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Chapter 8: Problem 44

Ten patients with advanced diabetic nephropathy (kidney complications of diabetes) were treated with captopril over an 8-week period [9]. Urinary protein was measured before and after drug therapy, with results listed in Table 8.16 in both the raw and In scale. What is the appropriate statistical procedure to test whether mean urinary protein has changed over the 8 -week period?

Short Answer

Expert verified
Use a paired t-test to assess changes in mean urinary protein.

Step by step solution

01

Understand the Data Structure

In this exercise, we have repeated measures of urinary protein for the same group of patients before and after treatment with captopril. Each patient has a before and after measurement.
02

Identify the Nature of the Data

The data involves paired measurements because each patient's urinary protein levels are recorded before and after the treatment. This pairing of data points suggests that we are dealing with dependent data.
03

Define the Statistical Test

Since we have paired data points, the appropriate statistical test to use is the paired t-test. This test is designed to compare means from two related groups, that is, the same subjects measured at different times.
04

Check Assumptions of the Paired t-test

Before performing the paired t-test, check assumptions: the differences between pairs (after - before values) should be approximately normally distributed. It is common to use visual methods like a histogram or a normality test to verify this assumption.
05

Implement the Paired t-test

Proceed with performing the paired t-test on the differences in the urinary protein levels before and after treatment. The null hypothesis for the test is that there is no difference in means, while the alternative hypothesis is that the means are different.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Dependent Data Analysis
When we talk about dependent data analysis, we're focusing on situations where each pair of data points is related. In our context, this means that the same group of patients is measured twice: once before and once after a treatment. This setup leads to paired observations, which means the data are not independent. Each patient's before-treatment and after-treatment measurements are directly linked.
This dependence is important because it affects how we analyze the data.
  • We cannot use tests that assume independence between observations, like those for two unrelated samples.
  • Instead, we use analyses, such as the paired t-test, that account for this relationship.
  • The pairing allows us to specifically assess the change or difference due to the treatment effect within each individual.
Understanding this dependency and how to handle it with appropriate statistical methods is crucial in accurately determining the effects of treatments.
Statistical Test for Treatment Effect
To evaluate the effect of a treatment, we must utilize the right statistical test. In cases where we have paired data, like the before and after results of the same patients, our tool of choice is the paired t-test.
The paired t-test is a reliable method for comparing two related groups. Its main focus is on the differences, specifically the difference in means between the two sets of dependent data.
  • We start by formulating hypotheses: the null hypothesis states there is no difference in means before and after the treatment.
  • The alternative hypothesis suggests there is a significant change or effect.
  • The test works with the differences (after - before) of each pair to see if this average difference significantly deviates from zero.
If the paired t-test indicates a statistically significant difference, we conclude that the treatment had an effect.
Biostatistics Education
Biostatistics is a critical field, especially when it comes to understanding health and biological phenomena. It blends statistical principles with biological data, helping scientists and healthcare professionals make informed decisions.
In our case study, learning how to properly apply a paired t-test is an essential skill. It reveals the treatment effect in medical studies.
  • Students learn to carefully choose the right statistical test based on the data structure, like paired measurements.
  • The key concepts include understanding hypothesis testing, checking the assumptions of tests, and correctly interpreting results.
  • This knowledge ensures accurate conclusions, which are vital in healthcare settings where patient decisions might be based on study results.
The goal of biostatistics education is to empower students to use data responsibly and innovate in medical research.

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Most popular questions from this chapter

The goal of the Swiss Analgesic Study was to assess the effect of taking phenacetin-containing analgesics on kidney function and other health parameters. A group of 624 women were identified from workplaces near Basel, Switzerland, with high intake of phenacetin-containing analgesics. This constituted the "study" group. In addition, a control group of 626 women were identified, from the same workplaces and with normal \(N\) -acetyl-P-aminophenyl (NAPAP) levels, who were presumed to have low or no phenacetin intake. The urine NAPAP level was used as a marker of recent phenacetin intake. The study group was then subdivided into high-NAPAP and low-NAPAP subgroups according to the absolute NAPAP level. However, both subgroups had higher NAPAP levels than the control group. The women were examined at baseline during 1967 and 1968 and also in 1969,1970,1971,1972,1975 and \(1978,\) during which their kidney function was evaluated by several objective laboratory tests. Data Set SWISS.DAT at www.cengagebrain.com contains longitudinal data on serum- creatinine levels (an important index of kidney function) and other indices of kidney functions. Documentation for this data set is given in SWISS.DOC at www.cengagebrain .com. A major hypothesis of the study is that women with high phenacetin intake would show a greater change in serum-creatinine level compared with women with low phenacetin intake. Can you assess this issue using the longitudinal data in the data set? (Hint: A simple approach for accomplishing this is to look at the change in serum creatinine between the baseline visit and the last follow- up visit. More complex approaches using all the available data are considered in our discussion of regression analysis in Chapter 11.)

What test can be used to assess whether the underlying mean change score differs for retired women vs. working women?

Cigarette smoking has important health consequences and is positively associated with heart and lung diseases. Less well known are the consequences of quitting smoking. A group of 10 nurses, from the Nurses' Health Study, ages \(50-54\) years, had smoked at least 1 pack per day and quit for at least 6 years. The nurses reported their weight before and 6 years after quitting smoking. A commonly used measure of obesity that takes height and weight into account is \(\mathrm{BMl}=\mathrm{wt} / \mathrm{ht}^{2}\) (in units of \(\mathrm{kg} / \mathrm{m}^{2}\) ). The BMI of the 10 women before and 6 years after quitting smoking are given in the last 2 columns of Table 8.32 What test can be used to assess whether the mean BMI changed significantly among heavy-smoking women 6 years after quitting smoking?

Suppose a similar study is planned among women who use exogenous hormones. How many participants need to be enrolled if the mean change in \(\log _{10}\) (plasma estradiol) is \(-0.08,\) the standard deviation of change is 0.20 and we want to conduct a two-sided test with an \(\alpha\) level of .05 and a power of .80?

Find the lower 2.5 th percentile of an \(F\) distribution with 14 and 7 df. What symbol is used to denote this?
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