instrument design essential concept behind the design bandit thinkhamrop, ph.d.(statistics)...

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

33

44

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