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Sampling and Sample Size Calculation Sources: -EPIET Introductory course, Thomas Grein, Denis Coulombier, Philippe Sudre, Mike Catchpole, Denise Antona -IDEA Brigitte Helynck, Philippe Malfait, Institut de veille sanitaire Modified: Viviane Bremer, EPIET 2004, Suzanne Cotter 2005, Richard Pebody 2006 Lazereto de Mahón, Menorca, Spain September 2006

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Objectives: sampling To understand: Why we use sampling Definitions in sampling Sampling errors Main methods of sampling Sample size calculation

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Why do we use sampling? Get information from large populations with: –Reduced costs –Reduced field time –Increased accuracy –Enhanced methods

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Definition of sampling Procedure by which some members of a given population are selected as representatives of the entire population

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Definition of sampling terms Sampling unit (element) Subject under observation on which information is collected –Example: children <5 years, hospital discharges, health events… Sampling fraction Ratio between sample size and population size –Example: 100 out of 2000 (5%)

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Definition of sampling terms Sampling frame List of all the sampling units from which sample is drawn –Lists: e.g. children < 5 years of age, households, health care units… Sampling scheme Method of selecting sampling units from sampling frame –Randomly, convenience sample…

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Survey errors Systematic error (or bias) Sample not typical of population –Inaccurate response (information bias) –Selection bias Sampling error (random error)

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Representativeness (validity) A sample should accurately reflect distribution of relevant variable in population Person e.g. age, sex Place e.g. urban vs. rural Time e.g. seasonality Representativeness essential to generalise Ensure representativeness before starting, Confirm once completed

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Sampling and representativeness Sample Target Population Sampling Population Target Population Sampling Population Sample

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Sampling error Random difference between sample and population from which sample drawn Size of error can be measured in probability samples Expressed as standard error –of mean, proportion… Standard error (or precision) depends upon: –Size of the sample –Distribution of character of interest in population

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Sampling error When simple random sample of size n is selected from population of size N, standard error (s) for population mean or proportion is: σ p(1-p) n Used to calculate, 95% confidence intervals Estimated 95% confidence interval

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Quality of a sampling estimate Precision & validity No precision Random error Precision but no validity Systematic error (bias)

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Survey errors: example Measuring height: Measuring tape held differently by different investigators loss of precision –Large standard error Tape shrunk/wrong systematic error –Bias (cannot be corrected afterwards)

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Types of sampling Non-probability samples Probability samples

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Non probability samples Convenience samples (ease of access) Snowball sampling (friend of friend….etc.) Purposive sampling (judgemental) You chose who you think should be in the study Probability of being chosen is unknown Cheaper- but unable to generalise, potential for bias

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Probability samples Random sampling –Each subject has a known probability of being selected Allows application of statistical sampling theory to results to: –Generalise –Test hypotheses

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Methods used in probability samples Simple random sampling Systematic sampling Stratified sampling Multi-stage sampling Cluster sampling

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Simple random sampling Principle –Equal chance/probability of drawing each unit Procedure –Take sampling population –Need listing of all sampling units (sampling frame) –Number all units –Randomly draw units

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Simple random sampling Advantages –Simple –Sampling error easily measured Disadvantages –Need complete list of units –Does not always achieve best representativeness –Units may be scattered and poorly accessible

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Example: evaluate the prevalence of tooth decay among 1200 children attending a school List of children attending the school Children numerated from 1 to 1200 Sample size = 100 children Random sampling of 100 numbers between 1 and 1200 How to randomly select? Simple random sampling

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EPITABLE: random number listing

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Also possible in Excel

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Simple random sampling

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Systematic sampling Principle –Select sample at regular intervals based on sampling fraction Advantages –Simple –Sampling error easily measured Disadvantages –Need complete list of units –Periodicity

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Systematic sampling N = 1200, and n = 60 sampling fraction = 1200/60 = 20 List persons from 1 to 1200 Randomly select a number between 1 and 20 (ex : 8) 1 st person selected = the 8 th on the list 2 nd person = = the 28 th etc.....

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Systematic sampling

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Stratified sampling Principle : –Divide sampling frame into homogeneous subgroups (strata) e.g. age-group, occupation; –Draw random sample in each strata.

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Stratified sampling Advantages –Can acquire information about whole population and individual strata –Precision increased if variability within strata is less (homogenous) than between strata Disadvantages –Can be difficult to identify strata –Loss of precision if small numbers in individual strata resolve by sampling proportionate to stratum population

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Multiple stage sampling Principle: consecutive sampling example : sampling unit = household –1 st stage: draw neighborhoods –2 nd stage: draw buildings –3 rd stage: draw households

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Cluster sampling Principle –Sample units not identified independently but in a group (or cluster) –Provides logistical advantage.

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Cluster sampling Principle –Whole population divided into groups e.g. neighbourhoods –Random sample taken of these groups (clusters) –Within selected clusters, all units e.g. households included (or random sample of these units)

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Example: Cluster sampling Section 4 Section 5 Section 3 Section 2Section 1

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Cluster sampling Advantages –Simple as complete list of sampling units within population not required –Less travel/resources required Disadvantages –Potential problem is that cluster members are more likely to be alike, than those in another cluster (homogenous)…. –This dependence needs to be taken into account in the sample size….and the analysis (design effect)

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Selecting a sampling method Population to be studied –Size/geographical distribution –Heterogeneity with respect to variable Availability of list of sampling units Level of precision required Resources available

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Sample size estimation Estimate number needed to reliably measure factor of interest detect significant association Trade-off between study size and resources…. Sample size determined by various factors: significance level (alpha) power (1-beta) expected prevalence of factor of interest

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Type 1 error The probability of finding a difference with our sample compared to population, and there really isnt one…. Known as the α (or type 1 error) Usually set at 5% (or 0.05)

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Type 2 error The probability of not finding a difference that actually exists between our sample compared to the population… Known as the β (or type 2 error) Power is (1- β) and is usually 80%

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A question? Are the English more intelligent than the Dutch? H 0 Null hypothesis: The English and Dutch have the same mean IQ Ha Alternative hypothesis: The mean IQ of the English is greater than the Dutch

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Type 1 and 2 errors Truth DecisionH 0 trueH 0 false Reject H 0Type I errorCorrect decision Accept H 0 CorrectType II error decision

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Power The easiest ways to increase power are to: –increase sample size –increase desired difference (or effect size) –decrease significance level desired e.g. 10%

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Steps in estimating sample size for descriptive survey Identify major study variable Determine type of estimate (%, mean, ratio,...) Indicate expected frequency of factor of interest Decide on desired precision of the estimate Decide on acceptable risk that estimate will fall outside its real population value Adjust for estimated design effect Adjust for expected response rate

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Sample size for descriptive survey z: alpha risk expressed in z-score p: expected prevalence q: 1 - p d: absolute precision g: design effect z² * p * q 1.96²*0.15*0.85 n = = 544 d²0.03² Cluster sampling z² * p * q 2*1.96²*0.15*0.85 n = g* = 1088 d² 0.03² Simple random / systematic sampling

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Case-control sample size: issues to consider Number of cases Number of controls per case Odds ratio worth detecting Proportion of exposed persons in source population Desired level of significance (α) Power of the study (1-β) –to detect at a statistically significant level a particular odds ratio

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Case-control: STATCALC Sample size

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Risk of alpha error 5% Power 80% Proportion of controls exposed20% OR to detect > 2

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Case-control: STATCALC Sample size

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Statistical Power of a Case-Control Study for different control-to-case ratios and odds ratios (with 50 cases)

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Conclusions Probability samples are the best Ensure –Representativeness –Precision …..within available constraints

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Conclusions If in doubt… Call a statistician !!!!

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