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Steroids and Height Does the use of inhaled steroids by children affect their height as adults? Excerpts from the abstract of a study about this are given. Read them and then answer the questions that follow. "Methods: We measured adult height in 943 of 1041 participants \((90.6 \%)\) in the Childhood Asthma Management Program; adult height was determined at a mean ( \(\pm \mathrm{SD}\) ) age of \(24.9 \pm 2.7\) years. Starting at the age of 5 to 13 years, the participants had been randomly assigned to receive \(400 \mu \mathrm{g}\) of budesonide, 16 \(\mathrm{mg}\) of nedocromil, or placebo daily for 4 to 6 years. Results: Mean adult height was \(1.2 \overline{\mathrm{cm}}\) lower \((95 \%\) confidence interval \([\mathrm{CI}],-1.9\) to \(-0.5\) ) in the budesonide group than in the placebo group \((\mathrm{P}=0.001)\) and was \(0.2 \mathrm{~cm}\) lower (95\% CI, \(-0.9\) to \(0.5\) ) in the nedocromil group than in the placebo group \((\mathrm{P}=0.61) . "\) a. Identify the treatment variable and the response variable. b. Was this a controlled experiment or an observational study? Explain. c. Does the first interval, \((-1.9\) to \(-0.5\) ), capture 0 ? What does that show? d. From the interval, can you conclude that the use of budesonide in childhood reduces the heights of the children when they become adults? Why or why not? e. Does the second interval, \((-0.9\) to \(0.5\) ), capture 0 ? What does that show? f. From the interval, can you conclude that the use of nedocromil in childhood reduces the heights of the children when they become adults? Why or why not?

Step by step solution

01

Identify the Variables

The treatment variable here is the type of drug given to the children (budesonide, nedocromil, or placebo) and the response variable is the adult height of these children at the age of 24.9 ± 2.7 years.

02

Type of Study

This is a controlled experiment because the treatment groups are randomly assigned. This ensures that the impact of confounding factors is minimized, thereby making the observed effects more reliable.

03

First Interval Interpretation

The first interval, (-1.9 to -0.5), does not capture 0. This means that the mean difference lies away from the null value (0 in this case) thereby giving evidence of a significant effect. The height of adults who took budesonide as children was, on average, shorter than the placebo group.

04

Drawing Conclusion from First Interval

Just from this interval, it appears that budesonide treatment in childhood could result in lower adult heights. However, it is also crucial to consider other potential factors and the individual variations too.

05

Second Interval Interpretation

The second interval, (-0.9 to 0.5), does capture 0. This indicates that the mean difference could be zero and there is not a significant effect. The height difference of adults who took nedocromil as children was not significantly different than the placebo group.

06

Drawing Conclusion from Second Interval

Based on this interval, we cannot conclude that the use of nedocromil in childhood reduces the heights of the children when they become adults because the confidence interval includes 0, indicating a possible lack of effect.

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Key Concepts

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

Controlled Experiment

Understanding a controlled experiment is vital when interpreting the effects of childhood asthma medication on adult height. A controlled experiment is a research study where participants are randomly assigned to different groups; each group receives a different treatment, or one group may receive a placebo—a substance with no therapeutic effect.

In the case of the Childhood Asthma Management Program study, children were assigned to receive either budesonide, nedocromil, or a placebo. This random assignment helps to ensure that the differences observed in adult height can be more confidently attributed to the medication rather than other variables. This is because randomization tends to balance out other factors that could influence the outcomes across the groups.

To further bolster the reliability of the experiment, it's important for the sample size to be sufficient, and the study design should include efforts to minimize bias. Additionally, tracking adherence to the treatment protocol and considering the impact of drop-outs or missing data are crucial in a controlled experiment. The goal is to reach a conclusion that is as accurate as possible about the cause-and-effect relationship between the treatment and the response variable—in this instance, adult height.

Adult Height Measurement

The accurate measurement of adult height is integral to studies examining the long-term impacts of medications taken during childhood. In the context of the study mentioned, the adult height was measured at a mean age of nearly 25 years, which is a suitable point for assessing final adult height, as most individuals have completed their growth by this age.

Adult height is often measured using a stadiometer, which is a calibrated device designed to take precise height measurements. Researchers will commonly take several measurements to account for normal variations, such as posture changes throughout the day, and use the average to ensure accuracy.

When reporting the results, it's important to consider the potential errors associated with the measurement process and to describe the techniques used to reduce these errors. This assures the reliability of the study results and supports a credible inference about the relationship between asthma medication used during childhood and eventual adult height.

Confidence Intervals

Conceptually, confidence intervals (CIs) are a range of values that are used to estimate the true effect of a treatment. In our study, the CIs give us a range within which we can be confident (to a certain statistical level, typically 95%) that the true average difference in height falls.

The first interval in the study group (-1.9 to -0.5) for the budesonide treatment does not include 0, which indicates that there is a statistically significant difference in height between the treated and placebo groups. Statisticians use the term 'significant' to denote that the observed effect is unlikely to be due to chance alone. However, this doesn't necessarily mean the effect is large or important in a practical sense—just that it is likely 'real'.

Moreover, the CI for the nedocromil group (-0.9 to 0.5) includes the 0 value, implying that the true effect could be no difference in height, or it could tilt either way. Therefore, from a statistical standpoint, we do not have evidence to conclude that nedocromil affects adult height, highlighting how CIs can inform us about both statistical significance and the precision of our estimates.

When interpreting CIs, we should consider their width. Narrow CIs suggest a more precise estimate of the effect, whereas wider intervals indicate less certainty. Researchers and readers alike should critically analyze confidence intervals to thoroughly understand their implications for the study's conclusions.