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International Journal of Epidemiology Jun 2014Sample size calculations are an important tool for planning epidemiological studies. Large sample sizes are often required in Mendelian randomization investigations.
BACKGROUND
Sample size calculations are an important tool for planning epidemiological studies. Large sample sizes are often required in Mendelian randomization investigations.
METHODS AND RESULTS
Resources are provided for investigators to perform sample size and power calculations for Mendelian randomization with a binary outcome. We initially provide formulae for the continuous outcome case, and then analogous formulae for the binary outcome case. The formulae are valid for a single instrumental variable, which may be a single genetic variant or an allele score comprising multiple variants. Graphs are provided to give the required sample size for 80% power for given values of the causal effect of the risk factor on the outcome and of the squared correlation between the risk factor and instrumental variable. R code and an online calculator tool are made available for calculating the sample size needed for a chosen power level given these parameters, as well as the power given the chosen sample size and these parameters.
CONCLUSIONS
The sample size required for a given power of Mendelian randomization investigation depends greatly on the proportion of variance in the risk factor explained by the instrumental variable. The inclusion of multiple variants into an allele score to explain more of the variance in the risk factor will improve power, however care must be taken not to introduce bias by the inclusion of invalid variants.
Topics: Humans; Mendelian Randomization Analysis; Monte Carlo Method; Random Allocation; Risk Factors; Sample Size
PubMed: 24608958
DOI: 10.1093/ije/dyu005 -
BMJ (Clinical Research Ed.) May 1993To gain population norms for the short form 36 health survey questionnaire (SF36) in a large community sample and to explore the questionnaire's internal consistency and...
OBJECTIVES
To gain population norms for the short form 36 health survey questionnaire (SF36) in a large community sample and to explore the questionnaire's internal consistency and validity.
DESIGN
Postal survey by using a booklet containing the SF36 and several other items concerned with lifestyles and illness.
SETTING
The sample was drawn from computerised registers of the family health services authorities for Berkshire, Buckinghamshire, Northamptonshire, and Oxfordshire.
SAMPLE
13,042 randomly selected subjects aged 18-64 years.
MAIN OUTCOME MEASURES
Scores for the eight health dimensions of the SF36.
RESULTS
The survey achieved a response rate of 72% (n = 9332). Internal consistency of the different dimensions of the questionnaire was high. Normative data broken down by age, sex, and social class were consistent with those from previous studies.
CONCLUSIONS
The SF36 is a potentially valuable tool in medical research. The normative data provided here may further facilitate its validation and use.
Topics: Adolescent; Adult; Age Factors; England; Female; Health Status; Health Surveys; Humans; Male; Middle Aged; Postal Service; Random Allocation; Sex Factors; Surveys and Questionnaires
PubMed: 8518639
DOI: 10.1136/bmj.306.6890.1437 -
JAMA Nov 2013In clinical and research settings worldwide, low-density lipoprotein cholesterol (LDL-C) is typically estimated using the Friedewald equation. This equation assumes a... (Comparative Study)
Comparative Study
IMPORTANCE
In clinical and research settings worldwide, low-density lipoprotein cholesterol (LDL-C) is typically estimated using the Friedewald equation. This equation assumes a fixed factor of 5 for the ratio of triglycerides to very low-density lipoprotein cholesterol (TG:VLDL-C); however, the actual TG:VLDL-C ratio varies significantly across the range of triglyceride and cholesterol levels.
OBJECTIVE
To derive and validate a more accurate method for LDL-C estimation from the standard lipid profile using an adjustable factor for the TG:VLDL-C ratio.
DESIGN, SETTING, AND PARTICIPANTS
We used a convenience sample of consecutive clinical lipid profiles obtained from 2009 through 2011 from 1,350,908 children, adolescents, and adults in the United States. Cholesterol concentrations were directly measured after vertical spin density-gradient ultracentrifugation, and triglycerides were directly measured. Lipid distributions closely matched the population-based National Health and Nutrition Examination Survey (NHANES). Samples were randomly assigned to derivation (n = 900,605) and validation (n = 450,303) data sets.
MAIN OUTCOMES AND MEASURES
Individual patient-level concordance in clinical practice guideline LDL-C risk classification using estimated vs directly measured LDL-C (LDL-CD).
RESULTS
In the derivation data set, the median TG:VLDL-C was 5.2 (IQR, 4.5-6.0). The triglyceride and non-high-density lipoprotein cholesterol (HDL-C) levels explained 65% of the variance in the TG:VLDL-C ratio. Based on strata of triglyceride and non-HDL-C values, a 180-cell table of median TG:VLDL-C values was derived and applied in the validation data set to estimate the novel LDL-C (LDL-CN). For patients with triglycerides lower than 400 mg/dL, overall concordance in guideline risk classification with LDL-CD was 91.7% (95% CI, 91.6%-91.8%) for LDL-CN vs 85.4% (95% CI, 85.3%-85.5%) for Friedewald LDL-C (LDL-CF) (P < .001). The greatest improvement in concordance occurred in classifying LDL-C lower than 70 mg/dL, especially in patients with high triglyceride levels. In patients with an estimated LDL-C lower than 70 mg/dL, LDL-CD was also lower than 70 mg/dL in 94.3% (95% CI, 93.9%-94.7%) for LDL-CN vs 79.9% (95% CI, 79.3%-80.4%) for LDL-CF in samples with triglyceride levels of 100 to 149 mg/dL; 92.4% (95% CI, 91.7%-93.1%) for LDL-CN vs 61.3% (95% CI, 60.3%-62.3%) for LDL-CF in samples with triglyceride levels of 150 to 199 mg/dL; and 84.0% (95% CI, 82.9%-85.1%) for LDL-CN vs 40.3% (95% CI, 39.4%-41.3%) for LDL-CF in samples with triglyceride levels of 200 to 399 mg/dL (P < .001 for each comparison).
CONCLUSIONS AND RELEVANCE
A novel method to estimate LDL-C using an adjustable factor for the TG:VLDL-C ratio provided more accurate guideline risk classification than the Friedewald equation. These findings require external validation, as well as assessment of their clinical importance. The implementation of these findings into clinical practice would be straightforward and at virtually no cost.
TRIAL REGISTRATION
clinicaltrials.gov Identifier: NCT01698489.
Topics: Adolescent; Adult; Child; Cholesterol, LDL; Cholesterol, VLDL; Female; Humans; Hypercholesterolemia; Male; Models, Theoretical; Practice Guidelines as Topic; Random Allocation; Reference Values; Risk Assessment; Triglycerides
PubMed: 24240933
DOI: 10.1001/jama.2013.280532 -
Cancers Sep 2023This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative... (Review)
Review
This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.
PubMed: 37835368
DOI: 10.3390/cancers15194674 -
Medical Archives (Sarajevo, Bosnia and... Oct 2019Inappropriate design of experimental studies in medicine inevitably leads to inaccurate or false results, which serve as basis for erroneous and biased conclusions. (Review)
Review
INTRODUCTION
Inappropriate design of experimental studies in medicine inevitably leads to inaccurate or false results, which serve as basis for erroneous and biased conclusions.
AIM
The aim of our study was to investigate prevalence of implementing basic principles of experimental design (local control, replication and randomization) in preclinical experimental studies, performed either on animals in vivo, or animal/human material in vitro.
MATERIAL AND METHODS
Preclinical experimental studies were retrieved from the PubMed database, and the sample for analysis was randomly chosen from the retrieved publications. Implementation rate of basic experimental research principles (local control, randomization and replication) was established by careful reading of the sampled publications and their checking against predefined criteria.
RESULTS
Our study showed that only a minority of experimental preclinical studies had basic principles of design completely implemented (7%), while implementation rate of single aspects of appropriate experimental design varied from as low as 9% to maximum 86%. Average impact factor of the surveyed studies was high, and publication date relatively recent, suggesting generalizability of our results to highly ranked contemporary journals.
CONCLUSION
Prevalence of experimental preclinical studies that did not implement completely basic principles of research design is high, raising suspicion to validity of their results. If incorrect and biased, results of published studies may mislead authors of future studies and cause conduction of fruitless research that will waste precious resources.
Topics: Animals; Biomedical Research; Control Groups; Humans; In Vitro Techniques; Random Allocation; Reproducibility of Results; Research Design
PubMed: 31819300
DOI: 10.5455/medarh.2019.73.298-302 -
Development and validation of a nonenhanced CT based radiomics model to detect brown adipose tissue.Theranostics 2023It has been reported that brown adipose tissue (BAT) has a protective effect regarding cardiovascular disease. Positron emission tomography-computed tomography (PET-CT)...
It has been reported that brown adipose tissue (BAT) has a protective effect regarding cardiovascular disease. Positron emission tomography-computed tomography (PET-CT) is the reference method for detecting active BAT; however, it is not feasible to screen for BAT due to the required radionuclides and high-cost. The purpose of this study is to develop and validate a nonenhanced CT based radiomics model to detect BAT and to explore the relationship between CT radiomics derived BAT and cardiovascular calcification. 146 patients undergoing F-FDG PET-CT were retrospectively included from two centers for model development (n = 86) and external validation (n = 60). The data for the model development were randomly divided into a training cohort and an internal validation cohort with a 7:3 ratio, while the external validation data were divided 1:1 into a propensity score matching (PSM) cohort and a randomly sex matched cohort. Radiomics features of BAT and non-BAT depots were extracted from regions of interest (ROI) on nonenhanced CT corresponding to PET studies. Inter-class correlation coefficient (ICC) and Pearson's correlation analysis were performed to select radiomics features with high consistency. Next, least absolute shrinkage and selection operator (LASSO) with linear regression model was used to select radiomics features for model construction. Support vector machine (SVM) was used to develop the model and a radiomics score (RS) was calculated for each depot. The diagnostic performance of the radiomics model was evaluated both on a per-depot and per-patient basis by calculating the area under the receiver operating characteristic curve (AUROC). We further divided patients into BAT-RS group and non-BAT-RS group based on radiomics score and compared their cardiovascular calcification by calculating calcium volume and score. A total of 22 radiomics features were selected for model construction. On a per-depot basis, the AUROCs were 0.87 (95% CI: 0.83-0.9), 0.85 (95% CI: 0.79-0.90), 0.72 (95% CI: 0.67-0.77) and 0.74 (95% CI: 0.69-0.79) for detecting BAT in the training, internal validation, external validation 1 and external validation 2 cohorts, respectively. On a per-patient basis, the radiomics model had high AUROCs of 0.91 (95% CI: 0.84-0.98), 0.77 (95% CI: 0.61-0.92) and 0.85 (95% CI: 0.72-0.98) in the training, external validation 1 and external validation 2 cohorts, respectively. When grouping based on the radiomics model, the BAT-RS group had lower odds of coronary artery calcium (CAC) and thoracic aorta calcium (TAC) compared with the non-BAT-RS group (CAC: 2.8% 20.3%, p = 0.001; TAC: 19.4% 39.2%, p = 0.009). The BAT-RS group had less CAC volume (4.1 ± 4.0 mm 147.4 ± 274.3 mm; p = 0.001), CAC score (2.8 ± 3.0 169.1 ± 311.5; p = 0.001), TAC volume (301.4 ± 450.2 mm 635.3 ± 1100.7 mm; p = 0.007) and TAC score (496.2 ± 132.6 749.2 ± 1297.3; p = 0.007) than the non-BAT-RS group. We developed and validated a nonenhanced CT based reliable radiomics model for detecting BAT with PET-CT findings as reference standard. Radiomics signatures from nonenhanced CT can reliably detect BAT and have promising potential to be used in routine clinical settings. Importantly, our study showed that patients with BAT had less cardiovascular calcification.
Topics: Female; Humans; Male; Adipose Tissue, Brown; Area Under Curve; Calcium; Cohort Studies; Positron Emission Tomography Computed Tomography; Retrospective Studies; Random Allocation
PubMed: 37056567
DOI: 10.7150/thno.81789 -
BMC Medical Research Methodology Aug 2021Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with... (Randomized Controlled Trial)
Randomized Controlled Trial
BACKGROUND
Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial.
METHODS
We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice.
RESULTS
Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size.
CONCLUSIONS
The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.
Topics: Computer Simulation; Humans; Likelihood Functions; Random Allocation; Sample Size; Selection Bias
PubMed: 34399696
DOI: 10.1186/s12874-021-01303-z -
Yonsei Medical Journal Sep 2021We aimed to develop a novel mortality scoring system for inpatients with COVID-19 based on simple demographic factors and laboratory findings.
PURPOSE
We aimed to develop a novel mortality scoring system for inpatients with COVID-19 based on simple demographic factors and laboratory findings.
MATERIALS AND METHODS
We reviewed and analyzed data from patients who were admitted and diagnosed with COVID-19 at 10 hospitals in Daegu, South Korea, between January and July 2020. We randomized and assigned patients to the development and validation groups at a 70% to 30% ratio. Each point scored for selected risk factors helped build a new mortality scoring system using Cox regression analysis. We evaluated the accuracy of the new scoring system in the development and validation groups using the area under the curve.
RESULTS
The development group included 1232 patients, whereas the validation group included 528 patients. In the development group, predictors for the new scoring system as selected by Cox proportional hazards model were age ≥70 years, diabetes, chronic kidney disease, dementia, C-reactive protein levels >4 mg/dL, infiltration on chest X-rays at the initial diagnosis, and the need for oxygen support on admission. The areas under the curve for the development and validation groups were 0.914 [95% confidence interval (CI) 0.891-0.937] and 0.898 (95% CI 0.854-0.941), respectively. According to our scoring system, COVID-19 mortality was 0.4% for the low-risk group (score 0-3) and 53.7% for the very high-risk group (score ≥11).
CONCLUSION
We developed a new scoring system for quickly and easily predicting COVID-19 mortality using simple predictors. This scoring system can help physicians provide the proper therapy and strategy for each patient.
Topics: Aged; COVID-19; Hospitalization; Humans; Proportional Hazards Models; Random Allocation; Risk Factors
PubMed: 34427066
DOI: 10.3349/ymj.2021.62.9.806 -
European Journal of Medical Research May 2023To develop a new, alternative sarcopenia risk score to screen for sarcopenia in type 2 diabetes patients in China and to demonstrate its validity.
OBJECTIVE
To develop a new, alternative sarcopenia risk score to screen for sarcopenia in type 2 diabetes patients in China and to demonstrate its validity.
RESEARCH DESIGN AND METHODS
The data for this study came from a multicenter, cross-sectional study that had been designed to estimate the prevalence of sarcopenia among adults with type 2 diabetes and had been conducted in several hospitals in Beijing, China. A total of 1125 participants were randomly divided into two groups: an exploratory population and a validation population. A multivariable logistic regression model using the backward stepwise likelihood ratio method to estimate the probability of sarcopenia was fitted with candidate variables in the exploratory population. A new, alternative sarcopenia risk score was developed based on the multivariable model. The internal and external validations were performed in the exploratory and validation populations. The study was registered at Chinese Clinical Trial Registry (ChiCTR-EOC-15006901).
RESULTS
The new, alternative sarcopenia risk score included five variables: age, gender, BMI, total energy intake per day, and the proportion of calories supplied by protein. The score ranged from - 2 to 19. The area under the receiver operating characteristic (ROC) curve of the risk score for the prediction of sarcopenia in type 2 diabetes patients was 0.806 (95% CI 0.741-0.872) and 0.836 (95% CI 0.781-0.892) in the exploratory and validation populations, respectively. At the optimal cutoff value of 12, the sensitivity and specificity of the score for the prediction of sarcopenia were 70.9% and 81.0% in the exploratory population and 53.7% and 88.8% in the validation population, respectively. The Hosmer-Lemeshow goodness-of-fit test showed a good calibration with the risk score in external validation (χ = 4.459, P = 0.813).
CONCLUSIONS
The new, alternative sarcopenia risk score appears to be an effective screening tool for identification of sarcopenia in Chinese patients with type 2 diabetes in clinical practice. Clinical trial registration Chinese Clinical Trial Registry, ChiCTR-EOC-15006901.
Topics: Adult; Humans; Cross-Sectional Studies; Diabetes Mellitus, Type 2; East Asian People; Risk Factors; Sarcopenia; Random Allocation
PubMed: 37161594
DOI: 10.1186/s40001-023-01127-1 -
Statistical Methods in Medical Research May 2019We present a randomization-based inferential framework for experiments characterized by a strongly ignorable assignment mechanism where units have independent...
We present a randomization-based inferential framework for experiments characterized by a strongly ignorable assignment mechanism where units have independent probabilities of receiving treatment. Previous works on randomization tests often assume these probabilities are equal within blocks of units. We consider the general case where they differ across units and show how to perform randomization tests and obtain point estimates and confidence intervals. Furthermore, we develop rejection-sampling and importance-sampling approaches for conducting randomization-based inference conditional on any statistic of interest, such as the number of treated units or forms of covariate balance. We establish that our randomization tests are valid tests, and through simulation we demonstrate how the rejection-sampling and importance-sampling approaches can yield powerful randomization tests and thus precise inference. Our work also has implications for observational studies, which commonly assume a strongly ignorable assignment mechanism. Most methodologies for observational studies make additional modeling or asymptotic assumptions, while our framework only assumes the strongly ignorable assignment mechanism, and thus can be considered a minimal-assumption approach.
Topics: Models, Statistical; Observational Studies as Topic; Probability; Propensity Score; Random Allocation; Research Design
PubMed: 29451089
DOI: 10.1177/0962280218756689