Profile

Scrapbook photo 1
gwern branwen
2,389 followers|1,397,811 views
AboutPostsPhotosVideos

Stream

gwern branwen

Shared publicly  - 
 
"An example of this approach is a study7 in Australia, which measured reported pain levels, swelling and other symptoms associated with osteoarthritis and chronic pain in 132 people taking different drugs over three years. For each person, measurements were taken every 2 weeks for 12-week periods, when the patient was either off or on a particular drug. By comparing the data collected before and after the different treatments, the researchers showed that, although initially costly, the formalized N-of-1 trials resulted in more-effective prescriptions."

Linked in http://www.nature.com/news/personalized-medicine-time-for-one-person-trials-1.17411#/b7
N-of-1 trials test treatment effectiveness within an individual patient.To assess (i) the impact of three different N-of-1 trials on both clinical and economic outcomes over 12 months and (ii) whether the use of N-of-1 trials to target patients’ ...
1
Add a comment...

gwern branwen

Shared publicly  - 
 
"CRISPR/Cas9-mediated gene editing in human tripronuclear zygotes"; abstract:

"Genome editing tools such as the clustered regularly interspaced short palindromic repeat (CRISPR)-associated system (Cas) have been widely used to modify genes in model systems including animal zygotes and human cells, and hold tremendous promise for both basic research and clinical applications. To date, a serious knowledge gap remains in our understanding of DNA repair mechanisms in human early embryos, and in the efficiency and potential off-target effects of using technologies such as CRISPR/Cas9 in human pre-implantation embryos. In this report, we used tripronuclear (3PN) zygotes to further investigate CRISPR/Cas9-mediated gene editing in human cells. We found that CRISPR/Cas9 could effectively cleave the endogenous β-globin gene (HBB). However, the efficiency of homologous recombination directed repair (HDR) of HBB was low and the edited embryos were mosaic. Off-target cleavage was also apparent in these 3PN zygotes as revealed by the T7E1 assay and whole-exome sequencing. Furthermore, the endogenous delta-globin gene (HBD), which is homologous to HBB, competed with exogenous donor oligos to act as the repair template, leading to untoward mutations. Our data also indicated that repair of the HBB locus in these embryos occurred preferentially through the non-crossover HDR pathway. Taken together, our work highlights the pressing need to further improve the fidelity and specificity of the CRISPR/Cas9 platform, a prerequisite for any clinical applications of CRSIPR/Cas9-mediated editing."

Discussions:

http://www.nature.com/news/chinese-scientists-genetically-modify-human-embryos-1.17378
http://phenomena.nationalgeographic.com/2015/04/22/editing-human-embryos-so-this-happened/
http://www.unz.com/gnxp/drakas-probably-not/
http://infoproc.blogspot.com/2015/04/crispr-edits-in-human-zygotes.html
http://marginalrevolution.com/marginalrevolution/2015/04/chinese-scientists-genetically-modify-human-embryos.html
4
1
Vivek Rai's profile photo
Add a comment...

gwern branwen

Shared publicly  - 
 
It's hard to imagine that Sacks intended to write such a searing indictment of himself &
'medical ethics' in which he identifies a possible treatment for a terminal condition and then stands idly by for years watching it kill his patient rather than attempt any kind of treatment or experiment, but I can't figure out how else one might supposed to be interpreting this essay.
What role did the car crash and the damage to his frontal lobes play in his decline? Credit Photograph by Noah Greenberg
3
Steven Rose's profile photoNeil Shepperd's profile photogwern branwen's profile photoEric Jain's profile photo
8 comments
 
+gwern branwen, I'd call it the "busy bystander effect". Someone like Sacks is flooded with too many requests to properly follow up on most of them. Having a dedicated "patient navigator" (as is common in cancer treatment) might help.
Add a comment...

gwern branwen

Shared publicly  - 
 
An interesting experiment: we worry about the 'garden of forking paths' in analysis where the results depend on the exact distributions, covariates, priors etc we choose, with unknown and hidden degrees of freedom; so generate a new virgin dataset and give it to a hundred analysts or so, ask them to answer the same question, and see how much they agree and whether they can come to a consensus. They can. Sort of.

http://andrewgelman.com/2014/04/28/crowdstorming-dataset/ https://osf.io/gvm2z/ https://osf.io/w6pi5/ http://home.uchicago.edu/~/npope/crowdsourcing_paper.pdf "Crowdsourcing data analysis: Do soccer referees give more red cards to dark skin toned players?"

"29 teams involving 61 analysts used the same data set to address the same research questions: whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players and whether this relation is moderated by measures of explicit and implicit bias in the referees' country of origin. Analytic approaches varied widely across teams. For the main research question, estimated effect sizes ranged from 0.89 to 2.93 in odds ratio units, with a median of 1.31. 20 teams (69%) found a significant positive effect and 9 teams (31%) observed a non-significant relationship. The causal relationship however remains unclear. No team found a significant moderation between measures of bias of referees' country of origin and red card sanctionings of dark skin toned players. Crowdsourcing data analysis highlights the contingency of results on choices of analytic strategy, and increases identification of bias and error in data and analysis. Crowdsourcing analytics represents a new way of doing science; a data set is made publicly available and scientists at first analyze separately and then work together to reach a conclusion while making subjectivity and ambiguity transparent.

After the first round of reporting, the 29 teams of analysts reported results with highly varying effect sizes, and moderate consensus. After feedback rounds and discussions, teams submitted their final reports. Analytical strategies still varied, yet 69% of teams reported significant result and 78% of the researchers concluded that the dataset suggests a positive association.

Data Analysts.  
Seventy-seven researchers expressed initial interest in participating and were given access to the Open Science Framework project page to obtain the data (https://osf.io/47tnc/). Individual analysts were welcome to form teams. Of the initial inquiries, 33 teams submitted a report in the first round, and 29 teams submitted a final report. In total, the project involved 61 data analysts plus the four authors who organized the project. Team leaders worked in 13 different countries and came from a variety of research backgrounds including Psychology, Statistics, Research Methods, Economics, Sociology, Linguistics, and Management. Of the 61 data analysts, 38 hold a PhD (62%) and 17 a Master's degree (28%). Researchers came from various ranks and included 8 Full Professors (13%), 9 Associate Professors (15%), 13 Assistant Professors (22%), 8 Post-Docs (13%) and 17 Doctoral students (28%). In addition, 27 participants (46%) have taught at least one undergraduate statistics course, 22 (37%) have taught at least one graduate statistics course, and 24 (39%) have published at least one methodological/statistical article.
 
Data set.  
From a company for sports statistics, we obtained player demographics from all soccer players ( N   = 2,053) playing in the first male divisions of England, Germany, France, and Spain in the 2012-2013 season. We also took from this source data about the interactions of those players with all referees ( N   = 3,147) that they encountered in their professional career. Thus the data entails a period of multiple years from a player's first professional match until the date this data was acquired (June 2014). This data included the number of matches players and referees encountered each other and our dependent variable, the number of red cards given to a player by a particular referee. The data set was made available as a list with 146,028 dyads of players and referees ( https://osf.io/47tnc/ ).   
 
Players' photos were available from the source for 1,586 out of 2,053 players. Profiles for which no photo was available tended to be relatively new players or players who had just moved up from a team in a lower league. The variable  player skin tone   was coded by two independent raters blind to the research question who, based on the profile photo, categorized players on a 5-point scale ranging    from 1 =  very light skin   to 5 =  very dark skin   with 3 =  neither dark nor light skin   as the center value  (  r   = 0.92;  rho   = 0.86) . This variable was re-scaled to be bounded by 0 ( very light skin  ) and 1 ( very dark skin  ) prior to the final analysis to ensure consistency among effect sizes between teams and to reflect the largest possible effect.
A range of potential independent variables was included in the data concerning the player, the referee, or the dyad. The complete codebook is available at: https://osf.io/9yh4x/. For players, data included their typical position, weight, and height at the time of data collection, and for referees, their country of origin. For each dyad, data included the number of games referees and players encountered each other and the number of yellow and red cards awarded. The variables of age, club, and league were only available for players at the time of data collection, not at the time of receiving the particular red card sanctioning.To protect their identities given the sensitivity of the research topic, referees were anonymized and listed by a numerical identifier for each referee and for each country of origin.  For the country of each referee, we included average scores of implicit and explicit preferences for light vs. dark skin tone that had been gathered in independent research by Project Implicit  (30, 31) . Implicit preference scores for each referee country had been calculated using a skin tone Implicit Association Test (IAT)  (32) , a speeded response task that assesses strength of associations. Higher scores on the IAT reflect a stronger automatic association between dark skin, relative to light skin, and negative valence. Explicit preference scores for each referee country were calculated using a feeling thermometer task, with higher values corresponding to greater self-reported feelings of positivity toward light skin tone versus dark skin tone. Both these national-level measures were created by aggregating data from many online users from referees' countries taking these tests on  Project Implicit ( https://implicit.harvard.edu/ ; see also (33) ).
 
At registration we asked team leaders for their present opinion regarding the research questions with a single question for each hypothesis, e.g. "How likely do you think it is 6 that soccer referees tend to give more red cards to dark skinned players?"

After removing description of the results, the structured summaries were collated into a single questionnaire and distributed to all the teams for peer review. The analytic approaches were presented in a random order and researchers were instructed to provide feedback on at least the first three approaches that they examined. Researchers were asked for both qualitative feedback as well as the assessment: "How confident are you that the described approach below is suitable for analyzing the research questions?", measured on a 7-point scale from 1=  Unconfident to 7 =  Confident   (see S3). Each team received feedback from an average of about 5 other teams (M = 5.32, SD = 2.87). The qualitative and quantitative feedback was aggregated into a single report and shared with all team members. As such, each team received peer review commentaries about their own and other teams' analysis strategies. Notably, these commentaries came from reviewers that were highly familiar with the data set, yet at this point teams were unaware of others' results (see https://osf.io/evfts/  and    https://osf.io/ic634/ for the complete survey and round-robin feedback)....When researchers scrutinized others' results, it became apparent that differences in results may have not only be due to variations in statistical models, but also due to variations in the choice of certain covariates. Doing a preliminary reanalysis, the leader of team 10 discovered that the controversial covariates league and country may be responsible for making some results appear non-significant. A debate emerged regarding whether the inclusion of these covariates was quantitatively defensible (see  https://osf.io/2prib/ ). The project coordinators thus asked the 10 teams who had included these variables in their final models to re-run their models without said covariates. Additionally, we asked these teams to decide whether to keep their prior version or use the results from the updated analysis. The results displayed in the manuscript reflect teams' choices of their final model 1 .

From the 79 researchers who initially registered for the crowdstorming project, 33 teams were formed and submitted an initial analytical approach. Of those, 29 teams also submitted a final report. Submitted analytical approaches were diverse, ranging from simple linear regression techniques to complex multilevel regression techniques and Bayesian approaches. Table 1 shows each team's analytic technique, reported effect size, and a number of characteristics describing how their model was specified (e.g., the number of covariates used in the analysis). In total, there were 21 unique combinations of covariates among the 29 teams. Apart from the variable 'games', which was used by all teams, just one covariate  (player position, 62%)  was used in more than half of the analytic strategies and three were used in just one analysis. Two sets of covariates were used by three teams each, and four sets of covariates were used by two teams each. All other 15 teams used a combination of covariates, which only their own team used. Table 1 shows variation in analytic strategies for number of covariates (M = 2.83 Stdev = 2.05), treatment of the non-independent structure of the data, statistical distribution chosen for theoutcome, and reported effect sizes. More detail regarding specific covariates chosen by each team can be seen in Table 2. Reasons that teams gave for their initial inclusion/exclusion of particular covariates can be found at  https://osf.io/sea6k/
.  
For the primary research question, researchers' conclusions varied regarding whether or not soccer referees were more likely to give red cards to dark skin toned players than light skin toned players. Fig. 1 shows the effect sizes and 95% confidence intervals alongside the description of the analytic approach provided by each team. Statistical results ranged from 0.89 (slightly and non-significantly negative) to 2.93 (moderately positive) in odds ratio units 2 , with a median of 1.31. From a null hypothesis significance testing standpoint, twenty teams (69%) found a significant positive effect and nine teams (31%) observed a non-significant relationship. No team reported a significant negative relationship.

Overall, teams who employed logistic or Poisson models reported estimates that were somewhat larger than teams using linear models. More specifically, 15 teams used logistic models (11/15 significant, median OR = 1.34, MAD = 0.07), six teams used Poisson models (4/6 significant, median OR = 1.36, MAD = 0.08), six teams used linear models (3/6 significant, median OR = 1.21, MAD = 0.05), and two teams used models classified as miscellaneous (2/2 significant).  
Teams also varied in their approaches to handling the non-independence of players and referees, which resulted in variability regarding both median estimated and rates of significance. In total, 15 teams used random effects (12/15 significant, Median OR = 1.32, MAD = 0.12), eight teams used clustered standard errors (4/8 significant, Median OR = 1.28, MAD = 0.13), five teams did not account for this artifact (4/5 significant, Median OR = 1.39, MAD = 0.28), and one team used fixed effects for the referee variable (0/1 significant, OR = 0.89).

After the discussion, and before seeing the draft of this report, most teams agreed moderately that the data showed a positive relationship between number of red cards and player skin-tone. In this final survey, a set of supplementary items assessing agreement with more nuanced beliefs (e.g., "There is little evidence for an effect," "The effect is positive and due to referee bias") revealed greatest endorsement (78% agreement) of the position that "The effect is positive and the mechanism is unknown" (M = 5.32,    SD = 1.47 on a scale ranging from 1 = strongly disagree   to 7 =  strongly agree  ; see S7 for more details).

Here, we demonstrate that variation in effect size is also present in the  same data   contingent on choices and assumptions in the analysis process. We observed variation in the effect estimates of whether soccer referees gave more red cards to dark skin toned players. We also observed convergence on the discrete judgment of whether there was a positive effect in the data. These crowdsourcing results illustrate both the contingency of effects as a function of analytic choices, and the opportunity for converging beliefs through shared examination and evaluation of a research question using a shared data set.  The median result (OR = 1.31) indicated that the odds were 31% higher for players rated as having the darkest skin tone to receive a red card when compared to players rated as having the lightest skin tone. Assuming a 40 game season, these results suggest that the probability of receiving at least one red card over a season is 15.2% for a player with the darkest skin tone and 11.8% for a player with the lightest skin tone. 4  The estimated effects ranged from 0.88 to 2.93 in odds ratio units (1.0 indicates a null effect), with zero teams finding a negative effect, nine teams finding no relationship, and twenty teams finding a positive effect. If, as in virtually all other research projects, a single team had conducted the study, selecting randomly from the present teams, there would have been a 69% likelihood of reporting a positive result and a 31% likelihood of reporting a null effect.

Crowdsourcing of data analysis is inefficient in that numerous analysts conduct multiple rounds of data analysis to answer a single research question. But, consider that inefficiency in comparison to the status quo in which a research question is examined and reported using a single analysis strategy. Conventional practice makes little accommodation for the possible contingency of the results on the analytic method  (2, 6) . Moreover, misspecification of results via analysis strategy is virtually undetectable without an ethic of open data and community review of analytic strategies. It is conceivable that the relative inefficiency trade-offs would actually produce a net benefit by having many independent analysts for a complex data set compared to the currently prevalent practice of individual analysis teams providing stand-alone analyses of privately held data. Further, the use of 29 independent teams helped us illustrate the variation in analytic strategies and conclusions, but - in practice - fewer independent teams may be needed to assess robustness of conclusions."

#statistics #replication  
4
Add a comment...

gwern branwen

Shared publicly  - 
 
"Genetics and the placebo effect: the placebome", Hall et al 2015 https://www.dropbox.com/s/scvomodvnm6pbav/2015-hall.pdf / http://sci-hub.bz/bde1e7da626c7c68403259529fddee20/10.1016@j.molmed.2015.02.009.pdf(via http://pipeline.corante.com/archives/2015/04/15/finding_placebo_responders.php); excerpts:

"Placebos are indispensable controls in randomized clinical trials (RCTs), and placebo responses significantly contribute to routine clinical outcomes. Recent neurophysiological studies reveal neurotransmitter pathways that mediate placebo effects. Evidence that genetic variations in these pathways can modify placebo effects raises the possibility of using genetic screening to identify placebo responders and thereby increase RCT efficacy and improve therapeutic care. Furthermore, the possibility of interaction between placebo and drug molecular pathways warrants consideration in RCT design. The study of genomic effects on placebo response, 'the placebome', is in its infancy. Here, we review evidence from placebo studies and RCTs to identify putative genes in the placebome, examine evidence for placebo-drug interactions, and discuss implications for RCTs and clinical care.

...Recent innovative neuroimaging [4] and physiological experiments [5] have fostered the current viewpoint that placebo effects are biological responses to psychosocial environmental cues surrounding the administration of inactive (or active) treatments. Such placebo research has established that the placebo response is more than patient report bias, regression to the mean, or spontaneous remission [6-8]. - 4. Atlas, L.Y. and Wager, T.D. (2014) A meta-analysis of brain mechanisms of placebo analgesia: consistent findings and unanswered questions. Handb. Exp. Pharmacol. 225, 37-69 - 5. Benedetti, F. (2013) Placebo and the new physiology of the doctor-patient relationship. Physiol. Rev. 93, 1207-1246 - 6. Benedetti, F. (2009) Placebo Effects: Understanding The Mechanisms In Health And Disease, Oxford University Press
- 7. Finniss, D.G. and Benedetti, F. (2005) Mechanisms of the placebo response and their impact on clinical trials and clinical practice. Pain 114, 3-6 - 8. Wechsler, M.E. et al. (2011) Active albuterol or placebo, sham acupuncture, or no intervention in asthma. N. Engl. J. Med. 365, 119-126

...Predicting who will be a placebo responder could be of value to both researchers and patients. In drug development, detecting a difference between active intervention and the placebo control is an underlying goal of RCTs. Being able to identify and exclude individuals who are more likely to respond to placebos could enhance trial designs seeking to find such a difference. Potential cost savings due to reduction of sample size could be of benefit for drug development [9]...In the past, scientists used behavioral instruments such as personality measures to predict placebo responders [10,11]. This approach has had limited success because these blunt instruments proved no match for the complex interplay of shifting states that may modify an individual's placebo response. Not only do clinical trial researchers have to contend with the type, duration, and severity of the condition, but the practitioner's 'bedside manner' and the patient's beliefs, hopes, expectations, and previous experiences [12] also make predicting the placebo response an ongoing challenge.

- 9. Servick, K. (2014) Outsmarting the placebo effect. Science 345, 1446-1447 - 10. Horing, B. et al. (2014) Prediction of placebo responses: a systematic review of the literature. Front. Psychol. 5, 1079
- 11. Kaptchuk, T.J. et al. (2008) Do 'placebo responders' exist? Contemp. Clin. Trials 29, 587-595 - 12. Finniss, D.G. et al. (2010) Biological, clinical, and ethical advances of placebo effects. Lancet 375, 686-695

...there have been many placebo-controlled RCTs with GWAS data, but they all lack a key dimension: a no-treatment control (NTC). A NTC is one of the few methodologies that can disentangle genuine psychosocial and physiological placebo responses to the symbols, rituals, and behaviors of the clinical encounter ('placebo effects') from spontaneous remission, regression to the mean, and the natural waxing and waning of illness. The main reason for this gap is simple: trials are interested in testing drug efficacy, and randomization to active treatment or placebo is thought to be a sufficient measure by which to allow clinical trial researchers to discern specific drug responses. Any improvement in subjects in the placebo arm has generally been ignored and viewed as an intrusive but necessary hurdle to overcome. However, without studies that have NTCs as a control for the placebo arm, an accurate and comprehensive view of the set of potential placebo genetic biomarkers (the placebome) may
not easily become available.
Despite this limitation, we can cull information about the genes involved in the placebome from three types of available studies in the literature: (i) a small RCT investigating placebo responses that included a NTC and conducted a candidate gene analysis; (ii) placebo-controlled RCTs in patients that included an analysis of candidate genes that coincide with genes implicated in the placebo response mechanism; and (iii) experimental studies in healthy subjects that examined candidate placebo genes.

...first published in 1978 followed by a series of studies on placebo effects in molar extraction [13]. In this and subsequent studies, Levine et al. demonstrated that the pain suppression system of the body could be induced by placebo and was, in turn, blocked by naloxone, an opioid receptor antagonist. Further studies by this group hypothesized that morphine and placebo might share a common opioidergic mechanism and estimated the placebo analgesic effect to be equivalent to up to 8 mg of morphine [14,15]. As the opioid system emerged as a major underlying biochemical mechanism involved in placebo analgesia, the role of mu opioid receptors in placebo analgesia was further confirmed in neuroimaging studies [16-19]. These studies used pain models to demonstrate that expectation of analgesia induced activity in key areas in the brain involved in endogenous opioid transmission and analgesia. Since these early studies, placebo researchers also raised the possibility that the opioidergic system is not exclusively responsible for placebo analgesia [12]...This growing list of neurotransmitters and neurological pathways mediating the placebo response provides a framework for candidate gene analyses. Indeed, treatment outcomes in the placebo arms of trials that have assessed genetic variation in the dopaminergic, opioid, cannabinoid, and serotonergic pathways suggest that genetic variation in the synthesis, signaling, and metabolism of these neurotransmitters contributes to variation in the placebo response (Table 1).

...Rs4680, the most studied polymorphism in dopamine metabolism, is in the gene encoding catechol-O-methyltransferase (COMT), an enzyme that metabolizes dopamine and other catecholamines [33]. The rs4680 SNP has been implicated in modifying clinical outcomes in both the placebo and drug treatment arms of numerous diverse trials [34- 44]. Rs4680 encodes a valine (val)to-methionine (met) change at codon 158 (val158met), resulting in a three four times reduction in enzymatic activity. Homozygotes of the less-active met allele have been associated with higher levels of dopamine in the prefrontal cortex, a region implicated in the placebo response pathway [45,46]. Rs4680 is a common polymorphism, and the prevalence of the less-frequent met allele or minor allele (MAF) is reported as 0.37 in Caucasians [47], but varies by race and/or ethnicity [48,49]. The high MAF of rs4680 translates to an estimated 20-25% of met/met individuals in Caucasian populations. Finding common SNPs is an important criterion when considering the feasibility of using genotype as a predictive placebo-response marker.
To our knowledge, the only candidate genetic association study that included a NTC and examined the effect of genetic variation in COMT on the placebo response [38] used an RCT designed to test whether placebo treatment could incrementally combine three components related to placebos: diagnosis and observation (NTC arm), therapeutic apparatus (placebo acupuncture), and apparatus plus a supportive patient-practitioner relation (placebo acupuncture plus a warm-caring provider) [50]. The RCT was a 3week trial in patients with irritable bowel syndrome (IBS), and the main outcome was reduction in IBS symptom severity. Patients in the arm that combined all the components, the strongest placebo treatment, reported the greatest symptom relief. The candidate genetic analysis performed on a subset of these patients, who gave genetic informed consent, looked at the association of rs4680 with IBS symptom severity, adequate relief, and quality of life in each of the treatment arms. Patients homozygous for the rs4680 low-activity met allele (met/met), known to have high levels of dopamine, had the greatest placebo response. The high-activity val allele homozygous (val/val) patients had the lowest placebo response. The val/met heterozygotes had an intermediate response. Similar results were reported for another COMT SNP, rs4633, which is closely linked to rs4680.
A subsequent small acute-pain model placebo neuroimaging study in healthy volunteers looked at genetic variation in COMT in relation to brain activity in the reward system using resting-state functional magnetic resonance imaging [51]. These researchers showed that placebo response to pain in healthy volunteers supported the IBS results, such that the number of rs4680 met alleles was linearly correlated with suppression of pain in the placebo expectation laboratory paradigm. While not having a NTC, the pain stimulation in this experiment was momentary, precise, and calibrated, so we can assume that spontaneous remission and waxing and waning of illness were not potential confounders.
Interestingly, a recent laboratory study found that the rs4680 high-activity val allele was associated with a higher frequency of nocebo effects (negative placebo adverse effect) using a model of learned immunosuppression [52]. Similarly, in the IBS placebo study discussed previously, the rs4680 high-activity val allele was associated with a higher frequency of complaint reporting [40]. This association of nocebo effect with the high-activity rs4680 val allele is not necessarily unexpected, given that in the absence of any significant improvements in symptoms derived from a placebo response, val/val individuals may have more complaints or experience more adverse effects.

- 33. Lachman, H.M. et al. (1996) Human catechol-O-methyltransferase pharmacogenetics: description of a functional polymorphism and its potential application to neuropsychiatric disorders. Pharmacogenetics 6, 243–250
- 38. Hall, K.T. et al. (2012) Catechol-O-methyltransferase val158met polymorphism predicts placebo effect in irritable bowel syndrome. PLoS ONE 7, e48135
- 39. Hall, K.T. et al. (2014) Polymorphisms in catechol-O-methyltransferase modify treatment effects of aspirin on risk of cardiovascular disease. Arterioscler. Thromb. Vasc. Biol. 34, 2160–2167
- 40. Hall, K.T. et al. (2015) Conscientiousness is modified by genetic variation in catechol-O-methyltransferase to reduce symptom complaints in IBS patients. Brain Behav. 5, e00294
- 50. Kaptchuk, T.J. et al. (2008) Components of placebo effect: randomised controlled trial in patients with irritable bowel syndrome. BMJ 336, 999–1003
- 51. Yu, R. et al. (2014) Placebo analgesia and reward processing: integrating genetics, personality, and intrinsic brain activity. Hum. Brain Mapp. 35, 4583–4593

[I'm val/val on this according to my 23andMe results.]

...In addition to COMT, there are several other polymorphisms in the dopamine pathway that are potential placebome candidates. Monoamine oxidase A (MAO-A) has been implicated in reward pathways through its role in catalyzing the oxidation of monoamines, including dopamine...The association of MAOA with treatment response to placebo was examined in a candidate gene analysis of patients with clinical depression from four combined small placebo-controlled RCTs of three selective serotonin reuptake inhibitor antidepressants (SSRIs), venlafaxine, sertraline, or fluoxetine [55]. The primary outcome was determined by the 17-item Hamilton Depression Rating Scale (HAM-D 17 ). Consistent with the findings described above for COMT, individuals with the low-activity MAOA genotypes and, therefore, higher basal dopamine tone, had a higher placebo response than those with the high-activity MAOA genotypes. The COMT rs4680 association with placebo response was also examined in this study, but the results were not significant. It is unclear whether the nonsignificant results with COMT were due to lack of power, a basic difference in the subject population, or other factors.
To our knowledge, the largest study of genetic variation in RCT patients randomized to placebo treatment examined 34 candidate genes (500 polymorphisms) in four trials of bupropion for major depressive disorder [43]. Although results for rs4680 were not reported in this trial, several other COMT SNPs were associated with placebo response and placebo remission (although these associations did not survive correction for multiple comparisons). The placebo response association with MAOA rs6609257, a SNP associated with dopamine basal tone, was one of the associations with treatment response in the placebo arm that was significant after correction, supporting the candidacy of MAOA in the placebome.
Genetic variations in dopamine receptor genes that modify dopaminergic signaling also modify the function of the brain reward circuit [56,57]. Rs6280 is a common serine-to-glycine coding polymorphism in dopamine receptor 3 (DRD3) that results in the DRD3 glycine form having a higher affinity for dopamine compared with the serine form [58]. A recent placebo-controlled RCT of a novel drug for treating symptoms of schizophrenia (ABT-925) examined the effects of genetic variation in DRD3 on the Positive and Negative Syndrome Scale (PANSS) [59]. Subjects homozygous for rs6280 serine allele (S/S) had significantly better outcomes in the placebo arm than when they were treated with increasing doses of ABT-95. Consistent with other studies, this study also showed that the COMT rs4680 met/met subjects had a higher placebo response...DBH was also one of the genes examined in the largest 34-candidate gene analysis of the placebo arm of the bupropion trial discussed above [43]. The DBH SNP rs2873804 survived the correction for multiple comparisons, reinforcing DBH as a potential candidate for a placebo response gene.

- 55. Leuchter, A.F. et al. (2009) Monoamine oxidase a and catechol-O- methyltransferase functional polymorphisms and the placebo response in major depressive disorder. J. Clin. Psychopharmacol. 29, 372–377
- 43. Tiwari, A.K. et al. (2013) Analysis of 34 candidate genes in bupropion and placebo remission. Int. J. Neuropsychopharmacol. 16, 771–781
- 56. Diaz, J. et al. (2000) Dopamine D3 receptors expressed by all mesencephalic dopamine neurons. J. Neurosci. 20, 8677–8684
- 57. Bouthenet, M.L. et al. (1991) Localization of dopamine D3 receptor mRNA in the rat brain using in situ hybridization histochemistry: comparison with dopamine D2 receptor mRNA. Brain Res. 564, 203–219
- 59. Bhathena, A. et al. (2013) Association of dopamine-related genetic to dopamine D3 receptor antagonist ABT-925 clinical response. Transl. Psychiatry 3, e245

...Endogenous opioids signal through opioid receptors, and genetic variation in the mu opioid receptor gene (OPRM1) has been shown to modify treatment outcomes in pain studies. The analgesic effects of placebo have been shown to be mediated through activation of endogenous opioid as well dopaminergic mechanisms. In a small experimental placebo study performed on healthy volunteers, signaling in the dopamine pathway was linked to opioid receptor signaling in antinociceptive responses to placebo [26]. Rs1799971 is a functional polymorphism in the OPRM1 gene that results in an asparagine-to-aspartic acid change at codon 40. The aspartic acid variant of the receptor was found to reduce receptor function across several studies [67,68]. The association of rs1799971 with placebo response in healthy volunteers was studied in an experimental model of placebo-induced analgesia [69]. In this study, placebo-induced activation of dopamine neurotransmission in the nucleus accumbens was greater in asparagine homozygotes compared with aspartic acid-allele carriers, suggesting that genetic variation in OPRM1 also contributes to variability in the placebo response.

- 26. Scott, D.J. et al. (2008) Placebo and nocebo effects are defined by opposite opioid and dopaminergic responses. Arch. Gen. Psychiatry 65, 220–231
- 67. Zhang, Y. et al. (2005) Allelic expression imbalance of human mu opioid receptor (OPRM1) caused by variant A118G. J. Biol. Chem. 280, 32618–32624
- 68. Kroslak, T. et al. (2007) The single nucleotide polymorphism A118G alters functional properties of the human mu opioid receptor. J. Neurochem. 103, 77–87
- 69. Pecina, M. et al. (2015) Effects of the mu opioid receptor polymorphism (OPRM1 A118G) on pain regulation, placebo effects and associated personality trait measures. Neuropsychopharmacology 40, 957–965

...The efficacy of a drug is determined by the difference between the aggregate outcomes of individuals randomized to drug versus placebo treatment. Therefore, the accuracy of the estimate of drug efficacy, especially in smaller trials depends on the randomization balancing the numbers of placebo responders by genotype across treatment arms. If by chance, in trials where the placebo response is known to be high (such as IBS [76]), there are more genetically predisposed placebo responders in the placebo arm than in the drug arm, the estimate of drug efficacy will be confounded by genotype and the results biased towards the null. If this imbalance is not accounted for, it would be expected to be more of a problem in smaller trials than larger trials. Ideally, RCTs would be designed such that the randomization balanced genetically predisposed placebo responders across all arms of a trial.

- 76. Patel, S.M. et al. (2005) The placebo effect in irritable bowel syndrome trials: a meta-analysis. Neurogastroenterol. Motil. 17, 332–340

...While studies have not as yet been conducted to identify genes and drugs that modify placebo response, hypothetically there may even be situations in which one might opt to intentionally use a drug to modify the placebo response. For instance, purposefully using a drug to inhibit the placebo response in clinical trials could minimize the placebo response and allow for a more accurate measurement of the drug effect. In this case, the placebo-modifying drug would be administered to both the drug treatment and placebo arm of the trial, and any potential drug-drug or gene-drug interactions would have to be well characterized. Given that so many future RCTs already include a placebo treatment arm and plan to collect -omics data, we propose that a cost-effective approach to elucidating the placebome would be to simply add NTCs to these studies.

...For example, good evidence suggests that persons homozygous for the low-activity met allele at COMT rs4680 (met/met) are more likely to respond to morphine than those homozygous for the val allele (val/ val) [87,88].

- 87. Rakvag, T.T. et al. (2008) Genetic variation in the catechol-O-methyltransferase (COMT) gene and morphine requirements in cancer patients with pain. Mol. Pain 4, 64
- 88. Rakvag, T.T. et al. (2005) The Val158Met polymorphism of the human catechol-O-methyltransferase (COMT) gene may influence morphine requirements in cancer pain patients. Pain 116, 73–78

...A key goal of the RCT is to detect a drug-placebo difference. There is a long and unsuccessful history of attempts to increase the efficiency of RCTs with placebo run-in periods that eliminate placebo responders [97- 99]. Could placebome data lead to new 'enrichment' strategies that could eliminate a priori high placebo responders in RCTs? Our discussion of placebo-drug interactions suggests that genetic profiles have the possibility of becoming an alternative strategy to make detection of drug-placebo difference more efficient.

- 97. Katz, N. (2005) Methodological issues in clinical trials of opioids for chronic pain. Neurology 65 (Suppl. 4), S32–S49
- 98. Lee, S. et al. (2004) Does elimination of placebo responders in a placebo run-in increase the treatment effect in randomized clinical trials? A meta-analytic evaluation. Depress. Anxiety 19, 10–19
- 99. Straube, S. et al. (2008) Enriched enrollment: definition and effects of enrichment and dose in trials of pregabalin and gabapentin in neuropathic pain. A systematic review. Br. J. Clin. Pharmacol. 66, 266–275"

Most of these variants are SNPs and available through commercial SNP providers; given the extreme inflation of effect sizes in the mentioned IBS trials (something like a 6x difference!), pinning down the genetics could make self-experiments much more effective and improve discourse around them - you could discard anecdotes from met/met on the COMT SNP, eg, and pay more attention to self-reports from val/vals.

#nootropics #quantifiedself #placebo  
Shared with Dropbox
2
Emil Ole William Kirkegaard's profile photogwern branwen's profile photo
4 comments
 
The placebos used in those kind of trials may simply not have been good enough. They don't always use 'active placebos' or do checks like ask patients to guess which arm they are in and construct a blinding index. (There are quasi-professional clinical trial guinea pigs and they claim to often be able to tell what they're getting.)
Add a comment...

gwern branwen

Shared publicly  - 
 
Speaking of causal nets, an interesting Facebook paper; "The Mystery Machine: End-to-end Performance Analysis of Large-scale Internet Services":

"Current debugging and optimization methods scale poorly to deal with the complexity of modern Internet services, in which a single request triggers parallel execution of numerous heterogeneous software components over a distributed set of computers. The Achilles’ heel of current methods is the need for a complete and accurate model of the system under observation: producing such a model is challenging because it requires either assimilating the collective knowledge of hundreds of programmers responsible for the individual components or restricting the ways in which components interact. Fortunately, the scale of modern Internet services offers a compensating benefit: the sheer volume of requests serviced means that, even at low sampling rates, one can gather a tremendous amount of empirical performance observations and apply “big data” techniques to analyze those observations. In this paper, we show how one can automatically construct a model of request execution from pre-existing component logs by generating a large number of potential hypotheses about program behavior and rejecting hypotheses contradicted by the empirical observations. We also show how one can validate potential performance improvements without costly implementation effort by leveraging the variation in component behavior that arises naturally over large numbers of requests to measure the impact of optimizing individual components or changing scheduling behavior. We validate our methodology by analyzing performance traces of over 1.3 million requests to Facebook servers. We present a detailed study of the factors that affect the end-to-end latency of such requests. We also use our methodology to suggest and validate a scheduling optimization for improving Facebook request latency."

Apparently the Internet giants like Facebook are now so large and so complex internally that it makes sense to start treating them as black boxes on par with human bodies and ecosystems and start applying techniques like randomization & causal networks rather than continue relying on human engineers.

In this case, they're using their server logs to infer causal networks linking their servers/infrastructure in order to infer where performance problems are.

Besides the unexpected connection of causal nets with server logs, one interesting aspect is that they seem to be using an algorithm which is about as dumb as possible to infer the causal net: they generate every possible causal net, and begin running each log item past the set of causal nets, deleting causal nets the instant they contradict a log item. If that sounds hard to you, well, this approach apparently requires an entire Hadoop cluster and a day of realtime to generate useful results... so it is.

And they only use a million or so log items. This is not so much 'big data' as 'big compute'.

#bayesnet #statistics #facebook #causalinference  
3
3
Evelyn Mitchell's profile photoJeffrey Cliff's profile photoIvan Pierre's profile photoHilmar Hoffmann's profile photo
3 comments
 
Thanks for finding this paper. That this sort of exhaustive testing is possible is a consequence of cheap and massive computing power.  That it is needed? <insert PHP joke here>.
Add a comment...

gwern branwen

Shared publicly  - 
 
Previously, with weight data: https://plus.google.com/103530621949492999968/posts/aBwZ57xFRjB

Here I look at my Zeo sleep data; more variables, more complex relations, and more unknown ones, but on the positive side, ~12x more data to work with.

zeo <- read.csv("http://www.gwern.net/docs/zeo/gwern-zeodata.csv")
zeo$Sleep.Date <- as.Date(zeo$Sleep.Date, format="%m/%d/%Y")

## convert "05/12/2014 06:45" to "06:45"
zeo$Start.of.Night <- sapply(strsplit(as.character(zeo$Start.of.Night), " "), function(x) { x[2] })
## convert "06:45" to 24300
interval <- function(x) { if (!is.na(x)) { if (grepl(" s",x)) as.integer(sub(" s","",x))
                                           else { y <- unlist(strsplit(x, ":")); as.integer(y[[1]])*60 + as.integer(y[[2]]); }
                                         }
                          else NA
                        }
zeo$Start.of.Night <- sapply(zeo$Start.of.Night, interval)
## correct for the switch to new unencrypted firmware in March 2013;
## I don't know why the new firmware subtracts 15 hours
zeo[(zeo$Sleep.Date >= as.Date("2013-03-11")),]$Start.of.Night <- (zeo[(zeo$Sleep.Date >= as.Date("2013-03-11")),]$Start.of.Night + 900) %% (24*60)

## after midnight (24*60=1440), Start.of.Night wraps around to 0, which obscures any trends,
## so we'll map anything before 7AM to time+1440
zeo[zeo$Start.of.Night<420 & !is.na(zeo$Start.of.Night),]$Start.of.Night <- (zeo[zeo$Start.of.Night<420 & !is.na(zeo$Start.of.Night),]$Start.of.Night + (24*60))

zeoSmall <- subset(zeo, select=c(ZQ,Total.Z,Time.to.Z,Time.in.Wake,Time.in.REM,Time.in.Light,Time.in.Deep,Awakenings,Start.of.Night,Morning.Feel))
zeoClean <- na.omit(zeoSmall)
# bnlearn doesn't like the 'integer' class that most of the data-frame is in
zeoClean <- as.data.frame(sapply(zeoClean, as.numeric))

Prior knowledge:

- `Start.of.Night` is temporally first, and cannot be caused
- `Time.to.Z` is temporally second, and can be influenced by `Start.of.Night` (likely a connection between how late I go to bed and how fast I fall asleep) & `Time.in.Wake` (since if it takes 10 minutes to fall asleep, I must spend >=10 minutes in wake) but not others
- `Morning.Feel` is temporally last, and cannot cause anything
- `ZQ` is a synthetic variable invented by Zeo according to an opaque formula, which cannot cause anything but is determined by others
- `Total.Z` should be the sum of `Time.in.Light`, `Time.in.REM`, and `Time.in.Deep`
- `Awakenings` should have an arrow with `Time.in.Wake` but it's not clear which way it should run

library(bnlearn)
## after a bunch of iteration, blacklisting arrows which violate the prior knowledge
bl <- data.frame(from=c("Morning.Feel", "ZQ", "ZQ", "ZQ", "ZQ", "ZQ", "ZQ", "Time.in.REM", "Time.in.Light", "Time.in.Deep", "Morning.Feel", "Awakenings", "Time.in.Light", "Morning.Feel", "Morning.Feel","Total.Z", "Time.in.Wake", "Time.to.Z", "Total.Z", "Total.Z", "Total.Z"),
                 to=c("Start.of.Night", "Total.Z", "Time.in.Wake", "Time.in.REM", "Time.in.Deep", "Morning.Feel","Start.of.Night", "Start.of.Night","Start.of.Night","Start.of.Night", "Time.to.Z", "Time.to.Z", "Time.to.Z", "Total.Z", "Time.in.Wake","Time.to.Z","Time.to.Z", "Start.of.Night", "Time.in.Deep", "Time.in.REM", "Time.in.Light"))

zeo.hc <- hc(zeoClean, blacklist=bl)
zeo.iamb         <- iamb(zeoClean, blacklist=bl)
## problem: undirected arc: Time.in.Deep/Time.in.REM; since hc inferred [Time.in.Deep|Time.in.REM], I'll copy that for iamb:
zeo.iamb <- set.arc(zeo.iamb, from = "Time.in.REM", to = "Time.in.Deep")
zeo.gs <- gs(zeoClean, blacklist=bl)
## same undirected arc:
zeo.gs <- set.arc(zeo.gs, from = "Time.in.REM", to = "Time.in.Deep")

## Bigger is better:
score(zeo.iamb, data=zeoClean)
# [1] -44776.79185
score(zeo.gs, data=zeoClean)
# [1] -44776.79185
score(zeo.hc, data=zeoClean)
# [1] -44557.6952
## hc scores best, so let's look at it:
zeo.hc
#   Bayesian network learned via Score-based methods
#
#   model:
#    [Start.of.Night][Time.to.Z|Start.of.Night][Time.in.Light|Time.to.Z:Start.of.Night]
#    [Time.in.REM|Time.in.Light:Start.of.Night][Time.in.Deep|Time.in.REM:Time.in.Light:Start.of.Night]
#    [Total.Z|Time.in.REM:Time.in.Light:Time.in.Deep][Time.in.Wake|Total.Z:Time.to.Z]
#    [Awakenings|Time.to.Z:Time.in.Wake:Time.in.REM:Time.in.Light:Start.of.Night]
#    [Morning.Feel|Total.Z:Time.to.Z:Time.in.Wake:Time.in.Light:Start.of.Night]
#    [ZQ|Total.Z:Time.in.Wake:Time.in.REM:Time.in.Deep:Awakenings]
#   nodes:                                 10
#   arcs:                                  28
#     undirected arcs:                     0
#     directed arcs:                       28
#   average markov blanket size:           7.40
#   average neighbourhood size:            5.60
#   average branching factor:              2.80
#
#   learning algorithm:                    Hill-Climbing
#   score:                                 BIC (Gauss.)
#   penalization coefficient:              3.614556939
#   tests used in the learning procedure:  281
#   optimized:                             TRUE

plot(zeo.hc)
## https://i.imgur.com/nD3LXND.png

fit <- bn.fit(zeo.hc, zeoClean); fit
#
#   Bayesian network parameters
#
#   Parameters of node ZQ (Gaussian distribution)
#
# Conditional density: ZQ | Total.Z + Time.in.Wake + Time.in.REM + Time.in.Deep + Awakenings
# Coefficients:
#    (Intercept)         Total.Z    Time.in.Wake     Time.in.REM    Time.in.Deep      Awakenings
# -0.12468522173   0.14197043518  -0.07103211437   0.07053271816   0.21121000076  -0.56476256303
# Standard deviation of the residuals: 0.3000223604
#
#   Parameters of node Total.Z (Gaussian distribution)
#
# Conditional density: Total.Z | Time.in.Wake + Start.of.Night
# Coefficients:
#    (Intercept)    Time.in.Wake  Start.of.Night
# 907.6406157850   -0.4479377278   -0.2680771514
# Standard deviation of the residuals: 68.90853885
#
#   Parameters of node Time.to.Z (Gaussian distribution)
#
# Conditional density: Time.to.Z | Start.of.Night
# Coefficients:
#    (Intercept)  Start.of.Night
# -1.02898431407   0.01568450832
# Standard deviation of the residuals: 13.51606719
#
#   Parameters of node Time.in.Wake (Gaussian distribution)
#
# Conditional density: Time.in.Wake | Time.to.Z
# Coefficients:
#   (Intercept)      Time.to.Z
# 14.7433880499   0.3289378711
# Standard deviation of the residuals: 19.0906685
#
#   Parameters of node Time.in.REM (Gaussian distribution)
#
# Conditional density: Time.in.REM | Total.Z + Start.of.Night
# Coefficients:
#      (Intercept)           Total.Z    Start.of.Night
# -120.62442964234     0.37864195651     0.06275760841
# Standard deviation of the residuals: 19.32560757
#
#   Parameters of node Time.in.Light (Gaussian distribution)
#
# Conditional density: Time.in.Light | Total.Z + Time.in.REM + Time.in.Deep
# Coefficients:
#   (Intercept)        Total.Z    Time.in.REM   Time.in.Deep
#  0.6424267863   0.9997862624  -1.0000587988  -1.0001805537
# Standard deviation of the residuals: 0.5002896274
#
#   Parameters of node Time.in.Deep (Gaussian distribution)
#
# Conditional density: Time.in.Deep | Total.Z + Time.in.REM
# Coefficients:
#   (Intercept)        Total.Z    Time.in.REM
# 15.4961459056   0.1283622577  -0.1187382535
# Standard deviation of the residuals: 11.90756843
#
#   Parameters of node Awakenings (Gaussian distribution)
#
# Conditional density: Awakenings | Time.to.Z + Time.in.Wake + Time.in.REM + Time.in.Light + Start.of.Night
# Coefficients:
#     (Intercept)        Time.to.Z     Time.in.Wake      Time.in.REM    Time.in.Light
# -18.41014329148    0.02605164827    0.05736596152    0.02291139969    0.01060661963
#  Start.of.Night
#   0.01129521977
# Standard deviation of the residuals: 2.427868657
#
#   Parameters of node Start.of.Night (Gaussian distribution)
#
# Conditional density: Start.of.Night
# Coefficients:
# (Intercept)
# 1413.382886
# Standard deviation of the residuals: 64.43144125
#
#   Parameters of node Morning.Feel (Gaussian distribution)
#
# Conditional density: Morning.Feel | Total.Z + Time.to.Z + Time.in.Wake + Time.in.Light + Start.of.Night
# Coefficients:
#     (Intercept)          Total.Z        Time.to.Z     Time.in.Wake    Time.in.Light
# -0.924662971061   0.004808652252  -0.010127269154  -0.008636841492  -0.002766602019
#  Start.of.Night
#  0.001672816480
# Standard deviation of the residuals: 0.7104115719

## some issues with big residuals at the extremes in the variables Time.in.Light, Time.in.Wake, and Time.to.Z;
## not sure how to fix those
bn.fit.qqplot(fit)
# https://i.imgur.com/fmP1ca0.png

library(lavaan)
Zeo.model1 <- '
    Time.to.Z ~ Start.of.Night
    Time.in.Wake ~ Total.Z + Time.to.Z
    Awakenings ~ Time.to.Z + Time.in.Wake + Time.in.REM + Time.in.Light + Start.of.Night
    Time.in.Light ~ Time.to.Z + Start.of.Night
    Time.in.REM ~ Time.in.Light + Start.of.Night
    Time.in.Deep ~ Time.in.REM + Time.in.Light + Start.of.Night
    Total.Z ~ Time.in.REM + Time.in.Light + Time.in.Deep
    ZQ ~ Total.Z + Time.in.Wake + Time.in.REM + Time.in.Deep + Awakenings
    Morning.Feel ~ Total.Z + Time.to.Z + Time.in.Wake + Time.in.Light + Start.of.Night
                   '
Zeo.fit1 <- sem(model = Zeo.model1,  data = zeoClean)
summary(Zeo.fit1)
# lavaan (0.5-16) converged normally after 183 iterations
#
#   Number of observations                          1379
#
#   Estimator                                         ML
#   Minimum Function Test Statistic               22.737
#   Degrees of freedom                                16
#   P-value (Chi-square)                           0.121
#
# Parameter estimates:
#
#   Information                                 Expected
#   Standard Errors                             Standard
#
#                    Estimate  Std.err  Z-value  P(>|z|)
# Regressions:
#   Time.to.Z ~
#     Start.of.Nght     0.016    0.006    2.778    0.005
#   Time.in.Wake ~
#     Total.Z          -0.026    0.007   -3.592    0.000
#     Time.to.Z         0.314    0.038    8.277    0.000
#   Awakenings ~
#     Time.to.Z         0.026    0.005    5.233    0.000
#     Time.in.Wake      0.057    0.003   16.700    0.000
#     Time.in.REM       0.023    0.002   10.107    0.000
#     Time.in.Light     0.011    0.002    6.088    0.000
#     Start.of.Nght     0.011    0.001   10.635    0.000
#   Time.in.Light ~
#     Time.to.Z        -0.348    0.085   -4.121    0.000
#     Start.of.Nght    -0.195    0.018  -10.988    0.000
#   Time.in.REM ~
#     Time.in.Light     0.358    0.018   19.695    0.000
#     Start.of.Nght     0.034    0.013    2.725    0.006
#   Time.in.Deep ~
#     Time.in.REM       0.081    0.012    6.657    0.000
#     Time.in.Light     0.034    0.009    3.713    0.000
#     Start.of.Nght    -0.017    0.006   -3.014    0.003
#   Total.Z ~
#     Time.in.REM       1.000    0.000 2115.859    0.000
#     Time.in.Light     1.000    0.000 2902.045    0.000
#     Time.in.Deep      1.000    0.001  967.322    0.000
#   ZQ ~
#     Total.Z           0.142    0.000  683.980    0.000
#     Time.in.Wake     -0.071    0.000 -155.121    0.000
#     Time.in.REM       0.071    0.000  167.090    0.000
#     Time.in.Deep      0.211    0.001  311.454    0.000
#     Awakenings       -0.565    0.003 -178.407    0.000
#   Morning.Feel ~
#     Total.Z           0.005    0.001    8.488    0.000
#     Time.to.Z        -0.010    0.001   -6.948    0.000
#     Time.in.Wake     -0.009    0.001   -8.592    0.000
#     Time.in.Light    -0.003    0.001   -2.996    0.003
#     Start.of.Nght     0.002    0.000    5.414    0.000

Again no major surprises, but one thing I notice is that ZQ does not seem to connect to Time.in.Light, though Time.in.Light does connect to Morning.Feel; I've long suspected that ZQ is a flawed summary and thought it was insufficiently taking into account wakes or something else, so it looks like it's Time.in.Light specifically which is missing.
`Start.of.night` also is more highly connected than I had expected.

Comparing graphs from the 3 algorithms, they don't seem to differ as badly as the weight ones did. Is this thanks to the much greater data or the constraints?

#bayesnet #statistics #R  
1
Add a comment...
Have him in circles
2,389 people
Joshua Fox's profile photo
Selem Delul's profile photo
Vanessa mendonça's profile photo
Greg Frascadore's profile photo
Daniel Feltey's profile photo
joshua tsatsu's profile photo
Open Data's profile photo
Edward McCaffrey's profile photo
Brian Forsberg's profile photo

gwern branwen

Shared publicly  - 
 
"Maladaptive daydreaming"

+Darcey Riley 
Should elaborate fantasies be considered a psychiatric disorder?
4
1
Nicholas Cotter's profile photo
Add a comment...

gwern branwen

Shared publicly  - 
 
"Universal love," said the cactus person. "Transcendent joy," said the big green bat. "Right," I said "I'm absolutely in favor of both those things. But before we go any further, could you tell me ...
5
3
gwern branwen's profile photoPaolo Marino's profile photoNick de Vera's profile photoPaul Hobbs's profile photo
3 comments
 
Perhaps the transcendent beings are specialists in 'cognitive disfluency'.
Add a comment...

gwern branwen

Shared publicly  - 
 
The non-parametric bootstrap was my first love. I was lost in a muddy swamp of zs, ts and ps when I first saw her. Conceptually beautiful, simple to …
View original post
5
1
Vít Tuček's profile photo
Add a comment...

gwern branwen

Shared publicly  - 
 
Bats in a birdless country,Sosuke put his hoe on his shoulder,Kosuke took his net in hand,Sosuke to the mountain, Kosuke to the seaCucumber flowers that bloom in the twilight,cicadas singing in the leaves of a distant mulberry,mountain paths cool with dew—Kosuke by the sea dreamed of them with envyTasty beach grasses on dunes near and far,boats drying by the distant summer tide,the voice of the sea resounding in the eelgrass—Sosuke on the mountai...
7
Andrei Mellas's profile photogwern branwen's profile photo
2 comments
 
It can be a surprisingly small Internet, sometimes.
Add a comment...

gwern branwen

Shared publicly  - 
 
Always interesting to become part of the story.
> /r/DarkNetMarkets has received its first known LE subpoena: a request for 5 accounts' data, including mine, related to Evolution and the...
12
Mursel Kukic's profile photoDavid Wright's profile photoEvelyn Mitchell's profile photo
3 comments
 
Oh Gwern!
Add a comment...
People
Have him in circles
2,389 people
Joshua Fox's profile photo
Selem Delul's profile photo
Vanessa mendonça's profile photo
Greg Frascadore's profile photo
Daniel Feltey's profile photo
joshua tsatsu's profile photo
Open Data's profile photo
Edward McCaffrey's profile photo
Brian Forsberg's profile photo
Links
Contributor to
Basic Information
Gender
Male