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# HealthyNumerics

HealthPoliticsEconomics | Quant Analytics | Numerics

# MetaAnalysis Articles

#### NumericalStats: What the Metropolis Markov Chain Monte Carlo sampler is good for

We use the Metropolis-Hasting algorithm to sample a 2-dimensional empirical distribution. #### How to climb the Matterhorn with a Markov Chain Monte Carlo walk

We let your robot climb the Matterhorn with a Markov Chain Monte Carlo walk #### MachineLearning: How to analyze longitudinal studies with time series clustering

With the increasing possibilities to gather longitudinal data, there is an interest in mining profiles in form of time series data. The key question is how to figure out and to group similarities and dissimilarities between the profiles. #### NumericalStats: WHY and HOW to involve medical researchers into the statistical model building

The Monty Hall problem came out of a US TV-game in the 1970's and got wide publicity about problem solving statistically. It can be transfered easely to medical research how to seek a strategy that proofs to be the most suitable to reach ceratin aims in healthcare. #### NumericalStats: WHY to switch to distribution vectors

Using the coin flipping example, we give some arguments WHY the use of distribution vectors can be helpful as a preparation for Monte Carlo Markov Chain models and others and how this changes the role of medical researchers. #### NumericalStats: WHY to switch from a decision tree to a Markov model

We give some arguments, why a change from a decision tree to a Markov model could be motivated. We provide a code of 7 lines to run a Markov model. #### MetaAnalysis: Evaluating a diagnostic test with continuous output

The output of a dignostic test is often not binary but continuous. The transformation of the continuous output into a binary variable influences the outcome of the test. #### MetaAnalysis: Screening with a binary diagnostic test

Given a diagnostic test with sensitivity 0.90 and specifity 0.95. Then, with a prevalence of 0.02, in medical practice 27% of the positive tests will be right and 73% of the positive tests will be wrong. #### NumericalStats: How to randomly sample your empirical arbitrary distribution

We provide a simple sampling engine which allows to generate random numbers that are distributed as an empirical and arbitrary distribution given as a data array. #### BasicStats: The secret of the CI

We reproduce numerically the meaning of the confidential intervall #### MetaAnalysis 02: A Medical Decision Tree Intro

A first glance at the numerical analysis of decision trees for medical decision making #### Meta Analysis 01: Clinical multi center study

We simulate a multi center study of a diastolic blood pressure decreasing drug. #### Numerical Stats 03: Linear regression by a Monte Carlo method

By performing linear regression by a Monte Carlo method we get an estimate (mean, standard deviation, standar error) of the slope and the intercept. #### Numerical Stats 02: π by Monte Carlo integration

We use a Monte Carlo method with a code of 6 lines for the integration of mathmatical functions. In the case of a circle we can determin π. #### Numerical Stats 01: Bootstrapping μ, σ and CI of the mean

As a first example for numerical statistics we introduce bootstrapping which belongs to the class of Monte Carlo methods. #### Basic Stats 32: Multiple linear Regression, ANOVA

Elementary methods for data flow and statistics with Python, Pandas, NumPy, StatsModels, Seaborn, Matplotlib #### Basic Stats 31: Linear Regression

Elementary methods for data flow and statistics with Python, Pandas, NumPy, StatsModels, Seaborn, Matplotlib #### Basic Stats 03: Distributions

We use Python to draw some distributions #### Basic Stats 02: A mechanical approach to the Bayesian rule

We use a simple contingency table to deduce Bayes rule #### Basic Stats 01: Rules of probability and bridge to the Bayesian formula

By summarizing the rules of probability we build a bridge to the Bayesian formula 