Exam 2018 Flashcards
what level in the NHMRC evidence hierarchy are cross sectional studies?
IV
What are some other names for a cross sectional study?
Also known as a cross-sectional analysis, transversal study, or prevalence study
What kind of study is a cross sectional study and what does it do?
- Is observational
• Is descriptive
• Collects data from a population at one specific point in time
How are the groups determined in a cross sectional study?
• Groups determined by existing differences, not random allocation
What are the advantages of a cross sectional study?
- ‘Snapshot’ of a population at one point in time
- Can draw inferences from
existing relationships or differences - Can use large numbers of subjects
- Relatively inexpensive
- Can generate odds ratio, absolute risk, relative risk, and prevalence
6 Could combine finding with other research to develop a hypothesis about why the prevalence of certain disease increases with “factor”
What are the disadvantages of a cross sectional study?
- Results are static (time bound). No indication of a sequence of events or historical or temporal contexts
- Does not randomly sample
- Cannot establish cause and effect relationships
What are the ethical considerations of cross sectional research?
Must promote
• aims of research (knowledge, truth, and avoidance of error)
• values that are essential to collaborative work (trust, accountability, mutual respect, and fairness)
• public support for research
• moral and social values (social responsibility, human rights, animal welfare, compliance with the law, and public health and safety)
What are the 14 CASP questions for a cross sectional study?
- Did the study address a clearly focused issue?
- Did the authors use an appropriate study design to answer their
question? - Were the subjects recruited in an acceptable way?
- Were the measures accurately measured to reduce bias?
- Were the data collected in a way that addressed the research aims?
- Did the study have enough participants to minimize the play of chance?
- Have the correct statistical methods been selected? Are they clearly described with rationales?
- Was the data analysis sufficiently rigorous?
- Have the authors taken account of the confounding factors in the
design and /or analysis phase? - How are the results presented and what was the main result?
- How precise was the result?
- Is there a clear statement of findings?
- Can the results be applied to the local population?
- Howvaluableistheresearch?
What are the five P’s?
Population Problem Prevalence Pos/Neg Clinical implications Proposal
What does Pearson’s correlation coefficient [Rho-ρ] measure?
linear relationship between two variables with ρ=0 suggesting ‘no
linear’ relationship [may have non-linear relationships?]
What do Pearson’s product–moment correlation analyses measure?
Whether the continuous outcome variables were associated with the set of independent variables
What is the problem with Pearson’s correlation coefficient and Pearson’s product-moment correlation?
They offer crude linear associations and unable to adjust for other variables, so need multiple linear regression
What is the purpose of regression modelling?
• To investigate whether an association exists between the variables of interest
• To measure the strength (as well as direction) of an association between the variables
• To study the form of relationships
For a continuous outcome, relationships can be examined by linear or non-linear regression models
For a categorical outcome, logistic regression is usually used to examine possible relationships
Consider a linear model with a positive slope: Y=3+2X
What would it mean If X=0, Y= 3 + 2 (0) = 3
►1 unit increase in X results in 2 units increase in Y
►A positive slope (+2) implies upward slopping line and a positive association
Consider another linear model with a negative slope: Y=3-2X
What would it mean If X=0, Y= 3 - 2 (0) = 3
►1 unit increase in X results in 2 units decrease in Y
►A negative slope (-2) implies downward slopping line and a negative association
What are the considerations for Linear regression?
- The response or outcome variable [DV] must be continuous (e.g. weight, balance measure)
- The independent variables can be categorical or continuous, or a combination of both
• In linear regression, we test the null hypothesis of no relationship between the DV and the IV. If β represents regression coefficient of the DV and the IV:
H0: β = 0
Ha: β ≠ 0 [two-sided]
• Alternative hypothesis can be one-sided (Ha: β>0 or Ha: β<0), depending on the research question.
What are the assumptions for Linear Regression?
- The relationship between DV and IVs is linear
- The observations are independent and randomly selected
- Homogeneity of variances – constant variance
- The residuals (differences between observed and predicted observations) are independent and normally distributed
- The effects are additive
- Absence of outliers and multi-collinearity
What are some steps to take before examining the data for relationships?
- Descriptive statistics of all variables
- Distribution of outcome variables using histogram, quantile-quantile (QQ) plot, normality tests
- Scatter plot to examine linearity
- Collinearity diagnostics
- Appropriate transformation for normality if an outcome variable is not normally distributed
- Use median, range, inter-quartile range to summarize non-normal data
If a variable had skewness=0 & kurtosis=0, what would it’s distribution be?
Normal
• The further the value is from zero, the more likely it is that the variable is not normally distributed.
How will the data be skewed if If the mean > median -
positively skewed
How will the data be skewed if If the mean < median -
negatively skewed
What would it mean if mean, median and mode were all equal?
Normally distributed variable
What does a positive value of skewness indicate?
A pile-up of scores on the left side of the distribution (positively skewed).
What does a positive value for kurtosis indicate?
A pointy and heavy tailed distribution.