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Lindy effect - Wikipedia
The Lindy effect is a theory that the future life expectancy of some non-perishable things like a technology or an idea is proportional to their current age, so that every additional period of survival implies a longer remaining life expectancy.[1] Where the Lindy effect applies, mortality rate decreases with time. In contrast, living creatures and mechanical things follow a bathtub curve where, after "childhood", the mortality rate increases with time. Because life expectancy is probabilistically derived, a thing may become extinct before its "expected" survival. In other words, one needs to gauge both the age and "health" of the thing to determine continued survival.
wiki  reference  concept  metabuch  ideas  street-fighting  planning  comparison  time  distribution  flux-stasis  history  measure  correlation  arrows  branches  pro-rata  manifolds  aging  stylized-facts  age-generation  robust  technology  thinking  cost-benefit  conceptual-vocab  methodology  threat-modeling  efficiency  neurons  tools  track-record 
3 days ago by nhaliday
The Benefits of Reading to Children, Tested With a Data Pool of One - Freakonomics Freakonomics
"One of the most controversial small points in Freakonomics was the claim that early childhood test scores are not correlated to the amount a child is read to at home."
parenting  correlation  causality  psychology 
17 days ago by pmigdal
Effects of low-carbohydrate- compared with low-fat-diet interventions on metabolic control in people with type 2 diabetes: a systematic review including GRADE assessments | The American Journal of Clinical Nutrition | Oxford Academic
Glycated hemoglobin declined more in people who consumed low-carbohydrate food than in those who consumed low-fat food in the short term (MD: –1.38%; 95% CI: –2.64%, –0.11%; very-low-certainty evidence). At 1 y, the MD was reduced to –0.36% (95% CI: –0.58%, –0.14%; low-certainty evidence); at 2 y, the difference had disappeared. There is low to high (majority moderate) certainty for small improvements of unclear clinical importance in plasma glucose, triglycerides, and HDL concentrations favoring low-carbohydrate food at half of the prespecified time points. There was little to no difference in LDL concentration or any of the secondary outcomes (body weight, waist circumference, blood pressure, quality of life) in response to either of the diets (very-low- to high-certainty evidence).
Conclusions
Currently available data provide low- to moderate-certainty evidence that dietary carbohydrate restriction to a maximum of 40% yields slightly better metabolic control of uncertain clinical importance than reduction in fat to a maximum of 30% in people with T2D.
triglycerides  fat  low  diet  diabetes  T2D  type  2  blood  pressure  carbohydrates  carb  fasting  quality  of  life  glucose  carbohydrate  waist  circumference  biomarkers  peer-reviewed  research  human  in  vivo  correlation  effects  benefit  nutrition  clinical  trial  review  systematic 
4 weeks ago by Michael.Massing
Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis - The Lancet Public Health
We studied 15 428 adults aged 45–64 years, in four US communities, who completed a dietary questionnaire at enrolment in the Atherosclerosis Risk in Communities (ARIC) study (between 1987 and 1989), and who did not report extreme caloric intake (<600 kcal or >4200 kcal per day for men and <500 kcal or >3600 kcal per day for women). The primary outcome was all-cause mortality. We investigated the association between the percentage of energy from carbohydrate intake and all-cause mortality, accounting for possible non-linear relationships in this cohort. We further examined this association, combining ARIC data with data for carbohydrate intake reported from seven multinational prospective studies in a meta-analysis. Finally, we assessed whether the substitution of animal or plant sources of fat and protein for carbohydrate affected mortality.

Findings

....In the ARIC cohort, after multivariable adjustment, there was a U-shaped association between the percentage of energy consumed from carbohydrate (mean 48·9%, SD 9·4) and mortality: a percentage of 50–55% energy from carbohydrate was associated with the lowest risk of mortality. In the meta-analysis of all cohorts (432 179 participants), both low carbohydrate consumption (<40%) and high carbohydrate consumption (>70%) conferred greater mortality risk than did moderate intake, which was consistent with a U-shaped association (pooled hazard ratio 1·20, 95% CI 1·09–1·32 for low carbohydrate consumption; 1·23, 1·11–1·36 for high carbohydrate consumption). However, results varied by the source of macronutrients: mortality increased when carbohydrates were exchanged for animal-derived fat or protein (1·18, 1·08–1·29) and mortality decreased when the substitutions were plant-based (0·82, 0·78–0·87).

Interpretation

Both high and low percentages of carbohydrate diets were associated with increased mortality, with minimal risk observed at 50–55% carbohydrate intake. Low carbohydrate dietary patterns favouring animal-derived protein and fat sources, from sources such as lamb, beef, pork, and chicken, were associated with higher mortality, whereas those that favoured plant-derived protein and fat intake, from sources such as vegetables, nuts, peanut butter, and whole-grain breads, were associated with lower mortality, suggesting that the source of food notably modifies the association between carbohydrate intake and mortality.

Funding: National Institutes of Health.
whole  grain  carb  carbohydrates  low  fat  loss  guidelines  nutrition  food  foods  protein  macronutrients  proportion  ratio  ketogenic  diet  ketosis  risk  benefit  peer-reviewed  research  human  in  vivo  correlation  mortality  morbidity  all-cause  meta-analysis  animal  meat  plant-based  prospective  large  cohort 
4 weeks ago by Michael.Massing
AI Needs More Why
An article on Judea Pearl's Book of WHY.
Quite an accessible introduction.
judeapearl  pearl  why  ai  explanation  correlation  introspection  counterfactual  retrospection  introduction 
5 weeks ago by drmeme
ON THE GEOMETRY OF NASH EQUILIBRIA AND CORRELATED EQUILIBRIA
Abstract: It is well known that the set of correlated equilibrium distributions of an n-player noncooperative game is a convex polytope that includes all the Nash equilibrium distributions. We demonstrate an elementary yet surprising result: the Nash equilibria all lie on the boundary of the polytope.
pdf  nibble  papers  ORFE  game-theory  optimization  geometry  dimensionality  linear-algebra  equilibrium  structure  differential  correlation  iidness  acm  linear-programming  spatial  characterization  levers 
5 weeks ago by nhaliday
An intuitive, visual guide to copulas
Copulas allow us to decompose a joint probability distribution into their marginals (which by definition have no correlation) and a function which couples (hence the name) them together and thus allows us to specify the correlation seperately. The copula is that coupling function.
python  statistics  distribution  science  correlation 
6 weeks ago by deprecated
maintenance - Why do dynamic languages make it more difficult to maintain large codebases? - Software Engineering Stack Exchange
Now here is the key point I have been building up to: there is a strong correlation between a language being dynamically typed and a language also lacking all the other facilities that make lowering the cost of maintaining a large codebase easier, and that is the key reason why it is more difficult to maintain a large codebase in a dynamic language. And similarly there is a correlation between a language being statically typed and having facilities that make programming in the larger easier.
programming  worrydream  plt  hmm  comparison  pls  carmack  techtariat  types  engineering  productivity  pro-rata  input-output  formal-methods  correlation  best-practices  composition-decomposition  error  causation  confounding  devtools  jvm  scala  open-closed 
6 weeks ago by nhaliday
If Correlation Doesn’t Imply Causation then What Does?
https://www.dropbox.com/s/4t8n5zfdhjcq0i0/If%20correlation%20doesn%E2%80%99t%20imply%20causation%2C%20then%20what%20does%20%20DDI.pdf?dl=0

Of course, while it’s all very well to piously state that correlation doesn’t imply causation, it does leave us with a conundrum: under what conditions, exactly, can we use experimental data to deduce a causal relationship between two or more variables?
...
Over the past few decades, a group of scientists have developed a theory of causal inference intended to address these and other related questions. This theory can be thought of as an algebra or language for reasoning about cause and effect. Many elements of the theory have been laid out in a famous book by one of the main contributors to the theory, Judea Pearl. Although the theory of causal inference is not yet fully formed, and is still undergoing development, what has already been accomplished is interesting and worth understanding.
Statistics  DataScience  DataAnalytics  Correlation  Causation  MichaelNielsen  DDI 
6 weeks ago by richardwinter
Everything Is Correlated - Gwern.net
Pretty impressive bibliography of writings on the "crud factor" in statistics.
statistics  correlation 
6 weeks ago by debrouwere
Mindfulness-based stress reduction is associated with improved glycemic control in type 2 diabetes mellitus: a pilot study. - PubMed - NCBI
Psychological distress is linked with impaired glycemic control among diabetics.

OBJECTIVE:
Estimate changes in glycemic control, weight, blood pressure, and stress-related psychological symptoms in patients with type 2 diabetes participating in a standard Mindfulness Based Stress Reduction (MBSR) program.

DESIGN:
Prospective, observational study.

SETTING:
Academic health center.

PATIENTS:
Adult patients with type 2 diabetes mellitus.

INTERVENTIONS:
Participation in MBSR program for heterogeneous patient population. Diet and exercise regimens held constant.

MAIN OUTCOME MEASURES:
Glycosylated hemoglobin A1c (HA1c), blood pressure, body weight, and Symptom Checklist 90-Revised (anxiety, depression, somatization, and general psychological distress scores).

RESULTS:
Eleven of 14 patients completed the intervention. At 1 month follow-up, HA1c was reduced by 0.48% (P = .03), and mean arterial pressure was reduced by 6 mmHg (P = .009). Body weight did not change. A decrease in measures of depression, anxiety, and general psychological distress was observed.
SMBG  diabetes  type  2  T2D  glycemic  control  glucose  management  blood  self  care  correlation  peer-reviewed  research  stress  distress  reduction  mindfulness  based 
8 weeks ago by Michael.Massing
Self-monitoring blood glucose improves glycemic control in type 2 diabetes without intensive treatment: A systematic review and meta-analysis. - PubMed - NCBI
SMBG was associated with a reduction of HbA1c at 12 weeks (-0.31%; 95% CI: -0.57 to -0.05) and 24 weeks (-0.34%; 95%CI: -0.52 to -0.17), but no difference was found for 1 year. Subgroup analysis including studies with baseline HbA1c greater than 8% showed a higher reduction of HbA1c: -0.83% (95% CI: -1.55 to -0.11) at 12 weeks, and -0.48% (95% CI: -0.77 to -0.19) at 24 weeks, with no difference for 1 year nor for the stratification for number the tests.
CONCLUSION:
SMBG seems to lead to a slightly better glycemic control in the short term in patients with T2D. Patients decompensated at baseline appear to have the greatest benefit.
SMBG  diabetes  type  2  T2D  glycemic  control  glucose  management  blood  self  monitoring  correlation  peer-reviewed  research  tight  frequency 
8 weeks ago by Michael.Massing

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