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Basic Analysis of Patient Data
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Basic analysis of patient data involves examining demographic, clinical, and treatment-related information to understand patterns and outcomes within a clinical study. This type of analysis helps researchers evaluate treatment effectiveness, patient characteristics, and overall study results.
Patient datasets typically include variables such as patient ID, age, gender, treatment group, laboratory results, and clinical outcomes. These variables can be summarized using descriptive statistics and simple comparisons.
# Example patient dataset
patient_data <- data.frame(
patient_id = 1:8,
age = c(45, 52, 37, 60, 49, 55, 42, 63),
gender = c("M", "F", "M", "F", "M", "F", "F", "M"),
treatment = c("Drug", "Drug", "Placebo", "Drug",
"Placebo", "Drug", "Placebo", "Drug"),
response = c(1, 1, 0, 1, 0, 1, 0, 1)
)The first step is to examine the structure and summary of the dataset.
str(patient_data)
summary(patient_data)Descriptive statistics can be used to summarize key variables.
| Measure | Purpose | R Function |
|---|---|---|
| Mean age | Average age of patients | mean(patient_data$age) |
| Gender count | Number of patients by gender | table(patient_data$gender) |
| Treatment count | Number of patients in each treatment group | table(patient_data$treatment) |
| Response rate | Proportion of patients who responded | mean(patient_data$response) |
Group-wise analysis helps compare outcomes between treatment groups.
library(dplyr)
patient_data %>%
group_by(treatment) %>%
summarise(
average_age = mean(age),
response_rate = mean(response)
)Visualization can also be used to compare treatment responses.
library(ggplot2)
ggplot(patient_data, aes(x = treatment, fill = factor(response))) +
geom_bar(position = "fill") +
labs(
x = "Treatment Group",
y = "Proportion",
fill = "Response"
)Basic analysis of patient data provides an overview of patient characteristics and treatment outcomes. It helps researchers identify trends, compare groups, and prepare data for more advanced statistical analysis.
