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Outline. Basic Concepts Sample Size Calculation Precision analysis Power analysis Power analysis for three most-frequently-used regressions Logistic Regression Cox Regression Linear Regression. Basic Concepts. There are two kinds of errors occur when testing hypotheses.

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Outline

Outline

  • Basic Concepts

  • Sample Size Calculation

    • Precision analysis

    • Power analysis

  • Power analysis for three most-frequently-used regressions

    • Logistic Regression

    • Cox Regression

    • Linear Regression


Basic concepts

Basic Concepts

  • There are two kinds of errors occur when testing hypotheses.

  • Type I error: If the null hypothesis is rejected when it is true, then a type I error occurs.

  • Type II error: If the null hypothesis is not rejected when it is false, then a type II error is made.


Basic concepts1

Basic Concepts

  • Probabilities of making type I and II errors

  • Significance level: an upper bound for .

  • Power: the probability of correctly rejecting the null hypothesis when the null hypothesis is false, i.e.


Sample size calculation

Sample Size Calculation

  • In practice, sample size may be determined based on either precision analysis or power analysis. Next a few slides will tell you what each analysis is in details. Since power analysis is more practical, the discussion will be focused on power analysis.


Precision analysis

Precision Analysis

  • For a confidence interval, the precision of the interval depends on its width. The narrower the interval is, the more precise the inference is. Therefore, the precision analysis for sample size determination is to consider the maximum half width of the confidence interval of the unknown parameter that one is willing to accept. The maximum half width of the confidence interval is usually referred to as the maximum error of an estimate of the unknown parameter.


Precision analysis1

Precision Analysis

  • For example, let be independent and identically distributed normal random variables with mean and variance . When is known, a confidence interval for can be obtained as

    where is the th percentile of the standard normal distribution.The maximum error, denoted by , is then defined as


Precision analysis2

Precision Analysis

  • Thus, the sample size required to achieve the desired maximum error can be chosen as

  • An Example: suppose that we wish to have a 95% assurance that the error in the estimated mean is less than 10% of the standard deviation (i.e., 0.1 ). The required sample size is


Power analysis

Power Analysis

  • Since a type I error is usually considered to be more important and serious error which one would like to avoid, a typical approach in hypothesis testing is to control at an acceptable level and try to minimize

    by choosing an appropriate sample size. In other words, the null hypothesis can be tested at pre-determined level of significance with a desired power ( ). This concept for determination of sample size is usually referred to as power analysis for sample size determination.


Power analysis1

Power Analysis

  • For determination of sample size based on power analysis, the investigator is required to specify the following information. First of all, select a significance level at which the chance of wrongly concluding that a difference exists when in fact there is no real difference (type I error). Typically, 0.05 is chosen. Secondly, select a desired power at which the chance of correctly detecting a difference when the difference truly exists. A conventional choice of power is either 90% or 80%.


Power analysis2

Power Analysis

  • Thirdly, specify a clinically meaningful difference, denoted by . The larger is, the larger the sample size is needed. Finally, the knowledge regarding the standard deviation( i.e. ), of the primary endpoint considered in the study is also required for sample size determination. A very precise method of measurement( small ) will permit detection of any given difference with a much smaller sample size than would be required with a less precise measurement.


Power analysis3

Power Analysis

  • Suppose there are two groups of observations, namely

    (treatment) and (control). Assume that and are independent and normally distributed with means and and variances and respectively. Suppose the hypotheses of interest are

    For illustration purpose, we assume (i) and are known, and (ii) . Under these assumptions, a Z-statistic can be used to test the mean difference.


Power analysis4

Power Analysis

  • The Z-test is given by

    Under the null hypothesis of no treatment difference, Z is distributed as N(0,1). Hence, we reject the null hypothesis when

    Under the alternative hypothesis that , Z is distributed as , where


Power analysis5

Power Analysis

  • The corresponding power is then given by

  • To achieve the desired power of , we set

  • This leads to the required sample size


Power analysis6

Power Analysis

  • An example: suppose the objective of the study is to compare a test drug with a control and the standard deviation for the treatment group is 1 and the standard deviation of the control group is 2. Then, by choosing , we have

  • Thus, a total of 106 subjects is required for achieving a 90% power for detection of a clinically meaningful difference of at the 5% level of significance.


Overview of programs for sample size calculation

Overview of Programs for Sample Size Calculation

  • There are a variety of programs that are available for sample size calculation. They are different in terms of cost and coverage of sample size calculation scenarios. For a comprehensive review, go to the following link:

    http://www.biostat.ucsf.edu/sampsize.html


Recommendation

Recommendation

  • While one can decide the type of the program to be used for sample size calculation, the program called PASS has been used as the most reliable, comprehensive and acceptable program in academic settings especially in NIH grant submissions. However, PASS is not a free program. The cost is about 650$ per license.


Simple logistic regression

Simple Logistic Regression

  • Simple logistic regression expresses the relationship between a binary response variable( ) and a covariate( ). The simple logistic regression model relates the probability of to by the formula

    where P is the probability of given the value of the covariate .


Power analysis for simple logistic regression continuous covariate

Power Analysis for Simple Logistic Regression: Continuous Covariate

  • Suppose one wants to test the null hypothesis that

    where is the odds ratio comparing the odds at one standard deviation of above the mean with the odds at the mean of .

  • Hsieh, Block, and Larsen (1998) gave the following sample size formula when is normally distributed.


Power analysis for simple logistic regression continuous covariate1

Power Analysis for Simple Logistic Regression : Continuous covariate

  • The sample size formula indicates that to determine the required sample size, one needs to know the following factors:

    • : Significance level

    • : desired Power

    • : odds ratio to be detected:

    • : probability of at the mean of the covariate


Power analysis for simple logistic regression continuous covariate2

Power Analysis for Simple Logistic Regression: Continuous Covariate

  • Example 1 : A study is to be undertaken to study the relationship between post-traumatic stress disorder and heart rate after viewing video tapes containing violent sequences. Heart rate is assumed to be normally distributed. The post-traumatic stress disorder rate is thought to be 7% among the soldiers with mean heart rate. The researchers want a sample size large enough to detect an odds ratio of 1.5 with 90% power at the 0.05 significance level with a two-sided test.


Power analysis for simple logistic regression continuous covariate3

Power Analysis for Simple Logistic Regression: Continuous Covariate

  • The example described on previous slide indicates that

  • Plugging these values into the sample size formula, we have


Power analysis for simple logistic regression continuous covariate4

Power Analysis for Simple Logistic Regression: Continuous Covariate

  • The following Splus codes can be used to carry out

    the hand-calculation on previous slide

    simple.logistic.regression.continuous<-function(alpha,beta,p0,B){

    # alpha---significance level

    # beta---one minus power

    # p0---probability at the mean of the covariate

    # B---odds ratio comparing the odds at one standard deviation of the covariate

    # above the mean with the odds at the mean

    N<-(qnorm(1-alpha/2)+qnorm(1-beta))**2/(p0*(1-p0)*(log(B))**2)

    N

    }

    simple.logistic.regression.continuous(0.05,0.1,0.07,1.5)


Power analysis for simple logistic regression continuous covariate5

Power Analysis for Simple Logistic Regression: Continuous Covariate

  • Summary statements: A logistic regression of post-traumatic stress disorder on heart rate (assuming normal distribution) with a sample size of 982 observations achieves 90% power at a 0.05 significance level to detect an odds ratio of 1.5: the ratio of the odds at the mean of heart rate to the odds at one standard deviation above the mean.


Power analysis for simple logistic regression binary covariate

Power Analysis for Simple Logistic Regression: Binary Covariate

  • When is a binary covariate, one also wants to test the null hypothesis that

    where is the odds ratio comparing the odds with the odds at . Notice that the interpretation of is different from that in the continuous covariate case.


Power analysis for simple logistic regression binary covariate1

Power Analysis for Simple Logistic Regression: Binary Covariate

  • Hsieh, Block, and Larsen (1998) also gave a sample size formula when is binomially distributed. the sample size formula is

    where


Power analysis for simple logistic regression binary covariate2

Power Analysis for Simple Logistic Regression : Binary Covariate

  • The sample size formula indicates that to determine the required sample size, one needs to know the following factors:

    • : Significance level

    • : desired Power

    • : odds ratio to be detected:

    • : probability of at

    • : the proportion of the sample with


Power analysis for simple logistic regression binary covariate3

Power Analysis for Simple Logistic Regression: Binary Covariate

  • Example 2: A study is to be undertaken to study the relationship between post-traumatic stress disorder and gender. The post-traumatic stress disorder rate is thought to be 7% among the males, and the proportion of female is 50%. The researchers want a sample size large enough to detect an odds ratio of 1.5 with 90% power at the 0.05 significance level with a two-sided test.


Power analysis for simple logistic regression binary covariate4

Power Analysis for Simple Logistic Regression: Binary Covariate

  • The example described on previous slide indicates that

  • To apply the sample size formula, we still need to calculate . They can be obtained by


Power analysis for simple logistic regression binary covariate5

Power Analysis for Simple Logistic Regression: Binary Covariate

  • Plugging these values into the sample size formula, we have


Power analysis for simple logistic regression continuous covariate6

Power Analysis for Simple Logistic Regression: Continuous Covariate

  • The following Splus codes can be used to carry out

    the hand-calculation on the previous slide

    simple.logistic.regression.binary<-function(alpha,beta,p0,B,R){

    # alpha---significance level

    # beta---one minus power

    # p0---probability at the mean of the covariate

    # B---odds ratio to be detected

    # R—the proportion of the sample with x1=1

    p1<-B*p0/(1-p0+B*p0)

    pbar<-(1-R)*p0+R*p1

    temp1<-pbar*(1-pbar)/R

    temp2<-p0*(1-p0)+p1*(1-p1)*(1-R)/R

    temp3<-(p1-p0)^2*(1-R)

    N<-(qnorm(1-alpha/2)*sqrt(temp1)+qnorm(1-beta)*sqrt(temp2))^2/temp3

    N

    }

    simple.logistic.regression.binary(0.05,0.2,0.1,2,0.5)


Power analysis for simple logistic regression binary covariate6

Power Analysis for Simple Logistic Regression: Binary Covariate

  • Summary statements: A logistic regression of post-traumatic stress disorder on gender with a sample size of 565 observations achieves 80% power at a 0.05 significance level to detect an odds ratio of 1.5: the ratio of odds when one is a female to the odds when one is a male .


Multiple logistic regression

Multiple Logistic Regression

  • Multiple logistic regression expresses the relationship between a binary response variable, , and two or more covariate, . The multiple logistic regression model relates the probability of

    to by the formula

    where P is the probability of Y=1 given the values of the

    covariates.


Power analysis for multiple logistic regression

Power Analysis for Multiple Logistic Regression

  • When there are multiple covariates, the following adjustment was given by Hsieh, Block, and Larsen (1998) to give the adjusted sample size,

  • Where is the sample size resulting from the simple logistic regression with (the variable of interest) being the covariate, and is the multiple correlation coefficient between

    and the remaining covariates, and is equal to the proportion of the variance of explained by the remaining covariates.


Power analysis for multiple logistic regression1

Power Analysis for Multiple Logistic Regression

  • Example 3 : A study is to be undertaken to study the relationship between post-traumatic stress disorder and heart rate after viewing video tapes containing violent sequences. Heart rate is assume to be normally distributed. The post-traumatic stress disorder rate is thought to be 7% among the soldiers with mean heart rate. In addition to heart rate, two more covariates: gender and age, are intended to be included in the model. The multiple correlation of heart rate with gender and age is 0.2. The researchers want a sample size large enough to detect an odds ratio of 1.5 with 90% power at the 0.05 significance level with a two-sided test.


Power analysis for multiple logistic regression2

Power Analysis for Multiple Logistic Regression

  • From example 1, when only heart rate is the covariate in the model, the required sample size is

    Thus the adjusted sample size with two more covariates with multiple correlation of 0.2 added in the model becomes


Power analysis for multiple logistic regression3

Power Analysis for Multiple Logistic Regression

  • Summary statements: A multiple logistic regression of post-traumatic stress disorder on heart rate, gender and age with a sample size of 1023 observations achieves 90% power at a 0.05 significance level to detect an odds ratio of 1.5: the ratio of the odds at the mean of heart rate to the odds at one standard deviation above the mean of heart rate, controlling for gender and age.


Cox regression

Cox Regression

  • Cox proportional hazards regression models the relationship between the hazard function

    of survival time and k covariates using the following formula

    where is the baseline hazard.


Power analysis for simple cox regression continuous covariate

Power Analysis for Simple Cox Regression: Continuous Covariate

  • Suppose one wants to test the null hypothesis

    where is the hazard ratio : the ratio of the hazard rate at one standard deviation of above the mean to the hazard rate at the mean of

  • Hsieh and Lavori (2000) gave the following sample size formula when is normally distributed.


Power analysis for simple cox regression continuous covariate1

Power Analysis for Simple Cox Regression: Continuous Covariate

  • The sample size formula indicates that to determine the required sample size, one needs to know the following factors:

    • : Significance level

    • : desired Power

    • : Hazard ratio to be detected:

    • : The proportion of subjects that become

      incidence cases

    • : The variance of


Power analysis for simple cox regression continuous covariate2

Power Analysis for Simple Cox Regression: Continuous Covariate

  • Compute required sample size to detect a hazard ratio of 1.5 for a continuous covariate of interest with standard deviation 0.3, assuming only 85% of subjects survive until the end of the study


Power analysis for simple cox regression continuous covariate3

Power Analysis for Simple Cox Regression: Continuous Covariate

  • Example 4: Compute required sample size to achieve power 80% in detecting a hazard ratio of 1.5 for a continuous covariate of interest with standard deviation 0.3, assuming only 85% of subjects survive until the end of the study


Power analysis for simple cox regression binary covariate

Power Analysis for Simple Cox Regression: Binary Covariate

  • When is binary covariate, one also wants to test the null hypothesis

    where is the hazard ratio : the ratio of the hazard at

    to the hazard at

  • Schoenfeld (1983) gave the following sample size formula when is binomally distributed.


Power analysis for simple cox regression binary covariate1

Power Analysis for Simple Cox Regression: Binary Covariate

  • The sample size formula indicates that to determine the required sample size, one needs to know the following factors:

    • : Significance level

    • : desired Power

    • : Hazard ratio to be detected:

    • : The proportion of subjects that become

      incidence cases

    • : The proportion of the sample with


Power analysis for simple cox regression binary covariate2

Power Analysis for Simple Cox Regression: Binary Covariate

  • The sample size formula indicates that to determine the required sample size, one needs to know the following factors:

    • : Significance level

    • : desired Power

    • : Hazard ratio to be detected:

    • : The proportion of subjects that become

      incidence cases

    • : The proportion of the sample with


Power analysis for simple cox regression binary covariate3

Power Analysis for Simple Cox Regression: Binary Covariate

  • Example 5: Compute required sample size to achieve power 80% in detecting a hazard ratio of 1.5 for a binary covariate of interest with exposure rate of 0.2, assuming only 85% of subjects survive until the end of the study


Power analysis for multiple cox regression

Power Analysis for Multiple Cox Regression

  • When there are multiple covariates, the following adjustment was given by Hsieh, and Lavori (2000) to give the adjusted sample size,

  • Where is the sample size resulting from the simple Cox regression with (the variable of interest) being the covariate, and is the multiple correlation coefficient between

    and the remaining covariates, is equal to the proportion of the variance of explained by the remaining covariates.


Power analysis for multiple cox regression1

Power Analysis for Multiple Cox Regression

  • From example 4, when only the continuous covariate is in the model, the required sample size is

    Thus the adjusted sample size with two more covariates with multiple correlation of 0.2 added in the model becomes


Linear regression

Linear Regression

  • Linear regression expresses the relationship between a continuous response variable, , and one or more covariate, . The multiple logistic regression model relates to by the formula

    where is a normally distributed random variable with mean

    0 and variance


Power analysis for linear regression

Power Analysis for Linear Regression

  • Suppose one wants to test the null hypothesis

    where C refers to the variable controlled, and T refers the variables tested.

  • Let be the achieved when is regressed on those in set C, and when on those in both sets T

    and C.


Power analysis for linear regression1

Power Analysis for Linear Regression

  • The formula for computing the power is

    where

    (1) is the (1- )% percentile of central F distribution with u and v degrees of freedom. The value of u is the number of variable in T, v=n-u-k-1, and k is the number of variables in C , and

    (2) F is distributed as a non-central F with u and v degrees of freedom and non-centrality parameter . The value of


Power analysis for linear regression2

Power Analysis for Linear Regression

  • The sample size formula indicates that to determine the required sample size, one needs to know the following factors:

    • : Significance level

    • : Sample size

    • u : the number of covariates tested

    • k : the number of covariates controlled

    • : the achieved when is regressed on those in set C

    • : the achieved when is regressed on those in set T

      and C


Power analysis for linear regression3

Power Analysis for Linear Regression

  • Example 6: A school district is designing a multiple regression study looking at the effect of gender, family income, mother's education and language spoken in the home on the English language proficiency scores of Latino high school students. The variables gender, family income, and Mother's education are control variables and not of primary research interest. The variable language spoken in the home, the variable of primary research interest, is a categorical research variable with three levels: 1) Spanish only, 2) both Spanish and English, and 3) English only. Since there are three levels, it will take two dummy variables to code language spoken in the home.


Power analysis for linear regression4

Power Analysis for Linear Regression

  • The full regression model will look something like this,

    engprof = b0 + b1(gender) + b2(income) + b3(momeduc) + b4(homelang1) + b5(homelang2)

  • Thus, the primary research hypotheses are the test of b4 = b5=0.


Power analysis for linear regression5

Power Analysis for Linear Regression

  • To begin with, we believe, from previous research, that the for the full-model with five predictor variables (3 control, and 2 dummy variables for the categorical variable) will be will be about 0.48.

  • We think that it will add about 0.03 to the R2 when the two dummy variables are added last to the model. This means that the for the model without the two dummy variables would be about 0.45.

  • The total number of variables controlled is three and the number being tested is two.


Power analysis for linear regression6

Power Analysis for Linear Regression

power.linear.regression<-function(N,alpha,kc,kt,Rsq.c,Rsq.tc){

#N--sample size

#alpha-significance level

#kc--the number of variables controlled

#kt--the number of variables tested

#Rsq.c--the R square when the variables controlled are in the model

#Rsq.c--the R square when both the variables controlled and the variables tested are in the model

u<-kt

v<-N-kc-kt-1

falpha<-qf(1-alpha,u,v)

fsq<-(Rsq.tc-Rsq.c)/(1-Rsq.tc)

lambda<-N*fsq

power<-1-pf(falpha,u,v,lambda)

power

}

power.linear.regression(170,0.05,3,2,0.45,0.48)


References

References

  • Hsieh, F.Y., Bloch, D.A., and Larsen, M.D. A Simple Method of Sample Size Calculation for Linear and Logistic Regression, Statistics in Medicine,17,1623-1634(1998)

    http://personal.health.usf.edu/ywu/logistic.pdf

  • Schoenfeld, D.A. Sample-size Formula for the Proportional-Hazards Regression Model. Biometrics, 39, 499-563(1983)

    http://personal.health.usf.edu/ywu/cox_bin.pdf

  • Hsieh, F.Y.,Lavori, P.W. Sample-Size Calculations for the Cox Proportional Hazards Regression Model with Nonbinary Covariates. Controlled Clinical Trials, 21 552-560(2000)

    http://personal.health.usf.edu/ywu/cox.pdf


Homework

Homework

  • Suppose somebody asks you to calculate the sample size for a matched case-control study, and you know that the sample size formula is available from the following paper:

    http://personal.health.usf.edu/ywu/matched_case_control.pdf

    1. Identify the sample size formula, and write a Splus function to carry out the sample size calculation;

    2. Use an example to illustrate how to use your Splus function, and write a statement to summarize the result of your power analysis.


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