Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public Health Khon Kaen University

Begin at the conclusion

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Caution about biases Selection bias Information bias Confounding bias Research Design -Prevent them -Minimize them

Caution about biases Selection bias (SB) Information bias (IB) Confounding bias (CB) If data available: SB & IB can be assessed CB can be adjusted using multivariable analysis

Sampling design Please refer to IPDET Handbook Module 9 Types of Random Samples –simple random samples –stratified random samples –multi-stage samples –cluster samples –combination random samples.

Summary of Random Sampling Process 1. Obtain a complete listing of the entire population 2. Assign each case a unique number. 3. Randomly select the sample using a random numbers table. 4. When no numbered listing exists or is not practical to create, use systematic random sampling: –make a random start –select every nth case.

Questionnaire design Design it with purpose, valid and reliable Wording and layout are important Question types –Multiple choice (radio button) –Multiple-item responses (checkbox) –Open-ended (blank or text area) Think aloud and improve the questionnaire Prepare manual of operation Pre-testing and improve them

Type of the study outcome: Key for selecting appropriate statistical methods Study outcome –Dependent variable or response variable –Focus on primary study outcome if there are more Type of the study outcome –Continuous –Categorical (dichotomous, polytomous, ordinal) –Numerical (Poisson) count –Even-free duration

Continuous outcome Primary target of estimation: –Mean (SD) –Median (Min:Max) –Correlation coefficient: r and ICC Modeling: –Linear regression The model coefficient = Mean difference –Quantile regression The model coefficient = Median difference Example: –Outcome = Weight, BP, score of ?, level of ?, etc. –RQ: Factors affecting birth weight

Categorical outcome Primary target of estimation : –Proportion or Risk Modeling: –Logistic regression The model coefficient = Odds ratio (OR) Example: –Outcome = Disease (y/n), Dead(y/n), cured(y/n), etc. –RQ: Factors affecting low birth weight

Numerical (Poisson) count outcome Primary target of estimation : –Incidence rate (e.g., rate per person time) Modeling: –Poisson regression The model coefficient = Incidence rate ratio (IRR) Example: –Outcome = Total number of falls Total time at risk of falling Total time at risk of falling –RQ: Factors affecting tooth elderly fall

Event-free duration outcome Primary target of estimation : –Median survival time Modeling: –Cox regression The model coefficient = Hazard ratio (HR) Example: –Outcome = Overall survival, disease-free survival, progression-free survival, etc. –RQ: Factors affecting survival

The outcome determine statistics Continuous Mean Median Categorical Proportion (Prevalence Or Risk) Count Rate per “space” Survival Median survival Risk of events at T (t) Linear Reg. Logistic Reg. Poisson Reg. Cox Reg.

Statistics quantify errors for judgments Parameter estimation [95%CI] Hypothesis testing [P-value] Parameter estimation [95%CI] Hypothesis testing [P-value]

n = 25 X = 52 SD = 5 Sample Population Parameter estimation [95%CI] Hypothesis testing [P-value] Parameter estimation [95%CI] Hypothesis testing [P-value]

5 = 1 5 Z = 2.58 Z = 1.96 Z = 1.64

n = 25 X = 52 SD = 5 SE = 1 Sample Population Parameter estimation [95%CI] : (1) to (1) to We are 95% confidence that the population mean would lie between and [95%CI] : (1) to (1) to We are 95% confidence that the population mean would lie between and Z = 2.58 Z = 1.96 Z = 1.64

n = 25 X = 52 SD = 5 SE = 1 Sample Hypothesis testing Hypothesis testing Population Z = 55 – H 0 :  = 55 H A :   55

Hypothesis testing H 0 :  = 55 H A :   55 If the true mean in the population is 55, chance to obtain a sample mean of 52 or more extreme is Hypothesis testing H 0 :  = 55 H A :   55 If the true mean in the population is 55, chance to obtain a sample mean of 52 or more extreme is Z = 55 – P-value = = SE +3SE

P-value vs. 95%CI (1) A study compared cure rate between Drug A and Drug B Setting: Drug A = Alternative treatment Drug 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 vs. 95%CI (2) Pa-Pb = 30% (95%CI: 26% to 34%; P< 0.05) Pa > Pb Pb > Pa

P-value vs. 95%CI (3) Adapted from: Armitage, P. and Berry, G. Statistical methods in medical research. 3rd edition. Blackwell Scientific Publications, Oxford page 99

Tips #6 (b) P-value vs. 95%CI (4) Adapted from: Armitage, P. and Berry, G. Statistical methods in medical research. 3rd edition. Blackwell Scientific Publications, Oxford page 99 There were statistically significant different between the two groups.

Tips #6 (b) P-value vs. 95%CI (5) Adapted from: Armitage, P. and Berry, G. Statistical methods in medical research. 3rd edition. Blackwell Scientific Publications, Oxford page 99 There were no statistically significant different between the two groups.

P-value vs. 95%CI (4) Save tips: –Always report 95%CI with p-value, NOT report solely p-value –Always interpret based on the lower or upper limit of the confidence interval, p-value can be an optional –Never interpret p-value > 0.05 as an indication of no difference or no association, only the CI can provide this message.

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