Articles by Klodian Dhana

Graphical Presentation of Missing Data; VIM Package

May 8, 2017 | 0 Comments

Missing data is a problem that challenge data analysis methodologically and computationally in medical research. Patients of the clinical trials and cohort studies may drop out of the study, and therefore, generate missing data. The missing data could be at random when participants who drop out of study are not ...
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How to create a loop to run multiple regression models

February 6, 2017 | 0 Comments

A friend asked me whether I can create a loop which will run multiple regression models. She wanted to evaluate the association between 100 dependent variables (outcome) and 100 independent variable (exposure), which means 10,000 regression models. Regression models with multiple dependent (outcome) and independent (exposure) variables are common in genetics. So models ... [Read more...]

Handling missing data with MICE package; a simple approach

June 6, 2016 | 0 Comments

This is a quick, short and concise tutorial on how to impute missing data. Previously, we have published an extensive tutorial on imputing missing values with MICE package. Current tutorial aim to be simple and user friendly for those who just starting using R. Preparing the dataset I have created ...
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Identify, describe, plot, and remove the outliers from the dataset

April 30, 2016 | 0 Comments

In statistics, a outlier is defined as a observation which stands far away from the most of other observations. Often a outlier is present due to the measurements error. Therefore, one of the most important task in data analysis is to identify and (if is necessary) to remove the outliers. ... [Read more...]

How to export Regression results from R to MS Word

March 15, 2016 | 0 Comments

In this post I will present a simple way how to export your regression results (or output) from R into Microsoft Word. Previously, I have written a tutorial how to create Table 1 with study characteristics and to export into Microsoft Word. These posts are especially useful for researchers who prepare ... [Read more...]

Table 1 and the Characteristics of Study Population

February 14, 2016 | 0 Comments

In research, especially in medical research, we describe characteristics of our study populations through Table 1. The Table 1 contain information about the mean for continue/scale variable, and proportion for categorical variable. For example: we say that the mean of systolic blood pressure in our study population is 145 mmHg, or 30% of ... [Read more...]

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