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Chapter 11: Problem 57

In December 2001 , the Department of Veterans Affairs announced that it would begin paying benefits to soldiers suffering from Lou Gehrig's disease who had served in the Gulf War (The New york Times, December 11,2001 ). This decision was based on an analysis in which the Lou Gehrig's disease incidence rate (the proportion developing the disease) for the approximately 700,000 soldiers sent to the Gulf between August 1990 and July 1991 was compared to the incidence rate for the approximately 1.8 million other soldiers who were not in the Gulf during this time period. Based on these data, explain why it is not appropriate to perform a formal inference procedure (such as the two-sample \(z\) test) and yet it is still reasonable to conclude that the incidence rate is higher for Gulf War veterans than for those who did not serve in the Gulf War.

Short Answer

Expert verified
Formal inference procedures like the two-sample \(z\) test rely on specific assumptions that are not met in this scenario, such as random and independent sampling and roughly equal variances. However, given the actual measurements of incidence rates in both groups, we can compare them directly and note a clear difference, making it reasonable to conclude that Gulf War veterans have a higher incidence rate of Lou Gehrig's disease.

Step by step solution

01

Understanding the exercise

The problem provides information about two groups: veterans that served in the Gulf War, and those that did not. The incidence rate of Lou Gehrig's disease in these groups is being compared. However, we can't apply a standard test of significance such as a two-sample \(z\) test. In this situation, it's important to identify why formal statistical procedures aren't applicable.
02

Recognizing limitations of statistical tests

Formal statistical tests like a two-sample \(z\) test, are designed to compare two samples drawn from the same population to assess if a significant difference exists between them. These tests rest on assumptions about the data, such as the samples being independently and randomly selected, and having roughly equal variances. In this scenario, these conditions are not met. Comparing soldiers deployed to the Gulf and those who were not doesn't constitute independent or random sampling. Moreover, the large discrepancy in the sizes of the two groups (700,000 versus 1.8 million) suggests that variance may not be approximately equal.
03

Forming a reasonable conclusion

Despite not being able to carry out a formal inference procedure, it's still reasonable to conclude that the incidence rate of Lou Gehrig's disease is higher among Gulf War veterans. This conclusion can be reached by observing the raw data and recognizing that the incidence rate is indeed higher in the Gulf War group. Since the incident rates are actual measurements and not estimates, we can directly compare them without a formal test. Therefore, even without the ability to prove causation or to quantify the measure of the difference, it's evident that a difference exists in the incidence rates of the two groups.

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Key Concepts

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

Incidence Rate Comparison
In the realm of research, the comparison of incidence rates focuses on understanding how frequently a specific event, such as the development of a disease, occurs in different groups. For this exercise, we are comparing the incidence rate of Lou Gehrig's disease among Gulf War veterans with those who did not serve in the Gulf.

The incidence rate is a crucial measure in epidemiology, representing the number of new cases of a disease in a population over a certain period. It helps researchers assess and compare disease risk across different populations. To compare these rates accurately, researchers look at the proportion of new cases in each group.
  • Gulf War veterans: Approximately 700,000 individuals were examined for disease incidence.
  • Non-Gulf War veterans: The incidence rate is established from a much larger group of about 1.8 million soldiers.
Analyzing the data involves observing the raw incidence rates. Even without complex formal tests, if one group shows a clearly higher incidence, it implies a greater risk or vulnerability within that group.
Limitations of Statistical Tests
Statistical tests, like the two-sample \(z\) test, help determine if there is a statistically significant difference between two group means. However, these tests come with assumptions. In the context of the Gulf War veterans study:
  • Random and Independent Sample Selection: These tests require each group to be random samples from the same population. The samples must be independent. Here, different environmental and historical contexts make these samples more complex.
  • Equal Variance: The assumption that both groups have similar variances does not hold, especially considering the huge sample size difference (700,000 vs. 1.8 million). Large imbalances can influence the variance.
Due to these constraints, using standard statistical inference techniques like the \(z\) test might not be appropriate. Instead, simply comparing the observed incidence rates directly can still provide valuable insights into differences between the groups.
Military Health Studies
Studies on military health are crucial as they help address specific health concerns related to unique exposures veterans may experience. Military personnel can be subject to different stressors, environments, and potential chemical exposures, influencing long-term health outcomes.

The Gulf War veterans' study highlights why such research is so essential:
  • Environmental Exposure: Veterans may have encountered conditions or chemicals not present in non-deployed groups, which can affect incidence rates of diseases like Lou Gehrig's.
  • Policy and Decision Making: Findings from these studies impact veteran benefits and healthcare policies. For instance, recognizing a higher disease incidence among Gulf War veterans led to new benefit entitlements.
Through ongoing research and better understanding of veteran health, military health studies aim to improve healthcare measures and support services for former military personnel.

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Most popular questions from this chapter

The authors of the paper "Adolescents and MP3 Players: Too Many Risks, Too Few Precautions" \((P \mathrm{e}-\) diatrics [2009]: e953-e958) concluded that more boys than girls listen to music at high volumes. This conclusion was based on data from independent random samples of 764 Dutch boys and 748 Dutch girls age 12 to \(19 .\) Of the boys, 397 reported that they almost always listen to music at a high volume setting. Of the girls, 331 reported listening to music at a high volume setting. Do the sample data support the authors' conclusion that the proportion of Dutch boys who listen to music at high volume is greater than this proportion for Dutch girls? Test the relevant hypotheses using a .01 significance level.

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"Smartest People Often Dumbest About Sunburns" is the headline of an article that appeared in the San Luis Obispo Tribune (July 19,2006 ). The article states that "those with a college degree reported a higher incidence of sunburn that those without a high school degree- \(43 \%\) versus \(25 \%\)." For purposes of this exercise, suppose that these percentages were based on random samples of size 200 from each of the two groups of interest (college graduates and those without a high school degree). Is there convincing evidence that the proportion experiencing a sunburn is higher for college graduates than it is for those without a high school degree? Answer based on a test with a .05 significance level.

The Insurance Institute for Highway Safety issued a press release titled "Teen Drivers Often Ignoring Bans on Using Cell Phones" (June 9,2008 ). The following quote is from the press release: Just \(1-2\) months prior to the ban's Dec. 1,2006 start, 11 percent of teen drivers were observed using cell phones as they left school in the afternoon. About 5 months after the ban took effect, \(12 \%\) of teen drivers were observed using cell phones. Suppose that the two samples of teen drivers (before the ban, after the ban) can be regarded as representative of these populations of teen drivers. Suppose also that 200 teen drivers were observed before the ban (so \(n_{1}=200\) and \(\hat{p}_{1}=.11\) ) and 150 teen drivers were observed after the ban. a. Construct and interpret a \(95 \%\) confidence interval for the difference in the proportion using a cell phone while driving before the ban and the proportion after the ban. b. Is zero included in the confidence interval of Part (c)? What does this imply about the difference in the population proportions?
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