Data analysis

“Statistical analysis allows us to put limits on our uncertainty, but not to prove anything.”Douglas G. Altman (in Altman DG. Practical Statistics for Medical Research. London, UK: Chapman & Hall; 1991.)

Quick Start Guide for R 

Link and courses

ANALYSIS PLAN

Aim:

Requirements:

Documentation:

Analysis Plan Guidelines (Per Study Type. see next section)


Responsibilities:

Executing Researcher:

Project Leaders:


How to Create an Analysis Plan:

DATA ANALYSIS

Objective

To gain an initial understanding of the dataset and to examine the relationship between exposure and outcome.

Data Prerequisites

Documentation

Study Population's Baseline Characteristics

Examine characteristics based on treatment/exposure. Distinctive results from the primary data analysis (such as odds ratios and hazard ratios) should be reported.

Roles and Responsibilities

Procedure

Post-hoc & sensitivity analyses

Objective

Ensure appropriate and correct execution of post-hoc and sensitivity analyses in dental research studies.

Requirements

Conduct post-hoc and sensitivity analyses as and when necessary.

Documentation

Roles and Responsibilities

Procedure

Data analysis documentation

Objective

To reinforce the reproducibility of dental research analyses using R.

Requirements

Documentation Protocol

Roles & Responsibilities

Guidelines

Ensuring easy reproducibility of data analyses via R in dental research is paramount. Adopt the practice of maintaining an R script detailing each analysis. The script should chronologically begin with the research question and conclude with a tentative or final answer.

Handling missing data

Objective

Equip dental researchers with a structured protocol and insights on addressing missing data.

Requirements

Documentation Protocol

Roles & Responsibilities

Guidelines

1. Understanding Missing Data

2. Addressing Missing Data Across Research Phases:

3. Techniques for Handling Missing Data:


Example in R for identifying missing data:

Identifying Percentage of Missing Values for Each Variable

In a dental research dataset using R, you can identify the percentage of missing values for each variable separately by employing the summary() function.

# Load data

mydata <- read.csv("dental_data.csv")


# Identify missing data for each variable separately

summary(mydata)


Executing the above will yield a summary of the dataset, detailing the number of missing values for each respective variable.


Imputing Missing Data

To handle missing data in a dental research dataset with R, the mice package is a robust solution. This package supports a variety of imputation techniques, such as mean imputation, regression imputation, and multiple imputation.

# Load data

mydata <- read.csv("dental_data.csv")


# Impute missing values using mean imputation

library(mice)

imp <- mice(mydata, method = "mean")


# Extract completed dataset with imputed values

mydata_imputed <- complete(imp)


By following the steps outlined above, the dataset's missing data will be imputed through mean imputation, and a completed dataset with these imputed values will be produced.


Deleting Missing Data

Should the need arise to remove missing data from a dental research dataset using R, the na.omit() function is an effective tool. This function will purge any rows containing missing values.

# Load data

mydata <- read.csv("dental_data.csv")


# Delete rows with missing values

mydata_clean <- na.omit(mydata)


After executing the above, the outcome will be a dataset devoid of rows containing missing values.