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Sample size Power Random allocation

Sample size Power Random allocation. R.Raveendran. Why is sample size important?. Validity Accuracy Finance Resources Ethics. What factors will affect the size of the sample? Degree of difference Type I error Type II error Variation of data Drop out Non-compliance.

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Sample size Power Random allocation

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  1. Sample sizePowerRandom allocation R.Raveendran

  2. Why is sample size important? • Validity • Accuracy • Finance • Resources • Ethics

  3. What factors will affect the size of the sample? • Degree of difference • Type I error • Type II error • Variation of data • Drop out • Non-compliance

  4. What methods can be used to determine the sample size? • Arbitrary numbers × • From previous studies ? • Nomograms & tables ! • Formulas • Computer programs

  5. How to calculate the sample size? • Assess the difference expected (0.5 kg) • Find out the SD of groups (0.4) • Set the level of significance (alpha - 0.05) • Set the beta level (0.02) • Select the appropriate formula (unpaired t) • Calculate the sample size u-v (diff/sd)/ • Give allowances for drop-outs & non-compliance

  6. Power Probability that a study can detect a difference Priori power determination : Power = 1 – beta Beta or type 2 error is the chances of missing a difference (false negative rate) Posteriori power calculation : Why? How? What is the implication?

  7. Random Allocation • Each unit in a sample has equal chance to be assigned a treatment • Simple • Block • Stratified • Cluster

  8. Thank you

  9. Degree of difference The minimum difference that is clinically or practically important e.g. A drug reduces BP by 2 mm of Hg (120 to 118). Is it clinically important? What about 4 mm of Hg? What about 6? 10? 20? 30? 40? Implication – Large difference needs small sample size Small difference needs large sample size

  10. Type I and II errors Question : Is the rice cooked? Possible Results : + (Yes) - (No) True TrueFalse False

  11. Type I and II errors Groups : Group A Vs BQuestion : Is there a difference between groups? Possible Results : + (Yes) - (No) True TrueFalse False Type I error = False + P Limit - 5% Type II error = False - P Limit - 20%

  12. Power Calculation • Why? • To find out whether a negative result is TRUE or FALSE • How? • Using the formula or computer programs • What information is needed? • The difference (0.5) • Alpha (0.05) • Sample size and SD both groups (5, 5 & 0.29, 0.33) • Statistical test used (Unpaired t) Implication? No power; redo the experiment / no diff.

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