PHCA9523 Introductory Biostatistics Assignment

QUESTION 1 DATA PREPARATION & VISULAIZATION (20 MARKS)

A cross-sectional study on Global AGEing and Adult Health (SAGE) is published in Sleep Health 6 (2020) 92-99 (https://doi.org/10.1016/j.sleh.2019.08.009) to investigate the association of sleep characteristics with suicidal ideation and suicide attempt among middle-aged and older adults with depressive symptoms in five low- and middle-income countries. The article is uploaded in the FLO site under Report.

The suicide attempt item asked, during this period, did you ever try to end your life? This variable is the main outcome variable and response options were 1=yes or 0=no for this item. Participants were asked to rate their sleep quality for the previous two nights with response options very good, good, moderate, poor, and very poor. We recategorized participants reporting sleep quality into two categories 1=poor/very poor or 0=good/very good/moderate sleep quality. For sleep length, participants were asked how many hours they slept the previous two nights. They provided responses in hours and minutes and calculated the mean duration of sleep hours

across the two nights. SAGE interviewers reported participants’ sex (1=male or 2=female) and age in single years. The SAGE dataset (SAGE.xlsx) is currently located in the FLO site.

1. Utilizea suitable R function to import the xlsx data file into the R environment.
2. Applya relevant data transformation function to select three variables: gender, age, and sleeplength, and save these variables into a new data file named "SAGE1.xlsx".
3. Analyseand interpret the insights gained from each of the diagrams, including:
   • Age: Construct a histogram to display the distribution of ages. Examine theshape of the histogram and discuss any notable patterns or trends
   • Gender: Create a basic bar chart to represent the distribution of males and Evaluate the proportions of males and females and provide an interpretation of the results.
   • Sleep length by gender: Generate a box plot to compare the sleep durationbetween genders. Analyse the differences in the median, quartiles, and outliers, and discuss any gender-related disparities in sleep

QUESTION 2 HYPOTHESIS TESTS (20 MARKS)

The retrospective cohort study identified pregnant women with a BMI >30kg/m2 recorded at their first antenatal visit, from within the ObstetriX and eMaternity databases from January 2013 to December 2017, within the Northern Sydney Local Health District in Sydney, NSW, Australia. The neonatal outcomes by obesity classes are provided in the following table. The data “neonata.xlsx” is currently available in the FLO site. The empty table was generated from the article “Obesity class impacts adverse maternal and neonatal outcomes independent of diabetes” through the following link https://www.frontiersin.org/articles/10.3389/fendo.2022.832678/full. The article is also available in the FLO.

1. Utilisea suitable R function to compute the mean and standard deviation of birthweight for the entire group of women and within different BMI obesity Conduct a relevant hypothesis test to determine the p-value.
2. Use an appropriate R function to calculate the median and the 25th-75th percentile ofgestational age for the overall group of women and within various BMI obesity Carry out a suitable hypothesis test to ascertain the p-value.
3. Utilisea suitable R function to compute the counts and percentages of each characteristic, namely LGA (large for gestational age), Hypoglycaemia, and NICU admission at Birth, for the entire group of women and within each BMI obesity Perform an appropriate hypothesis test to calculate the p-value for each characteristic.
4. Based on the obtained results, write a paragraph from the findings whether there is asignificantassociation between the BMI obesity groups and neonatal

QUESTION 3 CORRELATION & REGRESSION (20 MARKS)

A cross-sectional study on Global AGEing and Adult Health (SAGE) is published in Sleep Health 6 (2020) 92-99 (https://doi.org/10.1016/j.sleh.2019.08.009) to investigate the association of sleep characteristics with suicidal ideation and suicide attempt among middle-aged and older adults with depressive symptoms in five low- and middle-income countries. The article is uploaded in the FLO site under Report.

The age of participants was recorded as singles years of age. For sleep length, participants were asked how many hours they slept the previous two nights. They provided responses in hours and minutes and calculated the mean duration of sleep hours across the two nights. The SAGE dataset is currently located in the FLO site.

Interrogate the dataset provided to answer the following question:

1. Develop a statistical analysis plan (SAP) to examine the relationship between age andsleep length. The SAP should encompass the following steps:                                                                                                       (i) visually inspect the data,                                                                                                                                                                                                                                                                                                     (ii) calculate summary statistics,     
   (iii) compute the correlation coefficient,
   (iv) perform regression modelling,
   (v) assess assumptions,
   (vi) determine model coefficients and p-values, and
   (vii) calculate the adjusted R-squared.
2. Execute an analysis in R programming based on the previously defined
3. Examine the findings obtained from the analysis and engage in a thorough Based on this discussion, draw a conclusion that summarizes the key insights derived from the analysis.