Nonparametric Methods for Analyzing Longitudinal Data in Biostatistics Assignments
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The document provides an overview of nonparametric methods for analyzing longitudinal data, which involves collecting data on the same subjects over multiple time points. It discusses the benefits of these methods, especially for skewed or non-normal datasets, and outlines common techniques such as the Wilcoxon signed-rank test, Kruskal-Wallis test, Friedman test, and Kaplan-Meier estimator. Additionally, it emphasizes the importance of understanding data structure and using statistical software for effective biostatistics analysis.
![7
Introduction
• Purpose: Compare three or more
groups for ordinal or continuous
data.
• Example Scenario: Statistically
assessing whether patient
samples from three treatment
groups have different recovery
rates over time.
PROCEDURE
2. Kruskal-Wallis Test Tutorhelpdesk.com
• Analyse all the data and rank
them in order.
• compare mean ranks between
groups.
EXAMPLE IN PYTHON (USING S
from scipy.stats import kruskal
# Recovery rates in three treatment
groups
group1 = [3.2, 2.8, 3.5, 3.0]
group2 = [3.8, 3.7, 4.0, 4.1]
group3 = [2.9, 3.1, 3.3, 3.2]
# Kruskal-Wallis Test
stat, p = kruskal(group1, group2,
group3)
print(f"Kruskal-Wallis statistic:
{stat}, p-value: {p}")](https://image.slidesharecdn.com/nonparametricmethodsforanalyzinglongitudinaldataforbiostatisticsassignmenthelp-241126070950-6235b259/85/Nonparametric-Methods-for-Analyzing-Longitudinal-Data-in-Biostatistics-Assignments-7-320.jpg)
![9
Introduction
• Purpose: The estimate of time-
to-event data under no habit
of distribution.
• Example Scenario: A drug is
administered to the patients,
and the survival rates are
studied.
PROCEDURE
4. Kaplan-Meier Estimator
(Nonparametric Survival Analysis)
Tutorhelpdesk.com
• Estimate survival probability at
each time point.
• Plot survival curves.
EXAMPLE IN PYTHON
from lifelines import
KaplanMeierFitter
import matplotlib.pyplot as plt
# Time-to-event data
time = [5, 6, 6, 2, 4, 8, 10]
event = [1, 1, 0, 1, 0, 1, 1] # 1: Event
occurred, 0: Censored
# Kaplan-Meier Estimator
kmf = KaplanMeierFitter()
kmf.fit(time, event)
kmf.plot_survival_function()
plt.title("Kaplan-Meier Survival
Curve")](https://image.slidesharecdn.com/nonparametricmethodsforanalyzinglongitudinaldataforbiostatisticsassignmenthelp-241126070950-6235b259/85/Nonparametric-Methods-for-Analyzing-Longitudinal-Data-in-Biostatistics-Assignments-9-320.jpg)
Nonparametric Methods for Analyzing Longitudinal Data in Biostatistics Assignments
- 1. NONPARAMETRIC METHODS LONGITUDINAL DATA for Analyzing Tutorhelpdesk.co m BIOSTATISTICS ASSIGNMENT GUIDE
- 2. 2 Introduction WHAT IS LONGITUDINAL DATA? DEFINITION Longitudinal data is data that has been collected on the same subjects continuously and at multiple intervals. This type of data is used to track the changes and patterns which is essential for the field of biostatistics notably in medical and public health research. Tutorhelpdesk.com
- 3. 3 Introduction • Week-long recording of a patient’s blood pressure. • Measure a child’s height and weight and compare them later to ensure they follow the right growth pattern. • Measuring how well a new drug is performing in comparison to older drugs after two years of usage. EXAMPLES KEY FEATURES • Observations made multiple times over a period on the same individual. • Correlations between measurements on the same subject. Introduction Tutorhelpdesk.com
- 4. 4 Introduction • No Assumptions About Data Distribution: Non-parametric techniques are techniques in which data does not need to be normally distributed. • Robustness: Particularly suitable for skewed data, or data having outliers, or small sample sizes. • Flexibility: Applicable on categorical, ordinal, and other qualitative data. SCENARIOS WHERE NONPARAMETRIC METHODS ARE USEFUL • When the population is small. • When the distribution of the population from which the sample has been drawn cannot be ascertained or, it is non-normal. • Whenever you are working with the rankings or scores or ordinal outcome. Why nonparametric method is used for Longitudinal data? Tutorhelpdesk.com
- 5. 5 Introduction Common Nonparametric Techniques in Analysis of Longitudinal Data Tutorhelpdesk.com Sign Test and Wilcoxon Signed Rank Test Friedman Test Kruskal-Wallis Test Kaplan-Meier Estimator
- 6. 6 Introduction • Purpose: Compare two sets of results on the same subjects – paired data analysis (Before and After treatment). • Example Scenario: Conducting hypothesis whether a special diet type does or does not influence the cholesterol levels in the patients. PROCEDURE 1. Sign Test and Wilcoxon Signed Rank Test Tutorhelpdesk.com • Order the differences between paired measurements. • Don’t consider the signs of differences (either positive or negative). • Rather than the raw values, it is the ranks that have to be analyzed. EXAMPLE IN R # Hypothetical cholesterol levels before and after intervention before <- c(200, 220, 180, 210, 195) after <- c(190, 210, 185, 200, 185) # Wilcoxon Signed-Rank Test wilcox.test(before, after, paired = TRUE, exact = FALSE)
- 7. 7 Introduction • Purpose: Compare three or more groups for ordinal or continuous data. • Example Scenario: Statistically assessing whether patient samples from three treatment groups have different recovery rates over time. PROCEDURE 2. Kruskal-Wallis Test Tutorhelpdesk.com • Analyse all the data and rank them in order. • compare mean ranks between groups. EXAMPLE IN PYTHON (USING S from scipy.stats import kruskal # Recovery rates in three treatment groups group1 = [3.2, 2.8, 3.5, 3.0] group2 = [3.8, 3.7, 4.0, 4.1] group3 = [2.9, 3.1, 3.3, 3.2] # Kruskal-Wallis Test stat, p = kruskal(group1, group2, group3) print(f"Kruskal-Wallis statistic: {stat}, p-value: {p}")
- 8. 8 Introduction • Purpose: The many-measures data are analyzed over multiple conditions. • Example Scenario: Pain scores are measured at 3 different periods post surgery on patients (e.g., Day 1, Day 3, and Day 7). PROCEDURE 3. Friedman Test Tutorhelpdesk.com • Rank data for each subject across time points. • Compute the test statistics using rank sums. EXAMPLE IN R # Pain scores across time points pain_scores <- matrix(c(4, 3, 2, 5, 4, 3, 6, 5, 4), nrow = 3, byrow = TRUE # Perform Friedman test friedman.test(pain_scores)
- 9. 9 Introduction • Purpose: The estimate of time- to-event data under no habit of distribution. • Example Scenario: A drug is administered to the patients, and the survival rates are studied. PROCEDURE 4. Kaplan-Meier Estimator (Nonparametric Survival Analysis) Tutorhelpdesk.com • Estimate survival probability at each time point. • Plot survival curves. EXAMPLE IN PYTHON from lifelines import KaplanMeierFitter import matplotlib.pyplot as plt # Time-to-event data time = [5, 6, 6, 2, 4, 8, 10] event = [1, 1, 0, 1, 0, 1, 1] # 1: Event occurred, 0: Censored # Kaplan-Meier Estimator kmf = KaplanMeierFitter() kmf.fit(time, event) kmf.plot_survival_function() plt.title("Kaplan-Meier Survival Curve")
- 10. 10 Introduction • Complexity of Dependencies: Analzying the correlations between the same subject across time is not easy. • Reduced Power: Nonparametric tests can however be lower in terms of power as compared to parametric tests. • Computational Intensity: Certain techniques are computationally intensive particularly when handling big data. Difficulties in the analysis of longitudinal data Tutorhelpdesk.com
- 11. 11 Introduction • Versatile: Can manage many types of data (ordinal and categorical). • Fewer Assumptions: One does not need to normality or equal variance. • Robustness: Overall it is less affected by outliers and skewed data. Strengths of Nonparametric Techniques Tutorhelpdesk.com TIPS FOR STUDENTS • Understand the structure of data before proposing the method. • View the data using charts, like a box plot or time series plot for any trends or patterns. • Always check that the parametric assumptions are met before they prefer a nonparametric alternative. • Become conversant with statistical packages such as R, Python, SPSS, or SAS.
- 12. 12 Introduction Looking for Biostatistics Assignment Help? Tutorhelpdesk.com Our biostatistics assignment help is more than just a provider of solutions-it is your gateway toward mastery of the subject. By entrusting us with this task, students receive precise, well- structured solutions, which act as self-explanatory guide to understand how to solve biostatistical problems correctly. The guidance provided will assist in completing assignments and skills for the real-world application and knowledge of biostatistics. Our support covers popular statistical software, including R, Python, SPSS, SAS and Stata, ensuring you are well prepared for sound data analyses. Our team of biostatistics experts start from the basics of what hypothesis testing is, regressions, survival analysis, and other important topics. We cater to all study levels-from novices who have problems with the most-basic concepts to advanced learners holding complex datasets. Empower yourself with theoretical and practical knowledge for excelling in biostatistics, which will not only help you through your course but also boost your employment options. Choose us for dependable, educational support and pursue your academic issues with confidence.
- 13. Nonparametric methods are powerful and indispensable yet highly potent tools for longitudinal data analysis when data assumptions are violated. They show robustness, flexibility, and applicability to small or non-normal datasets. By knowing these methods, students will have an easier understanding of analyzing complex biostatistical data and improve their research and analysis skills. 13 Conclusion Tutorhelpdesk.com
- 14. 14 1.why nonparametric methods are more appropriate for skewed data in longitudinal studies? 2. Compare the differences between the Wilcoxon Signed- Rank Test and the Kruskal-Wallis Test? 3.In what way does the Kaplan-Meier estimator deal with censored data in survival analysis? Questions for Self-Review Tutorhelpdesk.com
- 15. 15 “Nonparametric Statistical Methods" by Myles Hollander and Douglas A. Wolfe Recommended Textbooks Tutorhelpdesk.com "Applied Longitudinal Analysis" by Garrett M. Fitzmaurice, Nan M. Laird, and James H. Ware "Practical Nonparametric Statistics" by W.J. Conover "Introduction to the Practice of Statistics" by Moore, McCabe, and Craig