Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics)...
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Instrument designInstrument designEssential concept behind the designEssential concept behind the design
Bandit Thinkhamrop, Ph.D.(Statistics)Bandit Thinkhamrop, Ph.D.(Statistics)Department of Biostatistics and DemographyDepartment of Biostatistics and Demography
Faculty of Public HealthFaculty of Public HealthKhon Kaen UniversityKhon Kaen University
Begin at the conclusionBegin at the conclusion
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Caution about biasesCaution about biases
Selection bias
Information bias
Confounding bias
Research Design-Prevent them-Minimize them
Caution about biasesCaution about biases
Selection bias (SB)
Information bias (IB)
Confounding bias (CB)
If data available:SB & IB can be assessedCB can be adjusted using multivariable analysis
Sampling designSampling designPlease refer to IPDET Handbook Module 9Please refer to IPDET Handbook Module 9Types of Random SamplesTypes of Random Samples– simple random samplessimple random samples– stratified random samplesstratified random samples– multi-stage samplesmulti-stage samples– cluster samplescluster samples– combination random samples.combination random samples.
Summary of Random Sampling ProcessSummary of Random Sampling Process
1.1. Obtain a complete listing of the entire populationObtain a complete listing of the entire population2.2. Assign each case a unique number.Assign each case a unique number.3.3. Randomly select the sample using a random Randomly select the sample using a random
numbers table.numbers table.4.4. When no numbered listing exists or is not When no numbered listing exists or is not
practical to create, use systematic random practical to create, use systematic random sampling:sampling:– make a random startmake a random start– select every nth case.select every nth case.
Questionnaire designQuestionnaire design
Design it with purpose, valid and reliableDesign it with purpose, valid and reliableWording and layout are importantWording and layout are importantQuestion typesQuestion types– Multiple choice (radio button)Multiple choice (radio button)– Multiple-item responses (checkbox)Multiple-item responses (checkbox)– Open-ended (blank or text area)Open-ended (blank or text area)
Think aloud and improve the questionnaireThink aloud and improve the questionnairePrepare manual of operationPrepare manual of operationPre-testing and improve themPre-testing and improve them
Type of the study outcome: Key for Type of the study outcome: Key for selecting appropriate statistical methodsselecting appropriate statistical methods
Study outcomeStudy outcome– Dependent variable or response variableDependent variable or response variable– Focus on primary study outcome if there are moreFocus on primary study outcome if there are more
Type of the study outcomeType of the study outcome– ContinuousContinuous– Categorical (dichotomous, polytomous, ordinal)Categorical (dichotomous, polytomous, ordinal)– Numerical (Poisson) countNumerical (Poisson) count– Even-free durationEven-free duration
Continuous outcomeContinuous outcome
Primary target of estimation: Primary target of estimation: – Mean (SD) Mean (SD) – Median (Min:Max)Median (Min:Max)– Correlation coefficient: r and ICC Correlation coefficient: r and ICC
Modeling:Modeling:– Linear regressionLinear regression
The model coefficient = Mean differenceThe model coefficient = Mean difference– Quantile regressionQuantile regression
The model coefficient = Median differenceThe model coefficient = Median differenceExample: Example: – Outcome = Weight, BP, score of ?, level of ?, etc.Outcome = Weight, BP, score of ?, level of ?, etc.– RQ: Factors affecting birth weightRQ: Factors affecting birth weight
Categorical outcomeCategorical outcome
Primary target of estimation : Primary target of estimation : – Proportion or Risk Proportion or Risk Modeling:Modeling:– Logistic regressionLogistic regression
The model coefficient = Odds ratioThe model coefficient = Odds ratio (OR)(OR)Example: Example: – Outcome = Disease (y/n), Dead(y/n), Outcome = Disease (y/n), Dead(y/n),
cured(y/n), etc.cured(y/n), etc.– RQ: Factors affecting low birth weight RQ: Factors affecting low birth weight
Numerical (Poisson) count outcomeNumerical (Poisson) count outcome
Primary target of estimation : Primary target of estimation : – Incidence rate (e.g., rate per person time) Incidence rate (e.g., rate per person time) Modeling:Modeling:– Poisson regressionPoisson regression
The model coefficient = Incidence rate ratio (IRR)The model coefficient = Incidence rate ratio (IRR)Example: Example: – Outcome = Total number of fallsOutcome = Total number of falls
Total time at risk of fallingTotal time at risk of falling– RQ: Factors affecting tooth elderly fallRQ: Factors affecting tooth elderly fall
Event-free duration outcomeEvent-free duration outcome
Primary target of estimation : Primary target of estimation : – Median survival time Median survival time Modeling:Modeling:– Cox regressionCox regression
The model coefficient = Hazard ratio (HR)The model coefficient = Hazard ratio (HR)Example: Example: – Outcome = Overall survival, disease-free Outcome = Overall survival, disease-free
survival, progression-free survival, etc.survival, progression-free survival, etc.– RQ: Factors affecting survivalRQ: Factors affecting survival
The outcome determine statisticsThe outcome determine statistics
Continuous
MeanMedian
Categorical
Proportion(PrevalenceOrRisk)
Count
Rate per “space”
Survival
Median survivalRisk of events at T(t)
Linear Reg. Logistic Reg. Poisson Reg. Cox Reg.
Statistics quantify errors for judgmentsStatistics quantify errors for judgmentsParameter estimation
[95%CI]
Hypothesis testing[P-value]
n = 25X = 52SD = 5
Sample
PopulationParameter estimation
[95%CI]
Hypothesis testing[P-value]
nSDSE
255
SE 5 = 1 5
Z = 2.58Z = 1.96Z = 1.64
n = 25X = 52SD = 5SE = 1
Sample
PopulationParameter estimation
[95%CI] : 52-1.96(1) to 52+1.96(1) 50.04 to 53.96We are 95% confidence that the population mean would lie between 50.04 and 53.96
Z = 2.58Z = 1.96Z = 1.64
n = 25X = 52SD = 5SE = 1
Sample
Hypothesis testing
Population
Z = 55 – 52 1 3H0 : = 55
HA : 55
Hypothesis testing
H0 : = 55HA : 55If the true mean in the population is 55, chance to obtain a sample mean of 52 or more extreme is 0.0027.
Z = 55 – 52 1 3 P-value = 1-0.9973 = 0.0027
5552-3SE +3SE
P-value P-value vs.vs. 95%CI 95%CI (1)(1)
A study compared cure rate between Drug A and Drug B
Setting:Drug A = Alternative treatmentDrug B = Conventional treatment
Results:Drug A: n1 = 50, Pa = 80%Drug B: n2 = 50, Pb = 50%
Pa-Pb = 30% (95%CI: 26% to 34%; P=0.001)
An example of a study with dichotomous outcome
P-value P-value vs.vs. 95%CI 95%CI (2)(2)
Pa-Pb = 30% (95%CI: 26% to 34%; P< 0.05)
Pa > Pb
Pb > Pa
P-value P-value vs.vs. 95%CI 95%CI (3)(3)Adapted from: Armitage, P. and Berry, G. Statistical methods in medical research. 3rd edition. Blackwell Scientific Publications, Oxford. 1994. page 99
Tips #6 Tips #6 (b)(b) P-value P-value vs.vs. 95%CI 95%CI (4)(4)
Adapted from: Armitage, P. and Berry, G. Statistical methods in medical research. 3rd edition. Blackwell Scientific Publications, Oxford. 1994. page 99
There were statistically significant different between the two groups.
Tips #6 Tips #6 (b)(b) P-value P-value vs.vs. 95%CI 95%CI (5)(5)
Adapted from: Armitage, P. and Berry, G. Statistical methods in medical research. 3rd edition. Blackwell Scientific Publications, Oxford. 1994. page 99
There were no statistically significant different between the two groups.
P-value P-value vs.vs. 95%CI 95%CI (4)(4)
Save tips:Save tips:– Always report 95%CI with p-value, NOT report Always report 95%CI with p-value, NOT report
solely p-valuesolely p-value– Always interpret based on the lower or upper Always interpret based on the lower or upper
limit of the confidence interval, p-value can be limit of the confidence interval, p-value can be an optional an optional
– Never interpret p-value > 0.05 as an indication Never interpret p-value > 0.05 as an indication of no difference or no association, only the CI of no difference or no association, only the CI can provide this message.can provide this message.
Q & AQ & AThank you