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PubMed1995

Survival estimates and sample size: what can we conclude?

Attempts to understand aging processes often involve life-span measurements from which a survival curve is constructed and model parameters estimated. The parameter estimates are then compared, and conclusions concerning the underlying biological processes are subsequently deduced, based upon the magnitude of the parameter differences. In this article we discuss the role of sample size and sample fluctuation on the parameter estimates and the profound effect that these factors may play in our arrival at meaningful biological conclusions. We then extend this discussion to examine one methodology that can help select sample sizes for specific parametric survival models.

Aging↗

PubMed2000

Differential progression of motor impairment in levodopa-treated Parkinson's disease.

OBJECTIVE: To monitor comparative progression of clinical impairment over 4 years in patients with Parkinson's disease (PD) who present on levodopa at two different levels of Hoehn and Yahr (HY) stages, II and III. BACKGROUND: The rate of clinical impairment progression in patients with PD being treated with levodopa has not been studied in detail using current, standardized assessment tools. Sample size estimates for all levodopa adjunctive treatment studies and proper definition of study groups require a solid estimate of longitudinal motor impairment progression. DESIGN/METHODS: From our computer database, we identified two groups of patients with PD being treated with levodopa based on their initial HY stage at presentation to our center (II or III). Fifty randomly selected subjects in each stage were monitored in the ON state with annual Unified Parkinson's Disease Rating Scale (UPDRS) motor scores, dyskinesia ratings, and antiparkinsonian medication doses using a repeated measures analysis of variance. RESULTS: The stage II and stage III subjects had similar disease duration. In stage II subjects, parkinsonian impairment was maintained without progression over 4 years, but in association with significantly higher dyskinesia scores and dopaminergic medication doses. In stage III subjects, UPDRS motor scores deteriorated despite more medication and increased dyskinesias. Of the established six factors comprising the UPDRS motor scale, bradykinesia accounted for the increased impairment. Initial UPDRS motor score and disease duration did not influence progression of motor impairment. CONCLUSION: In subjects with similar disease duration, progression of PD motor impairment differs significantly between stage II and stage III subjects over 4 years. Whereas in stage II subjects, parkinsonian impairment can be stabilized at the expense of increased dyskinesia and dopaminergic drugs, once subjects reach stage III, motor impairment progresses. Power estimates and sample size calculations for these groups of patients should be calculated separately.

Aged↗

PubMed1984

Statistical methodology for clinical trials of caries prophylactic agents--current knowledge.

Statistical methodology underlying clinical trials of caries prophylactic agents published up to 1982 is summarized. The various caries indices used to express an individual's caries increment over a period of time are considered, along with some standard models used to describe caries incremental and prevalence data. The parametric statistical techniques generally employed to test for significant differences between the caries preventive agents under investigation are robust for the departures from normality found in caries data with the large sample sizes involved. Methods of estimating sample sizes are considered along with the various multiple comparison tests suitable for analysing multi-group trials. The advantages of using either an analysis of covariance or stratification procedures in certain situations are discussed, along with suitable covariables which have been put forward. Ways of improving the efficiency of clinical trials by selecting subjects are also considered. Models put forward to describe diagnostic errors are examined. However, it is generally found that examiner error in long term trials is small. The use of sequential tests in clinical trials needs further development.

Analysis of Variance↗

PubMed1995

Precision of marker heterozygosity estimates.

Sample sizes may greatly affect the accuracy of estimates of marker heterozygosities. Therefore, indicating the precision of these estimates is strongly recommended. A computer program HETMAX was written and used to obtain unbiased estimates of heterozygosity, their standard errors and unit support intervals, based on a subset of GAW9 data. The accuracy of the estimates and the associated sample sizes are presented.

Chromosome Mapping↗

PubMed1984

Sample sizes for estimation of the odds ratio in unmatched case-control studies.

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.

Humans↗

PubMed1995

Measurement error in assessing the size of cortical cataracts from retroillumination photographs.

This study describes a new method of quantifying anteriorly located cortical cataracts using retroillumination photographs and computer planimetry. Cortical cataracts were graded clinically and then photographed using the Neitz retroillumination camera twice by each of 2 photographers. The cataract outlines were traced onto a transparent overlay, and computer planimetry was performed using a Scan Maker 600ZS, a MAC II Computer and specially developed software. We estimated the measurement error of the method and its associated effect on sample size estimates for clinical studies. We calculated that the variability in this technique would contribute about 21 additional subjects to overall sample size estimates in studies comparing the mean areas of cortical opacities. In many studies this would be a small addition to total sample size requirements. This technique provides clinically useful measurements of the size of a cortical opacity as seen on a retroillumination photograph. This may be useful for future clinical studies on natural progression of cortical cataracts as well as for clinical trials of anticataract drugs.

Cataract↗

PubMed1999

Estimating the sample size for a t-test using an internal pilot.

If the sample size for a t-test is calculated on the basis of a prior estimate of the variance then the power of the test at the treatment difference of interest is not robust to misspecification of the variance. We propose a t-test for a two-treatment comparison based on Stein's two-stage test which involves the use of an internal pilot to estimate variance and thus the final sample size required. We evaluate our procedure's performance and show that it controls the type I and II error rates more closely than existing methods for the same problem. We also propose a rule for choosing the size of the internal pilot, and show that this is reasonable in terms of the efficiency of the procedure.

Clinical Trials, Phase II as Topic↗

PubMed1999

Sample size calculations for trials in health services research.

The current orthodox way of estimating sample size for a trial is through a power calculation based on a significance test. It therefore carries the assumption that this test should be the centerpiece of the statistical analysis. However, it is increasingly the case that confidence intervals are preferred to significance tests in summarising the results of trials, particularly in health services research. We believe that the way sample size is estimated should reflect this change and focus on the width of the confidence interval rather than on the outcome of a significance test. Such a method of estimation is described here and shown to have additional advantages of simplicity and transparency, enabling a more informed debate about the proposed size of trials.

Confidence Intervals↗

PubMed1990

Statistical significance and statistical power in hypothesis testing.

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.

Research Design↗

PubMed2000

Cluster randomised trials in maternal and child health: implications for power and sample size.

BACKGROUND: Interventions based in the community can be evaluated by randomising clusters, such as general practices, rather than individuals, as in conventional randomised trials. This increases the sample size needed because of intracluster correlation. AIMS: To estimate sample size requirements for cluster randomised trials of interventions based in general practice directed at common health problems affecting mothers and infants. METHODS: Data were collected from a pilot trial of the effect of Citizen's Advice Bureau services involving six general practices. Outcome measures included the Edinburgh postnatal depression score, the Warwick child health and morbidity profile, number of visits to the general practitioner, and two questionnaires delivered at the beginning and end of the study. Intracluster correlation coefficients and inflation factors (the ratio of the sample size required for a cluster randomised trial to that required for an individually randomised trial) were calculated. RESULTS: Intracluster correlation coefficients ranged from 0 (sleeping problems, accidental injury, hospitalisation) to 0.09 (maternal smoking), with most being < 0.04 (for example, maternal depression, breast feeding, general health, minor illness, behavioural problems, and visits to the general practitioner). Assuming 50 cases/practice, cluster randomised trials require sample sizes up to 3 times greater than individually randomised trials for most health outcomes measured. CONCLUSIONS: These data enable sample sizes to be estimated for cluster randomised trials into a range of maternal and child health outcomes. Using such a design, approximately 40 practices would be sufficient to evaluate the effect of an intervention on maternal depression, sleeping, and behavioural problems, and non-routine visits to the general practitioner.

Analysis of Variance↗

PubMed1997

Use of the standard error as a reliability index of interest: an applied example using elbow flexor strength data.

The intraclass correlation coefficient (ICC) and the standard error of measurement (SEM) are two reliability coefficients that are reported frequently. Both measures are related; however, they define distinctly different properties. The magnitude of the ICC defines a measure's ability to discriminate among subjects, and the SEM quantifies error in the same units as the original measurement. Most of the statistical methodology addressing reliability presented in the physical therapy literature (eg, point and interval estimations, sample size calculations) focuses on the ICC. Using actual elbow flexor make and break strength measurements, this article illustrates a method for estimating a confidence interval for the SEM, shows how an a priori specification of confidence interval width can be used to estimate sample size, and provides several approaches for comparing error variances (and square root of the error variance, or the SEM).

Analysis of Variance↗

PubMed2000

Sample size calculation for clinical trials: the impact of clinician beliefs.

The UK Medical Research Council (MRC) randomized trial of gastric surgery, ST01, compared conventional (D1) with radical (D2) surgery. Sample size estimation was based upon the consensus opinion of the surgical members of the design team, which suggested that a change in 5-year survival from 20% (D1) to 34% (D2) could be realistic and medically important. On the basis of these survival rates, the sample size for the trial was 400 patients. However, this trial was exceptional in the way that a survey of surgeons' opinions was made at the start of the trial, in 1986, and again before results were analysed but after termination of the trial in 1994. At the initial survey, the three surgeons from the trial steering committee and 23 other surgeons experienced in treating gastric carcinoma were given detailed questionnaires. They were asked about the expected survival rate in the D1 group, anticipated difference in survival from D2 surgery, and what difference would be medically important and influence future treatment of patients. The consensus opinion of those surveyed was that there might be a survival improvement of 9.4%. In 1994, prior to closure of the trial, and before any survival information was disclosed, the survey was repeated with 21 of the original 26 surgeons. At this second survey, the opinion of the trial steering committee was that 9.5% difference was more realistic. This was in accord with the opinion of the larger group, which remained little changed since 1986. The baseline 5-year D1 survival was thought likely to be about 32%, which corresponded closely to the actual survival of recruited patients. Revised sample size calculations suggested that, on the basis of these more recent opinions, between 800 and 1200 patients would have been required. Both surveys assessed the level of treatment benefit that was deemed to be sufficient for causing surgeons to change their practice. This showed that the 13% difference in survival used as the study target was clinically relevant, but also indicated that many clinicians would remain unwilling to change their practice if the difference is only 9.5%. The experience of this carefully designed trial illustrates the problems of designing long-term, randomized trials. It raises interesting questions about the common practice of basing sample size estimates upon the beliefs of a trial design committee that may include a number of enthusiasts for the trial treatment. If their opinion of anticipated effect sizes drives the design of the trial, rather than the opinion of a larger community of experts that includes sceptics as well as enthusiasts, there is likely to be a serious miscalculation of sample size requirements.

Bayes Theorem↗

PubMed1993

Research cost analyses to aid in decision making in the conduct of a large prevention trial, CARET. Carotene and Retinol Efficacy Trial.

Because of their larger study populations and longer durations, prevention trials typically are more costly than treatment trials. Thus it is important to analyze costs systematically to aid in making cost-effective decisions during the conduct of prevention trials as well as in the original design. Cost analysis must be tied to sample size estimation because costs depend on such factors as the total number of person-years of follow-up and the number of trial outcomes, which are not basic design parameters but are derived quantities resulting from sample size estimation. We illustrate the use of cost analysis to decide among options for future conduct of an ongoing prevention trial with three issues that have arisen during the Carotene and Retinol Efficacy Trial (CARET): the trade-off between extending the duration of the trial or increasing the number of participants, the effect on costs of delay in accrual, and the cost effectiveness of particular retention activities.

Anticarcinogenic Agents↗

PubMed1991

[Estimation of sample size in randomized controlled clinical trials--elimination of various systematic biases by the utilization of computer network system].

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.

Database Management Systems↗

PubMed1978

An iterative approach to the analysis of EM autoradiographs. II. Estimates of sample sizes and confidence limits.

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.

Autoradiography↗

PubMed1988

On sample sizes to estimate the protective efficacy of a vaccine.

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.

Child, Preschool↗

PubMed1989

Variability of indexes for myocardial ischemia: a comparison of exercise treadmill test, ambulatory electrocardiographic monitoring and symptoms of myocardial ischemia.

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)

Ambulatory Care↗

PubMedDate not supplied

[Sample size for estimating attributable risk in cross-sectional studies].

The prevalence of a variety of risk factors and their strength of association with a disease can vary greatly among apparently similar communities. In small communities, risk estimates can also vary from year to year. An identification of important risk factors in each community is then needed, so that interventions can be specifically oriented towards the needs of each specific community. The attributable risk is the adequate measure of association for these purposes. The purpose of this paper is to determine the minimum sample size required to detect a given attributable risk in cross-sectional studies. A table was constructed, presenting the number of exposed subjects necessary to detect a given attributable risk for different combinations of prevalence of disease and prevalence of exposure to a given risk factor, with a power of 0.80 and alpha of 0.05.

Cross-Sectional Studies↗