Aggregated data from 2 study participants suggests with a high degree of confidence (p=1.3054943304997E-18, 95% CI 0.166 to 0.529) that Acetyl-L-Carnitine has a moderately positive predictive relationship (R=0.35) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 428.26 milligrams Acetyl-L-Carnitine per day. The lowest quartile of Overall Mood measurements were observed following an average 52.90791592548 mg Acetyl-L-Carnitine per day.
The objective of this study is to determine the nature of the relationship (if any) between Acetyl-L-Carnitine and Overall Mood. Additionally, we attempt to determine the Acetyl-L-Carnitine values most likely to produce optimal Overall Mood values.
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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.
It was assumed that 0.5 hours would pass before a change in Acetyl-L-Carnitine would produce an observable change in Overall Mood. It was assumed that Acetyl-L-Carnitine could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.
As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply correlation. We can never know for sure if one factor is definitely the cause of an outcome. However, lack of correlation definitely implies the lack of a causal relationship. Hence, we can with great confidence rule out non-existent relationships. For instance, if we discover no relationship between mood and an antidepressant this information is just as or even more valuable than the discovery that there is a relationship.
We can also take advantage of several characteristics of time series data from many subjects to infer the likelihood of a causal relationship if we do find a correlational relationship. The criteria for causation are a group of minimal conditions necessary to provide adequate evidence of a causal relationship between an incidence and a possible consequence. The list of the criteria is as follows:
1. Strength (effect size): A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. Specificity: Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.
4. Temporality: The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay).
5. Biological gradient: Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 130 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Acetyl-L-Carnitine values, the observed strength of the relationship will decline until it is below the threshold of significance. To it another way, in the case that we do find a spurious correlation, suggesting that banana intake improves mood for instance, one will likely increase their banana intake. Due to the fact that this correlation is spurious, it is unlikely that you will see a continued and persistent corresponding increase in mood. So over time, the spurious correlation will naturally dissipate. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.
|Aggregate QM Score||0.044817998748539|
|Number Of Correlations||2|
|Population Trait Correlation Pearson Correlation Coefficient||0.84557088547186|
|Average Daily High Cause||1241|
|Average Daily Low Cause||50|
|Average Effect Following High Cause||4|
|Average Effect Following Low Cause||3|
|Cause Variable Category Name||Treatments|
|Cause Variable Combination Operation||SUM|
|Cause Variable Common Unit Abbreviated Name||mg|
|Cause Variable Common Unit Name||Milligrams|
|Cause Variable Display Name||Acetyl-L-Carnitine intake|
|Cause Variable Name||Acetyl-L-Carnitine|
|Created At||2018-10-20 20:48:09|
|Critical T Value||1.655|
|Cumulative Value Over Duration Of Action Predicting High Outcome||428.26|
|Cumulative Value Over Duration Of Action Predicting Low Outcome||52.90791592548|
|Duration Of Action||604800|
|Duration Of Action In Hours||168|
|Effect Size||moderately positive|
|Effect Variable Category Name||Emotions|
|Effect Variable Common Unit Abbreviated Name||/5|
|Effect Variable Common Unit Name||1 to 5 Rating|
|Effect Variable Display Name||Overall Mood|
|Effect Variable Name||Overall Mood|
|Effect Variable Valence||positive|
|Forward Pearson Correlation Coefficient||0.3474|
|Grouped Cause Value Closest To Value Predicting High Outcome||500|
|Number Of Pairs||130|
|Number Of Users||2|
|Onset Delay In Hours||0.5|
|Optimal Pearson Product||0.63699675343992|
|Predictive Pearson Correlation Coefficient||0.011748519617646|
|Predictor Explanation||↑Higher Acetyl-L-Carnitine Intake Predicts ↑higher Overall Mood|
|Predicts High Effect Change||8|
|Predicts Low Effect Change||-4|
|Reverse Pearson Correlation Coefficient||0.3802498061214|
|Updated At||2018-10-20 20:48:09|