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This individual's Hunger is generally lowest after a daily total of 210 milligrams of Remeron intake over the previous 7 days.
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Blue represents the sum of Remeron intake over the previous 7 days
An increase in 7 days cumulative Remeron intake is usually followed by an decrease in Hunger. (R = -0.184)
Typical values for Hunger following a given amount of Remeron intake over the previous 7 days.
Typical Remeron intake seen over the previous 7 days preceding the given Hunger value.
This chart shows how your Remeron changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Remeron on each day of the week.
This chart shows the typical value recorded for Remeron for each month of the year.
This chart shows how your Hunger changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Hunger on each day of the week.
This chart shows the typical value recorded for Hunger for each month of the year.

Abstract

This individual's Hunger is generally 7% lower than normal after 210 milligrams Remeron per 7 days. This individual's data suggests with a high degree of confidence (p=0.00027988498237642, 95% CI -0.368 to -0) that Remeron has a weakly negative predictive relationship (R=-0.18) with Hunger. The highest quartile of Hunger measurements were observed following an average 185.69 milligrams Remeron per day. The lowest quartile of Hunger measurements were observed following an average 192.81690140845 mg Remeron per day.Hunger is generally 7% lower than normal after a total of 192.81690140845 milligrams of Remeron intake over the previous 7 days. Hunger is generally 12% higher after a total of 185.69 milligrams of Remeron intake over the previous 7 days.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Remeron and Hunger. Additionally, we attempt to determine the Remeron values most likely to produce optimal Hunger values.

Participant Instructions

Record your Remeron daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Hunger daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by one participant. Thus, the study design is consistent with an n=1 observational natural experiment.

Data Analysis

Remeron Pre-Processing
Remeron measurement values below 0 milligrams were assumed erroneous and removed. No maximum allowed measurement value was defined for Remeron. It was assumed that any gaps in Remeron data were unrecorded 0 milligrams measurement values.
Remeron Analysis Settings

Hunger Pre-Processing
Hunger measurement values below 1 out of 5 were assumed erroneous and removed. Hunger measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Hunger so any gaps in data were just not analyzed instead of assuming zero values for those times.
Hunger Analysis Settings

Predictive Analytics
It was assumed that 0 hours would pass before a change in Remeron would produce an observable change in Hunger. It was assumed that Remeron could produce an observable change in Hunger for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
89 raw Remeron measurements with 211 changes spanning 2265 days from 2013-01-12 to 2019-03-28 were used in this analysis. 475 raw Hunger measurements with 247 changes spanning 1596 days from 2014-11-09 to 2019-03-25 were used in this analysis.

Data Sources

Remeron 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.

Hunger 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.

Limitations

As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply causation. 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:
Strength (A.K.A. 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. There is a weakly negative relationship between Remeron intake and Hunger

Consistency (A.K.A. Reproducibility)
Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 356 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Remeron intake 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.

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.

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). The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations.

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.

Plausibility
A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 1 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Remeron intake and Hunger is coincidental.

Coherence
Coherence between epidemiological and laboratory findings increases the likelihood of an effect. It will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.

Experiment
All of human life can be considered a natural experiment. Occasionally, it is possible to appeal to experimental evidence.

Analogy
The effect of similar factors may be considered.

Relationship Statistics

Property Value
Cause Variable Name Remeron intake
Effect Variable Name Hunger
Sinn Predictive Coefficient 0.179
Confidence Level high
Confidence Interval 0.18391173481706
Forward Pearson Correlation Coefficient -0.184
Critical T Value 1.646
Total Remeron intake Over Previous 7 days Before ABOVE Average Hunger 185.69 milligrams
Total Remeron intake Over Previous 7 days Before BELOW Average Hunger 192.817 milligrams
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 356
Optimal Pearson Product 0.011468552301301
P Value 0.00027988498237642
Statistical Significance 0.9727
Strength of Relationship 0.18391173481706
Study Type individual
Analysis Performed At 2019-04-04

Remeron Statistics

Property Value
Variable Name Remeron
Aggregation Method SUM
Analysis Performed At 2019-03-30
Duration of Action 7 days
Kurtosis 28.998216800795
Mean 10.627 milligrams
Median 0 milligrams
Minimum Allowed Value 0 milligrams
Number of Changes 211
Number of Correlations 75
Number of Measurements 89
Onset Delay 0 seconds
Standard Deviation 17.794236652699
Unit Milligrams
UPC 863694000011
Variable ID 1431
Variance 316.63485805224

Hunger Statistics

Property Value
Variable Name Hunger
Aggregation Method MEAN
Analysis Performed At 2019-03-29
Duration of Action 7 days
Kurtosis 2.1332757611832
Maximum Allowed Value 5 out of 5
Mean 2.4343 out of 5
Median 2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 247
Number of Correlations 1189
Number of Measurements 475
Onset Delay 0 seconds
Standard Deviation 1.0227766256279
Unit 1 to 5 Rating
Variable ID 102685
Variance 1.0460720259307

Tracking Remeron

Record your Remeron daily in the reminder inbox or using the interactive web or mobile notifications.

Tracking Hunger

Record your Hunger daily in the reminder inbox or using the interactive web or mobile notifications.
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https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn