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Results for “Sample size estimation”

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PubMed1992

The efficacy of psychotropic drugs: implications for power analysis.

The estimation of the correct sample size to successfully test a hypothesis has become critical. A common approach to this problem is for the investigating team to complete a pilot study of a few patients to establish the "active drug-placebo" difference, using this "effect size" to perform the power analysis for sample size estimation. Given the variability evident in the effect size from completed and published studies, the pilot study approach may not be entirely dependable. The authors propose a method to obtain this initial "active drug-placebo" difference, in the field of psychotropic drug research. They apply meta-analysis to statistically summarize effect sizes obtained from an exhaustive review of the literature for a specific psychotropic drug in a given clinical condition. All double-blind, random assignment studies are used to calculate the effect size; therefore, no selection bias exists. These literature-based effect sizes are then used to perform the traditional power analysis for sample size estimation. The authors propose these estimations as a convenient reference source for future clinical investigators.

Humans↗

PubMed1997

Sample sizes for randomized trials measuring quality of life in cancer patients.

This paper describes the methods appropriate for calculating sample sizes for clinical trials assessing quality of life (QOL). An example from a randomized trial of patients with small cell lung cancer completing the Hospital Anxiety and Depression Scale (HADS) is used for illustration. Sample size estimates calculated assuming that the data are either of the Normal form or binary are compared to estimates derived using an ordered categorical approach. In our example, since the data are very skewed, the Normal and binary approaches are shown to be unsatisfactory: binary methods may lead to substantial over estimates of sample size and Normal methods take no account of the asymmetric nature of the distribution. When summarizing normative data for QOL scores the frequency distributions should always be given so that one can assess if non-parametric methods should be used for sample size calculations and analysis. Further work is needed to discover what changes in QOL scores represent clinical importance for health technology interventions.

Adult↗

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↗

PubMed2002

Sample size re-estimation in cluster randomization trials.

Cluster randomization trials in which families are the unit of allocation are commonly adopted for the evaluation of disease prevention interventions. Sample size estimation for cluster randomization trials depends on parameters that quantify the variability within and between clusters and the variability in cluster size. Accurate advance estimates of these nuisance parameters may be difficult to obtain and misspecification may lead to an underpowered study. Since families are typically recruited over time, we propose using a portion of the data to estimate the nuisance parameters and to re-estimate sample size based on the estimates. This extends the standard internal pilot study methods to the setting of cluster randomization trials. The effect of this design on the power, significance level and sample size is analysed via simulation and is shown to provide a flexible and practical approach to cluster randomization trials.

Cluster Analysis↗

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↗

PubMed2001

A SAS macro for sample size re-estimation.

The assessment of sample size in clinical trials comparing means requires a variance estimate of the main efficacy variable. If no reliable information about the variance of the key response is available at the beginning of a clinical trial, the use of data from the first 'few' patients entered in the trial ('internal pilot') may be appropriate to estimate the variance and thus to recalculate the required sample size. A SAS macro that implements the EM algorithm for carrying out and simulating such interim power evaluations without unblinding the treatment status is presented.

Algorithms↗

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↗

PubMed2002

Some statistical aspects of food intake assessment.

OBJECTIVE: To present the results of the statistical working group of the EFCOSUM project on estimating the minimum sample size for a pan-European dietary survey. BACKGROUND AND METHODS: Numerous statistical issues are involved when planning a nutritional survey aimed at evaluating various indicators, especially if it will be carried out in different countries. The plenary workshop of the EFCOSUM project has chosen four relevant statistical topics: the sample size estimation for dietary surveys, the number of repeated measurements needed to estimate usual intake for each individual; the statistical presentation of data; and the statistical procedures for estimating the usual intake distribution from a limited number of days of observation. This article deals with the first three topics mentioned. The participants of the EFCOSUM project answered a small questionnaire in order to get agreement on the method of estimating a minimum sample size in the context of a monitoring of dietary indicators. Data on the variability of dietary indicators of interest was also collected, in order to calculate a minimum sample size. RESULTS AND CONCLUSION: The main result was that a minimum sample size of 2000 adults in each European country will be needed in order to identify trends in the mean intake of the most relevant foods and nutrients in Europe. This sample size should be higher if trends have to be indentified for socio-demographic subgroups.

Data Interpretation, Statistical↗

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↗

PubMed2001

Type I error in sample size re-estimations based on observed treatment difference.

Sample size re-estimation based on an observed difference can ensure an adequate power and potentially save a large amount of time and resources in clinical trials. One of the concerns for such an approach is that it may inflate the type I error. However, such a possible inflation has not been mathematically quantified. In this paper the mathematical mechanism of this inflation is explored for two-sample normal tests. A (conditional) type I error function based on normal data is derived. This function not only provides the quantification but also gives mathematical mechanisms of possible inflation in the type I error due to the sample size re-estimation. Theoretically, based on their decision rules (certain upper and lower bounds), people can calculate this function and exactly visualize the changes in type I error. Computer simulations are performed to ensure the results. If there are no bounds for the adjustment, the inflation is evident. If proper adjusting rules are used, the inflation can be well controlled. In some cases the type I error can even be reduced. The trade-off is to give up some 'unrealistic power'. We investigated several scenarios in which the mechanisms to change the type I error are different. Our simulations show that similar results may apply to other distributions.

Clinical Trials as Topic↗

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↗

PubMed2000

Mammographic Localization Biopsy for Ductal Carcinoma In Situ: A Simple Mapping Technique for Sampling and Size Estimation.

Size, grade, margin status, and microscopic invasion are currently significant parameters for management of ductal carcinoma in situ (DCIS). Size estimation of DCIS is difficult or impossible if tissue is sampled haphazardly. Histologic examination of the entire biopsy to exclude microscopic invasion is cumbersome and expensive, and may not yield more information than less extensive but planned sampling. One hundred twenty-four mammographic localization biopsies with DCIS including 4 cases with 3 mm or less invasion are presented to address these issues. All were examined by a mapping technique utilizing specimen radiograph as a guide for sampling and a schematic drawing to record the sections. This involved sequential sampling of the entire tissue for smaller biopsies, and en bloc sampling of the mammographic abnormality and surrounding tissue with end-to-end sampling of the remaining tissue at regular intervals for larger biopsies. All tissue was examined histologically initially in 55 cases, while tissue away from the lesion was selectively omitted in 71 cases. To address the issue of occult invasion, all initially unsampled tissue of 44 biopsies was submitted for histologic examination after completion of the case and the findings were recorded separately. Size estimates ranged from 4 to 70 mm. The solitary focus of microscopic invasion in four cases was present in the initial sections at the site corresponding to the mammographic abnormality. No invasion was identified in the additional tissue in any of the 44 cases. Margin status did not change for any. Using specimen radiographs as a guide, all the necessary information for DCIS, including the size and microscopic invasion, can be obtained by a planned sampling of tissue with diagrammatic documentation. Sequential sections of the entire tissue for small biopsies and sampling at regular intervals to include tissue at and around the mammographic abnormality for larger biopsies is appropriate. Microscopic invasion, when focal, is likely to be identified at the site of mammographic abnormality in the initial en bloc sections.

Journal Article↗

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↗

PubMedDate not supplied

Sample size re-estimation: recent developments and practical considerations.

Interim findings of a clinical trial often will be useful for increasing the sample size if necessary to provide the required power against the null hypothesis when the alternative hypothesis is true. Strategies for carrying out the interim examination that have been described over the past several years include "internal pilot studies", blinded interim sample size adjustment and conditional power. Simulation studies show that the alternative methods generally control the type I error rate satisfactorily, although the power properties are more variable. The important issues associated with sample size re-estimation are strategic, not numeric. Clearly expressed regulatory preferences suggest that methods not requiring unblinding the data before completion of the trial would be most appropriate. Extending a trial has its risks. The investigators/patients enrolled later in the course of a trial are not necessarily the same as those recruited/entered early. Re-activating the enrollment process may be sufficiently complicated and expensive to justify enrolling more investigators/patients at the outset. Since sample size re-estimation adjusts the sample size on the basis of variability while efficacy interim analysis adjusts the sample size based on the basis of estimated effect size, both principles can be used in the same trial. Sample size re-estimation may not be advisable for trials involving extended follow-up of individual patients or, more generally, when the follow-up time is long relative to the recruitment time. In such cases, it may be better to estimate the sample size conservatively and introduce an interim efficacy evaluation.

Clinical Trials as Topic↗