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A method is presented to obtain sample sizes for cases and controls that are required to provide approximate confidence intervals on the log odds ratio of predetermined width 2d and probability of coverage as a function of assumed exposure rates in the control group, assumed odds ratio psi, required d, and ratio C:1 of controls to cases.
Experimental design requires estimation of the sample size required to produce a meaningful conclusion. Often, experimental results are performed with sample sizes which are inappropriate to adequately support the conclusions made. In this paper, two factors which are involved in sample size estimation are detailed--namely type I (alpha) and type II (beta) error. Type I error can be considered a "false positive" result while type II error can be considered a "false negative" result. Obviously, both types of error should be avoided. The choice of values for alpha and beta is based on an investigator's understanding of the experimental system, not on arbitrary statistical rules. Examples relating to the choice of alpha and beta are presented, along with a series of suggestions for use in experimental design.
Although sample size calculation is mandatory in clinical trials, the power of most of the published small size clinical studies, was only about 0.25 instead of 0.80 which is normally required in a standard randomized controlled trial. This phenomenon is probably due to the publication bias. In a cancer clinical study, the ideal sample size needed in a trial exceeds over a thousand, when significance level was fixed under 0.05 and treatment difference is estimated from 10 to 15%. In such cases, evaluation of a new treatment in less common cancers or in a specified strata become unfeasible. Although statistically plausible clinical trial is difficult, small trials of newly advocated treatment is likely to be performed elsewhere, and presentation of these haphazard results might propagate a wrong information about the new treatment. To prevent the dissemination of these biased results, 1) preregistration of all planned clinical trials to an authorized organization before the initiation of the trial, 2) randomization of the patients entered in the trial from the first case, and 3) registration of all available data of the patients and meta-analysis of preregistered multiple trials, should be the most effective counterplans. In order to achieve those functions, installation of a computer assisted coordinating center is considered to be the best solution for the proper evaluation of the clinical trial as well as for the evaluation of a new treatment. With the collaboration of regional affiliating hospitals, a pilot study have started to establish a computer network system.
The errors inherent in EM autoradiography are discussed and certain of them deemed to be of particular practical significance in the quantitative assessment of preparations. A method is described for estimating the standard errors attributable to each of several sources of variation and thence for obtaining the overall standard error value to be attached to relative activity estimates obtained in the method of Downs & Williams (1978). In an appendix, a fully worked example is given illustrating clearly the strategy of the method and the magnitudes of error estimates that are to be attached to the final specific activity values.
To estimate vaccine protective efficacy, defined as VE = 1 - ARV/ARU where ARV is the disease attack rate in the vaccinated group and ARU is the disease attack rate in the controls, investigators have used both cohort and case-control designs. For each design, we present a method for calculation of the sample size required to provide an approximate confidence interval for VE of predetermined width and probability of coverage. The required sample size is a function of the desired width of the confidence interval, the probability of coverage, the assumed VE, and, for cohort designs, the assumed disease attack rate in the controls, and for case-control designs, the assumed vaccine exposure prevalence for the controls.
Fifty-four patients with chronic stable angina were studied to determine and compare weekly variability of indexes for the detection of myocardial ischemia. All patients underwent three single-blind placebo periods, each lasting 1 week. An exercise treadmill test, 24 h ambulatory electrocardiographic (Holter) monitoring (analyzed blindly) and an accurate diary of anginal attacks and nitroglycerin use were obtained at the end of each placebo period. An unbalanced, completely random component of variance analysis was used to calculate a component for within subject variability and a component for among subject variability. The coefficient of variation and percent variation (within subjects) of onset of chest pain during exercise were 19% and 30%, respectively; the corresponding values were 28% and 33% for onset of 1 mm ST depression, 15% and 15% for exercise duration, 44% and 27% for number of ischemic episodes/24 h, 56% and 43% for anginal frequency and 55% and 27% for nitroglycerin consumption, respectively. With use of this statistical method and variation within subjects, the change in the value of each variable necessary to exceed those attributable to spontaneous variation was determined. The trade-off between repeated measurements and number of subjects, the sample size estimated for planning studies and the minimal sample size for using various designs were also determined. Although the data indicate that all indexes for myocardial ischemia, both during exercise and during daily activity, vary considerably, but the exercise variables have less variability and are more reproducible.(ABSTRACT TRUNCATED AT 250 WORDS)
General guidelines are presented for the use of cluster-sample surveys for health surveys in developing countries. The emphasis is on methods which can be used by practitioners with little statistical expertise and no background in sampling. A simple self-weighting design is used, based on that used by the World Health Organization's Expanded Programme on Immunization (EPI). Topics covered include sample design, methods of random selection of areas and households, sample-size calculation and the estimation of proportions, ratios and means with standard errors appropriate to the design. Extensions are discussed, including stratification and multiple stages of selection. Particular attention is paid to allowing for the structure of the survey in estimating sample size, using the design effect and the rate of homogeneity. Guidance is given on possible values for these parameters. A spreadsheet is included for the calculation of standard errors.
Twenty-six eyes with posterior subcapsular opacities of various sizes were photographed with the Neitz-Kawara Retroillumination camera. The outline of the opacity in a single photograph of each opacity was traced onto a transparent plastic overlay twice by two independent outliners. Two methods were used to estimate the area within the outlines of the opacities. In the first, a transparent overlay with a standard grid was used to count the number of boxes within the outlines. The second method used computer planimetry to estimate the area within the tracings. We estimated the measurement error associated with a single outlining of an opacity and the contribution of the measurement error to overall sample size requirements in studies comparing the mean areas of posterior subcapsular opacities. Variability in the measurement techniques contributed fewer than 20 additional subjects to overall sample size estimates, a small contribution to total sample size requirements in most studies. An outliner's inherent variability in outlining an opacity was a much larger contributor to the measurement error than was variability in assessing the area of the outline of the opacity. While within outliner variability was similar for the two persons outlining the opacities, there were systematic differences in the way the two traced the outlines. Variability from the use of separate photographs of the same opacity taken by different photographers was minimal.
In planning case-control studies with matched sets, the calculation of exact sample sizes is difficult, because this calculation depends on some nuisance parameters that are usually unknown in practice. Using the Pitman efficiency of Miettinen's test relative to McNemar's test, Schlesselman and Stolley (Case-control studies: design, conduct, analysis. Oxford: Oxford University Press, 1982:144-70) derived an approximate sample size formula which requires the assumption that the difference in exposure rates between cases and controls is small. Furthermore, on the basis of an assumption similar to that used in Schlesselman and Stolley's approach, Taylor (Stat Med 1986;5:29-36) proposed another approximation formula. In this paper, an alternative and explicit formula that does not require the exposure difference to be small between case and control groups has been derived. Monte Carlo studies are given for comparing the accuracy of these three procedures. The results indicate that when odds ratios of exposure between cases and controls are small (less than or equal to 4) and there is more than one matched control per case, the formula derived in this paper seems to be the best. When odds ratios are large (greater than or equal to 5), however, Taylor's more conservative estimate is recommended, unless the exposure prevalence in the general population is large (0.9).
While most epidemiologic cohort studies are preceded by some sort of feasibility study, details of such prior investigations are rarely reported. Yet it is during such feasibility studies that critical decisions are made, such as site selection and definition of exposure. Here we present the details on one such feasibility study, conducted to determine the possibility of a cohort mortality study of workers exposed to ethylene oxide. Issues discussed include methods for estimating sample size and power, for estimating levels of exposure, and for assessing the adequacy of personnel records.
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The prevalence of vitamin A deficiency among two to ten years old children in a rural area of Mali was assessed by ophthalmic examination, determination of plasma retinol levels and impression cytology with transfer tests. A Public Health problem of vitamin A deficiency was identified in this rural area by: the prevalence of nightblindness significantly (p < 0.001) above the cut-off (1%) defined by the World Health Organization (WHO); the prevalence of corneal scarring significantly (p < 0.001) above the WHO's cut-off (0.05%); the percentage of subjects with plasma retinol levels below 0.35 mumol/l (10 micrograms/dl) significantly (p < 0.001) higher than the WHO's threshold (5%); and 52.8 +/- 8.2% children with "Abnormal" impression cytology as determined by the impression cytology test (IC). This preliminary survey confirmed widespread vitamin A deficiency in Mali. The minimum sample size required for a study using the impression cytology test to determinate a Public Health problem in a population was calculated for different situations. Ophthalmic examination indicated a very high rate of active trachoma (29.6 +/- 7.0%), and a relationship between active trachoma and impression cytology results was identified.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Sample sizes are calculated for unmatched case-control (or cohort) studies where the goal is interval estimation of the odds ratio. The procedure used gives the smallest sample size for which a 100(1-alpha)% confidence interval for the log odds ratio will not exceed a specified width with specified probability (1-gamma). Tables of sample sizes for various choices of parameter values are presented. Considerable disagreement is found with a published method which has as its basis expected cell counts.
BACKGROUND AND PURPOSE: The purpose of this study was to analyze recovery of motor function in a cohort of patients presenting with an acute occlusion in the carotid distribution. Analysis of recovery patterns is important for estimating patient care needs, establishing therapeutic plans, and estimating sample sizes for clinical intervention trials. METHODS: We prospectively measured the motor deficits of 104 stroke patients over a 6-month period to identify earliest measures that would predict subsequent motor recovery. Motor function was measured with the Fugl-Meyer Assessment. Fifty-four patients were randomly assigned to a training set for model development; 50 patients were assigned to a test set for model validation. In a second analysis, patients were stratified on basis of time and stroke severity. The sample size required to detect a 50% improvement in residual motor function was calculated for each level of impairment and at three points in time. RESULTS: At baseline the initial Fugl-Meyer motor scores accounted for only half the variance in 6-month motor function (r2 = 0.53, p less than 0.001). After 5 days, both the 5-day motor and sensory scores explained 74% of the variance (p less than 0.001). After 30 days, the 30-day motor score explained 86% of the variance (p less than 0.001). Application of these best models to the test set confirmed the results obtained with the training set. Sample-size calculations revealed that as severity and time since stroke increased, sample sizes required to detect a 50% improvement in residual motor deficits decreased. CONCLUSIONS: Most of the variability in motor recovery can be explained by 30 days after stroke. These findings have important implications for clinical practice and research.