Basic Epi Flashcards

Question

What is non-random sampling?

Answer 1

Non-probabilistic Judgement (purposive) - selection based on personal (expert) belief about representativeness Accessibility (convenience) - select the most easily obtained participants Quota - judgement and accessibility used to achieve specifies group sizes It does not involve random selection May or may not represent the population well Used when researcher lacks a sampling frame for the population These methods may introduce bias

Answer 2

Sample selected based on (subjective) judgement of researcher This is often used It is a type of non-probability sampling You are not attempting to be generalisable Potential for bias is high

Answer 3

Quota sampling - people on the street, widely used in market research, strata corresponding to different characteristics e.g. age, sx, race No sampling frame Problem -> stratum may not be random

Answer 4

Con - selection units up to investigator - high bias Pro - convenient, less expensive than probability sampling

Answer 5

Selection of participants from the population is random. It is any method that utilises some form of random selection. Different units in the population should have a known probability of being chosen. AKA probability sampling Removes the possibility of bias

Answer 6

The elements being sampled (e.g. the postcode rather than the individual)

Answer 7

the list of all units able to be sampled (the list of all postcodes included)

Answer 8

Collection of sampling units drawn from the sampling frame

Answer 9

Estimate = true value + random error + systematic error (bias) Sample size (n) Sampling design Non-response (unit or item) Measurement: - observer or interviewer - participant or respondent - instrument e.g. questionnaire - mode of administration

Answer 10

Accuracy - accuracy is crudely defined as the difference between the population value and sample estimate

Answer 11

How closely repeated measurements or observations come to duplicating measured or observed values

Answer 12

With replacement - the chance of selecting the each unit does not change from selection to selection. The same unit could be selected more than once. Without replacement - the chance of selecting each unit does change from selection. under this strategy the same unit may not be selected more than once.

Answer 13

gold standard everyone has equal property n/N could be 100/100,000 therefore 1/1000 probability to be picked. This is sampling without replacement True SRS can be expensive but has low bias, might be inefficient to do as well

Answer 14

1. obtain a sampling frame that includes all units 2. number each unit 1-N 3. generate n random numbers between 1 and N 4. if any random number selected is greater than N or already selected ignore it 5. select each unit corresponding to the random numbers

Answer 15

may be more convenient than simple process 1. put numbers in a sequence 2. divide the study population size by required sample size to determine interval size 3. choose a random number between 1 and k 4. start at random number and add part 2 each time e.g. n/10,000 - 200/10,000 pick random number for k, 200/k if k = 27, 27+50 = 77, 77+50 = 127

Answer 16

We may want independent results for different sub-population e.g. sex, age or location Simple random sampling are taken for each strata

Answer 17

increases the chance that sample mean is a precise estimate (e.g. less random error) of the population mean because the sample resembles the study population better useful if interested in sub-pops strat samples useful when interested in comparing groups can use different sample designs if different strata administrative convenience

Answer 18

size of the strata in the pop must be known information on strata characterstics must be known in advance a weighted analysis is required if not proportionate to the population distribution of strata

Answer 19

populations may consist of large number of groups e.g. households, schools or hospitals, these can be referred to as clusters we first take a simple random sample of n clusters and include all sample members of the chosen clusters. the sampling unit is the cluster not the individual essentially SRS applied to groups

Answer 20

hard to define cluster less efficient than SRS members of the same cluster tend to be more similar Samples might not be independent: - tends to be less precise - requires a large sample than srs

Answer 21

often cheap and convenient to draw sample only for admin convenience often we do not have info of the individuals only a group e.g. address of household

Answer 22

1. draw up list of all first stage units (called primary sampling units) 2. select random sample of PSUs 3. for each PSU selected draw up a sampling frame of second-stage units 4. select a random sample of the second stage units (SSUs)

Answer 23

can reduce cost if collecting data in person usually less precise, have larger SEs can be extended to 3 or more stages req. specially weighted analysis

Answer 24

relatively cheap and easy, able to cover wide geography, larger poor response rate, can be completed by not the respondent, not possible in some settings, may appear probabilistic - but actually not

Answer 25

high response rate, often greater details given, able to take other non-recorded data e.g. samples cons - v costly, enumerators can influence and bias responses, hard to over come geographical differences

Answer 26

very cheap and easy to collect usually biased, non-probabilistic, difficult to analyse

Answer 27

important to ensure good estimates of population parameters as they reduce bias (systematic error) and measure precision (standard errors). random sample reduces chance of bias. not always possible to do SRS so stratified, cluster and two stage is req. more complex designs tend to increase sample size

Answer 28

Adjusted by age

Answer 29

Direct and indirect

Answer 30

Age-specific death rates from a standard population are applied to the index population - provides a standardised mortality ratio and indirectly standardised rates. It does not require age specific mortality rates of the index population.

Answer 31

age specific rates are taken from the population that we are standardising and applied to a standard population. Provides directly standardised rates. Age specific-rates are a necessity.

Answer 32

Total number of observed deaths / total number of expected deaths x 100 = SMR

Answer 33

1. apply age-specific rates from the standard population and calculate the expected number of deaths 2. sum up number of expected deaths 3. divide the observed deaths in the index population by the number of expected deaths

Answer 34

can only compare the index population with the standard population. An SMR of 100 = similar expected rates >100 = larger mortality rate <100 = lower mortality rate Should include a 95% CI

Answer 35

1. Calculate local population death rates for each category 2. apply the age specific mortality rates from the index population to the standard population to calculate expected number of deaths 3. sum up expected number of deaths for the index population using the standard population age groups 4. calculate overall age standardised mortality rates for the index

Answer 36

1. the direct method requires availability of age-specific rates for the study population of interest 2. the indirect method requires the total number of cases in each population 3. if numbers are small the indirect method is advisable

Answer 37

the study of characteristics of human populations

Answer 38

* Stillbirth – born after 24 or more weeks completed gestation and which did not, at any time, breathe or show signs of life * Perinatal – stillbirths plus early neonatal deaths * Early neonatal: deaths under seven days * Neonatal – deaths at under 28 days * Postneonatal – deaths between 28 days & one year * Infant – deaths under one year * Rates – neonatal, postneonatal and infant mortality rates are reported per 1,000 live births

Answer 39

Outbreak investigations Studying uncommon diseases Studying diseases with long latency Generating and testing hypothesis Quick and cheap to conduct

Answer 40

With the disease

Answer 41

Need a precise case definition - must be objective with a validated test e.g. blood test, histology, X-rays or sonograms It can be classified by diagnostic certainty e.g. confirmed, probable or possible Whilst precise definitions can limit generalisability, it increases validity Often req. inclusion/exclusion criteria (who, what, when, where) Must report on numbers that meet exclusion/inclusion Must decide whether incident or prevalent cases (i.e. all new cases within fixed time period, or taking all new and old ones)

Answer 42

Useful if exposure is associated with recovery or survival Greater representativeness at its timely Disease behaviour change less likely

Answer 43

Pro - can be used when its difficult to establish date of onset e.g. study of H.pylori infection Con - may not be representative of all cases, pts with long course disease tend to be over represented and recall bias

Answer 44

Population based studies try and recruit all from a defined population over a fixed period of time. It requires tracing subjects and has issues with completeness and refusal to participate. Hospital based studies require a clear case definition and protocol adherent to minimise bias as they are more prone to bias (note this won't score a star in some systems)

Answer 45

Controls should be free of disease and representative of the population. They must have the potential to become diseased. They must be from the: - same source population - same inclusion/exclusion criteria - identified as cases if they had the disease while under investigation

Answer 46

Population controls (random from registry/list/directory) Neighbourhood controls Friends/family controls Hospital controls

Answer 47

Can gain a random sample Limitations: can be difficult and time consuming, healthy people may not participate, may be low response rate, selection bias (e.g. if using a register with phone numbers some may not have phone)

Answer 48

Pro No need for population register Controls for social factors e.g. deprivation Con Poor cooperation Can be time inefficient

Answer 49

Pro - no need for pop registers, quick and efficient, easily to control for social factors so may reduce confounding Con - overmatching, selection bias (i.e. family members may be different from gen pop)

Answer 50

Pro - easy to identify, relatively cooperative Cons - overmatching (more likely to be sicker and have higher risk factor exposures), selection bias (catchment population differs for different diseases)

Answer 51

If ample cases can be 1:1 for sufficient power If rare outcomes can go 1:1 - 1:4 , >1:4 is a waste of resources as minimal increase in statistical power

Answer 52

Study design methods - randomisation (can't do this one) - restriction (can be done) - matching (can be done, risk of over-matching) Analysis methods - stratification - multivariable analysis

Answer 53

Matching is too close or elaborate. Controls become difficult to find and may fail to find a true causal association or may underestimate the association.

Answer 54

Odds ratio is simply an association, with no indication of temporality. Incidence of disease is unknown. Relative risk is usually interpreted as the risk of having outcome of interest if you have the exposure i.e. if temporality is indicated. Odds tend to approximate OR. If the prevalence of the disease is <10%, the relative risk and OR can approximate each other.

Answer 55

measurement bias (observer, responder and limitations of instruments) and selection bias (selection of cases and controls)

Answer 56

Good for study for rare diseases Can use small sample size Makes use of available data Rapid Low cost Suitable for diseases with long latency Can examine multiple exposures for a single disease

Answer 57

Cannot directly measure relative risk Not suitable for rare exposure Temporal relationship exposure-disease difficult to establish Prone to several biases - selection of controls, recall when collecting data Loss of precision due to sampling

Answer 58

an observational study which follows up two or more groups of people from exposure to outcome. A simple cohort study has an exposed or unexposed group. Participants that develop the outcome are recorded and rates in the two groups compared.

Answer 59

free of disease at the start of the study and at risk of the outcome being studied. for common exposures, cohort may be drawn from gen pop. for rare exposures, a specific group may be more suitable

Answer 60

prospective cohort studies - start before outcome has occurred. cohort is disease free at the start of follow up. data is captured prospectively retrospective (historical) cohort studies - start after outcome has occurred. relies on medical records or routinely collected data. data on measurements, exposure and outcome are collected retrospectively after the events have happened

Answer 61

the probability that an event will occur

Answer 62

in RCTs and cohort studies

Answer 63

the risk of a particular outcome (Disease) when a particular exposure (risk factor) is present

Answer 64

absolute risk

Answer 65

I(subscore)e

Answer 66

Incidence in p - incidence in unexposed / incidence in the whole population

Answer 67

To assess the extra disease incidence in the whole study population that can be attributed to the exposure we can use measures of population impact

Answer 68

measures strength of association between exposure and outcome can be generalised to other populations also known as risk ratio and rate ratio

Answer 69

measure the impact of an association i.e. number of cases that could be prevented by eliminating the exposure relies on baseline incidence in the unexposed which vary in different populations, therefore cannot be generalised also known as excess risk, risk difference, rate difference, attributable risk

Answer 70

to measure incidence find aetiology quantify risk factors describe prognosis evaluate treatment outcome

Answer 71

can directly measure: incidence in exposed and unexposed groups, true relative risk temporal relationship between exposure and disease is clear can examine multiple effects for a single exposure less prone to selection biases (outcome not known - prospective)

Answer 72

require large sample size not suitable for rare disease not suitable for disease with long latency problems with losses to follow up can be difficult to measure multiple exposure exposures may change over time time-consuming and costly

Answer 73

in survival analysis, usually cancer trials outcomes can be negative or positive (e.g. relapse vs remission, disability vs disease-free, death vs cure)

Answer 74

an instantaneous event rate the probability that a person will experience an event at a specific point in time (rather than cumulatively)

Answer 75

the effect of a particular intervention on a particular outcome (negative or positive) per unit time

Answer 76

time to event curve

Answer 77

to see whether a treatment shortens an illness duration relative risk of a complication in treatment vs control types of individuals more likely to experience an event first

Answer 78

1. ask 2. acquire 3. appraise 4. apply 5. evaluate

Answer 79

Does this study address a clearly focused question? (PICO) Did the study use valid methods to address the question? Are the valid results of this study important? Are these valid and important results applicable to my patient or population?

Answer 80

Critical appraisal checklists, critical appraisal skills programme (CASP) checklists are one example but widely used

Answer 81

a planned experiment on human beings which is designed to evaluate the effectiveness of two or more forms of treatment

Answer 82

we start with can we give therapy to should we give therapy.

Answer 83

testing in small group of people (20-80) to determine a safe dosage range and pharmacological effects of a drug. generally first time testing in humans

Answer 84

initial study of efficacy, dose or technique based on phase 1, collects adverse events data. some are randomised, other's aren't.

Answer 85

full scale evaluation of treatment, study efficacy in large groups (100-1000s) comparing new intervention to a standard intervention. designed to detect a clinically meaningful difference. adverse event monitored. this is only done if 1and2 are okay though.

Answer 86

post marketing surveillance, to assess longer term risks assoc. with intervention

Answer 87

controlled - the responses of a group of patients on the new treatment with a control group of similar patients receiving a standard treatment / placebo randomised - each patient should be randomly assigned to a new treatment group or control group, for unbiased evaluation. there has to be a known, often equal chance of being assigned to each group

Answer 88

a blueprint for the study, who, what, when, where, why, how, how much

Answer 89

simple randomisation (tossing a coin) blocked (restricted) randomisation - used to keep number in each group close at all times stratified randomisation - classed into subgroups (strata), random allocation in each subgroup minimisation - adaptive design to ensure best possible balance at all times

Answer 90

accidental bias, where a study fails to balance groups, which is more prone in small sample sizes 'gaming' the system when allocation is predictable e.g. using DOB for randomisation, small block sizes, non-opaque envelopes

Answer 91

sealed, opaque envelopes tables of random numbers computer generated randomisation lists central allocation (inc. telephone, web-based and pharmacy controlled randomisation) sequentially numbered drug containers of identical appearance

Answer 92

keeps randomisation status secret from patients (single blinded) patients and clinicians (double blinded) patients, clinicians and researchers (triple blinded) psychological effects in patients, recording bias by clinicians

Answer 93

needs a clearly defined, measureable and objective measurement. this is used to determine sample size. secondary outcomes need to be defined a priori to avoid fishing expeditions

Answer 94

intention to treat analysis. this is designed to manage non-compliance, non-adherence and losses to follow up. all randomised participants are analyses whether they completed/received treatment they were randomised to. failure to include these will lead to biases results.

Answer 95

side effects forgetting to take meds withdrawing consent choosing alternative treatment can lead to reduced statistical power and bias

Answer 96

selection bias due to differences in baseline characteristics with respect to diagnosis performance bias due to differences in case (other than treatment) attrition bias (due to differences in withdrawal from trial) detection (ascertainment) bias due to differences in outcome assessments

Answer 97

randomised to a sequence of treatments each person serves as their own control. washout period between the treatments. switching from placebo to treatment for example.

Answer 98

multiple treatments occurring at once. could be placebo + some type of treatment or vice versa or any combination

Answer 99

clinical equipoise (uncertainty in the expert community informed consent methodological rigour (statistical and operational) registration and reporting via registries inc. CONSORT (inc. statement, checklist and flow diagram)

Answer 100

is the proposed treatment safe? can the treatment be ethically withheld? are all potential participants suitable for randomisation? is it ethical to use a placebo or use blinding? may need to stop a trial early

Answer 101

independent data monitoring committee which can recommend ending a trial

Answer 102

most reliable scientific evidence high internal validity as exposure to treatment is random, potentially can eradicate bias and isolate the treatment effect provide true measure of efficacy and allow for meta-analysis

Answer 103

expensive and time consuming limited external validity (generalisability) strict eligibility or insufficient suitable participants limited scope (difficult for rare events/distant outcomes) conflicts of interest ethical considerations

Answer 104

>2million article a year and >20k boomed journals need for consistency and precision informed decision making inform research agenda

Answer 105

review of published/unpublished studies to identify, select, appraise and synthesise all relevant evidence to address a specific question. it is designed to be systematic and reproducible approach to minimise bias. It takes into account quality of evidence and may or may not include a meta-analysis

Answer 106

PICO (population, intervention, comparison, outcome) SPICE (setting, population, intervention, comparison, evaluation) SPIDER (sample, phenomenon of interest, design, evaluation, research type)

Answer 107

databases, study registers, references, key journals, contact with expert in the field need to be looked at. 1. search general databases e.g. medline 2. search specialised databases e.g. cochrane 3. check reference list of key articles 4. hand search key journals 5. contact experts in the field

Answer 108

eligibility of studies is checked independently by two reviewers and disputes req. a third reviewer. This is based via exclusion/inclusion criteria. A prisma Flow chart should be used to show excluded papers.

Answer 109

critically appraised and scored to assess quality and bias. a standard tool to assess quality is used e.g. Jadad scale, Cochrane risk of bias tool or a standard tool to assess the quality of evidence overall e.g. GRADE

Answer 110

whether randomisation was used AND described was the study double blind AND described how it was double blind are withdrawals / dropouts acknowledged

Answer 111

are results consistent? how are outcomes defined and measured. is there heterogeneity or subgroup analysis? can a meta analysis be used? the data analysed should be defined a priori

Answer 112

Continuous - change from baseline, standard mean differences Dichotomous data - OR, RR Time till event - HR Others - interrupted time series; incidence or prevalence If not combinable, needs a descriptive or narrative synthesis

Answer 113

Variability between studies can be due to participant characteristics, outcome measures, interventions or methodologies. Chi2 = variability due to change I2 = variation in magnitude and direction, describes the amount of variability

Answer 114

>60% avoid pooled analysis, narrative synthesis only and use sub-group analysis

Answer 115

fixed (low heterogeity) or random effects (high heterogeneity) model (fixed assumes effect of intervention is constant in all studies population) drug dosages vary between studies missing data do the results still stand if you use only the best quality studies (lowest risk of bias)

Answer 116

funnel plots a plot of each trial's effect size against some measure of sample size statistically significant results tend to get published you will see a gap on the non-statistically significant side of a funnel plot

Answer 117

precision of treatment effect - how wide are 95% CIs what are implication for clinical/public health implications for research

Answer 118

whether true association i.e. causal vs non-causal if result is due to either chance, confounding or bias

Answer 119

any systematic error in an epidemioglocial study that results in an incorrect estimate of the association between exposure and outcome

Answer 120

a difference in how participants get into the study, this could be an error in identification or preferential selection of partipcants based on case/control status or exposure status

Answer 121

ascertainment bias when members of a population are more likely to be included that others due to surveillance systems or diagnostics or referral/admission to hospital, can skew outcome e.g. wealthier countries might have higher rates of breast cancer because of better detection due to screening rather than higher rates of breast ca sampling bias means there is an inappopriate comparison group when the control group isn't appropriately selected or representatiative of general pop participation bias is when people in the study are different to those that are part of general population this could be due to volunteerism, worried well, non-response, refusal or survival (health worker effect)

Answer 122

selection bias

Answer 123

difference in how data on participants are collected differences in accuracy of exposure data/outcome data for cases/controls or study subjects wrongly classified

Answer 124

reporting bias (recall bias) observer bias (interviewer or instrument bias) misclassification bias (participants wrongly categorised)

Answer 125

measurement error leads to assigning wrong exposure or outcome category

Answer 126

non-differential - when all individuals have some probability of being wrongly classified. Random error, unrelated to exposure or outcome status, not a bias but weakens measure of association. differential when errors in exposure or outcome status depends on the outcome or exposure. This is a systematic error, related to the exposure or outcome status, this results in bias and affects the measure of association in any direction

Answer 127

standardise methods of data collection with objective questions/measures and training/blinding of interviewers. use multiple sources of information and use prompts to aid recall

Answer 128

ascertainment bias, participation bias and interviewer bias (exposure and disease have already occurred; differential selection or data gathering in cases and non-cases recall bias - cases may remember exposures differently from controls

Answer 129

loss to follow up (main concern) as major source of bias, do we assume they do or do not develop outcome? ascertainment and interviewer bias (knowing exposure may influence how outcome determined) non-response or refusals, little concern bias only arises if related to both exposure and outcome recall bias is not a problem as exposure is determined at time of enrolment

Answer 130

point and period prevalence

Answer 131

incidence proportion (cumulative incidence) and incidence rate (incidence density)

Answer 132

number of cases of disease at a specific time divided by population at risk at the specific time

Answer 133

number of cases of disease during a time period divided by population at risk midway through the time period

Answer 134

increase in new cases longer duration of the disease increase in survival (without cure) in-migration of cases out-migration of healthy people improved diagnosis or better reporting

Answer 135

decrease in new cases shorter duration of disease high case-fatality rate from the disease in-migration of healthy people out-migrations of cases improved cure rate

Answer 136

number of new cases of disease during a period divided by the population at risk at the start of the period

Answer 137

number of new cases of disease during a period / total person time at risk this can be expressed as person-years as long as it is time and person aka force of morbidity or mortality incidence density

Answer 138

the probability than an event will occur

Answer 139

probability than an event will happen / probability that an event will not happen

Answer 140

incidence of death in a population

Answer 141

( total no. of deaths from all causes in 1 year / no. of persons in the population at mid year ) x 1000

Answer 142

crude mortality rates - total number of deaths per year per 1,000 (or 100,000) people age-specific mortality rate - total number of deaths per year per 1,000 people of a given age cause-specific rate - number of deaths due to a particular cause per year per 1,000 (or 100,000) population

Answer 143

no. of people dying during a specified time after disease onset or diagnosis / no. of individuals with the specified disease ) x 100 mortality rates denominator = entire population at risk of dying CFR denominator = those who already have the disease

Answer 144

number of deaths from a specific cause during a specified time period / all deaths during that time period. it shows the relative importance of certain cause of deaths in relation to all deaths in that population. hard to compare different populations due to varying denominators

Answer 145

no. of pregnancy related deaths by place and time / average number of women of reproductive age in the same population or time frame x 100,000

Answer 146

deaths at age under one year per 1,000 live births

Answer 147

foetal births from 28 (24) week per 1000 total live and still births

Answer 148

stillbirths and deaths in first week of life per 1000 live and stillbirth

Answer 149

deaths at age under 28 days per 1000 live births

Answer 150

deaths age under 5 years per 1000 live births

Answer 151

Ratios allow comparison by considering the number of cases in relation to the size of different populations, making it easier to make meaningful comparisons regardless of population size.

Answer 152

A proportion is a fraction where the numerator is a part of the denominator, representing a part of a whole, while a ratio compares two separate quantities, and can be used to compare two different population groups.

Answer 153

Point Prevalence: Measures the proportion of the population with a specific condition at a single point in time (e.g., the number of people experiencing an asthma attack on a particular day). Period Prevalence: Measures the proportion of the population that experienced the condition over a defined period (e.g., the number of people who had an asthma attack in the month of January). Lifetime Prevalence: Measures the proportion of the population that has ever had the condition at any point in their lives (e.g., the percentage of people who have ever had an asthma attack).

Answer 154

The risk is the probability that a subject within a pop- ulation will develop a given disease, or other health out- come, over a specified follow-up period. Risk = Number of subjects developing the disease over a time period / Total number of subjects followed over that time period

Answer 155

It can be calculated by dividing the number of subjects developing a disease by the total time at risk for all people to get the disease. The denominator of this formula includes a measure of time instead of just a number of subjects. The incidence rate should therefore be interpreted as an instantaneous con- cept, like speed. Incident rate = number of subjects developing the disease / total time at risk for the disease of all subjects followed

Answer 156

Under conditions in which rates do not change with time (a steady state), the incidence rate can be interpreted as the reciprocal of the average time until an event occurs, also called the waiting time. For example, in the calcula- tion of the incidence rate of vascular access infections in HD patients, the average waiting time for such an episode to occur would be 1/0.54 = 1.85 years. When calculated over a short period of time, the risk and the incidence rate will be rather similar, because the influence of loss to follow-up and competing risks which may flaw risk will only be small

Answer 157

Population level information Snapshot in time Examine association between amount of risk factor in an area and amount of disease in the area Uses aggregated data at the population level Does not use data on individual subjects and therefore subject to ecological fallacy Often use scatterplots to demonstrate associations * Think fish consumption and heart disease. Ecological studies are useful for generating hypotheses

Answer 158

Surveys - Census, Welsh Health Survey, National Attitudes and Lifestyle Survey Information not available from routine data to answer specific questions: What is the caesarean section rate in hospitals in England and Wales? What is the prevalence of psychological distress following the summer 2012 floods in South Yorkshire? Usually descriptive but can be analytical

Answer 159

Population attributable risk (PAR) is the proportion of the incidence of disease in a population (exposed and non exposed) that is due to exposure. PAR = Incidence rate in total population – incidence rate unexposed. It is the incidence of a disease in the population that would be eliminated if exposure was eliminated.

Answer 160

The proportion of the disease in the population that is due to the exposure, assuming causality = (Ipop - Iu)/Ipop also = [Pe x (RR-1)] / [Pe x (RR-1) + 1] Ipop = incidence rate in the population Iu = incidence rate in unexposed Pe = proportion of population exposed aka = prevalence of exposure RR = relative risk

Answer 161

What proportion of the new cases of disease observed in the study is attributable to a risk factor?

Answer 162

PAR & PAF depend on both: the strength of the association (RR) the prevalence of exposure in the population (Pe) to have a large impact on the population the exposure must be common PAF provides important information about the potential impact of prevention programmes and interventions in public health

Answer 163

CS vs cohort Sample Size small vs large Cost/Time less/short vs more/long Rare disease good vs bad Rare exposure bad vs good Multiple exposures good vs bad Multiple outcomes bad vs good Rates of disease novs yes Recall/selection bias yes vs no Loss to follow up no vs yes

Answer 164

1. Identify population of interest, select sample i.e. using sampling frame 2. record baseline measurements on entire sample 3. randomly allocate subjects to group A (intervention) and group B (control). 4. ITT analysis of results

Answer 165

1. Appropriateness of the strategy 2. Representativeness of sample - can result be generalised 3. has random allocation worked? 4. has bias been introduced through losses to the trial? 5. is the trial clinically relevant? is the outcome a disease, or simply a mechanism in a disease? 6. is the power of the trial adequate? is the trial large enough and sensitive enough? are the results statistically significant but clinically unimportant? 7. are the results consistent with evidence from other sources. Hence Bradford criteria for testing an association 8. is the trial a test of management or of disease i.e. efficiency and effectiveness in Cochrane terms 9. cost - other strategies and risk/benefit balance

Answer 166

random sequence generation allocation concealment blinding selective reporting other bias

Answer 167

1. strength of association - the stronger the more causal (OR/RR) 2. consistency - if relationship is causal we would expect the finding to be consistent with other data 3. specificity - this suggests one exposure = one disease, harder to use now 4. temporality - does exposure occur before disease development 5. biological gradient (dose response) - as dose of exposure increases risk of disease also increases 6. plausibility - sits within current body of evidence 7. coherence - replication of finding in different situations, consistent with sub-groups and different population unless plausible cause 8. analogy - similar findings between observed association and other associations 9. experimental evidence - similar finding between lab run and observational studies

Answer 168

The incidence of an event in exposed people

Answer 169

The difference between the incidence in exposed people minus the incidence in unexposed people (Ie-Iu).

Answer 170

Incidence of event in exposed group/incidence of event in the unexposed group (Ie/Iu)

Answer 171

Total number (or proportion) of a group of people who experience a new event during a specified time period

Answer 172

Observe a population at a point in time. They are descriptive studies. Useful in assessing prevalence rates etc. They can show association but not direction of causality due to lack of longitudinal data.

Answer 173

The statistical analysis of the data/results from homogeneous studies with the same outcome of interest to produce an overall, pooled result of the treatment/interventional effect. Most commonly used for RCTs but can be used for observational studies too. Useful when studies are too small to show a significant difference, combining the results of several studies increases the sample size and power enabling a more precise measure of effect

Answer 174

This implies severe/strong heterogeneity (1 mark) * Refit the analysis with a random effects model (1 mark) * Investigate the heterogeneity using subgroups (1 mark) * Carry out a sensitivity analysis (1 mark) * If you can’t explain the heterogeneity - do not pool the data (1 mark)

Answer 175

The leading outcomes that determine the success or effectiveness of an intervention

Answer 176

The graphical/visual result of a pooled meta-analysis, it presents the pooled effect size and confidence interval

Answer 177

A comparison of study size and effect size, it is a tool to assess publication bia

Answer 178

Publication bias is the term used to describe the tendency for positive and negative results to be unequally reported in published literature. Negative results are more likely to not be reported. Reviewers should aim to minimise publication bias. Funnel plot can be used to assess bias. 8

Answer 179

Internal validity means that the study measured what it set out to; external validity is the ability to generalise from the study to the reader’s patients.

Answer 180

* participant characteristics e.g. age group * outcome measures e.g. pain-free vs disability * interventions e.g. drug dosage, Rx duration * methodology e.g. randomisation vs none

Answer 181

Temporal ecological studies measure exposure and the outcome in the same population at different points in time Or between 2 different populations They can also be referred to as time-trend analysis or longitudnal ecological studies This data enables a dynamic view of a population's health These studies can be used to examine rates of disease mortality changes in health behaviours Comparisons can be geographical, different time periods or patterns of change over time Generate hypothese, identify trends Do NOT prove causality

Answer 182

An alternative approach is to use a design in which a case-control study is nested within the cohort. In this approach, the cohort is identified and followed up until a sufficient number of cases develop. All these cases, and a random sample of controls among those who have not developed disease in the cohort, become the "case-control" study. Additional information is usually collected and the analysis is carried out.

Answer 183

This design is a type of population based case-control study as both cases and controls are drawn from the same population (the cohort). In this study design, the controls can be selected in three different ways: - Controls are sampled form the population still at risk at the end of the follw up period i.e. those still disease-free at the end of the study. In this circumstance, a cohort subject can only be a case or a control but not both. - Controls are sampled from those who are still at risk at the time each case is diagnosed, so that controls are time-matched with cases. In this circumstance, a cohort subject originally selected as a control can later become a case. - Controls are sampled from those who were at risk at the start of the cohort. This design is called a "case-cohort" study. In this circumstance, a cohort subject can be a control and then become a case and vice versa.

Answer 184

It can be mathematically shown that the odds (of exposure) ratio from a nested (or a population based) case-control study can provide an unbiased estimate of the risk ratio and rate ratio, and the odds (of diasese) ratio depending on how controls were selected. Controls sampled from those who are disease free at the end of follow up = estimate of odds of disease ratio Controls selected from those at risk at the time each case develops = estimate of rate ratio (time-matched analysis required). Controls selected from all those initially at risk = estimate risk ratio

Answer 185

how subjects get into the study or errors in the process of identifying the study population

Answer 186

differences in surveillance (detection), hospitalisation (referral, admission or diagnostic). If exposed cases have a difference chance of admission to controls this can lead to an overestimate in size of effect.

Answer 187

inappropriate comparison group. How representative are the controls of the population giving rise to the cases. This can lead to over/under estimation.

Answer 188

self-selection or volunteerism, non-response or refusal. It is how the responders differ from non-responders which lead to an over/under estimation of effect.

Answer 189

you need to improve your choice of study population. this can be done via clear definition of study population; using a consistent case definition; trying to enrol all cases; ensuring cases/controls are from same population

Answer 190

biases in how data is collected if study subjects are wrongly classified this becomes misclassification bias

Answer 191

when study subjects are wrongly categories due to inaccurate diagnosis or admin error etc.

Answer 192

recall bias, where cases may more clearly recall exposures than controls

Answer 193

interviewer bias - interviewers case/control status and probes cases differently as a result data collection instruments - calibration errors or measurement in interviews

Answer 194

measurement error leads to assigning wrong exposure or outcome category this can be non-differential i.e: random error; unrelated to exposure or outcome status; not a bias but weakens measure of association. differential is a systematic error, related to exposure or outcome status; it introduces bias and can distort measure of association in any direction

Answer 195

I - reject null when it is actually true II - accept null when it is incorrect increase power of study and select appropriate sample size

Answer 196

A review of published and unpublished studies focussed on a single question Aims to identify, appraise, select and synthesise all evidence relevant to the question Take into account quality of the evidence

Answer 197

1. Ask a focussed question (PICO) 2. Define inclusion and exclusion criteria 3. Locate studies – Database search, grey literature 4. Select studies 5. Data extraction – assess study quality – NOS: observational studies/ Jadad – RCTS. 6. Analyse results: Individual study results Meta-analysis if appropriate/ forest plot Heterogeneity – fixed and random effects model Sensitivity analysis 7. Interpret results

Answer 198

Chi2 test: null hyp = no heterogeneity. Therefore a small p-value = heterogeneity. If there are only a few studies the test is under powered so a cut of 0.10 is used to assess heterogeneity. I2 test: Depends on magnitude and direction of effects. Should be used in combo with Chi2 statistic i.e. evidence for hetergeneity. Rough guide – 0-40% litte, 40 – 75% Moderate, 75%+ Considerable. Fixed effects model: Use if heterogeneity is not deemed to be a problem. Underlying assumption that there is a ‘fixed’ or common feature that underlies all of the studies in the analysis. It treats the studies as if there was no heterogeneity. Random effects model: Use if there is considerable heterogeneity between studies. Underlying assumption is that the studies are estimating different treatment effects. Looks at the distribution of effects across different studies. If you try both models and they give similar results it is unlikely that heterogeneity is NOT a problem and you can use either.