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Tight Control of Blood Sugar "Tight glycemic control" means that the blood sugar is kept within a narrow range. Read the abstract below, and then answer the questions that follow it. "Methods: In this two-center, prospective, randomized trial, we enrolled 980 children, 0 to 36 months of age, undergoing surgery with cardiopulmonary bypass. Patients were randomly assigned to either tight glycemic control ... targeting a blood glucose level of 80 to \(110 \mathrm{mg}\) per deciliter \(\ldots\) or standard care in the cardiac intensive care unit. Results: A total of 444 of the 490 children assigned to tight glycemic control ( \(91 \%\) ) received insulin versus 9 of 490 children assigned to standard care \((2 \%) \ldots\) [T]ight glycemic control was not associated with a significantly decreased rate of health careassociated infections \((8.6\) vs. \(9.9\) per 1000 patient-days, \(\mathrm{P}=0.67\) ). Conclusions: Tight glycemic control can be achieved with a low hypoglycemia rate after cardiac surgery in children, but it does not significantly change the infection rate \(\ldots .\) as compared with standard care." a. Identify the treatment variable and the response variable. b. Was this a controlled experiment or an observational study? Explain. c. What does the p-value show? d. Can you conclude that the use of tight glycemic control affects the rate of infections? Why or why not?

Step by step solution

01

Identify the treatment variable and the response variable

From the description, the treatment variable, that is the one being manipulated in order to test its effects, is whether patients are assigned to tight glycemic control or standard care. The response variable, the outcome that the researchers were interested in, is the rate of health-care associated infections.

02

Determine if this is a controlled experiment or an observational study

In this case, it is a controlled experiment because the patients are randomly assigned to either tight glycemic control or standard care and the effects on the health care-associated infections are observed.

03

Understand the role of P-value

The P-value is a statistical measure that helps scientists determine whether their hypotheses are correct. In this case, the P-value is 0.67 which suggests that, assuming there was no true effect of the tight glycemic control on the rate of infections, there would be a 67% chance of observing a difference as large as (or larger than) the one in the study due to random sampling variability.

04

Conclude if the use of tight glycemic control affects the rate of infections

The conclusion, based on the data, is that the use of tight glycemic control does not significantly affect the rate of infections. The reason for this is that the p-value is so high (0.67) which means that it is likely that the observed effects could be due to random chance.

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Key Concepts

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

Treatment Variable

In a controlled experiment, the treatment variable is the factor that is deliberately changed or manipulated to observe its effect. In our exercise, the treatment variable is the type of care patients receive: tight glycemic control versus standard care. This variable is pivotal because researchers want to assess whether manipulating blood glucose levels can impact other health outcomes, such as the rate of infections. By controlling this variable, researchers aim to isolate its effects and draw a clearer conclusion. Thus, understanding the treatment variable helps determine the direct cause-effect relationship in an experiment.

Response Variable

The response variable, or the dependent variable, is the outcome researchers are interested in measuring. In this exercise, the response variable is the rate of health-care associated infections. The goal of the research is to see if altering the treatment variable affects this response. It is crucial in understanding how changes in the treatment variable can directly or indirectly influence the outcome. By focusing on the response variable, researchers can collect data that may confirm or refute their initial hypothesis concerning the efficacy of the treatment applied.

P-value

A p-value is a statistical measure that helps scientists understand the strength of their experimental results. It quantifies the probability of obtaining results at least as extreme as the ones observed, assuming that the null hypothesis (no effect) is true. In our scenario, the p-value is 0.67. This indicates that there is a 67% chance of observing a change as large as in this study by random chance alone if tight glycemic control truly has no effect on infection rates.

• Low p-value (typically <0.05) suggests strong evidence against the null hypothesis, indicating a significant effect.
• High p-value, as in this case, suggests weak evidence against the null hypothesis, so no significant effect is detected.

By understanding the p-value, researchers can better interpret their experimental outcomes in the context of statistical significance.

Statistical Significance

Statistical significance refers to whether the results of an experiment are likely to be true or occurred by random chance. If results are statistically significant, it means they are unlikely to occur if the null hypothesis were true. In the exercise provided, the p-value of 0.67 indicates a lack of statistical significance.

• Statistical significance helps researchers judge how conclusive their findings are.
• Without statistical significance, any observed differences can likely be attributed to variability or error in the sample data rather than a real effect.

In this study, since the results are not statistically significant, it implies that tight glycemic control does not have a meaningful impact on infection rates when compared to standard care.