instrument design essential concept behind the design bandit thinkhamrop, ph.d.(statistics)...
DESCRIPTION
3TRANSCRIPT
![Page 1: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/1.jpg)
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
![Page 2: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/2.jpg)
Begin at the conclusionBegin at the conclusion
![Page 3: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/3.jpg)
33
![Page 4: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/4.jpg)
44
![Page 5: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/5.jpg)
Caution about biasesCaution about biases
Selection bias
Information bias
Confounding bias
Research Design-Prevent them-Minimize them
![Page 6: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/6.jpg)
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
![Page 7: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/7.jpg)
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.
![Page 8: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/8.jpg)
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.
![Page 9: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/9.jpg)
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
![Page 10: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/10.jpg)
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
![Page 11: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/11.jpg)
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
![Page 12: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/12.jpg)
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
![Page 13: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/13.jpg)
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
![Page 14: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/14.jpg)
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
![Page 15: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/15.jpg)
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.
![Page 16: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/16.jpg)
Statistics quantify errors for judgmentsStatistics quantify errors for judgmentsParameter estimation
[95%CI]
Hypothesis testing[P-value]
![Page 17: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/17.jpg)
n = 25X = 52SD = 5
Sample
PopulationParameter estimation
[95%CI]
Hypothesis testing[P-value]
![Page 18: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/18.jpg)
nSDSE
255
SE 5 = 1 5
Z = 2.58Z = 1.96Z = 1.64
![Page 19: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/19.jpg)
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
![Page 20: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/20.jpg)
n = 25X = 52SD = 5SE = 1
Sample
Hypothesis testing
Population
Z = 55 – 52 1 3H0 : = 55
HA : 55
![Page 21: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/21.jpg)
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
![Page 22: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/22.jpg)
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
![Page 23: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/23.jpg)
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
![Page 24: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/24.jpg)
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
![Page 25: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/25.jpg)
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.
![Page 26: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/26.jpg)
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.
![Page 27: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/27.jpg)
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.
![Page 28: Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public](https://reader036.vdocuments.net/reader036/viewer/2022062401/5a4d1b517f8b9ab0599a7c0f/html5/thumbnails/28.jpg)
Q & AQ & AThank you