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Results for “Sample size estimation”

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PubMed1997

Design for sample size re-estimation with interim data for double-blind clinical trials with binary outcomes.

Estimation of sample size in clinical trials requires knowledge of parameters that involve the treatment effect and variability, which are usually uncertain to medical researchers. The recent release within the European Union of a Note for Guidance from the Commission for Proprietary Medical Products (CPMP) highlights the importance of this issue. Most previous papers considered the case of continuous response variables that assume a normal distribution; some regarded the portion up to the interim stage as an 'internal pilot study' and required unblinding. In this paper, our concern is with the case of binary response variables, which is more difficult than the normal case since the mean and variance are not distinct parameters. We offer a design with a simple stratification strategy that enables us to verify and update the assumption of the response rates given initially in the protocol. The design provides a method to re-estimate the sample size based on interim data while preserving the trial's blinding. An illustrative numerical example and simulation results show slight effect on the type I error rate and the decision making characteristics on sample size adjustment.

Clinical Trials as Topic↗

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↗

PubMed2006

[Methodological quality of an article on the treatment of gastric cancer adopted as protocol by some Chilean hospitals].

BACKGROUND: Surgery is a curative treatment for gastric cancer (GC). As relapse is frequent, adjuvant therapies such as postoperative chemo radiotherapy have been tried. In Chile, some hospitals adopted Macdonald's study as a protocol for the treatment of GC. AIM: To determine methodological quality and internal and external validity of the Macdonald study. MATERIAL AND METHOD: Three instruments were applied that assess methodological quality. A critical appraisal was done and the internal and external validity of the methodological quality was analyzed with two scales: MINCIR (Methodology and Research in Surgery), valid for therapy studies and CONSORT (Consolidated Standards of Reporting Trials), valid for randomized controlled trials (RCT). Guides and scales were applied by 5 researchers with training in clinical epidemiology. RESULTS: The reader's guide verified that the Macdonald study was not directed to answer a clearly defined question. There was random assignment, but the method used is not described and the patients were not considered until the end of the study (36% of the group with surgery plus chemo radiotherapy did not complete treatment). MINCIR scale confirmed a multicentric RCT, not blinded, with an unclear randomized sequence, erroneous sample size estimation, vague objectives and no exclusion criteria. CONSORT system proved the lack of working hypothesis and specific objectives as well as an absence of exclusion criteria and identification of the primary variable, an imprecise estimation of sample size, ambiguities in the randomization process, no blinding, an absence of statistical adjustment and the omission of a subgroup analysis. CONCLUSION: The instruments applied demonstrated methodological shortcomings that compromise the internal and external validity of the.

Antineoplastic Combined Chemotherapy Protocols↗

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↗

PubMed2005

Do polymorphic loci require large sample sizes to estimate genetic distances?

The coefficient of variation of estimates of three genetic distances (standard genetic distance of Nei, chord distance, FST) was examined with computer simulation to determine if large samples (per population) are necessary to precisely estimate genetic distances at loci with high levels of polymorphism. These simulations showed that loci with high mutation rates produce estimates of genetic distance with lower coefficients of variation than loci with lower mutation rates--without requiring larger sample sizes from each population. In addition, the rate at which increasing sample sizes decreases the coefficient of variation of estimates of genetic distances was shown to be approximately determined by the value of FST between the populations being sampled. When FST was greater than 0.05, sampling fewer than 20 individuals (per population) should be sufficient. When FST was less than 0.01, sampling 100 individuals (per population) or more will be useful.

Alleles↗

PubMed2006

[Aspects of sample size determination and power calculation illustrated on examples from rehabilitation research].

Often it is reported in medical studies that an expected effect could not be detected. This may be the case if the sample size had been too small to detect an effect which actually exists. This often is due to the fact that sound sample size estimation had been omitted prior to the study outset. As a result, it is not known how many persons should have been involved in the study to detect this effect if present. On the other hand, if sample size estimation has not been realized, more persons than needed might be included in the study. This is problematic for economic and in particular for ethical reasons. The aim of this paper is to point out the principles of sample size estimation as well as to emphasize its importance not only in general but also in medical rehabilitation research.

Clinical Trials as Topic↗

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↗

PubMed2006

Analysing multitrait-multimethod data with structural equation models for ordinal variables applying the WLSMV estimator: what sample size is needed for valid results?

Convergent and discriminant validity of psychological constructs can best be examined in the framework of multitrait-multimethod (MTMM) analysis. To gain information at the level of single items, MTMM models for categorical variables have to be applied. The CTC(M-1) model is presented as an example of an MTMM model for ordinal variables. Based on an empirical application of the CTC(M-1) model, a complex simulation study was conducted to examine the sample size requirements of the robust weighted least squares mean- and variance-adjusted chi(2) test of model fit (WLSMV estimator) implemented in Mplus. In particular, the simulation study analysed the chi(2) approximation, the parameter estimation bias, the standard error bias, and the reliability of the WLSMV estimator depending on the varying number of items per trait-method unit (ranging from 2 to 8) and varying sample sizes (250, 500, 750, and 1000 observations). The results showed that the WLSMV estimator provided a good -- albeit slightly liberal -- chi(2) approximation and stable and reliable parameter estimates for models of reasonable complexity (2-4 items) and small sample sizes (at least 250 observations). When more complex models with 5 or more items were analysed, larger sample sizes of at least 500 observations were needed. The most complex model with 9 trait-method units and 8 items (72 observed variables) requires sample sizes of at least 1000 observations.

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↗