Here is why we exist and how we want to empower our readers

From Pexels by Pixabay

Most of the scientists without a programming background, trying to test their hypothesis or explore data and who are new to some Data Science, face the same problems… They get lost with excessive, out-of-context, detailed information, which most of the time focuses on the technical capabilities of a given software. Then, they end up as they were before, wasting precious time.

By scientists, we do not mean someone in a lab with a coat and a test tube. We mean anyone who…


Linear regression in Python for Epidemiologists in 6 steps

From Pexels by Lukas

In this tutorial we will cover the following steps:

1. Open the dataset

2. Explore data

3. Make a research question (that can be answered using a linear regression model)

4. Running and reading a simple linear regression

5. Running and reading a multiple linear regression

6. Answering our research question

Back to Python…

1. Open the dataset

We are going to extract a publically available version of the Framingham heart study from; https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset/version/1

On this website, you can also consult the coding for the different variables. For example, regarding gender (variable male; male=1 means male and male=2 means female)

First Download the file to your computer.

Here the Jupyter Notebook version (Dataset downloaded from Kaggle).

Here is the Kaggle Notebook so you can run everything on your browser.

First import all relevant packages

import numpy as np 
import pandas as pd
import statsmodels.formula.api

6 simple steps to design, run and read a logistic regression analysis

From Pexels by Lukas

In this tutorial we will cover the following steps:

1. Open the dataset

2. Explore data

3. Make a research question (that can be answered using a logistic regression model)

4. Running and reading a simple logistic regression

5. Running and reading a multiple logistic regression

6. Answering our research question

For more on descriptive analysis using RStudio check this post

1. Open the dataset

We are going to extract a publically available version of the Framingham heart study from;

https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset/version/1

On this website, you can also consult the coding of the different variables. For example, regarding gender (variable male; male=1 means male and male=2 means female).

First Download the file to your computer.

Then go to RStudio and open the file via the following options (this is the easiest way).

File -> Import Dataset -> From Text (base) (this is a .csv file)


6 simple steps to design, run and read a logistic regression analysis

From Pexels by Lukas

In previous tutorials, we approached basic descriptive statistics. Now, we will take the next step, inferential analysis using regression to study association.

In this tutorial, we will run and interpret a logistic regression analysis using Stata.

In this tutorial we will cover the following steps:

1. Open the dataset

2. Explore data

3. Make a research question (that can be answered using a logistic regression model)

4. Running and reading a simple logistic regression

5. Running and reading a multiple logistic regression

6. Answering our research question

Before we get started…

For more on descriptive analysis using Stata check this post

Now, let’s roll the numbers (and open our data)!

1. Open the dataset

Let’s revisit the publically available version of the Framingham heart study from https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset/version/1

First Download the file to your computer

Then go to Stata and open the file via the following options (the easiest way)

File -> Import -> Text data (delimited, *.csv…)


6 simple steps to design, run and read a linear regression analysis

From Pexels by Lukas

In this tutorial we will cover the following steps:

1. Open the dataset

2. Explore data

3. Make a research question (that can be answered using a linear regression model)

4. Running and reading a simple linear regression

5. Running and reading a multiple linear regression

6. Answering our research question

For more on the fundamentals behind a linear regression analysis check this post

For more on descriptive analysis using RStudio check this post

1. Open the dataset

We are going to extract a publically available version of the Framingham heart study from;

https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset/version/1

On this website, you can also consult the coding for the different variables. For example, regarding gender (variable male; male=1 means male and male=2 means female)

First Download the file to your computer.

Then go to RStudio and open the file via the following options (this is the easiest way)

File -> Import Dataset -> From Text (base) (this is a…


6 simple steps to design, run and read a linear regression analysis

From Pexels by Lukas

In previous tutorials, we approached basic descriptive statistics. Now, we will take the next step, inferential analysis using regression to study association.

In this tutorial, we will run and interpret a linear regression analysis using Stata.

In this tutorial we will cover the following steps:

1. Open the dataset

2. Explore data

3. Make a research question (that can be answered using a linear regression model)

4. Running and reading a simple linear regression

5. Running and reading a multiple linear regression

6. Answering our research question

Before we get started…

For more on the fundamentals behind a linear regression analysis check this post

For more on descriptive analysis using Stata check this post

Now, let’s roll the numbers (and open our data)!

1. Open the dataset

Let’s revisit the publically available version of the Framingham heart study from https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset/version/1

First Download the file to your computer

Then go to Stata and open the file via the following options (the easiest way)

File -> Import -> Text data…


Without a strategy, you lost before you started

from Pexels by Vlada Karpovich

In my career in clinical research, I have realized, with the help of great mentors and some costly mistakes, that research is not a random game. There should be a plan, a chart, a strategy, to avoid (or at least lessen) future headaches.

Just like a chess game, there are some core rules one cannot forget. This list could be infinite but let’s talk about the top 8 fundamental rules that I wished I have mastered sooner.

1) Develop your pieces — A single finished project outperforms 1,000 ideas

Moving your Knight back and forth will not develop your game. You need to break…


How to plan your data analysis

by Andrea Piacquadio from Pexels

You have a great research idea and you have a very broad idea of how to get the project done. However, while writing your statistical analysis plan, once again, you end up stuck in the “statistics” section. Then, you waste long hours checking GitHub and Reddit threads and watching jargon-full YouTube tutorials. With a headache, once again, you procrastinate and jump to the next thing.

You are not alone…

This article shares some simple steps on how to plan your data analysis by anticipating what you want to “show” in the final work (manuscript, poster, oral communication, you name it!).

These steps are

1…


Why “p-value<0.05” is not enough

by Lum3n from Pexels

The interpretation of p-value is one of the most misunderstood subjects in science and statistics, not only by the general public but also for Academia. This leads to low-quality publications and misleading news, with a consequent distrust in Science. Just stating “p<0.05 = good” and “p-value≥0.05 = bad” is a dangerous oversimplification.

This article tries to debug some concepts, focusing on:

  1. Data distribution and description
  2. Hypothesis testing
  3. The value of the p-value
  4. Practical limitations of the p-value
  5. How to choose the test to calculate the p-value?

1. Data distribution and description

Let’s take, for example, the height in adult men vs women. Empirically, we all…


The fundamentals behind linear regression

From Pexels by rodolfoclix

A classic taximeter is a linear regression machine. Right after you sit on the backseat and close the door the taximeter starts doing its math.

Here, image a linear regression function, where the xx axis represents the distance in Km, or independent variable, and the yy axis represents price in EUR (€), our dependent variable.

Santiago Rodrigues Manica

Physician, epidemiology enthusiast, and entrepreneur. Learning every day. Fueled by curiosity and challenges @R_M_Santiago #Lisbon

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