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PubMed1996

Sample size effects on temporal reliability of language sample measures of preschool children.

The present study examined the temporal reliability of four quantitative measurements of operationally defined linguistic behaviors observed in a naturalistic setting. The measures of language production were computed using the Systematic Analysis of Language Transcripts (SALT) software from two 20-minute language samples obtained 3 to 14 days apart for each of 20 preschool-aged children. Samples were edited to different sizes based either on duration (12 or 20 minutes) or on limits of the total number of complete and intelligible utterances (from 25 to 175 in increments of 25). Inadequate reliability was found for the language sample measure, total number of words; hence, the validity of this measure is questionable. In contrast, very high temporal reliability coefficients (r > .92) were obtained for the language sample measures of number of different words, mean length of utterance in morphemes, and mean sentence length in morphemes when derived from a large number (> or = 175) of complete and intelligible utterances. The temporal reliability of these measures reached acceptable levels, not only for research purposes, but for diagnostic purposes as well.

Child, Preschool↗
PubMed1990

Shortcut method to calculate the sample size in trials of screening for chronic disease.

One of the first questions arising in the planning of a randomized trial to evaluate mortality reduction by screening concerns the sample size of the trial required to detect an expected mortality reduction in the study group for given significance level alpha, and power 1 - beta. If estimates exist of the underlying average annual incidence rate of the disease ra and the annual mortality rate delta a or survival data for patients in the population under consideration before screening started, then a simple formula for the probability of dying from the disease within T years after entry into the trial can be given for the control group. Standard formulas may then be used for sample size calculations in randomized trials, which compare the risk of death from the disease in the control and the study group accrued at T years after entry. A simple correction for loss of follow-up, due to mortality from other causes or, for instance, migration is possible.

Age Factors↗
PubMed2004

Radiography as primary outcome in rheumatoid arthritis: acceptable sample sizes for trials with 3 months' follow up.

OBJECTIVES: To investigate whether plain radiographs can show changes in joint damage due to rheumatoid arthritis (RA) within 3 months. METHODS: 188 film pairs taken with a 3 month interval were evaluated. They were scored with (chronological) and without (paired) knowledge of the sequence of the films according to the Sharp/van der Heijde method. Changes in joint damage were analysed on a group and an individual level for different subsets of patients. Sample sizes required to detect statistically and clinically significant differences were estimated based on the percentages of patients with progression larger than the smallest detectable change (SDC). RESULTS: Changes in joint damage were seen by both the chronological and the paired scoring method. The percentage of patients with progression of joint damage larger than the corresponding SDCs (1.7 and 2.4) varied in the subsets from 18% to 64% if based on the chronological change-scores and from 9% to 36% using paired change-scores. Acceptable sample size estimates were seen in several subsets, depending on (a) how the investigated drug would reduce the individual risk of progression of joint damage (by an absolute or a relative risk reduction model); (b) how damage was scored (chronological or paired); (c) the baseline risk; and (d) whether a two sided or one sided test would be used. CONCLUSIONS: Changes in joint damage due to RA can be detected reliably already within 3 months. This finding can be used to plan short term, randomised controlled trials with radiographic progression as primary outcome.

Adult↗
PubMed1988

Confidence interval estimation of a rate and the choice of sample size.

The problem of estimating a rate or proportion is considered. Four methods for constructing an approximate confidence interval are discussed and compared via a simulation study. The most accurate method is found. Also, for each method a sharp upper bound (dependent only on the sample size) is given for the length of the confidence interval. By choosing an appropriate sample size this bound enables the practitioner to achieve a prespecified maximum length for the confidence interval without knowing the population rate. The striking result is that the most accurate method has the smallest bound, thus requiring the least sample units.

Computer Simulation↗
PubMed2001

Sample size and power calculations in repeated measurement analysis.

Controlled clinical trials in neuropsychopharmacology, as in numerous other clinical research domains, tend to employ a conventional parallel-groups design with repeated measurements. The hypothesis of primary interest in the relatively short-term, double-blind trials, concerns the difference between patterns or magnitudes of change from baseline. A simple two-stage approach to the analysis of such data involves calculation of an index or coefficient of change in stage 1 and testing the significance of difference between group means on the derived measure of change in stage 2. This article has the aim of introducing formulas and a computer program for sample size and/or power calculations for such two-stage analyses involving each of three definitions of change, with or without baseline scores entered as a covariate, in the presence of homogeneous or heterogeneous (autoregressive) patterns of correlation among the repeated measurements. Empirical adjustments of sample size for the projected dropout rates are also provided in the computer program.

Controlled Clinical Trials as Topic↗
PubMed2006

Power and sample size for testing associations of haplotypes with complex traits.

Evaluation of the association of haplotypes with either quantitative traits or disease status is common practice, and under some situations provides greater power than the evaluation of individual marker loci. The focus on haplotype analyses will increase as more single nucleotide polymorphisms (SNPs) are discovered, either because of interest in candidate gene regions, or because of interest in genome-wide association studies. However, there is little guidance on the determination of the sample size needed to achieve the desired power for a study, particularly when linkage phase of the haplotypes is unknown, and when a subset of tag-SNP markers is measured. There is a growing wealth of information on the distribution of haplotypes in different populations, and it is not unusual for investigators to measure genetic markers in pilot studies in order to gain knowledge of the distribution of haplotypes in the target population. Starting with this basic information on the distribution of haplotypes, we derive analytic methods to determine sample size or power to test the association of haplotypes with either a quantitative trait or disease status (e.g., a case-control study design), assuming that all subjects are unrelated. Our derivations cover both phase-known and phase-unknown haplotypes, allowing evaluation of the loss of efficiency due to unknown phase. We also extend our methods to when a subset of tag-SNPs is chosen, allowing investigators to explore the impact of tag-SNPs on power. Simulations illustrate that the theoretical power predictions are quite accurate over a broad range of conditions. Our theoretical formulae should provide useful guidance when planning haplotype association studies.

Computer Simulation↗
PubMed2001

Sample sizes for batch acceptance from single- and multistage designs using two-sided normal tolerance intervals with specified content.

One quality control test in the pharmaceutical industry is a test for uniformity of content of a batch prior to release of the batch to market. For batch acceptance by this or other quantitative tests of batch quality, one approach uses two-sided tolerance intervals of specified content. If the tolerance interval falls entirely within an acceptance interval, the batch is accepted. This has the form of a statistical hypothesis test. Once we recognize this approach as a statistical test, we can ask what sample size is required to be able to accept the batch with a desired power. The power for a single-stage design is a bivariate noncentral t probability and can be determined using previously published algorithms. Using standard methods for interim analyses, the approach is extended to multistage designs. Power and sample size for multistage designs are validated with simulations. We demonstrate it is possible to design one- and two-stage designs for batch acceptance with desired power and specified type I level.

Algorithms↗
PubMed1993

Sample size requirements and length of study for testing interaction in a 2 x k factorial design when time-to-failure is the outcome [corrected].

This paper provides equations for calculating the sample size necessary to test an interaction effect in an 2 x k factorial design when time-to-failure is the outcome of interest. The results are a direct extension of those used by George and Desu and Makuch and Simon who provide sample size requirements for comparing two and k treatment groups, respectively. Duration of a clinical trial concerned with an interaction is calculated using the results of Rubinstein, Gail, and Santner. The results in the present paper can also be used to investigate the impact that an unforeseen interaction has on the power to detect a treatment main effect. This impact can be substantial, even for moderate interactions.

Aspirin↗
PubMed1989

Measuring change in nutritional status: a comparison of different anthropometric indices and the sample sizes required.

The usefulness of different anthropometric indices to detect nutritional changes at the community level, ie, in a number of children considered as a group, was compared by using data from a longitudinal study from rural Bangladesh which followed up quarterly an average of 413 children aged 6-35 months from December 1984 to December 1987. Weight change, mid-upper arm circumference and weight-for-height responded most quickly to seasonal variations of the food situation. Height-for-age was more responsive to long-term variations. Although similar conclusions were reached when proportions of children below a cut-off point or mean indices were compared, the comparison of mean indices required a smaller sample size to detect changes. The difference in sample size needed ranged from 48 to 61 per cent. All indices varied significantly with age, which suggests that precise knowledge of age is essential for proper interpretation of nutritional surveillance data.

Aging↗
PubMed1983

Sample size for estimating the quantiles of endothelial cell-area distribution.

The estimation of corneal endothelium mean cell area (and, hence, mean cell density) is an important problem in clinical ophthalmology. Mitotic division of these cells is not known to occur, and cell deaths are followed by the enlargement of adjacent cells. As a consequence, cell-area distributions change drastically as functions of age and disease. Changes in cell-area distributions, in particular multimodality and skewness due to aging, are observed, and give rise to some difficult sampling problems. In this paper, sample quantiles are investigated as an alternative to the use of the sample mean. Asymptotic approximations are provided for the sample sizes required to estimate population quantiles with a desired precision. Asymptotic sample sizes are then compared with those obtained from tolerance limits. Empirical sample quantiles that can be used as benchmarks to compare corneas of normal individuals against corneas with unknown cell-area distributions are also presented. Aspects that merit further investigation are noted.

Adolescent↗
PubMed2002

Sample size calculations for comparative studies of medical tests for detecting presence of disease.

Technologic advances give rise to new tests for detecting disease in many fields, including cancer and sexually transmitted disease. Before a new disease screening test is approved for public use, its accuracy should be shown to be better than or at least not inferior to an existing test. Standards do not yet exist for designing and analysing studies to address this issue. Established principles for the design of therapeutic studies can be adapted for studies of screening tests. In particular, drawing upon methods for superiority and non-inferiority studies of therapeutic agents, we propose that confidence intervals for the relative accuracy of dichotomous tests drive the design of comparative studies of disease screening tests. We derive sample size formulae for a variety of designs, including studies where patients undergo several tests and studies where patients receive only one of the tests under evaluation. Both cohort and case-control study designs are considered. Modifications to the confidence intervals and sample size formulae are discussed to accommodate studies where, because of the invasive nature of definitive testing, true disease status can only be obtained for subjects who are positive on one or more of the screening tests. The methods proposed are applied to a study comparing a modified pap test to the conventional pap for cervical cancer screening. The impact of error in the gold standard reference test on the design and evaluation of comparative screening test studies is also discussed.

Case-Control Studies↗
PubMed1992

Meta-analysis of second-line antirheumatic drugs: sample size bias and uncertain benefit.

Placebo controlled trials of methotrexate, auranofin, penicillamine, azathioprine, sulphasalazine, gold sodium thiomalate and chloroquines were subjected to meta-analysis. The difference between drugs and placebo in the erythrocyte sedimentation rate was 8.8 mm/hr [95% confidence interval (CI), 6.4-11.3]. In multiple linear regression analyses, with the physician's global evaluation and relative change in joint tenderness count as outcome variables, a substantial sample size bias was demonstrated. The effect decreased with increasing sample size. The risk of dropping out from any cause was larger on drug than on placebo (odds ratio, 1.17; CI, 0.99-1.38). No evidence of a worthwhile effect on radiological changes was found. Of the 3439 patients, 4 went into complete remission on drug. We conclude that the benefit of second-line drugs is uncertain.

Arthritis, Rheumatoid↗
PubMed1985

Noise exposure--sample size and confidence limit calculation.

In a previous paper a method for assessing noise exposure levels of workers from the same trade was presented. There was also discussed how to apply the NIOSH sample size method to noise exposed populations. In this paper both subjects are further discussed by including the calculation of confidence limits for the mean noise exposure as well as for the percentage of workers with noise exposure levels beyond a certain level. The calculation of the sample size of a population where standard deviation is known is also discussed.

Environmental Exposure↗
PubMed2006

Planning genetic studies on primary adult-onset dystonia: sample size estimates based on examination of first-degree relatives.

Primary adult-onset dystonia is thought to be partly genetic, but families large enough for a genome wide search are difficult to find. We examined the first-degree relatives of 76 primary adult-onset dystonia patients to assess the feasibility of model-free nonparametric methods that allow either screening of candidate loci (case-control design, transmission disequilibrium test [TDT], and sibling-TDT [S-TDT]) or identification of novel genes (affected sib-pair [ASP] method). Among the examined relatives, 1/34 parents, 13/149 siblings and 10/125 offspring were affected by adult-onset dystonia. The predicted sample sizes to detect a gene conferring an Odds ratio of 3.0 were 99 for case-control and TDT methodology, 148 for S-TDT, and 107 to 173 for an ASP study assuming three major loci. Based on our family structure, TDT, S-TDT, and ASP methods would required screening of about 220, 700, and 580 to 939 probands respectively. Analysing subpopulations with different types of dystonia, TDT required fewer probands with cervical/hand dystonia, S-TDT needed fewer probands with cranial dystonia. These sample size estimates suggest that the S-TDT may be feasible, whereas collection of cases for both TDT and ASP approaches would represent a major collaborative challenge.

Adolescent↗
PubMed1992

Sample size requirement for repeated measurements in continuous data.

In this paper we extend Bloch's discussion on the usefulness and the limitations in the application of repeated measurements per subject in study designs. We derive general sample size formulae for any finite number of comparison groups to calculate the required number of subjects with repeated measurements, that do not have to be conditionally independent. For fixed total cost, we discuss the optimal sample allocation for repeated measurements needed to maximize the power and the underestimation when using Bloch's sample size formula if in the hypothesis testing procedure the variance parameters are unknown. We have also included a quantitative investigation of the effectiveness of taking repeated measurements per subjects to reduced the required number of subjects for a given power at a given alpha-level.

Analysis of Variance↗
PubMed1994

The effect of poor compliance and treatment side effects on sample size requirements in randomized clinical trials.

Treatment side effects and associated noncompliance have methodological implications vital to the testing of new drugs. In this paper, we quantify the impact of these factors on sample size requirements in clinical trials. In the Lipid Research Clinics Trial, side effects caused treatment group compliance (50.8%) to be lower than placebo compliance (67.3%). Cholesterol reduction among treatment noncompliers was 35.2% of the reduction among compliers. Had treatment group compliance been as high as placebo compliance, 41% fewer patients would have been required to achieve the same statistical power and an expected 31% more coronary events would have been prevented. We conclude: Because they discourage patient compliance, treatment side effects can (1) cause large sample size increases, (2) lead to underestimates of true efficacy, and (3) contribute to potentially invalid negative conclusions in clinical trials. The impact of side effects goes well beyond the complications and patient discomforts with which they are associated.

Cholestyramine Resin↗
PubMed2026

Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.

OBJECTIVE: Radiogenomics aims to non-invasively predict tumour genotypes from imaging, but most studies assume molecular homogeneity by assigning a single biopsy-derived label to all lesions within a patient. This approach risks substantial label noise given well-documented interlesional heterogeneity. We investigated whether anchoring training to biopsy-confirmed lesions improves radiogenomic model performance and generalisability. MATERIALS AND METHODS: We retrospectively analysed 1646 patients (11473 segmented lesions) with contrast-enhanced CT and EGFR mutation status from next-generation sequencing at the Netherlands Cancer Institute, alongside an external NSCLC radiogenomics cohort (n = 158). All visible lesions were segmented, and the exact biopsy site was matched to its segmentation. Radiomic features were extracted, and machine learning models were trained with three lesion selection strategies: all lesions, non-biopsied lesions only, and biopsy-confirmed lesions only. To disentangle label quality from sample size, we created size-matched variants (one lesion per patient) for all-lesion and non-biopsied strategies. RESULTS: All models achieved significant discrimination of EGFR status on internal validation (AUC = 0.62-0.68). However, performance of the all-lesion and non-biopsied models declined on external validation (AUC = 0.55-0.63), while the biopsy-anchored model maintained stable performance (AUC = 0.62), despite having only 1/10th of the training sample size. When training sets were size-matched, the biopsy-anchored approach significantly outperformed a model trained on all available lesions on external validation (p = 0.037). CONCLUSIONS: Radiogenomic models trained on biopsy-confirmed lesions outperform conventional all-lesion strategies in external validation, despite using an order of magnitude fewer samples. Prioritising lesion-level label fidelity can mitigate heterogeneity-driven noise, enhancing robustness and clinical translation of imaging-based genomic prediction. KEY POINTS: Question Does assigning biopsy-derived molecular labels to all lesions introduce heterogeneity-driven label noise that reduces the generalisability of radiogenomic models? Findings Models trained exclusively on biopsy-confirmed lesions demonstrated superior external generalisability compared with all-lesion approaches, despite being trained on substantially fewer samples. Clinical relevance Biopsy-anchored radiogenomics improves the reliability of non-invasive mutation prediction by accounting for tumour heterogeneity, potentially supporting clinical decision-making when tissue sampling is limited or molecular results are discordant across lesions.

Humans↗
PubMed2005

Estimating live birth rates after ovulation induction in polycystic ovary syndrome: sample size calculations for the pregnancy in polycystic ovary syndrome trial.

Polycystic ovary syndrome (PCOS) affects approximately 5% of the female population, and is a leading cause of infertility, primarily secondary to anovulation. Clomiphene citrate has been standard therapy for ovulation induction in patients seeking pregnancy, but recent evidence suggests that insulin sensitizing agents such as metformin may also be effective. The National Institute of Child Health and Human Development's Reproductive Medicine Network has begun a randomized, double-blind trial of clomiphene vs. metformin vs. clomiphene plus metformin for the induction of ovulation in patients with PCOS seeking pregnancy, with live birth rate as the primary outcome. Because the available literature was largely limited to surrogate outcomes such as ovulation and pregnancy rates, we created a Markov model to derive estimates of likely live birth rates in each arm. Using these estimates, we then constructed an algorithm that allowed only two formal comparisons between the three arms. First, we assumed that combination therapy would have to be superior to the next best single-agent therapy in order to be preferred, because of complexity, costs, increased side effects, etc. If combination therapy is not superior to the next best single agent, then the only other comparison of interest is between the two single agent therapies. Because the third possible comparison, between the best and worst of the three therapies, is not clinically relevant, it can be eliminated from formal statistical consideration, with subsequent reduction in sample size. Based on the opinion of the Network Steering Committee that a 15% absolute difference in live birth rates would be clinically relevant, our methodology resulted in a sample size of 226 per arm, or a total of 678 subjects. The PPCOS trial should definitively answer the question of the relative efficacy of metformin, clomiphene, and combination therapy in the treatment of infertile women with PCOS.

Adolescent↗