Introduction to Biostatistics Assignment

Question 1 -

Read the paper DeereK, Williams C, Leary S, et al (2009). Myopia and later physical activity in adolescence: a prospective study. British Journal of Sports Medicine, 43,542-544.

Critically appraise of the statistical material in this paper against items 10, 12-17 of the STROBE checklist. Present your review as a 400-500 word (approx.) report.

Notes:

Only review the provided paper Deere et al, 2009. Do not read any other papers. 

Restrict your review to how well Deere et al have documented their statistical methods - that is, items 10, 12-17 of STROBE only. You may not have to address every item; just describe the major strengths and weaknesses of the authors' descriptions of their statistical methods and results.

For each important STROBE item:

• state whether you believe the STROBE item is met or not,
• support your judgment with proof or examples from the paper, and
• describe why this inclusion or exclusion is important / how it will impact on the reader's understanding and decision making.

The 400-500 words is a guideline not a rule. There are no penalties for exceeding this guideline.

There are no marks for adding a reference list. Referencing is optional.

Question 2 -

Using the data set assigned to you for your Assignments address the following research question:

Does myopia status predict sedentary hours per week after correcting for age in the population of young adult Australians?

Notes:

You should only use the variables 'sed', 'myopia' and 'age'.

Correcting for 'age' is just including 'age' in the regression model.  When age is in the model all other variables are corrected for it.

Do report the results of your descriptive analyses

• Well labelled graphs can be copied from R Commander
• Summary statistics and tables should be manually typed
• Summarise the main findings of your descriptive analyses in words and describe how these findings inform your expectations and interpretation of the more complex models.

Do report the results of your statistical inference and/or regression models

• Any fitted regression models should be manually typed and described in the text.
• Any hypothesis tests should contain all relevant information (use the 5 step method to be sure)
• Any other results such as confidence intervals should be manually typed and described in the text.
• Summarise the main findings of your regression model and statistical inference in one or two paragraphs.

Do remember to answer the research question

Write a final paragraph which summarises the key findings of your analysis and your answer to the research question.

Do check the Learning Guide for the marking criteria

Do write your answers yourself and keep them private.

Description of your data set -

All entry level government employees were asked to complete a health and lifestyle survey. Among many variables, the survey measured both the physical activity levels of these young people and whether or not they were short sighted (myopia). Researchers interested in the relationship between physical activity levels and myopia extracted a data set consisting of 349 randomly selected survey participants (individuals) and 7 measurements (variables).

The variables are:

myopia - myopia status of respondent (coded as "myopia" or "normal")

sex - gender of respondent (coded as "male" or "female")

age - age in years of respondent

educ - Highest education level achieved (coded as "completed tertiary", "completed secondary" or "less").

MVPA - the respondent's self-reported hours of moderate to vigorous physical activity per week.

logMVPA - logarithm transformed MVPA (the logarithm transformation is used to produce a variable containing the same information as MVPA but with a more symmetric distribution).

sed - the respondents self-reported sedentary hours per week

For all analyses please assume that this data file presents measurements from a random sample of 349 young adult Australians. (But please remember that this is a completely artificial data file. It does not reflect any real world measurements or associations.)

Within R Commander the data set is called 'shortsight'. There are 349 lines of data. An 8th column has been added to the data set which shows your student number. If this is not your student number, you are working on the wrong data file!

Attachment:- Assignment Files.rar