Statistics Flashcards

Question

What is a randomised controlled trial? What are the advantages and disadvantages?

Answer 1

* Prospective * Sample at risk population, give half treatment, other half = control, after follow up period look at incidence of outcome * Treatment group allocation must be random to prevent confounding problems * Ideally would have a double-blind trial – neither patient nor physician aware of treatment – prevents bias (differential follow-up or care otherwise) * Most convincing evidence of cause-and-effect relationships but expensive, complicated by non-compliance, not always ethical

Answer 2

For any type of analytical study, data can be summarised in a 2X2 table with exposed and not exposed on the left and diseased and not diseased on the top. RD = a/(a+c)- b/(b+d) RR = (a × (b+d))/(b × (a+c)) OR = (a × d)/(b × c) For case-control studies, only OR is meaningful

Answer 3

Non-probability samples: • Convenience (ease of access), snowball (friend of friend), purposive (you choose who) • Probability of being chosen is unknown • Cheaper + easier to implement but unable to generalise, high potential for bias

Answer 4

* Each member of population has equal probability of being selected * Use random mechanism (random number table) * Need complete list of population – time-consuming, does not always achieve best representation - by bad luck sample not evenly spread across all sections of population

Answer 5

* Members of population selected at equal intervals | * Need complete list of population, less precise than SRS but easy to carry out

Answer 6

* Population partitioned into strata and sample selected from within each stratum (by SRS) – should be low within-strata variability * Requires sampling frame, prior info about population being sampled, costly but increases representativeness and more statistically precise

Answer 7

* Population partitioned into clusters and sample selected (by SRS) – all people in selected clusters are included in sample, should be low between-groups variability * Don’t need complete list (just list of clusters), cheap but decreases statistical precision

Answer 8

Inferential statistics uses sample data to draw inferences about population represented 2 approaches: confidence intervals (estimate range of plausible values for population parameter) + tests of significance (assess degree of uncertainty in evidence using a p-value)

Answer 9

Chance evidence is due to variability • Different samples produce different estimates of effects • Sampling error 2 types of variability: • Within sample: measured using standard deviation (learn equation) • Between samples: measured using standard error (learn equation)

Answer 10

‘Normal reference range’ in clinical practice: ± 2 SDs Normal distribution not normally encountered – few biological variables conform, most medically important variables are not symmetrical

Answer 11

Had we repeatedly sampled a population + calculated the sample means, the sampling distribution of those means would be approx normal

Answer 12

SE is low when sample is large + variability in data is low | Lower SE leads to lower uncertainty of mean + better precision of mean

Answer 13

More confidence --> wider interval --> less precision 95% CI: for every 100 studies performed, we expect 5 to produce an interval that does not contain the true population value, 95% of the time the CI would include the true population parameter CI for difference: if interval excludes zero, effect is statistically significant CI for ratio: if interval excludes 1, effect is statistically significant

Answer 14

statement being tested – statement of no difference, no association, no effect

Answer 15

P-value is probability of an effect as large/ larger than observed effect in sample, assuming null hypothesis is true • Small p-value: sample results unlikely when null hypothesis is true – data contradicts, reject null hypothesis – statistically significant effect o <0.05 is often accepted as statistically significant • Large p-value: sample results likely when null hypothesis is true – cannot reject null hypothesis – inconclusive Statistical significance does not necessarily mean that effect is clinically significant – needs to be big enough to make worthwhile difference

Answer 16

Multivariable regression analysis = valuable tool for diagnostic, prognostic + aetiognositic research problems: • Diagnosis: if disease is present • Prognosis: future course of patient’s current standing + how this depends on choice of intervention • Aetiology: factors that cause disease Applications of Regression: • Developing model for risk prediction of a clinical outcome • Isolating effect of single variable on clinical outcome • Identifying multiple important predictors of a clinical outcome and how they jointly affect outcome • Covariate adjustment to improve efficiency in RCTs Crude (unadjusted) does not take into account effect of confounding variables Adjusted accounts for confounding variable – generated using multivariate regression analysis • May still be residual confounding ``` Regression relates 2 kinds of variables: • Outcome (or response) variable o Eg BP, 90 day mortality • Explanatory variables (or predictors) o Eg age, comorbid conditions ```

Answer 17

Model: Linear Regression Outcome: Continuous What is modelled?: Mean Measure of effect: Mean Difference Model: Logistic Regression Outcome: Binary What is modelled? : Log (odds) Measure of effect: Odds Ratio Model: Poisson Regression Outcome: Binary (count data) What is modelled?: Log (incidence rate) Measure of effect: Incidence rate ratio (IRR) Model: Cox Regression Outcome: Time to event What is modelled?: Log ( Hazard Rate) Measure of effect: Hazard ratio (HR)

Answer 18

Simple as only one ‘X’ variable (how does outcome Y change with X) Look for correlation – linear relationship / = positive, \ = negative, --- = no relationship Mean y = a + b x Mean y = outcome variable A = intercept B = slope (∆y/∆x) X = predictor variable

Answer 19

Mean y = a + b1X1 + b2X2 + b3X3 …. P-values included in table to show which predictors have no statistically significant effect on birth weight • P > 0.05 = not significant Standard errors can be used to calculate confidence interval for the b coefficients: • b ± 1.96 SE • b coefficients quantify effect of each predictor on__________, adjusting for all other predictors o eg study looking at mean birth weight values: b = 9 for mother’s height: increase of 1 cm in mother’s height is expected to produce an average increase in birth weight of 9g o -ve coefficient means a decrease

Answer 20

= random error (imprecision) that produces different observations for replicate experiments or repeated samples Controlling: • Prevention: sufficiently large sample size • Detection + evaluation: confidence intervals, p-values

Answer 21

= any systematic error in the design/ conduct of research resulting in an estimated effect which is different from the truth, 2 types: • Selection bias: participants (inc factors affecting recruitment/ retention of subjects) • Information bias: information get from participants (eg due to improperly calibrated measuring device, recall or interviewer bias) Controlling: • Before study: appropriate selection of study participants + correct data measurements + definitions • After study: assess how well this was done Good precision: SE small, all results close together Good accuracy: bias low, all results in expected range

Answer 22

= distortion in measure of effect as other variables (that will have an effect) are not controlled for Confounding pulls observed (crude) effect away from the true effect – can over- or under-estimate the truth Controlling • Before study: randomise (comparable groups), restrict (eliminates confounders – ie only use one gender in study) + matching (control group that resembles case group with respect to confounders) • After study: stratified analysis, regression modelling, adjusted effect measures Criteria for confounding: • Confounder must be associated with the outcome • Confounder must be associated with the exposure • Confounder cannot be an intervening variable between exposure + outcome Effect modifier has different effects across strata (statistically significant differences) – belongs to nature so is useful

Answer 23

Strength of association • How far from null value measure of effect is Temporal sequence • Did exposure precede outcome? Consistency of effect • Has the effect been seen by others? Is study reproducible in different settings? Dose-response relation • Does increased exposure result in greater effect? Biological plausibility • Does association make biological sense? Experimental evidence • Has an RCT been done?