# SAMPLE SIZE TABLES FOR LOGISTIC REGRESSION

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1 STATISTICS IN MEDICINE, VOL. 8, (1989) SAMPLE SIZE TABLES FOR LOGISTIC REGRESSION F. Y. HSIEH* Department of Epidemiology and Social Medicine, Albert Einstein College of Medicine, Bronx, N Y 10461, U.S.A. SUMMARY Sample size tables are presented for epidemiologic studies which extend the use of Whittemore s formula. The tables are easy to use for both simple and multiple logistic regressions. Monte Carlo simulations are performed which show three important results. Firstly, the sample size tables are suitable for studies with either high or low event proportions. Secondly, although the tables can be inaccurate for risk factors having double exponential distributions, they are reasonably adequate for normal distributions and exponential distributions. Finally, the power of a study varies both with the number of events and the number of individuals at risk. KEY WORDS Logistic regression Sample size INTRODUCTION AND ASSUMPTIONS Logistic regression is commonly used in the analysis of epidemiologic data to examine the relationship between possible risk factors and a disease. In follow-up studies the proportion of individuals with disease (event) is usually low, but it is higher in case-control studies. In this paper I present tables of the required number of subjects in such studies for event proportions ranging from 001 to 0.50, covering most follow-up and case-control studies. In the logistic regression model, the dependent variable (the disease status) is a dichotomous variable taking the values 0 for non-occurrence and 1 for occurrence. If the independent variable (such as the risk factor) is also dichotomous, the approximate required sample size can be found from published sample size tables for the comparison of two proportions. For matched casecontrol studies, sample size calculations can be obtained from Dupont.2 The sample size tables which I present in this paper are derived from Whittemore s f~rrnula.~ The tables assume that the risk factors are continuous and have a joint multivariate normal distribution. The following section describes the sample size tables and their use. THE SAMPLE SIZE TABLES Tables I to V display the required sample size for a study using logistic regression with only one covariate (that is, risk factor). To use the tables one must specify (1) the probability P of events at the mean value of the covariate, and (2) the odds ratio r of disease corresponding to an increase of one standard deviation from the mean value of the covariate. The tables give five choices of percentage values for the one-tailed significance level a per cent and the power 1 -B per cent: (I) a=5, 1-Q=70 (11) a=5, 1-8=80 (111) a=5, 1-j3=90 * Current address: Anaquest, BOC Health Care, 100 Mountain Avenue, Murray Hill, NJ 07974, U.S.A /89/ \$ by John Wiley & Sons, Ltd. Received September 1988 Revised January 1989

2 796 F. Y. HSIEH (IV) a=5, 1 -b=95 (V) a= 1, 1 -/?=95. As explained in Appendix I, the sample size for an odds ratio r is the same as that required for an odds ratio l/r. For example, the sample sizes for odds ratios of 2 and 2.5 are the same as those required for odds ratios 0.5 and 0-4, respectively. When there is more than one covariate in the model, multiple logistic regression may be used to estimate the relationship of a covariate to disease, adjusting for the other covariates. The sample size required to detect such a relationship is greater than that listed in Tables I to V. For the calculation of sample size, let p denote the multiple correlation coefficient relating the specific covariate of interest to the remaining covariates. One must specify (1) the probability P of an event at the mean value of all the covariates, and (2) the odds ratio r of disease corresponding to an increase of one standard deviation from the mean value of the specific covariate, given the mean values of the remaining covariates. The sample size read from Tables I to V should then be divided by the factor 1 - p2 to obtain the required sample size for the multiple logistic regression model. This method yields an approximate upper bound rather than an exact value for the sample size needed to detect a specified association. Unlike Whittemore s f~rmula,~ this method does not require the user to specify the coefficients of the remaining covariates. EXAMPLES Whittemore3 used Hulley s data4 to calculate the sample size for a follow-up study designed to test whether the incidence of coronary heart disease (CHD) among white males aged is related to their serum cholesterol level. For this study, the probability of a CHD event during an 18-month follow-up for a man with a mean serum cholesterol level is To detect an odds ratio of 1.5 for an individual with a cholesterol level of one standard deviation above the mean using a one-tailed test with a significance level of 5 per cent and a power of 80 per cent, we need 614 individuals (from Table 11). To detect the same effect while controlling for the effects of triglyceride, and assuming that the correlation coefficient of cholesterol level with log triglyceride level is 0.4, we would need 614/(1-0.16)=731 individuals for the study. DISCUSSION These sample size tables do not explicitly require knowledge of the number of covariates in the regression model. The results in Appendix1 indicate that the number of covariates is not important, and that the inclusion of new covariates which do not increase the multiple correlation coefficient with the covariate of interest does not affect sample size. An adjustment of the overall P-value for multiple significance testing may be needed when several covariates are of interest as potential risk factors for the disease. Where one is interested in the effect of one specific covariate:, Appendix I also shows that the sample sizes given in the tables may be used whatever the values of the coefficients of the remaining covariates. The results of Monte Carlo simulations in Appendix I1 indicate that, when there is only one covariate in the model, the given sample sizes are reasonably accurate for both normal and exponential distributions of the covariate, although the tables can be inaccurate for some distributions, such as the double exponential. When there are two covariates having a bivariate normal distribution, the values in the tables overestimate the required sample size, but to an acceptable degree. The simulations also show that the power of the test varies both with the number of events and with the number of individuals at risk. Whittemore3 has found the required sample size to be very sensitive to the distribution of the covariates. I recommend that when a covariate is not normally distributed, leaving the adequacy

3 ~~ SAMPLE SIZE TABLES FOR LOGISTIC REGRESSION 797 Table I. Sample size required for univariate logistic regression having an overall event proportion P and an odds ratio r at one standard deviation above the mean of the covariate when a= 5 per cent (one-tailed) and 1-8=70 per cent P lo I Note: To obtain sample sizes for multiple logistic regression, divide the number from the table by a factor of 1 - p2. where p is the multiple Table 11. Sample size required for univariate logistic regression having an overall event proportion P and an odds ratio r at one standard deviation above the mean of the covariate when a=5 per cent (one-tailed) and 1-8=80 per cent P Note: To obtain sample sizes for multiple logistic regression, divide the number from the table by a factor of 1 -p2, where p is the multiple of the calculated sample size in doubt, one should perform a transformation of the covariate to achieve normality before using Tables I to V. In conclusion, the methods in this paper provide slightly conservative estimates of the required sample size for normally distributed covariates. The tables are simple to use and are suitable for a variety of epidemiologic studies.

4 ~~~~~ ~ 798 F. Y. HSIEH Table 111. Sample size required for univariate logistic regression having an overall event proportion P and an odds ratio rat one standard deviation above the mean of the covariate when a=5 per cent (one-tailed) and 1-8=90 per cent P I I Note: To obtain sample sizes for multiple logistic regression, divide the number from the table by a factor of 1 -p2. where p is the multiple Table IV. Sample size required for univariate logistic regression having an overall event proportion P and an odds ratio r at one standard deviation above the mean of the covariate when a= 5 per cent (one-tailed) and 1-8=95 per cent P Q Note: To obtain sample sizes for multiple logistic regression, divide the number from the table by a factor of 1 - p2. where p is the multiple APPENDIX I: SAMPLE SIZE FORMULAE Let Y denote the disease status, and let Y= 1 if the disease occurs and Y=O otherwise. Let XI, X,,..., X, denote the covariates, which are assumed to have a joint multivariate normal distribution. The logistic regression model specifies that the conditional probability of disease

5 SAMPLE SIZE TABLES FOR LOGISTIC REGRESSION 799 Table V. Sample size required for univariate logistic regression having an overall event proportion P and an odds ratio r at one standard deviation above the mean of the covariate when CI= 1 per cent (one-tailed) and 1 -!3=95 per cent P I Note: To obtain sample sizes for multiple logistic regression, divide the number from the table by a factor of 1 -p2, where p is the multiple occurrence P = P(X) = P(Y = 1 IX,,...,Xk) is related to X,,...,Xk by iog[p/(i-p)]=80+8,x, kxk. Assume, without loss of generality, that among the k covariates X, is the covariate of primary interest. We wish to test the null hypothesis of H,: 8= [0, tl,,...,ok] against the alternative hypothesis H,: 8 = [e*, 8,,... \$k]* Let p denote the multiple correlation coefficient of X, with X,,...,Xk. If each of the covariates Xi has been normalized to have mean zero and variance one, the sample size N needed to test at level a and power 1 -p can be approximated, according to Whittem~re,~ by N exp (0,) = [ v1l2 (eo)z, + vl/, (e*)z,~~/e*~, (1) where v(e)=[exp(em/2)(1 -pz)]-i, 8, = (0, e,,...,&.), e*=(e*, e,,...,&), C is the correlation matrix of X,,...,xk, and Z, and Z, are standard normal deviates with probabilities a and /?, respectively, in the upper tail. When there is only one covariate in the model, (1) reduces to NP = [z, + exp( - e*2/4)z,]2/e*2. (2) Note that (2) relates the power of the test directly to the expected number of events NP, implying that power will be independent of sample size N given a fixed number of events. However, because of deviations from the approximate formula (l), the above statement is not accurate. Monte Carlo simulations in Appendix I1 show that the power of the test is an increasing function of sample size even when the number of events remains constant. Whittemore3 suggested that the approximation could be improved by a multiplying factor of 1 + 2PS, where 6 = [ 1 + (1 + e* )e~p(50*~/4)] [ 1 + exp (- 13*~/4)] - l. (3)

6 800 F. Y. HSIEH The required sample size may then be written as a function of P and 8* as follows: N, = [za + exp( - 8*2/4)Z,]2 (1 + 2~s)/(~e*~). (4) In this formula the value of 8* represents the log odds ratio of disease corresponding to an increase in X of one standard deviation from the mean. In practice, as in Tables I to V, one would specify the value of the odds ratio I instead of the value of 8*. Because the standard normal distribution is symmetric about the mean 0, the sample size to detect a log odds ratio 8*, or an odds ratio r=exp(o*), is the same as a log odds ratio - 8*, or an odds ratio l/r=exp( - 8*). According to Whittemore s f~rmula,~ the sample size calculation for the multivariate case requires specification of the coefficients for each covariate and their correlation matrix. This is impractical for routine use. Whittemore has already pointed out that the inclusion of covariates which are correlated with x, but independent of the event (that is, o,=...=8,=0) leads to a loss of power when testing the relationship of X, to the event. Since V(0) = V( -0) 3 0, it can be shown from (1) that the sample size required for 82 =... = & = 0 is an approximate upper bound after applying the correction of (3). Therefore, I suggest using approximate sample size NM, derived from (1) by substituting 8, =... =8, = 0, for multiple logistic regression: NM = N,/(1-p2). (5) This formula provides maximum sample sizes and does not require the specification of coefficients for each covariate, or the full correlation matrix. APPENDIX 11: MONTE CARL0 POWER SIMULATIONS To check the accuracy of the calculated sample size tables, Monte Carlo simulations were carried out for various proportions of events and for covariates with different distributions. A set of lo00 trials was generated for five event proportions, four odds ratios and three distributions. Let m and s be the mean and standard deviation, respectively, of the covariates; let U and G be standard uniform and standard normal variables, respectively. The distributions of covariates were generated as follows: 1. Normal distribution: m + s G. 2. Double exponential distribution: m + Zslog U, where I = -- 1 if U * -= 0.5 and I = 1 otherwise. U* is a standard uniform variable independent of U. 3. Exponential distribution: m - s - s log U. Under the alternative hypothesis that the specified association between covariate and disease occurrence exists, the above mean and standard deviation are specified by the odds ratios through the following equations: Diseased group: Non-diseased group: m = log(odds ratio) and s = exp( - m2/4), m= 0 and s=l. When two covariates (X,, X,) have a joint bivariate normal distribution with a correlation coefficient p, the first covariate (XI) was generated by the above procedure 1 and the second covariate (X,) was obtained from X2 = px, + G(l -p2)l12. The results are shown in Table VI. Formula (4) seems to slightly underestimate the power for a covariate with a normal distribution and severely overestimate the power for a double exponential covariate. For an exponential covariate, formula (4) overestimates the power when both odds ratio

7 ~ ~ SAMPLE SIZE TABLES FOR LOGISTIC REGRESSION 801 Table VI. Estimated power from Monte Carlo simulations (lo00 repetitions) for normal, double exponential and exponential covariates, compared with formula (4) Event No. of 1.30 proportion events/ Sig. level P sample size Source /1o00 Formula (4) Normal Double exp Exponential /600 Formula (4) Normal Double exp Exponential /400 Formula (4) 0, Normal Double exp Exponential /300 Formula (4) Normal Double exp Exponential 0, /200 Formula(4) Normal Double exp Exponential Sig. level Sig. level , , , Q Sig. level & QOO o o o00 1.OOo Table VII. Estimated power from Monte Carlo simulations ( lw repetitions) for bivariate normal covariates compared with formula (4) Event No. of proportion events/ Correlation Sig. level Sig. level Sig. level P sample size Source coefficient /600 Formula (4) 0.0 Normal /300 Formula (4) 0.0 Normal /200 Formula (4) 0.0 Normal , ~o o00 1.o

8 ~ 802 F. Y. HSIEH Table VIII. Estimated power from Monte Carlo simulations (1000 repetitions) for different sample sizes and number of events Odds ratio I Number of Sample Sig. level Sig. level Sig. level Sig. level events size ~ ~ , ~ ~ ~OOO Qoo 1 ~Ooo ~Ooo OOO 1 ~OOO and event rate are low and underestimates the power otherwise. In general, if the covariate is normally distributed, we are assured that the sample size obtained from the tables will be slightly conservative. Table VII shows that formula (4) underestimates the power for bivariate normal covariates, but to an acceptable degree. Table VIII shows the results of simulations using normal covariates relating the number of events and the sample size to the power of the test. They show that when the number of events remains constant, the power of the test varies with sample size. ACKNOWLEDGEMENTS I thank the reviewers for very helpful comments. REFERENCES 1. Fleiss, J. Statistical Methods for Rates and Proportions, Wiley, New York, Dupont, W. D. Power calculations for matched case-control studies, Biometrics, 44, (1988). 3. Whittemore, A. Sample size for logistic regression with small response probability, Journal of the American Statistical Association, 76, (1981). 4. Hully, S. B., Rosenman, R. A., Bowol, R. and Brand, R. Epidemiology as a guide to clinical decisions: the association between triglyceride and coronary heart disease, New England Journal of Medicine, 302, (1980). 5. Sokal, R. R. and Rohlf, F. J. Biometry, Freeman, San Francisco, 1969.

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