Now use univariable logistic regression to estimate the unadjusted odds ratios of death for haemoglobin and all three potential confounders (age, sex, and malari

Repeat parts 3a) & 3b) to investigate whether the association between haemoglobin and the log odds of death is linear. Briefly comment on whether the association is linear and state the null hypothesis being tested here. (5 marks) 4. (Multivariable logistic regression models – Confounding; 14 marks) Now use univariable logistic regression to estimate the unadjusted odds ratios of death for haemoglobin and all three potential confounders (age, sex, and malaria). Then use multivariable logistic regression (including all four variables) to estimate the adjusted odds ratios. Include haemoglobin and age as categorical variables with the following groupings – age (< 3 & ≥ 3 years, with ≥ 3 years as the reference group) and haemoglobin (<9 (low), 9-14 (normal), >14 (high) g/dL, with the haemoglobin group 9- 14 g/dL set as the reference group) [Hint – use ‘gen’ and ‘replace’ commands to create new variables]. a) Present in a table two columns – the unadjusted Odds Ratios (95% Confidence Intervals) and the adjusted Odds Ratios (95% Confidence Intervals) for the association between haemoglobin, age, sex, and malaria and the odds of death. (5 marks) b) Comment on any confounding observed by considering any changes in the odds ratio of haemoglobin (categorical version) from the univariable to the multivariable logistic regression. (3 marks) c) Investigate the confounding by exploring any univariable associations (in the controls only) between haemoglobin and the potential confounders. (3 marks) d) Comment on the associations between the potential confounders and the outcome (after adjusting for the exposure of interest, haemoglobin). Together with what you found in 4c, comment on which variables are confounding the association between haemoglobin and death. (3 marks)