Abstract
This mega-study analyzes Depression (Emotions) using aggregated N-of-1 observational data from 115 participants who contributed 961 measurements . We identified 262 statistically significant predictor-outcome relationships involving Depression.
Depression primarily acts as an outcome, influenced by 212 different factors. Effect sizes are reported as percent change from baseline following above-average predictor exposure.
Our analysis employs within-subject comparisons to control for individual differences, temporal precedence analysis to assess causality direction, and the Predictor Impact Score (PIS) to quantify causal evidence. See the ranked results below to explore the full list of factors predicting Depression.
Keywords: Depression, Emotions, N-of-1 trials, real-world evidence, causal inference, Predictor Impact Score, observational study
Full Methodology: Framework for Real-World Evidence-Based Pharmacovigilance: Aggregated N-of-1 Trials for Quantifying Treatment Effects
High Confidence: With 115 participants, these findings have strong statistical power. Results are significant at p < 0.05.
Results
Our analysis identified 262 statistically significant relationships involving Depression. These represent factors that predict changes in Depression.
Click any relationship in the tables below to view the full study page with detailed charts, statistical analysis, temporal parameters, and methodology for that specific predictor-outcome pair.
Relationship Network
The network graph below visualizes the relationships between Depression and related variables. Nodes represent variables, and edges represent statistically significant relationships. Click any node or edge to explore that relationship.
Predictors Network Graph
Causal Flow Diagram
The Sankey diagram below illustrates the flow of influence between predictors, Depression, and outcomes. The width of each flow corresponds to the strength of the relationship. Click any flow to see the detailed study.
Predictors Flow Chart
Causes of Depression
User reported causes of Depression based on their intuition.
Conditions Resulting from Depression
User-reported conditions resulting from Depression based on their intuition.
| Resulting Condition | Agree |
|---|---|
| Binge Eating | 77% |
| Anxiety / Nervousness | 69% |
| Anger | 67% |
| Adrenal Fatigue | 59% |
| Chronic Pain Syndrome | 58% |
| Obesity | 57% |
| Fatigue | 55% |
| Fibromyalgia | 53% |
| Insomnia | 48% |
| Low Libido | 47% |
| Tobacco Addiction | 43% |
| Aging | 40% |
| Psoriasis | 38% |
| Vulvodynia | 31% |
| Arthritis | 26% |
| Lower Back Pain | 18% |
Treatments
User-reported effectiveness in treating Depression
| Treatment | Major Improvement | Moderate Improvement | Much Worse | No Effect | Worse | Responses |
|---|---|---|---|---|---|---|
| Massage | 15% | 47% | 2% | 34% | 2% | 59 |
| Progressive Relaxation | 6% | 51% | 2% | 37% | 4% | 95 |
| Exercise | 21% | 51% | 1% | 25% | 3% | 1301 |
| Chiropractic Care | 6% | 27% | 3% | 60% | 5% | 177 |
| Tai Chi | 9% | 35% | 2% | 50% | 4% | 109 |
| Acupuncture | 6% | 28% | 2% | 60% | 5% | 221 |
| Reiki | 3% | 34% | 0% | 59% | 3% | 29 |
| Alcohol | 2% | 16% | 24% | 29% | 29% | 417 |
| Sam-E | 7% | 21% | 4% | 60% | 7% | 136 |
| Marijuana | 17% | 42% | 8% | 25% | 8% | 12 |
| Yoga | 12% | 48% | 1% | 37% | 3% | 488 |
| Low Dose Naltrexone (LDN) | 35% | 29% | 6% | 18% | 12% | 17 |
| Qi Gong | 0% | 75% | 0% | 25% | 0% | 8 |
| Light Therapy | 9% | 45% | 1% | 42% | 3% | 206 |
| Imipramine | 6% | 22% | 16% | 46% | 10% | 50 |
| Psychotherapy | 18% | 46% | 2% | 29% | 5% | 464 |
| Swimming | 11% | 52% | 1% | 34% | 1% | 157 |
| Paleo Diet | 31% | 23% | 0% | 38% | 8% | 13 |
| Minocycline | 10% | 0% | 0% | 70% | 20% | 10 |
| Magnesium | 3% | 19% | 2% | 74% | 3% | 356 |
| Provigil | 7% | 36% | 7% | 38% | 12% | 91 |
| Krill Oil | 5% | 32% | 5% | 59% | 0% | 22 |
| Massage Therapy | 10% | 47% | 1% | 39% | 3% | 373 |
| Meditation | 13% | 46% | 1% | 35% | 4% | 572 |
| Adderall | 19% | 35% | 7% | 31% | 8% | 113 |
| Reflexology | 7% | 21% | 7% | 43% | 21% | 14 |
| Xanax | 12% | 46% | 6% | 29% | 7% | 211 |
| Rhodiola Rosea | 4% | 27% | 1% | 63% | 4% | 73 |
| Nortriptyline | 7% | 21% | 7% | 64% | 0% | 14 |
| Effexor XR | 14% | 29% | 13% | 31% | 13% | 371 |
| Fish Oil | 1% | 16% | 1% | 77% | 4% | 683 |
| Mirtazapine | 13% | 31% | 13% | 31% | 11% | 61 |
| Amitriptyline (Elavil) | 6% | 20% | 18% | 41% | 15% | 71 |
| SSRIs | 18% | 40% | 7% | 27% | 8% | 771 |
| Adequate Sleep | 14% | 43% | 1% | 39% | 3% | 1220 |
| Relaxation | 7% | 41% | 1% | 45% | 6% | 772 |
| Zinc | 2% | 10% | 3% | 83% | 2% | 131 |
| Vitamin D3 | 5% | 25% | 2% | 66% | 2% | 363 |
| Armour | 17% | 28% | 0% | 50% | 6% | 18 |
| Zoloft | 14% | 30% | 10% | 34% | 11% | 462 |
| Lexapro | 13% | 31% | 11% | 35% | 9% | 351 |
| St. John's Wort | 3% | 18% | 4% | 70% | 6% | 426 |
| Diet Changes | 13% | 43% | 2% | 38% | 4% | 384 |
| Savella | 13% | 13% | 20% | 53% | 0% | 15 |
| Cymbalta | 14% | 29% | 14% | 35% | 8% | 243 |
| Tryptophan | 2% | 25% | 5% | 64% | 4% | 145 |
| Eye Movement Desensitization and Reprocessing (EMDR) | 17% | 30% | 6% | 38% | 10% | 71 |
| Transcranial Magnetic Stimulation | 20% | 45% | 5% | 30% | 0% | 20 |
| Desipramine | 6% | 29% | 18% | 35% | 12% | 34 |
| Masturbation | 3% | 28% | 5% | 52% | 13% | 424 |
| 5-HTP | 7% | 27% | 5% | 54% | 7% | 192 |
| Valerian | 0% | 13% | 8% | 66% | 13% | 85 |
| Abilify (Aripiprazole) | 5% | 15% | 20% | 38% | 22% | 65 |
| Spend Time With Pet | 16% | 55% | 0% | 27% | 2% | 735 |
| Play Outside | 17% | 57% | 0% | 23% | 3% | 304 |
| Music With Exercise | 19% | 52% | 0% | 26% | 3% | 288 |
| Music Therapy | 21% | 56% | 2% | 18% | 3% | 149 |
| Daily Aerobic Exercise | 21% | 52% | 2% | 24% | 2% | 156 |
| Daily Natural Sunlight Exposure | 16% | 45% | 0% | 37% | 2% | 263 |
| Art Therapy | 17% | 49% | 2% | 30% | 2% | 178 |
| Cognitive Behavior Therapy | 16% | 48% | 2% | 28% | 6% | 507 |
| Distance Walking | 15% | 53% | 1% | 27% | 5% | 147 |
| Mindful Meditation | 11% | 51% | 1% | 33% | 4% | 417 |
| Talk Therapy | 13% | 48% | 2% | 30% | 6% | 980 |
| Bibliotherapy | 14% | 50% | 2% | 30% | 3% | 119 |
| Group Sports | 18% | 44% | 3% | 29% | 5% | 126 |
| Breathwork | 9% | 50% | 1% | 36% | 5% | 191 |
| Volunteer | 14% | 50% | 0% | 28% | 9% | 58 |
| Anti-Inflamatory Whole Foods Diet | 13% | 39% | 1% | 46% | 0% | 82 |
| Talking With Family/friends | 13% | 42% | 2% | 33% | 10% | 950 |
| Personal Growth Workshops | 15% | 41% | 3% | 34% | 7% | 162 |
| Dialectical Behavioral Therapy | 26% | 35% | 7% | 23% | 9% | 57 |
| Keep Daily Routine | 11% | 37% | 2% | 46% | 4% | 492 |
| Salsa Dancing | 13% | 47% | 0% | 33% | 7% | 30 |
| Sleep Hygiene Maintenance | 9% | 41% | 1% | 47% | 3% | 128 |
| Venlafaxin | 17% | 36% | 6% | 38% | 3% | 95 |
| Transdermal Estorgen/Progestin Therapy | 24% | 47% | 6% | 18% | 6% | 17 |
| Neurofeedback | 9% | 48% | 2% | 39% | 2% | 46 |
| Warm Soaking Bath | 11% | 78% | 0% | 11% | 0% | 9 |
| Suffering From Pseudoscience | 100% | 0% | 0% | 0% | 0% | 3 |
| Warm Shower | 7% | 42% | 1% | 45% | 4% | 206 |
| Gardening | 40% | 10% | 0% | 50% | 0% | 10 |
| Sertralin | 21% | 34% | 10% | 30% | 5% | 102 |
| Avoid Intoxicants | 11% | 31% | 1% | 53% | 4% | 750 |
| Hobbies | 13% | 47% | 0% | 33% | 7% | 15 |
| Thyroxine | 4% | 48% | 0% | 48% | 0% | 25 |
| Alpha Stim CES | 13% | 50% | 0% | 38% | 0% | 8 |
| Wake Therapy | 50% | 25% | 0% | 0% | 25% | 4 |
| Quilonum | 75% | 0% | 25% | 0% | 0% | 4 |
| Klonopin | 14% | 43% | 7% | 27% | 9% | 108 |
| Dancing to 80s Music | 5% | 51% | 5% | 37% | 2% | 41 |
| Marplan | 100% | 0% | 0% | 0% | 0% | 1 |
| Infrared Brain Stimulation | 100% | 0% | 0% | 0% | 0% | 1 |
| Tango Dancing | 20% | 40% | 0% | 20% | 20% | 10 |
| Synthroid | 12% | 34% | 2% | 44% | 7% | 82 |
| Housecleaning | 6% | 47% | 3% | 36% | 8% | 635 |
| Anafranil | 30% | 30% | 15% | 15% | 10% | 20 |
| Sipralexa (Escitalopram Oxalate) | 18% | 36% | 9% | 36% | 0% | 11 |
| Trevilor | 33% | 33% | 22% | 11% | 0% | 9 |
| Invega | 14% | 43% | 0% | 29% | 14% | 7 |
| Volunteer on Mental Health Call Line | 25% | 33% | 8% | 17% | 17% | 12 |
| Bioidentical Hormone Replacement Therapy | 20% | 34% | 11% | 29% | 6% | 35 |
| Support Groups | 14% | 40% | 6% | 28% | 13% | 215 |
| Hobbioes | 33% | 33% | 0% | 0% | 33% | 3 |
| Tianeptine | 0% | 100% | 0% | 0% | 0% | 1 |
| Faster EFT | 25% | 0% | 0% | 75% | 0% | 4 |
| Lexotan | 22% | 22% | 11% | 44% | 0% | 9 |
| Vagus Nerve Stimulation (VNS) | 0% | 50% | 0% | 50% | 0% | 2 |
| Cynomel / Cytomel | 17% | 17% | 4% | 63% | 0% | 24 |
| Deep Brain Stimulation DBS | 33% | 0% | 0% | 33% | 33% | 3 |
| Quetiapine | 0% | 33% | 0% | 67% | 0% | 3 |
| Nardil | 17% | 17% | 0% | 50% | 17% | 6 |
| Hormonal Birth Control | 17% | 17% | 0% | 50% | 17% | 6 |
| Doxepin (Aponal, Deptran, Sinquan) | 9% | 36% | 9% | 45% | 0% | 11 |
| Viibryd | 0% | 40% | 0% | 40% | 20% | 5 |
| DL-Phenylalanine | 0% | 20% | 0% | 80% | 0% | 5 |
| Testosterone | 12% | 35% | 6% | 41% | 7% | 69 |
| Relora (Magnolia Bark) | 15% | 23% | 0% | 38% | 23% | 13 |
| Shiatsu | 11% | 34% | 5% | 42% | 8% | 38 |
| Electro Convulsive Therapy ECT | 17% | 17% | 8% | 50% | 8% | 12 |
| Stop Eating Sugar | 13% | 27% | 3% | 49% | 8% | 133 |
| Methyltetrahydrasulfate | 0% | 33% | 33% | 33% | 0% | 3 |
| Vyvanse | 14% | 32% | 14% | 36% | 5% | 22 |
| Concerta | 8% | 54% | 23% | 8% | 8% | 13 |
| Milnacipran (Ixel) | 25% | 6% | 13% | 44% | 13% | 16 |
| Being in a Relationship | 13% | 39% | 11% | 29% | 8% | 138 |
| Journaling | 6% | 38% | 3% | 44% | 9% | 663 |
| Aripiprazole | 0% | 25% | 50% | 25% | 0% | 4 |
| Discontinue Hormonal Birth Control | 19% | 19% | 10% | 29% | 24% | 21 |
| Parnate | 14% | 21% | 21% | 36% | 7% | 14 |
| Agomelatine | 0% | 0% | 17% | 67% | 17% | 6 |
| Bupropion (Wellbutrin) | 13% | 35% | 9% | 34% | 9% | 537 |
| Regular Eating Times (At Least 3x a Day) | 7% | 26% | 1% | 62% | 4% | 477 |
| Resting Before Bedtime | 6% | 31% | 2% | 54% | 7% | 351 |
| Cannabis (Sativa) | 16% | 36% | 13% | 23% | 11% | 349 |
| Cut Down on Internet | 6% | 44% | 10% | 29% | 11% | 72 |
| Citalopram | 4% | 40% | 12% | 40% | 4% | 52 |
| Lamictal (Lamotragine) | 13% | 31% | 12% | 36% | 8% | 119 |
| Psychiatric Inpatient Care | 16% | 37% | 16% | 20% | 10% | 129 |
| Niacin (Megadose) | 4% | 18% | 7% | 64% | 7% | 28 |
| Cold Shower | 5% | 33% | 6% | 50% | 6% | 167 |
| Pristiq | 15% | 27% | 27% | 23% | 8% | 26 |
| Tyrosine | 2% | 25% | 6% | 56% | 12% | 52 |
| Watching Emotional or Spiritual Movies | 3% | 39% | 9% | 34% | 16% | 70 |
| Celexa | 11% | 29% | 9% | 40% | 11% | 256 |
| Vitamin B | 3% | 21% | 1% | 72% | 3% | 588 |
| Retired | 9% | 22% | 15% | 38% | 15% | 65 |
| Reducing Red Meat Consumption | 4% | 16% | 6% | 69% | 5% | 124 |
| Olanzapine (Zyprexa) | 6% | 23% | 26% | 29% | 17% | 35 |
| Chromium Picolinate | 2% | 5% | 5% | 88% | 1% | 106 |
| Prozac | 12% | 31% | 15% | 29% | 13% | 480 |
| Cigarettes | 0% | 30% | 11% | 47% | 12% | 118 |
| Flaxseed Oil | 1% | 12% | 4% | 79% | 4% | 220 |
| Risperidone (Risperdol) | 9% | 11% | 29% | 29% | 23% | 35 |
| Trazodone | 10% | 21% | 14% | 40% | 16% | 185 |
| Lithium | 10% | 13% | 19% | 43% | 15% | 89 |
| Caffeine | 3% | 26% | 5% | 50% | 16% | 844 |
| Effexor | 8% | 24% | 16% | 37% | 13% | 225 |
| Depakote | 8% | 17% | 27% | 25% | 22% | 63 |
| Paxil | 9% | 25% | 17% | 32% | 17% | 305 |
Treatments Causing Depression
User-reported treatments that exhibited the side effect of Depression
| Treatment | Percent of Reports |
|---|---|
| Zanaflex | 27% |
| Meditation | 18% |
| Xanax | 23% |
| Melatonin | 9% |
| Hydrocortisone | 27% |
| Atenolol | 41% |
| Copaxone | 37% |
| Leuprolide | 76% |
| Depo-Provera | 58% |
| Vaginal Estrogen Cream (Estrace, Premarin) | 30% |
| Alprazolam (Xanax) | 23% |
| Testosterone | 24% |
| Lithium | 44% |
| Caffeine | 19% |
| Topiramate (Topamax) | 34% |
| Tizanidine | 27% |
| Amphetamines | 29% |
Predictors of Depression
The table below ranks predictors by their impact on Depression. Each row shows the percent change in Depression following above-average predictor exposure. Click any row to see the full analysis.
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Daily Distribution
Average by Year
Average by Month
Average by Day of Week
Predictors Label
Summary Statistics
Depression Info
| Property | Value |
|---|---|
| Variable Name | Depression |
| Aggregation Method | MEAN |
| Analysis Performed At | 2020-12-19 |
| Duration of Action | 24 hours |
| Kurtosis | 1.7807894944403 |
| Maximum Allowed Value | 5 out of 5 |
| Mean | 3.0073956521739 out of 5 |
| Median | 2.9946369565217 out of 5 |
| Minimum Allowed Value | 1 out of 5 |
| Number of Aggregate Predictors | 212 |
| Number of Aggregate Outcomes | 50 |
| Number of Measurements | 961 |
| Number of Measurements (including those generated by tagged, joined, or child variables) | 946 |
| Public | true |
| Onset Delay | 0 seconds |
| Standard Deviation | 0.35810502382401 |
| Unit | 1 to 5 Rating |
| User Variables | 115 |
| UPC | 0 |
| Variable Category | Emotions |
| Variable ID | 86810 |
| Variance | 0.3645803387576 |
Introduction
Background
Depression (Emotions) is primarily a health outcome that may be influenced by various factors. Understanding the predictors and outcomes associated with Depression has important implications for personalized health optimization, clinical decision-making, and public health interventions. Traditional randomized controlled trials (RCTs), while the gold standard for causal inference, are often impractical for studying the full range of factors that may influence emotions.
Research Questions
This mega-study addresses the following research questions:
- What factors most strongly predict changes in Depression?
- Which predictors are modifiable vs. non-modifiable?
- What is the magnitude of predictor effects (percent change from baseline)?
- How confident can we be in these relationships?
Study Overview
We employ an aggregated N-of-1 observational study design, combining data from multiple individual longitudinal natural experiments. This approach leverages within-subject comparisons to control for stable individual differences while aggregating across participants to identify population-level patterns.
Discussion
Interpretation of Findings
The ranked tables in the Results section provide a comprehensive list of predictors, ordered by their effect size on Depression. Rather than focusing on any single relationship, the value lies in the full spectrum of factors identified and their relative effect sizes.
Context and Prior Research
These findings should be interpreted in the context of existing literature on Depression. While our observational design cannot establish causality with the certainty of randomized trials, the large sample size, within-subject design, and temporal precedence analysis provide converging evidence for the relationships identified.
Practical Implications
Individuals seeking to optimize their Depression may consider the predictors identified in this analysis, particularly those that are directly modifiable (e.g., behaviors, treatments, environmental factors). However, individual responses may vary, and these population-level findings should not replace personalized medical advice.
Future Directions
Future research should examine:
- Subgroup analyses to identify individual differences in response
- Potential confounders and mediators of the observed relationships
- Optimal dosing and timing for modifiable predictors
- Confirmation of key findings through prospective or randomized designs
Conclusion
The ranked tables above provide the complete list of factors predicting Depression, ordered by effect size.
These findings may inform evidence-based strategies for optimizing Depression. Individual responses may vary; consult healthcare providers for personalized guidance.
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Methods
Study Design
Our analysis of Depression is based on aggregated data from 115 separate N-of-1 observational natural experiments. Unlike traditional clinical trials, our approach captures relationships in everyday life conditions, providing insights into how factors actually affect people outside controlled laboratory settings. Each participant serves as their own control, reducing between-subject confounding.
Baseline & Outcome Measurement
For each participant \(i\), we compute the mean predictor value and partition measurements into baseline (below-average exposure) and follow-up (above-average exposure) periods:
The primary effect size is expressed as percent change from baseline:
This metric is interpretable ("15% reduction in symptoms"), scale-invariant, and consistent with FDA efficacy assessments.
Temporal Analysis & Causality Direction
Our analysis accounts for two critical temporal parameters:
- Onset Delay (\(\delta\)): Time lag between predictor exposure and observable outcome change (0-100 days)
- Duration of Action (\(\tau\)): Time window over which predictor influence persists (10 min - 90 days)
We compute both forward correlations (predictor → outcome) and reverse correlations (outcome → predictor) to calculate the temporality factor:
A temporality factor approaching 1.0 indicates the predictor reliably precedes the outcome, supporting a causal interpretation. Values near 0.5 suggest ambiguous temporal direction, while values approaching 0 suggest reverse causation.
Temporal Parameter Optimization
Different predictor-outcome pairs have different optimal temporal alignments. We employ hyperparameter optimization to find the onset delay and duration that maximize correlation strength:
The search begins with category-appropriate defaults (e.g., 30-minute onset for treatments) and explores physiologically plausible ranges. To prevent overfitting, we restrict searches to biologically plausible ranges and require minimum sample sizes.
Statistical Methods
We employ multiple statistical techniques:
-
Pearson Correlation Coefficient:
$$r = \frac{\sum_{j=1}^{n}(p_j - \bar{p})(o_j - \bar{o})}{\sqrt{\sum_{j=1}^{n}(p_j - \bar{p})^2} \cdot \sqrt{\sum_{j=1}^{n}(o_j - \bar{o})^2}}$$
-
Z-Score Normalization: Effect magnitude relative to baseline variability:
$$z = \frac{|\Delta\%|}{\text{RSD}_{\text{baseline}}}$$where \(z > 2\) indicates \(p < 0.05\) (statistically significant)
- Two-Tailed T-Tests: Statistical significance assessed at \(\alpha = 0.05\)
- 95% Confidence Intervals: \(\text{CI}_{95\%} = \bar{r} \pm 1.96 \cdot \text{SE}_{\bar{r}}\)
Effect Size Classification
Correlation strength is classified based on the absolute coefficient value:
| Classification | Correlation Range |
|---|---|
| Very Strong | \(|r| \geq 0.8\) |
| Strong | \(0.6 \leq |r| < 0.8\) |
| Moderate | \(0.4 \leq |r| < 0.6\) |
| Weak | \(0.2 \leq |r| < 0.4\) |
| Very Weak | \(|r| < 0.2\) |
Data Quality Requirements
To ensure reliable results, we enforce minimum thresholds:
- ≥ 5 distinct value changes in both predictor and outcome variables
- ≥ 30 overlapping measurement pairs (per Central Limit Theorem)
- ≥ 10% of data in both baseline and follow-up periods
- Non-zero variance in both predictor and outcome
Our filling strategy is deliberately conservative: zero-filling for treatments assumes non-adherence when no measurement exists, biasing toward null findings rather than false positives.
Predictor Impact Score (PIS)
We calculate a composite Predictor Impact Score that quantifies how much a predictor impacts an outcome:
Where:
- \(|r|\) = absolute correlation coefficient (strength)
- \(S = 1 - p\) = statistical significance
- \(\phi_z = \frac{|z|}{|z| + 2}\) = normalized z-score factor (effect magnitude)
- \(\phi_{\text{temporal}}\) = temporality factor (forward vs. reverse causation)
- \(f_{\text{interest}}\) = interest factor (penalizes spurious variable pairs)
Higher PIS values indicate predictors with greater, more reliable impact on the outcome.
Bradford Hill Criteria for Causality
While correlation does not prove causation, our PIS operationalizes six of the nine Bradford Hill criteria:
| Criterion | How Addressed | Metric |
|---|---|---|
| Strength | Effect size magnitude | \(|r|\), \(\Delta\%\) |
| Consistency | Cross-participant replication | \(N\), \(n\), SE, CI |
| Temporality | Forward vs. reverse correlation | \(\phi_{\text{temporal}}\) |
| Biological Gradient | Dose-response analysis | \(\phi_{\text{gradient}}\) |
| Specificity | Category appropriateness | \(f_{\text{interest}}\) |
| Plausibility | Community voting | Up/down votes |
Confidence Levels
Each relationship is assigned a confidence level based on multiple factors:
- High Confidence: \(p < 0.01\), or \(N > 100\) participants, or \(n > 500\) pairs
- Medium Confidence: \(p < 0.05\), or \(N > 10\) participants, or \(n > 100\) pairs
- Low Confidence: Meets minimum thresholds but requires more data
Limitations
Key limitations of this observational framework:
- Cannot prove causation: Unmeasured confounders may influence results
- Self-selection bias: Health trackers may differ from general population
- Measurement error: Self-reported data may contain recall bias
- Confounding by indication: Sicker patients may take more treatments
These findings represent population-level trends and should not replace personalized medical advice. Within-subject comparison and temporal precedence analysis partially mitigate these limitations.
Population Analysis
With 115 participants contributing data, our analysis benefits from the Law of Large Numbers: as sample size increases, random noise diminishes and true relationships become more apparent. Population-level estimates are computed as:
Principal Investigator
Program & Methods
Cite This Study
@misc{sinn_86810_2026,
author = {Sinn, Mike P.},
title = {Depression Mega-Study: Systematic Review of Predictive Factors},
year = {2026},
publisher = {The Decentralized FDA},
url = {http://studies.dfda.earth/variables/Depression},
note = {Accessed: January 7, 2026},
howpublished = {N=115 participants}
}
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:
- 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]
- 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]
- Pearl, J. (2009). Causality: Models, Reasoning, and Inference . Cambridge University Press. [Causal inference]
- Hernán, M.A., & Robins, J.M. (2020). Causal Inference: What If . Chapman & Hall/CRC. [Free textbook]
- FDA (2018). Framework for FDA's Real-World Evidence Program . U.S. Food and Drug Administration. [Regulatory context]
- Duan, N., et al. (2013). Single-patient (n-of-1) trials: a pragmatic clinical decision methodology . Journal of Clinical Epidemiology, 66(8), S21-S28.
- 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