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Results for “Sample size estimation”

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PubMed2006

Approaches for sampling the twospotted spider mite (Acari: Tetranychidae) on clementines in Spain.

Tetranychus urticae Koch (Acari: Tetranychidae) is an important pest of clementine mandarins, Citrus reticulata Blanco, in Spain. As a first step toward the development of an integrated crop management program for clementines, dispersion patterns of T. urticae females were determined for different types of leaves and fruit. The study was carried out between 2001 and 2003 in different commercial clementine orchards in the provinces of Castelló and Tarragona (northeastern Spain). We found that symptomatic leaves (those exhibiting typical chlorotic spots) harbored 57.1% of the total mite counts. Furthermore, these leaves were representative of mite dynamics on other leaf types. Therefore, symptomatic leaves were selected as a sampling unit. Dispersion patterns generated by Taylor's power law demonstrated the occurrence of aggregated patterns of spatial distribution (b > 1.21) on both leaves and fruit. Based on these results, the incidence (proportion of infested samples) and mean density relationship were developed. We found that optimal binomial sample sizes for estimating low populations of T. urticae on leaves (up to 0.2 female per leaf) were very large. Therefore, enumerative sampling would be more reliable within this range of T. urticae densities. However, binomial sampling was the only valid method for estimating mite density on fruit.

Animals↗
PubMed2001

Outcome markers for clinical trials in cerebral amyloid angiopathy.

Determining the effectiveness of candidate treatments for preventing hemorrhagic strokes caused by cerebral amyloid angiopathy will require clinical drug trials. This article explores tvo potential outcome markers for such trials: (1) clinical recurrence of hemorrhagic stroke, and (2) the appearance of small, clinically silent hemorrhagic lesions on gradient-echo MRI. Using pilot data from our cohort of survivors of lobar hemorrhage, we estimated the sample sizes required to demonstrate efficacy with each of these outcome markers as the study endpoint. A study with recurrent hemorrhagic stroke as the endpoint was estimated to require 145 patients per treatment arm to demonstrate a 50% reduction in the recurrence rate over a 24-month follow-up period, while a study using new hemorrhagic lesions on MRI was estimated to require 70 patients per arm for a 17-month follow-up interval. The required sample sizes could be further reduced (to 105 and 52 patients, respectively) by limiting the analysis to those at highest risk of recurrence, defined according to apolipoprotein E genotype or the presence of more than one hemorrhagic lesion at study entry. This analysis suggests that radiographic detection of small hemorrhages may be an efficient surrogate endpoint for pilot trials of promising therapeutic approaches to cerebral amyloid angiopathy.

Aged↗
PubMed1995

Sample size: how many patients are necessary?

The need for sample size calculations is briefly reviewed: many of the arguments against small trials are already well known, and we only cursorily repeat them in passing. Problems that arise in the estimation of sample size are then discussed, with particular reference to survival studies. However, most of the issues which we discuss are equally applicable to other types of study. Finally, prognostic factor analysis designs are discussed, since this is another area in which experience shows that far too many studies are of an inadequate size and yield misleading results.

Clinical Trials as Topic↗
PubMedDate not supplied

From diagnostic accuracy to accurate diagnosis: interpreting a test result with confidence.

BACKGROUND: The Standard for Reporting of Diagnostic Accuracy statement promotes the reporting of confidence intervals (CIs) for indices of diagnostic test accuracy. However, these indices must be combined with an estimate of pretest probability to properly interpret the results of such tests, thus yielding positive and negative predictive values. For small sample sizes, CI estimation for predictive values based on the classical logit transformation has been found to be very conservative. A method based on computer simulation has therefore been suggested as an alternative. METHODS: ACI procedure for predictive values that yields limits completely contained in those provided by the logit transformation is proposed and evaluated. RESULTS: The proposed approach to CI construction maintains nominal coverage very well even when sample sizes are small. CONCLUSION: Accurate CIs for positive and negative predictive values can be obtained without using computer simulation.

Bayes Theorem↗
PubMed1997

Responsiveness of endpoints in osteoporosis clinical trials.

The usefulness of an endpoint depends in part on its responsiveness to clinically important change. From existing randomized controlled trials, the responsiveness of endpoints currently employed in osteoporosis clinical trials were examined. The responsiveness is presented as the sample size per group needed to show a statistically significant difference. The large variation found means that careful attention needs to be given to the responsiveness of the population studied when estimating the sample size.

Bone Density↗
PubMed2006

Monte Carlo simulation to reconstruct formaldehyde exposure levels from summary parameters reported in the literature.

OBJECTIVES: This study presents a procedure allowing the numerical synthesis of exposure data reported in different ways in the literature, including summary parameters and single measurements. The procedure was applied to literature regarding formaldehyde exposure in the reconstituted wood panels industry, including oriented-strand board (OSB), medium density fibre board (MDF) and particle board (PB). METHODS: For each publication providing summary parameters we estimated geometric means (GM) and geometric standard deviations (GSD) by assuming lognormality of exposure levels. Monte Carlo simulation was performed to re-create datasets from the sample sizes and estimated GMs and GSDs, allowing their subsequent formatting together with the single measurements. The precision and bias of the methods used to estimate GMs and GSDs were evaluated. RESULTS: Altogether, the 13 articles included in our study yielded a final database of 874 data, of which 732 were simulated. For both area and personal data, exposures corresponding to MDF and PB were similar while OSB levels were lower. The most recent available personal levels (1985-1994) were highest in PB for jobs performed in the vicinity of the press (GM=0.63 mg m-3). Corresponding area levels were highest for PB in the main production zone (GM=0.43 mg m-3). Mixed-effects models fitted to area PB data explained 38% of the total variability. A 6-fold decrease in exposures from 1965 to 1995 was estimated. Replication of the simulation process yielded relative standard deviations of the calculated GMs and GSDs between 10 and 20%. The relative biases of the methods used to estimate GMs and GSDs varied across methods and decreased with higher sample sizes (from approximately 15% for n=5 to less than 5% for n=30, in absolute value). The precision also varied across methods and improved with higher sample sizes (from approximately 30% for n=5 to approximately 10% for n=30). DISCUSSION: This methodology constitutes a new meta-analysis tool that should improve the interpretation of industrial hygiene literature data, but needs to be further validated.

Air Pollutants, Occupational↗
PubMed1987

Effective sample sizes for confidence intervals for survival probabilities.

We examine various methods to estimate the effective sample size for construction of confidence intervals for survival probabilities. We compare the effective sample sizes of Cutler and Ederer and Peto et al., as well as a modified Cutler-Ederer effective sample size. We investigate the use of these effective sample sizes in the common situation of many censored observations that intervene between the time point of interest and the last death before this time. We note that there is no a priori reason to treat upper and lower confidence intervals in a symmetric fashion since censored survival data are by nature asymmetric. We recommend the use of the Cutler-Ederer effective sample size in construction of upper confidence intervals and the Peto effective sample size in construction of lower confidence intervals. Two examples with real data demonstrate the differences between confidence intervals formed with different effective sample sizes. This study also illustrates the need for caution in the application of simulation studies to real problems.

Bacterial Infections↗
PubMed1990

Sample size determination for pair-matched case-control studies where the goal is interval estimation of the odds ratio.

Samples sizes are calculated for case-control studies where 1:1 matching has been employed, and where the goal is the interval estimation of the odds ratio. The optimal sample size is defined to be the smallest value for which a 100(1 - alpha)% confidence interval for the log odds ratio will not exceed a specified width 2 delta with specified probability (1 - gamma). This approach is similar in spirit to the power-based approach for sample size determination when significance testing is the goal. Tables of sample sizes are presented for various choices of parameters. We also find considerable disagreement with a published method based on expected numbers of discordant pairs.

Case-Control Studies↗
PubMed2002

Sample size considerations for studies of intervention efficacy in the occupational setting.

OBJECTIVE: Due to a shared environment and similarities among workers within a worksite, the strongest analytical design to evaluate the efficacy of an intervention to reduce occupational health or safety hazards is to randomly assign worksites, not workers, to the intervention and comparison conditions. Statistical methods are well described for estimating the sample size when the unit of assignment is a group but these methods have not been applied in the evaluation of occupational health and safety interventions. We review and apply the statistical methods for group-randomized trials in planning a study to evaluate the effectiveness of technical/behavioral interventions to reduce wood dust levels among small woodworking businesses. METHODS: We conducted a pilot study in five small woodworking businesses to estimate variance components between and within worksites and between and within workers. In each worksite, 8 h time-weighted dust concentrations were obtained for each production employee on between two and five occasions. With these data, we estimated the parameters necessary to calculate the percent change in dust concentrations that we could detect (alpha = 0.05, power = 80%) for a range of worksites per condition, workers per worksite and repeat measurements per worker. RESULTS: The mean wood dust concentration across woodworking businesses was 4.53 mg/m3. The measure of similarity among workers within a woodworking business was large (intraclass correlation = 0.5086). Repeated measurements within a worker were weakly correlated (r = 0.1927) while repeated measurements within a worksite were strongly correlated (r = 0.8925). The dominant factor in the sample size calculation was the number of worksites per condition, with the number of workers per worksite playing a lesser role. We also observed that increasing the number of repeat measurements per person had little benefit given the low within-worker correlation in our data. We found that 30 worksites per condition and 10 workers per worksite would give us 80% power to detect a reduction of approximately 30% in wood dust levels (alpha = 0.05). CONCLUSIONS: Our results demonstrate the application of the group-randomized trials methodology to evaluate interventions to reduce occupational hazards. The methodology is widely applicable and not limited to the context of wood dust reduction.

Dust↗
PubMed1994

Spatial patterns of Anopheles freeborni and Culex tarsalis (Diptera: Culicidae) larvae in California rice fields.

Spatial patterns of Anopheles freeborni Aitken and Culex tarsalis Coquillett larvae were studied during summer by sampling with a standard mosquito dipper in 104 rice fields in northern California. Culex tarsalis larval abundance was highest initially, then decreased and remained low through late summer. An. freeborni larval abundance was low initially, increased steadily, and peaked in mid-August. The degree of aggregation for both species as measured using Taylor's power law and Iwao's Patchiness Regression was highest among the first instars and then decreased as the larvae aged. Seasonal peaks in the degree of aggregation were observed. Analysis of covariance showed that for Taylor's model both instar and time effects were statistically significant, with instar showing the largest effect. In comparison, all slopes resulting from Iwao's model were significantly different, indicating that this model was affected by specific combinations of instar, week, and location and, thus, was less useful in developing an area-wide sampling plan. Optimal sample size was estimated using two methods. One method calculated the number of dips needed to estimate population abundance at three fixed-precision levels. The second calculated the minimum number of dips needed to collect at least one larva. The latter requires a substantially smaller sample size and may provide an effective method for monitoring larval mosquito abundance for control purposes.

Animals↗
PubMed1985

Consideration of covariates and stratification in sample size determination for survival time studies.

Sample size determination for survival time studies is discussed, taking into account stratification. We present formulas and tables which also apply in the case of multicenter clinical trials. In addition, the same method is used to estimate the sample size requirement when a covariate is grouped into strata. Simulation studies compare the results to covariate adjustment by the Cox proportional hazards model.

Follow-Up Studies↗
PubMed1996

Formulae and tables for the determination of sample sizes and power in clinical trials for testing differences in proportions for the two-sample design: a review.

This paper is a compendium of exact and asymptotic formulae and tables for estimating the sample size in a clinical trial with two treatment groups and a dichotomous outcome. The paper provides separate formulae for equal and unequal treatment group sizes, formulae for the calculation of power given the sample size, and complete references for all formulae and tables cited.

Binomial Distribution↗
PubMed1983

A method for the rapid assessment of sample size in dietary studies.

Critical readers should be suspicious about the inability of a dietary study to discriminate between the energy intakes of two groups when small sample sizes have been used. The possibility of a false-negative (type II error) should be considered. This problem could be avoided if investigators used adequate sample sizes. A review of 26 dietary studies published in the American Journal of Clinical Nutrition between 1979 and 1981 revealed that the median "SD of energy intakes" was 525 kcal/day. This figure was used to illustrate a simple method for estimating appropriate sample sizes assuming type I and type II error probabilities of 0.05. Prospective use of this method should increase the reproducibility of conclusions drawn from dietary studies.

Calorimetry↗
PubMed2003

Feasibility of a randomized trial on adjuvant radio-iodine therapy in differentiated thyroid cancer.

BACKGROUND: Justification for adjuvant radio-iodine (I-131) therapy in differentiated thyroid cancer (DTC) is purely based on retrospective data. This is true for ablative therapy and even more so for high-dosage adjuvant schedules. Randomized trials on the latter application are considered impossible due to anticipated formidable sample sizes required in a disease with an overall excellent prognosis like DTC. OBJECTIVE: To develop and validate a model that could stratify for risk of recurrence, rather than survival, as is usually done in prognostic indices, and secondly, to use this model to estimate the sample size required for a randomized trial. DESIGN, PATIENTS AND RESULTS: From databases of three large Dutch centres, we identified 342 consecutive patients without known residual DTC after (near-) total thyroidectomy. Using Cox proportional hazards analysis, a model was validated that clearly distinguished risk categories of recurrence using commonly available baseline variables. The model included age, N stage at presentation and T stage in papillary carcinoma. According to this stratification, a subset of patients at substantial risk for relapse (30-40%) was identified. They could be eligible for a trial assessing the impact of high-dose adjuvant I-131 on recurrence rates. Assuming a clinically relevant effect of 30% reduction of relapses, 290 patients would have to be entered in either arm (alpha 0.05, power 80%). CONCLUSION: We conclude that even though a randomized trial on this issue will be difficult to design and conduct, sample size is not the main problem.

Adult↗
PubMed2002

Nursing turnover in Taiwan: a meta-analysis of related factors.

A meta-analytic study was conducted to investigate the causal relationships among individual, organizational and environmental factors related to nurses' intention to stay at or leave their jobs in Taiwanese hospitals. A total of 129 studies related to nursing turnover from 1978-1998 were reviewed. A total of 4032 subjects were selected for the study. Data were integrated by estimation of parametric correlation coefficients, and analyzed using Friedman's two-way analysis of variance by ranks following weight adjustment of sample size and estimation of correlation effect on size of variables. The results of this study merit attention by nursing administrators in order to develop strategies for stabilizing the nursing work force.

Factor Analysis, Statistical↗
PubMed1986

Evaluation of sample size and power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance, and stratification.

When designing a clinical trial to test the equality of survival distributions for two treatment groups, the usual assumptions are exponential survival, uniform patient entry, full compliance, and censoring only administratively at the end of the trial. Various authors have presented methods for estimation of sample size or power under these assumptions, some of which allow for an R-year accrual period with T total years of study, T greater than R. The method of Lachin (1981, Controlled Clinical Trials 2, 93-113) is extended to allow for cases where patients enter the trial in a nonuniform manner over time, patients may exit from the trial due to loss to follow-up (other than administrative), other patients may continue follow-up although failing to comply with the treatment regimen, and a stratified analysis may be planned according to one or more prognostic covariates.

Analysis of Variance↗
PubMed2003

Meta-analysis of the effects of endothelin receptor blockade on survival in experimental heart failure.

BACKGROUND: Although an initial study of endothelin receptor blockade reported positive findings, subsequent experiments and clinical trials in humans found little or no benefit. METHODS: We applied meta-analytic methods to assess the methodologic rigor of preclinical studies of endothelin blockade and to quantitatively evaluate the totality of evidence regarding the effect of endothelin receptor blockers in experimental heart failure. A total of 396 animals were assigned to control and 594 were assigned to experimental therapy in the pooled analysis. Of the 9 studies identified, no study reported a priori sample size justification. Although there was a tendency to increased mortality with early administration (relative risk 1.39, P=.15) and decreased mortality with late administration (relative risk 0.85, P=.6), in the overall analysis, there was no significant evidence of benefit or harm (relative risk 1.03, P=.9). Studies with a small sample size had estimated effects that tended to deviate further from the pooled estimate of all studies. CONCLUSIONS: Consideration of mortality effects in the totality of studies revealed no significant effect of endothelin antagonists in animal models of experimental heart failure. Given the potential for between-study variability, reliance on studies with small sample size may lead to unrealistic expectations when extrapolating preclinical experimental results to future research.

Animals↗