Causal inference literature in statistics, and in the biomedical and social sciences focus on what Aristotle called "efficient causes." We can try to predict actions, and possibly even reasons, but again the recent developments in causal inference literature in statistics and the biomedical and social sciences focus more on "efficient causes." Most of the work in the biomedical and social sciences on causal inference has focused on this sufficient condition of counterfactual dependence in thinking about causes. Some areas that might have exciting developments in the future include causal inference with network data, causal inference with spatial data, causal inference in the context of strategy and game theory, and the bringing together of causal inference and machine learning.
First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses.
The disease, Niemann-Pick type C (NPC), causes cholesterol build up in the neurons, leading to enlarged organs, lung damage, muscle stiffness, seizures, dementia and difficulty speaking. The test, which is called the NPC Neurological Severity Score, helps assess eye movement, gait, speech, swallowing, fine motor skills, cognition, hearing, memory and presence and severity of seizures. The historical data showed patients' scores increased - meaning the disease worsened - an average of 2.9 points per year. The progressive, fatal condition, officially known as Niemann-Pick disease type C, causes enlarged organs, lung damage, muscle stiffness, dementia and difficulty speaking.
A new study, in which IBM Watson took just 10 minutes to analyze a brain cancer patient's genome and suggest a treatment plan, demonstrates the potential of artificially intelligent medicine to improve patient care. Both the NYGC clinicians and Watson identified mutations in genes that weren't checked in the panel test, but which nonetheless suggested potentially beneficial drugs and clinical trials. Both Watson and the expert team received the patient's genome information and identified genes that showed mutations; went through the medical literature to see if those mutations had figured in other cancer cases; looked for reports of successful treatment with drugs; and checked for clinical trials that the patient might be eligible for. IBM's Parida notes that the cost of sequencing an entire genome has plummeted in recent years, opening up the possibility that whole-genome sequencing will soon be a routine part of cancer care.
The development of pilotless passenger aircraft could be worth 35 billion US dollars (£27 billion) to the aviation industry and cut fares for passengers, according to research. Analysis by investment bank UBS found that technology to enable remotely controlled planes carrying people and cargo could appear by 2025. For this reason, pilotless cargo aircraft may happen more swiftly than for passengers.' He said: 'Boeing and Airbus are both cooperating with US FAA and European Aviation Safety Agency on some pilotless aircraft studies but as the current generation of airliners - B787, A350 - are new into service I cannot see this as a reality for at least 20/25 years.
Researchers at Princeton Baby Lab studied how babies and young children learn to see, talk and comprehend the world. International researchers teamed up with researchers from Princeton University and found infants as young as 20 months of age could accurately and efficiently process two languages separately. The researchers then experimented with language switches called code switches that are regularly heard by children in bilingual communities like "That one looks fun! They found that on hearing switched-language sentences, both bilingual infants and adults incurred a processing "cost."
To date, most research on the significance of edible algae to fertility has involved animal studies. For instance, two studies found enhanced male reproductive function for boar when spirulina was incorporated into their feed, while a third found this same effect for bulls. Other studies showed spirulina minimising the adverse effects of chemotherapy on testicular function of both rats and mice, while yet another study showed spirulina protecting the reproductive function of male mice from gamma radiation. The provisional take home message from the early research is nonetheless clear: various studies give cause for hope that edible algae could significantly boost reproductive function in men, notably where it is threatened by industrial toxins.
Early this year, I wrote a piece that discussed how emerging technologies such as artificial intelligence (AI) and blockchain will drive precision medicine this year. AI represents a $150 billion savings opportunity for healthcare, across a wide range of applications: robot-assisted surgery, clinical diagnosis and treatment options, and operational efficiencies, to name a few. Health plans: There is considerable traction today applying RPA tools and AI technologies for improving productivity and efficiencies in health plans. As healthcare transitions from a fee-for-service to a value-based care era, the need for advanced technologies for everything from precision medicine to increased operational efficiencies and improved patient engagement will drive the adoption rates for these technologies.
One approach is to design more robust algorithms where the testing error is consistent with the training error, or the performance is stable after adding noise to the dataset. For example, using "r" as a measure of similarity in the registration of low-contrast images can produce cases where "close to unity" means 0.998 and "far from unity" means 0.98, and there's no way to compute a p-value due to the extremely non-Gaussian distributions of pixel values involved. Robust statistics are also called nonparametric precisely because the underlying data can have almost any distribution and they will still produce a number that can be associated with a p-value. So while losing signal information can reduce the statistical power of a method, degrading gracefully in the presence of noise is an extremely nice feature to have, particularly when it comes time to deploy a method into production.
That is, each study participant would see as many varied product offerings (or product profiles) as the experiment required, and would evaluate each. For instance, eight features or attributes, each varied in three ways, would require 18 runs in the experiment--or 18 product profiles. This was overcome with the use of a machine learning method, Hierarchical Bayesian Analysis. This method provides highly accurate estimates by learning from the data where an individual's responses are spotty or missing.