HRSS SEM 2

Question 1

Cross-sectional study design

Answer 1

Observational or descriptive
Collects data from a population at 1 specific point in time
Groups determined by existing differences, not random allocation

Question 2

Advantages of cross-sectional study design (5)

Answer 2

• Snapshot of a population at one time
• Can draw inferences from existing relationships or differences
• Large numbers of subjects
• Relatively inexpensive
• Can generate odds ratio, absolute/relative risk and prevalence

Question 3

Disadvantages of cross-sectional study designs

Answer 3

• Results are static: no sequence of events
• Doesn’t randomly sample
• Can’t establish cause and effect relationship

Question 4

Pearson’s correlation co-efficient

Answer 4

Measures linear relationship between 2 variables
P=0 suggests no linear relationship
Coefficients offer crude linear association and are unable to adjust for other variables

Question 5

Regression modelling

Answer 5

Investigates if an association exists between variables of interest
Measures strength and direction of association between variables
Studies the form of relationships

Question 6

How are continuous linear relationships examined

Answer 6

By linear or non-linear regression models

Question 7

How are categorial outcomes examined

Answer 7

By logistic regression

Question 8

Describe Linear Regression

Answer 8

Ho = no relationship between DV and IV

IV and DV must be continuous; IV can be continuous or categorical

Question 9

Assumptions for linear regression

Answer 9

Linear relationship between DV and iV
Observation independently and randomly selected
Effects are additive
Homogeneity of variances
Residuals are independent + normally distributed
Absence of outliers and multi-collinearity

Question 10

Describe skewness and kurtosis, mean/median/mode for a normally distributed variable

Answer 10

both = 0
- the further the value is from 0 the more likely it is that the variable isn’t normally distributed
mean median and mode should be equal for a normally distributed variable

Question 11

Tests of normality

Answer 11

Tests of normality (Shapiro-Wilk) compare the shape of the sample distribution to the shape of a normally distributed curve

• non significant tests suggest distribution of sample isn’t sig different from normal distribution
• significant tests suggest distribution in question is sig different from a normal distribution

Question 12

Multicollinearity

Answer 12

Refers to IV’s that are correlated with other IV’s

In presence of multi-collinearity, regression models may not give valid estimates of individual predictors

Question 13

Variance inflation factor (VIF)

Answer 13

Measure of how much the variance of the estimated regression coefficient is inflated by the existence of correlation among IV’s in the model
VIF = 1 : no correlation among predictors
VIF >4 : warrants further investigation
VIF > 10: signs of serious multicollinearity

Question 14

In a simple linear regression model if B > 0 …

Answer 14

Positive association between IV and DV

For each unit increase in IV, the DV would increase by (B) value units.

Question 15

Building a regression model

Answer 15

If IV associated with outcome (DV) and no affected by multi-collinearity, then can build multivariable multiple linear regression model for DV
Fitted regression model presents regression coefficients representing adjusted associations between DV and IV; adjusted for each other

Question 16

Interpreting a regression model

Answer 16

For each unit increase in IV, the estimated DV unit would increase by (B) value, after adjusting for other IV’s
May change association

Question 17

R^2

Answer 17

% of variability explained by fitted model

Question 18

Observational studies

Answer 18

Subjects observed in natural state; can be measure and tested by no intervention or treatment

Question 19

Cohort longitudinal studies

Answer 19

Population of subjects identified by common link
Researcher can follow across time to see what happens
Useful for establishing natural Hx of a condition
Cohort can be divided at outset into subgroups of people whose experience is to be compared
Can identify those most likely to develop outcome

Question 20

Internal comparison

Answer 20

1 cohort involved study

Sub classified and internal comparison done

Question 21

External comparison

Answer 21

> 1 cohort in study for purpose of comparison

Question 22

Strengths of cohort studies

Answer 22

Can give incidence rate and risk
Cause-Effect
Good when exposure is rare
Minimises selection and information bias

Question 23

Weaknesses

Answer 23

Loss to follow up
Requires large sample
Ineffective for rare disease
Time-consuming and expensive

Question 24

Chi Square Test (+ assumption)

Answer 24

Determines if there is a significant relationship between 2 categorial variables
Assumption: expected frequency in each cell >5

Question 25

Fisher's exact test

Answer 25

Used if Chi Square Test doesn't meet assumption (i.e. cells have frequency < 5)

Question 26

Independent samples t-test

Answer 26

Used to compare means of a normally distributed continuous variable for 2 groups

Question 27

Odds Ratio

Answer 27

Measure of strength of association between exposure and outcome. Represents odds an outcome will occur given a particular exposure; compared to outcome occurring in absence of exposure OR = 1: exposure doesn't affect odds of outcome OR = >1 : exposure associated with higher odds of outcome OR = <1 : exposure associated with lower odds of outcome

Question 28

Confidence Intervals and OR

Answer 28

If CI of OR contains 1 (null value of OR) the relationship is likely to be insignificant If CI of OR doesn't contain 1 the relationship is likely to be significant

Question 29

Logistic Regression

Answer 29

A regression w an outcome variable that is categorical and IV that can be a mix of continuous/categorical Predicts which of the possible events are going to happen given certain other information on IV Identifies factors that determine whether an individual is likely to benefit from a certain type of rehab program/outcome

Question 30

Logistic Regression Assumptions

Answer 30

Ration of cases to variables (enough responses in a given category) Linearity in the logic (regression equation should have a linear relationship w the logic form of the outcome) Absence of multicollinearity and outliers Independence of results

Question 31

Logistic Regression Models

Answer 31

Dichotomous outcome - Binary LR Polychromous outcome - multinomial LR Ordered outcome - ordinal logistic regression

Question 32

What test to examine relationship between outcome variable and DV

Answer 32

Chi Square test Fishers test T-Test

Question 33

LR - Omnibus Test

Answer 33

Asks if null model is an improvement | If p<0.05 then fitted model (new model) as a whole fits significantly better than a null model w/o a predictor

Question 34

LR model summary

Answer 34

- 2 log likelihood suggests new model > null model as in a better fit - Nagelkerke R^2 value shows how much variation is explained by new model

Question 35

Goodness of fit test

Answer 35

Used to examine if estimated LR model fits sample data P<0.05 = poor fit p>0.05 = good fit

Question 36

RCT

Answer 36

Individuals allocated at random to receive one of number of interventions (min 2) = chance of allocation to each intervention NOT determined by researched, NOT predictable

Question 37

Random allocation

Answer 37

Eliminates bias Allows researchers to make casual inferences - randomisation ensures any difference between groups is due to chance Covariates are distributed across groups at baseline = unbiased distribution of confounders

Question 38

Sources of bias in RCT

Answer 38

Selection bias - inadequate concealment of allocation/incorrect generation of randomisation sequence Performance bias - inadequate blinding/masking Detection bias Attrition bias Reporting bias

Question 39

How are sample and effect size related

Answer 39

Sample size inversely associated with effect size

Question 40

Intention to treat analysis

Answer 40

Compares treatment groups as originally allocated, irrespective of whether patients received or adhered to treatment protocol Promotes external validity

Question 41

Per protocol analysis

Answer 41

Compares treatment groups as originally allocated but includes only those patients who completed treatment protocol which compromises internal validity

Question 42

Longitudinal Data Analysis

Answer 42

Assesses change in response variable over time, measures temporal patterns of response to Rx, identifies factors that influence change 1. Mixed effects model - compares individual change over time 2. Marginal mode (GEE) - compares population over time, evaluates interventions/informs public policy

Question 43

Generalised Estimating Equations (GEE)

Answer 43

GEE an extension of general linear model of statistical regression for modelling clustered/correlated data Offers robust estimate of standard errors to allow for clustering of observations Produces consistent estimates of regression coefficients and their standard errors Can deal with normal and non-normal outcome data If GEE is significant, two interventions are significantly different

Question 44

Assumptions of GEE

Answer 44

Assumptions of general linear model Responses from known family of distribution with specified mean and variance where variance is function of mean Mean is a linear function of predictors A correlation structure for the responses must be specified Any missing data are either missing completely at random or data is missing at random

Question 45

Sources of variation

Answer 45

In LDA the unexplained variation is divided into components, making the ultimate error variance smaller LDA decreases unexplained variability in response - provides better estimates of effect

Question 46

Post estimation distribution of residuals

Answer 46

Insignificant p value supports normality of residuals, no significant difference between residuals