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401077 Introduction to Biostatistics Assignment 3 Answer

    401077 Introduction to Biostatistics, Autumn 2019

    Assignment 3

     Due Sunday June 9, 2019

    Please answer each question in the template document provided and submit via Turnitin on or before the due date.  The marks allocated to each question are shown in the assignment. A total of 40 marks are available and this assignment is worth 40% of your overall grade.

    Question 1 (18 marks)

    Read the paper Weston, G., Zilanawala, A., Webb, E., Carvalho, L. A. &McMunn, A. (2019). Long work hours, weekend working and depressive symptoms in men and women: findings from a UK population-based study. J Epidemiology Community Health, 0, 1-10.

    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.

    Note: 

    • Only review the provided paper Weston et al, 2019. Do not read any other papers.  
    • Restrict your review to how well Western 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(22 marks)

    Using R Commander and the data set from the sample of full-time workers in Sydney assigned to you address the following research questions:

    1. By how much do self-reported work hours differ between male and female full-time workers on average in Sydney after correcting for age? (You should address this question using linear regression and include associated descriptive analyses.)
    2. Using the model in a), predict the number of self-reported work hours for 25-year-old male workers. Repeat for 25-year-old female workers.

    Note: To answer this question, you need to use R Commander and the data set assigned to you for assignments. This data set contains the (fictitious) data from a random sample of full-time workers in Sydney, Australia and is the same data set as that you have previously used in Assignments 1 and 2. See ‘Description of your data set.docx’ for the descriptions of the variables.

    Note: This assignment is assessing your skills, not the skills of the computer. You will need to include graphs from R Commander into your assignment but all other R Commander output will attract 0 marks and is discouraged. It is your task to identify the relevant results in the R Commander output and write these up in your assignment.

    Also note: 

    • You should only use the variables ‘work’, ‘sex’ 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.
    • Documenting your analysis plan is recommended but not required. (If your analysis report is complete then your plan must have been complete also.)
    • 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 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 questions
      • Write a final paragraph which summarises the key findings of your analysis and your answers to the research question.
    • Do check the Learning Guide for the marking criteria
    • Do write your answers yourself and keep them private.

    Answer

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    Question 1:

    Provide your appraisal of the strengths and weaknesses of the presentation of the statistical material in Weston et al (2019) against items 10, 12-17 of STROBE (about 400-500 words, 18 marks)

    Answer 1: Item 10 – Seperated/divorced/widowed: Marital status impacts a lot on how long a person works. We can see that if they are married then they work for extra hours as compared to when they are separated. We can also see that if Men are separated/divorced/widowed it impacts on how long they work in weekday as well as on weekends. The numbers tell us that they work less than women on weekdays and weekends which results in depression of more men than women.

    Item 12: Children None: If a person doesn’t have any children then their working hours are above average and they put in extra working hours on weekdays as well as on weekends and we can relate it with the study and say that in this category women working more than 55 hour/week are more depressed than men.

    Item 13: Children 0-4 Years: Children affects the working hours of both men and women but if we only compare them on how long they work given the situation that they have a child at home, we can see that men give extra hours on weekdays and weekends as compared to women wherein if a woman is <35 years then they give extra hours on weekdays as well as weekends but if they are >35 years then they tend to give extra hours on weekends than on weekdays.

    Item 14: Children 5-9 years: Children affects the working hours of both men and women but if we only compare them on how long they work given the situation that they have a child at home, we can see that it’s the opposite of what was in 0-4 years. Men give less hours on weekdays as well as weekends whereas if a woman is <35 years then they give extra hours on weekdays but not on weekends and if they are >35 years then they do not give any extra hours on weekdays or weekends.

    Item 15: Children 10-15 years: Children affects the working hours of both men and women but if we only compare them on how long they work given the situation that they have a child at home, it is similar to what we understood in 5-9 years, where men don’t give extra hours on weekdays as well as on weekends but women gives long working hours on weekdays as well as weekends. We can say that women work more than men in this category.

    We can relate this with the results that the depression symptoms in women is more than men if women are working for more than 55 hours/week but it is opposite in the case of men where the depression rate is lower if they are working more than 55 hours/week.

    Item 16: Degree: It is seen that those having degree work more than average hours on weekdays as well as weekends compared to those having A level, GSCE or other qualification. This means that the depression rate in the other categories other than Degree holders is more as they don’t have work to do and they sit idle more often or not.

    Question 2: Note: Students will get different answers as the data sets differ.

    Present the findings of your descriptive analyses (graphs, tables and about 100-150 words, 8 marks)

    I have trained two different linear regression models to predict workhours from age. First model is trained on data of male workers and second is trained on data of female workers.

    For linear regression model for male, size of train and test set is:

    • Train: 200 samples 
    • Test: 66 samples

    For linear regression model for female, size of train and test set is:

    • Train: 200 samples 
    • Test: 32 samples

    The average work hours mentioned bellow are noted for test data, as evaluating model in train data is not a good idea.

    Graph of Linear regression model for male:

    Graph of Linear regression model for male

    Graph of Linear regression model for female:

    linear regression for female

    Present the findings of relevant regression models and inferential analyses (about 150-200 words, 10 marks)

    Coefficients of linear regression model for males:

    Intercept = 43.289958, age coefficient = - 0.008368

    Coefficients of linear regression model for females:

    Intercept = 37.01796, age coefficient = - 0.00235

    Conclusions: 

    1. On an average, work hours for female are slightly lesser than male. The difference in work-hours for male and females is around 6 hours.
    2. Generally, we can see from the model coefficients that ‘as age increases, work hour tends to decrease’.
    3. As the age co-efficient for linear regression model for male is - 0.008368 and for female is - 0.00235, the rate of decrease in work hours wrt increase in age is slightly higher for male.
    4. Average Residual of Linear regression model for male is -1.157838e-16, and average residual for female is 1.762718e-16. This means that generally Linear regression model for male is slightly overestimating the work hours for male, whereas Linear regression model for female is slightly underestimating the work hours for female.

    Provide your answer to the research question (40-80 words, 4 marks)

    A.

    1. Average predicted self-reported work hours after correcting for age for male = 42.94688
    2. Average predicted self-reported work hours after correcting for age for female = 36.90736

    So, difference between male and female full-time workers on average in Sydney after correcting for age is 6.03952.

    B.

    1. Self-reported work hours for 25-year-old male workers: 43.08076
    2. Self-reported work hours for 25-year-old female workers: 36.95921

    References, optional (0 marks)

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