string(25) "Israel Science Foundation"
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1656) "Background:

Drugs and vaccines can influence outcomes through both physiological and non-physiological pathways. For example, Canagliflozin, used for type 2 diabetes, may lead to higher HbA1c levels if patients become less diligent about diet and exercise. Clinical trials aim to measure physiological effects, often using blinding to eliminate behavioral influences. Previous research has identified key conditions, such as measuring a "belief variable," to help estimate treatment effects accurately, even when blinding fails.



Objective:

To derive bounds for the true treatment effect in cases of broken blinding, even when the key conditions are not met.



Study Design:

This is a statistical methodological study. We will demonstrate the application of the proposed bounds and sensitivity analysis using real clinical trial data.



Participants:

We aim to analyze one or two clinical trials that meet two criteria: (1) The treatment potentially has a non-physiological effect, and (2) blinding is imperfect due to strong side effects or notable treatment effects.



Primary and Secondary Outcome Measures:

The treatment effect is defined as the causal contrast 

E(Y^{a=1,m}) vs. E(Y^{a=0,m}), where 

E denotes expectation, Y is the outcome (e.g., weight loss), a is the treatment arm, and m is the "message indicator," which reflects the participant's belief about their treatment assignment.



Statistical Analysis:

We will use Linear Programming techniques and sensitivity analysis to construct bounds for the treatment effect."
  ["project_brief_bg"]=>
  string(2313) "It is well known that medications and vaccines can affect desired medical outcomes through physiological mechanisms. However, they may also influence outcomes through behavioral pathways [1,2,3]. For example, individuals treated with insulin may justify consuming more food because they perceive the insulin injection as a safeguard against hyperglycemia [1,4], potentially leading to an underestimation of insulin's effect on weight loss.



To isolate the physiological effect of a treatment and eliminate behavioral influences, clinical trials typically use a "blinding" procedure, where participants are unaware of their treatment assignment. Ideally, the control group receives a placebo with side effects similar to those of the actual treatment. However, several studies have shown that blinding is often unsuccessful, with participants correctly guessing their treatment group [5,6]. This unblinding can occur due to an ineffective placebo choice [9] or because the treatment itself produces noticeable effects.



[10] developed a method to separate physiological and behavioral effects using a "belief variable," which reflects an individual's belief about whether they received the actual treatment. This method is only valid under two conditions: (1) the "Y dismissible component condition" is met, ensuring no common causes between the belief variable and the outcome, and (2) the belief variable is measured during the study. However, in many trials, one or both conditions are not met. For example, personality traits like optimism may influence both the belief about receiving the treatment and the likelihood of engaging in behaviors that affect the outcome, such as exposure to infectious agents [11].



This project aims to improve the accuracy of treatment effect estimates by addressing behavioral confounders in clinical trials for both vaccines and drugs. We will develop methods to separate physiological and behavioral effects even when blinding is imperfect, and when the belief variable or the "Y dismissible component condition" is not available. The findings will enhance the reliability of treatment efficacy estimates and improve the understanding of real-world treatment effects, ultimately informing both scientific research and public health."
  ["project_specific_aims"]=>
  string(1337) "This project aims to provide partial identification of the physiological treatment effect in two key settings: (1) when the Y dismissible component condition is violated, and (2) when the belief variable is unmeasured. For the first setting, we derive bounds using two strategies. The first strategy employs the linear programming approach developed in [12], which generates nonparametric, valid, and tight bounds, evaluated without any assumptions. The second strategy incorporates assumptions about monotone relationships between the unmeasured variable, the belief variable, and the outcome. To determine broken blinding, we will test for significant differences in side effects between treatment groups.



When the belief variable is unmeasured, we propose a sensitivity analysis approach to address potential biases.



To summarize, our main objectives are:



To demonstrate how unmeasured common causes can impact clinical trial results, using real examples and directed acyclic graphs.

To develop a framework—based on bounds or sensitivity analysis—to distinguish between physiological and behavioral effects, even when the belief variable is unmeasured or common causes are present.

To apply our methods to real data and estimate bounds for the true physiological effects."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(8) "meth_res"
    ["label"]=>
    string(23) "Methodological research"
  }
  ["project_purposes"]=>
  array(2) {
    [0]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(34) "research_on_clinical_trial_methods"
      ["label"]=>
      string(34) "Research on clinical trial methods"
    }
  }
  ["project_research_methods"]=>
  string(1123) "We will include clinical trials with (1) patient-level datasets containing outcomes, side effects, and treatment group indicators, (2) treatments with non-physiological effects, such as diabetes medications, Crohn's disease drugs, or the COVID-19 vaccine, and (3) evidence of broken blinding due to noticeable side effects or a strong treatment effect.

As we are uncertain which trials have fully available side effect data or report adverse event rates by treatment group, we are requesting multiple datasets. We will screen these datasets to confirm eligibility and select one or two trials with the most significant differences in side effect rates to apply our methodology for treatment effect estimation.





Inclusion/Exclusion Criteria for Each Trial

Inclusion criteria: All participants who met the study's eligibility requirements, complied with the protocol, and received the required treatment doses.

Exclusion criteria: Participants who did not meet the eligibility requirements, failed to comply with protocol procedures, or did not receive the required treatment doses."
  ["project_main_outcome_measure"]=>
  string(859) "Canagliflozin and JNJ-28431754 (Diabetes Treatment):

Primary Outcome: Change in Glycosylated Hemoglobin (HbA1c) from baseline to Week 18/26.

Secondary Outcome: Percent change in body weight from baseline to Week 18.



Ad26.COV2.S (COVID-19 Vaccine):

Primary Outcome: Number of participants with the first occurrence of molecularly confirmed moderate to severe/critical COVID-19 in seronegative participants, with onset at least 14 days after the second vaccination.



Ustekinumab/Infliximab/Remicade (Crohn’s Disease Treatment):

Primary Outcome: Number of participants achieving clinical remission at Week 44 or clinical response at Week 6/4.



Topiramate (Hypertension Treatment):

Primary Outcome: Percent change in body weight and/or sitting diastolic blood pressure from baseline."
  ["project_main_predictor_indep"]=>
  string(802) "The main predictor in all studies is the treatment arm (A), categorized into two groups:

Treatment (A=1): Subjects who received the active treatment being tested (Canagliflozin, Ad26.COV2.S, Ustekinumab/Infliximab/Remicade, or Topiramate).

Control (A=0): Subjects who received the placebo.



Another key predictor needed is the "belief variable," which has not been directly measured within the trial. To demonstrate our method, we generate a synthetic belief variable based on the assumption that experiencing side effects increases the likelihood of believing one received active treatment. While the synthetic model may influence results, our goal is to illustrate the methodology, which can be applied in future trials where the belief variable is directly measured [13]."
  ["project_other_variables_interest"]=>
  string(760) "Side Effects / Adverse Events (S):

The presence of side effects or adverse events will be categorized into two groups:

Yes (S=1): Subjects who reported adverse events.

No (S=0): Subjects who did not report adverse events.

All selected trials include "safety" in their title, so we expect this variable to be present in the data. The exact definition of adverse events will depend on the specific trial data.

Other Variables:

Additional variables collected in the clinical trials, such as age, sex, and comorbidities, will be used to stratify the main outcome measure. Alternatively, these variables may be used to estimate the inverse probability of treatment weighting (IPTW) and incorporated into the final analysis."
  ["project_stat_analysis_plan"]=>
  string(1951) "This is a statistical methodology study, not a reanalysis aimed at deriving new medical insights. Our goal is to demonstrate our novel methods using real clinical data.



We will estimate treatment effectiveness by comparing the main outcome for each treatment arm. For binary outcomes (e.g., infection status in the Ad26.COV2.S/Ustekinumab/Infliximab/Remicade trials), we will calculate the rate difference or rate ratio. For continuous outcomes (e.g., HbA1c in the Canagliflozin/Topiramate trials), we will calculate the mean difference or mean ratio.



Our focus is on scenarios where direct estimation of treatment effects is biased due to unobserved common causes. To address this, we will provide two alternatives for estimating directly the treatment effects: bounds and a sensitivity analysis framework. These methods are based on conditional expectations of the outcome in each treatment arm. For binary outcomes, the expectation is the event rate (e.g., infection rate). For continuous outcomes, the expectation is the mean (e.g., mean HbA1c).



Regarding missing data, if the proportion of missing outcomes is low, we will exclude subjects with missing data and perform a complete case analysis. As a sensitivity analysis, we will impute missing outcomes using the Multivariate Imputation by Chained Equations (MICE) algorithm (implemented in the R mice package). If the proportion of missing data is high, we will prioritize imputation.



We are requesting multiple clinical trials due to uncertainty about which trial will best meet the two conditions outlined in the Data Source section. Since we do not plan to conduct a meta-analysis, no special methods are required to combine datasets.



Our primary aim is to present a new estimation method, not to make statistical inferences. However, we will compute non-parametric confidence intervals using the bootstrap method. "
  ["project_software_used"]=>
  array(2) {
    ["value"]=>
    string(7) "rstudio"
    ["label"]=>
    string(7) "RStudio"
  }
  ["project_timeline"]=>
  string(133) "Estimated start date: January 2025

Estimated analysis completion: March 2025

Estimated manuscript submission: June 2025"
  ["project_dissemination_plan"]=>
  string(255) "We plan to submit our research findings to one of the top peer-reviewed journals in biostatistics, i.e., statistics for biological and medical research. Examples of these journals are Biometrics, Biostatistics, and Statistical Methods in Medical Research."
  ["project_bibliography"]=>
  string(3424) "
1. Hannele Yki-Järvinen, Leena Ryysy, Marjut Kauppila, Eila Kujansuu, Jorma Lahti, Tapani Marjanen, Leo Niskanen, Sulo Rajala, Seppo Salo, Pentti Seppälä, Timo Tulokas, Jorma Viikari, Marja-Riitta Taskinen, Effect of Obesity on the Response to Insulin Therapy in Noninsulin-Dependent Diabetes Mellitus, The Journal of Clinical Endocrinology & Metabolism, Volume 82, Issue 12, 1 December 1997, Pages 4037–4043.
2. Serisier, Aimee, Sarah Beale, Yamina Boukari, Susan Hoskins, Vincent Nguyen, Thomas Byrne, Wing Lam Erica Fong et al. “A case-crossover study of the effect of vaccination on SARS-CoV-2 transmission relevant behaviours during a period of national lockdown in England and Wales.” Vaccine 41, no. 2 (2023): 511-518.
3. Hossain, Md Emran, Md Sayemul Islam, Md Jaber Rana, Md Ruhul Amin, Mohammed Rokonuzzaman, Sudipto Chakrobortty, and Sourav Mohan Saha. “Scaling the changes in lifestyle, attitude, and behavioral patterns among COVID-19 vaccinated people: insights from Bangladesh.” Human vaccines & immunotherapeutics 18, no. 1 (2022): 2022920.
4. Heller, Simon. “Weight gain during insulin therapy in patients with type 2 diabetes mellitus.” Diabetes research and clinical practice 65 (2004): S23-S27.
5. Bang, Heejung, Liyun Ni, and Clarence E. Davis. “Assessment of blinding in clinical trials.” Controlled clinical trials 25, no. 2 (2004): 143-156.
6. Bang, Heejung, Stephen P. Flaherty, Jafar Kolahi, and Jongbae Park. “Blinding assessment in clinical trials: a review of statistical methods and a proposal of blinding assessment protocol.” Clinical Research and Regulatory Affairs 27, no. 2 (2010): 42-51.
7. Fisher, Seymour, and Roger P. Greenberg. “How sound is the double-blind design for evaluating psychotropic drugs?.” The Journal of nervous and mental disease 181, no. 6 (1993): 345-350.
8. Freed, Brian, Brian Williams, Xiaolu Situ, Victoria Landsman, Jeehyoung Kim, Alex Moroz, Heejung Bang, and Jongbae J. Park. “Blinding, sham, and treatment effects in randomized controlled trials for back pain in 2000–2019: a review and meta-analytic approach.” Clinical Trials 18, no. 3 (2021): 361-370.
9.Boutron, Isabelle, Candice Estellat, and Philippe Ravaud. “A review of blinding in randomized controlled trials found results inconsistent and questionable.” Journal of clinical epidemiology 58, no. 12 (2005): 1220-1226.
10. Stensrud, Mats J., Daniel Nevo, and Uri Obolski. “Distinguishing immunologic and behavioral effects of vaccination.” Epidemiology 35, no. 2 (2024): 154-163.
11. Bang, Heejung. “Random guess and wishful thinking are the best blinding scenarios.” Contemporary clinical trials communications 3 (2016): 117-121.
12. Balke, Alexander, and Judea Pearl. “Counterfactual probabilities: Computational methods, bounds and applications.” In Uncertainty in artificial intelligence, pp. 46-54. Morgan Kaufmann, 1994.
13. Obolski, U., Stensrud, M. J., & Nevo, D. (2024). A call for blinding assessments in dengue vaccine trials. The Lancet Infectious Diseases, 24(1), e10.
 
 
 
 
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pi affil
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products
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