CAM625 Module 3 Assignment
\nInterpretation of statistical results
\nInstructions
\nYour submission should consist of a word document addressing the questions set out below. The number of words suggested next to each part below is guide for approximately what we expect. They are not strict limits, but you should be brief and your responses should be clear and concise. We expect short answers rather than an essay. This assignment is worth 10% of your final mark.
\nPlease do not discuss (neither personally nor on the discussion board) specific findings or results from this assignment. Weanticipatethatyoumayhavesomequeriesbutpleasetrytophraseyourquestionscarefully when posting to the discussion board. If in doubt contact us both privately by email:
\nPetr.Otahal@utas.edu.au and Karen.Wills@utas.edu.au
\nDue Date The Module 3 Assignment is due for submission in the MyLO Dropbox at 5pm on Sunday 25th September. The Final Deadline for submission is 12pm Thursday 29th September.
\nLow Birth Weight Data set
\nThe dataset we will be using for this assessment task is the Low Birth Weight data collected at Baystate Medical Center, Springfield, Massachusetts during 1986, and used as an example of fitting a multiple logistic regression model in the text: Applied Logistic Regression: Third Edition by Hosmer, D.W., Lemeshow, S. and Sturdivant, R.X. (2013).
\nStory behind the data
\nLow birth weight is an outcome that has been of concern to physicians for years. This is due to the fact that infant mortality rates and birth defect rates are very high for low birth weight babies. A woman\u2019s behavior during pregnancy (including diet, smoking habits, and receiving prenatal care) can greatly alter the chances of carrying the baby to term and, consequently, of delivering a baby of normal birth weight.
\nThe study variables have been shown to be associated with low birth weight in the obstetrical literature. The goal of the study was to ascertain if these variables were important in the population being served by the medical center where the data were collected.
\nList of variables
\nID – Identification Code
\nLOW – Low Birth Weight (0 = Birth Weight = 2500g, 1 = Birth Weight 2500g) low_f – Factor variable for LOW (No = Birth Weight = 2500g, Yes = Birth Weight 2500g)
\nAGE – Age of the Mother in Years
\nLWT – Mother\u2019s Weight in Pounds at the Last Menstrual Period
\nRACE – Race (1 = White, 2 = Black, 3 = Other)
\nSMOKE – Smoking Status During Pregnancy (0 = No, 1 = Yes)
\nsmoke_f – Factor variable for SMOKE (No\/Yes) PTL – History of Premature Labor (0 = None 1 = One, etc.) ptl_f – Factor variable for PTL recoded as a binary variable (No\/Yes)
\nHT – History of Hypertension (0 = No, 1 = Yes) ht_f – Factor variable for HT (No\/Yes)
\nUI – Presence of Uterine Irritability (0 = No, 1 = Yes)
\nui_f – Factor variable for UI (No\/Yes)
\nFTV – Number of Physician Visits During the First Trimester, (0 = None, 1 = One, 2 = Two, etc.) BWT Birth Weight in Grams
\nAssignment overview
\nThis assignment is about the interpretation of statistical results.
\nFor the assignment we will consider the associations of low birth weight with mother\u2019s history of hypertension, smoking status during pregnancy, age and mother\u2019s weight at last menstrual period. You will not be required to perform any analysis; the analyses have been done and your task is to interpret the results. The assignment will focus on one key relationship under investigation, however you will be also be required to interpret some other associations that may be of interest.
\nOutcome variable The outcome variable to be used for the Module 3 Learning Activity and Assignment is BWT, the continuous variable for birth weight in grams.
\nExposure variables The primary exposure of interest is ht_f, the factor variable coding for history of hypertension.
\nConfounders Potential confounders of the association between birth weight and hypertension are:
\n– smoke_f, smoking status during pregnancy
\n– AGE, Mother\u2019s age in years,
\n– LWT, Mother\u2019s Weight in pounds at the last menstrual period.
\nAssignment Tasks
\n1. Summary statistics (100-200 words)
\nExamine the participant characteristics in Table 1 which are stratified by the binary variable ht_f, indicating whether the individual has a history of hypertension. Provide a brief summary for each of the variables describing the differences or similarities based on hypertension status. Use only the data provided, do not run any statistical tests.
\nTable 1: Participant characteristics stratified by history of hypertension<\/p>\n
Total No Yes
\n(N=189) (N=177) (N=12)
\nAge (years) Mean (SD) 23.2 (5.30) 23.3 (5.36) 22.9 (4.44)
\nSmoking status during pregnancy No 115 (61 %) 108 (61 %) 7 (58 %)
\nYes 74 (39 %) 69 (39 %) 5 (42 %)
\nHistory of preterm labour No 159 (84 %) 149 (84 %) 10 (83 %)
\nYes 30 (16 %) 28 (16 %) 2 (17 %)
\nBirth weight (grams)
\nMean (SD) 2945 (729) 2972 (709) 2537 (917)
\nPresence of uterine irritability No 161 (85 %) 149 (84 %) 12 (100 %)
\nYes 28 (15 %) 28 (16 %) 0 (0 %)
\nWeight at last menstrual period (pounds)
\nMean (SD) 130 (30.6) 128 (28.4) 158 (47.0)
\n2. Bivariate relationships (100-200 words)
\na) Figures 1 to 3 are graphical displays of the relationships between birth weight (BWT) and:
\n\u2022 ht_f, history of hypertension
\n\u2022 smoke_f, smoking status during pregnancy
\n\u2022 AGE, Mother\u2019s age in years,
\n\u2022 LWT, Mother\u2019s Weight in pounds at the last menstrual period. Briefly describe the patterns you observe from the plots.<\/p>\n
Age (years)
\nFigure 1: Scatter plot of birth weight and mother\u2019s age<\/p>\n
100 150 200 250
\nMother’s weight at last menstrual period (pounds) Figure 2: Scatter plot of birth weight and mother\u2019s weight<\/p>\n
History of hypertension
\nFigure 3: Boxplot for birth weight by history of hypertension<\/p>\n
Current Smoker
\nFigure 4: Boxplot for birth weight by smoking status during pregnancy
\nb) Table 2 displays the correlation coefficients, 95% confidence intervals and p-values for the association of birth weight with mother\u2019s age, AGE and weight at last menstrual period, LWT. Comment on the magnitude of the association indicated by the test statistic, and the statistical significance of the association.
\nTable 2: Associations of birth weight with mother\u2019s age and weight at last menstrual period
\nCorrelation coefficient (r) 95% CI P-value
\nMother\u2019s Age 0.0899 -0.054, 0.23 0.2188
\nWeight at last menstrual period 0.1858 0.044, 0.32 0.0105
\n3. Univariate associations (200-400 words)
\nExamine the results of the regression models for birth weight presented in Table 3 below. The table displays the variable names and the results for the univariable and multivariable regression models, including \u00df coefficients, 95% confidence intervals (CI) and p-values.
\nThe variables included in the models are:
\nOutcome: – Birth weight (BWT)
\nExposure:
\n– History of hypertension coded as a binary factor variable (ht_f) Note: \u201cNo\u201d is the reference category
\nConfounders:
\n– Mother\u2019s age in years (AGE)
\n– Smoking status during pregnancy coded as a binary factor variable (smoke_f)
\n– Mother\u2019s weight in pounds at last menstrual period (LWT)
\nNote: a separate model was fitted for each variable for the univariable results, and a single model containing all four variables was fitted for the multivariable results.
\nPlease refer to the descriptions at the start for assignment for information about each variable to ensure your interpretation is appropriate for the scale of measurement.
\na) Interpret the beta coefficients and 95% confidence intervals from the univariable models for history of hypertension (ht_f), smoking status (smoke_f), mother\u2019s age (AGE) and mother\u2019s weight in pounds at last menstrual period (LWT) .
\nb) For each univariable model, comment on the direction and magnitude of the estimated effect, and include an interpretation of the p-value.
\n4. Multivariable (adjusted) associations (100-200 words)
\na) Interpret the beta coefficients, 95% CIs, and p-values for each variable in the multivariable model in Table 3.
\nb) Compare the coefficients with those in the univariable models and briefly describe what you observe.
\nc) Additionally, comment on the magnitude of the effect of the primary exposure, hypertension, in terms of clinical relevance\/importance.
\nTable 3: Linear regression estimates for low birth weight<\/p>\n
Beta 95% CI P-value Beta 95% CI P-value
\nHypertension -435.6 -858.3, -12.8 0.045 -579.5 -999.7, -159.2 0.008
\nSmoking -281.7 -491.4, -72.1 0.009 -260.9 -464.9, -57.0 0.013
\nAge 12.4 -7.28 , 32.0 0.219 5.50 -13.7 , 24.7 0.574
\nMother\u2019s weight 4.43 1.07 , 7.79 0.010 5.17 1.75 , 8.59 0.003
\n5. Diagnostics (100-200 words)
\nExamine Fig 5 and Fig 6 which show regression diagnostics for the multivariable model.
\na) Comment on the model fit.
\nb) Are there sufficient diagnostics to judge the model fit? Why or why not?
\nResiduals vs Fitted<\/p>\n
2200 2400 2600 2800 3000 3200 3400 3600
\nFitted
\nFigure 5: Residual vs fitted plot for the multivariable model
\n2
\n1
\n0
\n-1
\n-2
\n-3
\nTheoretical Quantiles
\nFigure 6: Q-Q plot for the multivariable model<\/p>\n","protected":false},"excerpt":{"rendered":"
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