Higher Jdtic Intake Predicts Very Slightly Lower Facebook Pages Liked for Population

Higher Jdtic Intake Predicts Very Slightly Lower Facebook Pages Liked for Population

Contents

Variables

A
Jdtic 20
A
Facebook Pages Liked 916

Categories

A
Treatments 9356
A
Social Interactions 78

Tags

Low Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 20.3% average increase in Facebook Pages Liked following above average Jdtic Intake.

Abstract

Facebook Pages Liked was generally 19% higher than average after a total of 0.1 milligrams of Jdtic over the previous 7 days.

Aggregated data from 2 study participants suggests with a LOW degree of confidence (p=0.394, 95% CI -0.31 to 0.188) that Jdtic has a very weakly negative predictive relationship (R=-0.0607) with Facebook Pages Liked.

The highest quartile of Facebook Pages Liked measurements were observed following an average 0.01 milligrams Jdtic per day.

The lowest quartile of Facebook Pages Liked measurements were observed following an average 0.008 milligrams of Jdtic per day.

After an onset delay of 30 minutes, Facebook Pages Liked is typically 2% lower than average over the 7 days following around 0.008 milligrams of Jdtic Jdtic.

Keywords: Jdtic, Facebook Pages Liked, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 2 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 83 paired observations from 2 participants revealed a moderate improvement in Facebook Pages Liked following above-average Jdtic exposure.

+20.6%
Change from Baseline
Moderate effect on Facebook Pages Liked
0.01
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
-0.061
Correlation (r)
p = 0.004
Significance
z = 0.06
Effect Magnitude
φ = 0.32
Temporality

What This Means

When participants had above-average Jdtic:

  • Facebook Pages Liked increased by 20.6% on average
  • Temporal analysis suggests possible reverse causation—interpret with caution
  • This relationship is statistically significant (p = 0.004)

Interpreting the Predictor Impact Score

The Predictor Impact Score (PIS) integrates multiple Bradford Hill causal criteria into a single metric. Use this guide to interpret the score:

PIS Range Interpretation Recommended Action
≥ 0.5 Strong evidence High priority for RCT validation
0.3 - 0.5 Moderate evidence Consider for experimental investigation
0.1 - 0.3 Weak evidence Monitor for additional data
< 0.1 Insufficient evidence Low priority; may be noise

Note: PIS is a prioritization heuristic, not proof of causation. High scores indicate relationships worth investigating, not confirmed causal effects. With only 2 participants, these scores are preliminary and will become more reliable as additional data is collected.

Optimal Daily Values (Precision Dosing)

Based on the observed relationship, we can estimate the predictor values associated with the best and worst outcomes. These values enable personalized dosing recommendations.

⚠️ Preliminary Data: With 2 participants and 83 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

0.1 mg
Value Predicting Higher Facebook Pages Liked
Average Jdtic when Facebook Pages Liked exceeded its mean
0.0 mg
Value Predicting Lower Facebook Pages Liked
Average Jdtic when Facebook Pages Liked was below its mean

What This Suggests

Facebook Pages Liked tended to be lowest (best) when Jdtic was around 0.0 mg.

Important: These values reflect correlations, not guaranteed causal effects. Individual responses may vary. Use as a starting point for personal experimentation, not as a definitive prescription. Consult healthcare providers before making treatment decisions.

Population Correlation

Jdtic Distribution

Facebook Pages Liked Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Jdtic Intake
Effect Variable Name Facebook Pages Liked
Sinn Predictive Coefficient 0.0055015215835781
Confidence Level LOW
Confidence Interval 0.24901
Forward Pearson Predictive Coefficient -0.0607
Critical T Value 1.66
Total Jdtic Intake Over Previous 7 days Before ABOVE Average Facebook Pages Liked 0.01 milligrams
Total Jdtic Intake Over Previous 7 days Before BELOW Average Facebook Pages Liked 0.008 milligrams
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 83
Optimal Pearson Product -0.00454105108186
P Value 0.39368
Statistical Significance 0.0036
Strength of Relationship 0.24901
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Jdtic Info

Property Value
Variable Name Jdtic (mg)
Aggregation Method SUM
Analysis Performed At 2021-08-29
Duration of Action 21 days
Filling Value 0
Kurtosis 31.282755348613
Mean 0.00866265 milligrams
Median 0 milligrams
Minimum Allowed Value 0 milligrams
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 20
Number of Measurements 33
Number of Measurements (including those generated by tagged, joined, or child variables) 33
Public true
Onset Delay 30 minutes
Standard Deviation 0.024675205973947
Unit Milligrams
User Variables 2
Variable Category Treatments
Variable ID 108992
Variance 0.00074979872449181

Facebook Pages Liked Info

Property Value
Variable Name Facebook Pages Liked
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 7 days
Filling Value 0
Kurtosis 273.83865391441
Mean 0.27514379698492 event
Median 0.020100502512563 event
Minimum Allowed Value 0 event
Number of Aggregate Predictors 732
Number of Aggregate Outcomes 184
Number of Measurements 164257
Number of Measurements (including those generated by tagged, joined, or child variables) 151770
Public true
Onset Delay 0 seconds
Standard Deviation 1.0850111721454
Unit Event
User Variables 461
UPC 0
Variable Category Social Interactions
Variable ID 5969791
Variance 3.5942247713855

Introduction

Background

Jdtic (Treatments) and Facebook Pages Liked (Social Interactions) are both important factors in understanding human health and well-being. This study investigates the relationship between these two variables using real-world observational data.

Traditional randomized controlled trials (RCTs), while the gold standard for causal inference, are often impractical, expensive, or unethical for studying many health relationships. Aggregated N-of-1 observational studies offer a complementary approach that leverages within-subject comparisons across large populations to identify meaningful patterns.

Research Question

Does Jdtic affect Facebook Pages Liked?

Additionally, we seek to determine:

  1. What is the direction and magnitude of any effect?
  2. How confident can we be in this relationship based on the available data?
  3. What are the optimal levels of Jdtic for maximizing Facebook Pages Liked?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Jdtic and Facebook Pages Liked. Additionally, we attempt to determine the Jdtic (mg) values most likely to produce optimal Facebook Pages Liked values.

Study Overview

This is a population-level observational study using aggregated N-of-1 methodology. By aggregating individual N-of-1 experiments, we can identify population-level patterns while accounting for the substantial individual variation that exists in most health relationships. Effect sizes are reported as percent change from baseline, enabling intuitive interpretation and comparison across different measures.

Full Methodology: Framework for Real-World Evidence-Based Pharmacovigilance: Aggregated N-of-1 Trials for Quantifying Treatment Effects

Discussion

Interpretation of Findings

Participants experienced a 20.6% improvement in Facebook Pages Liked following above-average Jdtic exposure. The Predictor Impact Score (PIS) of 0.01 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.004).

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Facebook Pages Liked is not statistically significant at a 95% confidence interval. This suggests that the Jdtic value may not have a significant influence on the Facebook Pages Liked value, or that more data is needed to detect an effect.

After treatment, a 20.3% increase (0.0244 event) from the mean baseline 0.118 event was observed. The relative standard deviation at baseline was 336.9%. The observed change was 0.061258 times the standard deviation.

A common rule of thumb considers a change greater than twice the baseline standard deviation on two separate pre-post experiments may be considered significant. This occurrence would have only a 5% likelihood of resulting from random fluctuation (a p-value < 0.05).

T-Test Details
Observed t-value: 0.163
Critical t-value: 1.660

Since t = 0.16 < 1.66, we cannot reject the null hypothesis.

Biological Plausibility

A plausible bio-chemical mechanism between predictor and outcome is critical for interpreting observational findings. This is where human judgment excels beyond statistical analysis.

Community feedback on the biological plausibility of this relationship is still being collected. Consider the known mechanisms by which Jdtic might influence Facebook Pages Liked.

Bradford Hill Criteria Assessment

The Bradford Hill criteria provide a framework for assessing causality in observational studies. Our methodology operationalizes six of the nine criteria through the Predictor Impact Score (PIS):

Criterion How Addressed Metric
Strength Effect size magnitude Percent change from baseline (Δ%), z-score
Consistency Cross-participant replication Number of users (N), number of pairs (n)
Temporality Predictor precedes outcome Temporality factor (φ), onset delay (δ > 0)
Biological Gradient Dose-response relationship Gradient coefficient (φgradient)
Plausibility Biological mechanism assessment Community votes on mechanism plausibility
Specificity Category appropriateness Interest factor (finterest)

Predictor Impact Score (PIS)

The PIS integrates multiple Bradford Hill criteria into a composite metric quantifying how reliably a predictor affects an outcome. Higher scores indicate stronger evidence:

Population-Level PIS:

$$\text{PIS}_{\text{agg}} = |r_{\text{forward}}| \cdot w \cdot \phi_{\text{users}} \cdot \phi_{\text{pairs}} \cdot \phi_{\text{change}} \cdot \phi_{\text{gradient}}$$

Where φ-factors are saturation functions approaching 1 as evidence accumulates:

  • φusers = 1 - e-N/10 (user saturation)
  • φpairs = 1 - e-n/nsig (pair saturation)
  • φchange = 1 - e-Δspread/Δsig (effect spread saturation)
  • w = weighted average of plausibility votes

Temporality Assessment

We assess evidence for correct causal direction using the temporality factor:

$$\phi_{\text{temporal}} = \frac{|r_{\text{forward}}|}{|r_{\text{forward}}| + |r_{\text{reverse}}|}$$

Values approaching 1 indicate the predictor precedes the outcome (supporting causation); values near 0.5 suggest ambiguous directionality; values near 0 suggest reverse causation or confounding by indication.

Limitations

As with any observational study, correlation does not prove causation. Key limitations include:

  • Unmeasured confounders: Variables not tracked may influence results
  • Self-selection bias: Health trackers may differ from the general population
  • Measurement error: Self-reported data may contain recall bias
  • Confounding by indication: Sicker individuals may use more treatments

However, within-subject comparison and temporal precedence analysis partially mitigate these limitations. If the relationship is merely coincidental, as participants independently modify their Jdtic values, the observed strength will decline over time. Spurious correlations naturally dissipate as more data is collected.

Future Directions

Future research should examine:

  • Subgroup analyses to identify individual differences in response
  • Potential confounders and mediators of the observed relationship
  • Optimal dosing and timing for Jdtic
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 2 participants, these results are based on limited data. Effect sizes and confidence will improve as more participants contribute data. Consider these findings directional rather than definitive.

Above-average Jdtic was associated with a 20.6% improvement in Facebook Pages Liked—a moderate effect. The Predictor Impact Score of 0.01 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.01 and a 20.6% effect size, this relationship currently lacks sufficient evidence. Continue monitoring as more data becomes available. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Jdtic may influence Facebook Pages Liked in real-world conditions. The within-subject design and temporal analysis provide confidence in these relationships, though observational limitations remain.

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Methods

Study Design

This study is based on data donated by 2 participants. Thus, the study design is equivalent to the aggregation of 2 separate n=1 observational natural experiments.

This within-subject design is powerful because it controls for all stable individual characteristics (genetics, baseline health status, socioeconomic factors) that might otherwise confound the relationship between variables.

Data Analysis

Temporal Assumptions

The analysis incorporates temporal assumptions about the relationship between variables:

  • Onset Delay: It was assumed that 30 minutes would pass before a change in Jdtic would produce an observable change in Facebook Pages Liked.
  • Duration of Action: It was assumed that Jdtic could produce an observable change in Facebook Pages Liked for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Jdtic values and subsequent Facebook Pages Liked values. Individual correlations were then aggregated using Fisher's z-transformation to produce a population-level estimate:

Individual Correlation:

$$r_i = \frac{\sum(x_{ij} - \bar{x}_i)(y_{ij} - \bar{y}_i)}{\sqrt{\sum(x_{ij} - \bar{x}_i)^2 \sum(y_{ij} - \bar{y}_i)^2}}$$

Fisher's Z-Transformation:

$$z_i = \frac{1}{2} \ln\left(\frac{1 + r_i}{1 - r_i}\right)$$

Aggregated Correlation:

$$\bar{r} = \tanh(\bar{z}) \quad \text{where} \quad \bar{z} = \frac{1}{N}\sum_{i=1}^{N} z_i$$

Effect Size Calculation

Effect sizes are reported as percent change from baseline. For each participant, we compare the outcome following above-average predictor values to the overall baseline outcome:

$$\Delta\%_{\text{baseline}} = \frac{\bar{O}_{\text{follow-up}} - \bar{O}_{\text{baseline}}}{\bar{O}_{\text{baseline}}} \times 100$$

Effect Magnitude (Z-Score)

To assess effect magnitude relative to natural variability, we calculate the z-score:

$$z = \frac{|\Delta\%_{\text{baseline}}|}{\text{RSD}_{\text{baseline}}}$$

where RSDbaseline is the relative standard deviation of outcome during baseline period

A z-score > 2 indicates statistical significance (p < 0.05), meaning the observed change exceeds typical baseline fluctuation and is unlikely due to random variation.

Statistical Significance

Correlation significance is assessed using a two-tailed t-test:

$$t = \frac{r\sqrt{n-2}}{\sqrt{1-r^2}}$$

We reject the null hypothesis (ρ = 0) at α = 0.05 when |t| exceeds the critical value, providing statistical evidence that the observed relationship is not due to chance.

Data Sources

Jdtic data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Facebook Pages Liked data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Data Quality

Data quality measures were applied to ensure reliable results:

  • Minimum Data Requirement: Only participants with sufficient paired observations were included in the analysis.
  • Outlier Handling: Extreme values were winsorized to reduce the influence of measurement errors.
  • Missing Data: Days with missing values were handled using appropriate filling strategies based on the variable type.
  • Test User Exclusion: Test accounts and invalid users were excluded from all analyses.

Principal Investigator

Program & Methods

Mike P. Sinn
Sinn, M. P.

Designed and implemented data collection, aggregation, causal inference pipeline, and automated study generation framework. Developed the Predictor Impact Score methodology operationalizing Bradford Hill criteria for ranking causal relationships in observational data. When he tells people this at parties, they usually say they have to go check on their car.

Individual study outputs are automated, reproducible, and open to external audit. (Which I would seriously recommend.)

Contact

Cite This Study

APA Format
Sinn, M. P. (2026). Causal Analysis: Does Jdtic (mg) Affect Facebook Pages Liked?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-108992-effect-5969791-population-study
BibTeX 
@misc{sinn_cause_108992_effect_5969791_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Jdtic (mg) Affect Facebook Pages Liked?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-108992-effect-5969791-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Jdtic (mg) Affect Facebook Pages Liked?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-108992-effect-5969791-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Jdtic (mg) Affect Facebook Pages Liked?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-108992-effect-5969791-population-study [Accessed January 10, 2026].

Study Type: Aggregated N-of-1 Observational Mega-Study
Evidence Level: Level II (Real-World Evidence)
Methodology: Bradford Hill Criteria with Predictor Impact Score (PIS)

References

This framework was originally developed in 2013 based on the Bradford Hill criteria. Subsequent literature has independently validated similar approaches to causal inference from observational data:

  1. Hill, A.B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295-300. [Bradford Hill criteria]
  2. Lillie, E.O., et al. (2011). The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized Medicine, 8(2), 161-173. [N-of-1 methodology]
  3. Pearl, J. (2009). Causality: Models, Reasoning, and Inference . Cambridge University Press. [Causal inference]
  4. Hernán, M.A., & Robins, J.M. (2020). Causal Inference: What If . Chapman & Hall/CRC. [Free textbook]
  5. FDA (2018). Framework for FDA's Real-World Evidence Program . U.S. Food and Drug Administration. [Regulatory context]
  6. Duan, N., et al. (2013). Single-patient (n-of-1) trials: a pragmatic clinical decision methodology . Journal of Clinical Epidemiology, 66(8), S21-S28.
  7. Platt, R., et al. (2018). The FDA Sentinel Initiative—an evolving national resource . New England Journal of Medicine, 379(22), 2091-2093.

This information is for research and educational purposes only, not medical advice. Consult a healthcare provider before making health decisions. Terms of Service

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