Uncertainty Analysis in Population-Based Disease Microsimulation Models

Objective. Uncertainty analysis (UA) is an important part of simulation model validation. However, literature is imprecise as to how UA should be performed in the context of population-based microsimulation (PMS) models. In this expository paper, we discuss a practical approach to UA for such models. Methods. By adapting common concepts from published UA guidelines, we developed a comprehensive, step-by-step approach to UA in PMS models, including sample size calculation to reduce the computational time. As an illustration, we performed UA for POHEM-OA, a microsimulation model of osteoarthritis (OA) in Canada. Results. The resulting sample size of the simulated population was 500,000 and the number of Monte Carlo (MC) runs was 785 for 12-hour computational time. The estimated 95% uncertainty intervals for the prevalence of OA in Canada in 2021 were 0.09 to 0.18 for men and 0.15 to 0.23 for women. The uncertainty surrounding the sex-specific prevalence of OA increased over time. Conclusion. The proposed approach to UA considers the challenges specific to PMS models, such as selection of parameters and calculation of MC runs and population size to reduce computational burden. Our example of UA shows that the proposed approach is feasible. Estimation of uncertainty intervals should become a standard practice in the reporting of results from PMS models.

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How should a clinician interpret results of randomized controlled trials?

Acta medica Lituanica ◽

10.15388/amed.2010.21689 ◽

2010 ◽

Vol 17 (1-2) ◽

pp. 30-34

Author(s):

Virginijus ŠAPOKA ◽

Vytautas KASIULEVIČIUS ◽

Janina DIDŽIAPETRIENĖ

Keyword(s):

Randomized Controlled Trials ◽

Sample Size ◽

Controlled Trial ◽

Sample Size Calculation ◽

Study Groups ◽

Controlled Trials ◽

Random Allocation ◽

Equivalence Testing ◽

Randomized Controlled ◽

Selection Of

Randomized controlled trials (RCTs) and systematic reviews are the most reliable methods of determining the effects of treatment. The randomization procedure gives a randomized controlled trial its strength. Random allocation means that all participants have the same chance of being assigned to each of the study groups. The choice of which end point(s) to select is critical to any study design. Intention-to-treat is the preferred approach to the analysis of clinical trials. Sample size calculations and data analyses have an important impact on the planning, interpretation, and conclusions of randomized trials. In this article, we discuss the problematic areas that can affect the outcome of a trial, such as blinding, sample size calculation, randomization; concealment allocation; intention of treating the analysis; selection of end points; selection of traditional versus equivalence testing, early stopped trials, selective publications. Keywords: randomized controlled trials, sample size, outcomes, type of analyses

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Common deficiencies found in generic Finished Pharmaceutical Product (FPP) applications submitted for registration to the South African Health Products Regulatory Authority (SAHPRA)

Journal of Pharmaceutical Policy and Practice ◽

10.1186/s40545-021-00398-5 ◽

2022 ◽

Vol 15 (1) ◽

Author(s):

Lerato Moeti ◽

Madira Litedu ◽

Jacques Joubert

Keyword(s):

Sample Size ◽

Sample Size Calculation ◽

Pharmaceutical Product ◽

Systematic Sampling ◽

Multi Stage ◽

African Health ◽

Therapeutic Category ◽

Health Products ◽

Container Closure ◽

Selection Of

Abstract Background The aim of the study was to investigate the common deficiencies observed in the Finished Pharmaceutical Product (FPP) section of generic product applications submitted to SAHPRA. The study was conducted retrospectively over a 7-year period (2011–2017) for products that were finalised by the Pharmaceutical and Analytical pre-registration Unit. Methods There were 3148 finalised products in 2011–2017, 667 of which were sterile while 2089 were non-sterile. In order to attain a representative sample for the study, statistical sampling was conducted. Sample size was obtained using the statistical tables found in literature and confirmed by a sample size calculation with a 95% confidence level. The selection of the products was according to the therapeutic category using the multi-stage sampling method called stratified-systematic sampling. This resulted in the selection of 325 applications for non-sterile products and 244 applications for sterile products. Subsequently, all the deficiencies were collected and categorised according to Common Technical Document (CTD) subsections of the FPP section (3.2.P). Results A total of 3253 deficiencies were collected from 325 non-sterile applications while 2742 deficiencies were collected from 244 sterile applications. The most common deficiencies in the FPP section for non-sterile products were on the following sections: Specifications (15%), Description and Composition (14%), Description of the Manufacturing Process (13%), Stability Data (7.6%) and the Container Closure System (7.3%). The deficiencies applicable to the sterile products were quantified and the subsection, Validation and/or Evaluation (18%) has the most deficiencies. Comparison of the deficiencies with those reported by other agencies such as the USFDA, EMA, TFDA and WHOPQTm are discussed with similarities outlined. Conclusions The overall top five most common deficiencies observed by SAHPRA were extensively discussed for the generic products. The findings provide an overview on the submissions and regulatory considerations for generic applications in South Africa, which is useful for FPP manufacturers in the compilation of their dossiers and will assist in accelerating the registration process.

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A Practical ANOVA Approach for Uncertainty Analysis in Population-Based Disease Microsimulation Models

Value in Health ◽

10.1016/j.jval.2017.01.002 ◽

2017 ◽

Vol 20 (4) ◽

pp. 710-717 ◽

Cited By ~ 2

Author(s):

Behnam Sharif ◽

Hubert Wong ◽

Aslam H. Anis ◽

Jacek A. Kopec

Keyword(s):

Uncertainty Analysis ◽

Population Based ◽

Microsimulation Models

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Construction and Selection of Six Sigma Quick Switching Sampling System: Sample Size Tightening

International Journal of Innovative Research in Computer and Communication Engineering ◽

10.15680/ijircce.2015.0303002 ◽

2015 ◽

pp. 1410-1418

Author(s):

Dr. D. Senthil kumar

Keyword(s):

Sample Size ◽

Six Sigma ◽

Sampling System ◽

Selection Of

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Is Hydraulics Over-Used or Under-Used in Storm Drainage?

Water Science & Technology ◽

10.2166/wst.1994.0651 ◽

1994 ◽

Vol 29 (1-2) ◽

pp. 53-61

Author(s):

Ben Chie Yen

Keyword(s):

Unsteady Flow ◽

Computational Time ◽

Time Step ◽

Flow Routing ◽

Kinematic Wave Equation ◽

Storm Drainage ◽

Saint Venant Equations ◽

Unsteady Flow Routing ◽

Selection Of

Urban drainage models utilize hydraulics of different levels. Developing or selecting a model appropriate to a particular project is not an easy task. Not knowing the hydraulic principles and numerical techniques used in an existing model, users often misuse and abuse the model. Hydraulically, the use of the Saint-Venant equations is not always necessary. In many cases the kinematic wave equation is inadequate because of the backwater effect, whereas in designing sewers, often Manning's formula is adequate. The flow travel time provides a guide in selecting the computational time step At, which in turn, together with flow unsteadiness, helps in the selection of steady or unsteady flow routing. Often the noninertia model is the appropriate model for unsteady flow routing, whereas delivery curves are very useful for stepwise steady nonuniform flow routing and for determination of channel capacity.

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Integrating genetics with newborn metabolomics in infantile hypertrophic pyloric stenosis

Metabolomics ◽

10.1007/s11306-020-01763-2 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

João Fadista ◽

Line Skotte ◽

Julie Courraud ◽

Frank Geller ◽

Sanne Gørtz ◽

...

Keyword(s):

Pyloric Stenosis ◽

Hypertrophic Pyloric Stenosis ◽

Dried Blood Spots ◽

Population Based ◽

Case Control ◽

Infantile Hypertrophic Pyloric Stenosis ◽

Sphincter Muscle ◽

Feeding Patterns ◽

Matched Case ◽

Selection Of

Abstract Introduction Infantile hypertrophic pyloric stenosis (IHPS) is caused by hypertrophy of the pyloric sphincter muscle. Objectives Since previous reports have implicated lipid metabolism, we aimed to (1) investigate associations between IHPS and a wide array of lipid-related metabolites in newborns, and (2) address whether detected differences in metabolite levels were likely to be driven by genetic differences between IHPS cases and controls or by differences in early life feeding patterns. Methods We used population-based random selection of IHPS cases and controls born in Denmark between 1997 and 2014. We randomly took dried blood spots of newborns from 267 pairs of IHPS cases and controls matched by sex and day of birth. We used a mixed-effects linear regression model to evaluate associations between 148 metabolites and IHPS in a matched case–control design. Results The phosphatidylcholine PC(38:4) showed significantly lower levels in IHPS cases (P = 4.68 × 10−8) as did six other correlated metabolites (four phosphatidylcholines, acylcarnitine AC(2:0), and histidine). Associations were driven by 98 case–control pairs born before 2009, when median age at sampling was 6 days. No association was seen in 169 pairs born in 2009 or later, when median age at sampling was 2 days. More IHPS cases than controls had a diagnosis for neonatal difficulty in feeding at breast (P = 6.15 × 10−3). Genetic variants known to be associated with PC(38:4) levels did not associate with IHPS. Conclusions We detected lower levels of certain metabolites in IHPS, possibly reflecting different feeding patterns in the first days of life.

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Simulation-Based Sample-Size Calculation for Designing New Clinical Trials and Diagnostic Test Accuracy Studies to Update an Existing Meta-Analysis

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x1301300302 ◽

2013 ◽

Vol 13 (3) ◽

pp. 451-473 ◽

Cited By ~ 6

Author(s):

Michael J. Crowther ◽

Sally R. Hinchliffe ◽

Alison Donald ◽

Alex J. Sutton

Keyword(s):

Clinical Trials ◽

Sample Size ◽

Diagnostic Test ◽

Meta Analysis ◽

Sample Size Calculation ◽

Diagnostic Test Accuracy ◽

Test Accuracy ◽

Simulation Based

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The most frequently asked question to a statistician: The sample size

Multiple Sclerosis Journal ◽

10.1177/1352458517695471 ◽

2017 ◽

Vol 23 (5) ◽

pp. 644-646 ◽

Cited By ~ 2

Author(s):

Maria Pia Sormani

Keyword(s):

Clinical Study ◽

Sample Size ◽

Study Design ◽

Treatment Effect ◽

Sample Size Calculation ◽

Ethical Implications ◽

Number Of Patients ◽

Reducing Costs ◽

Correct Size

The calculation of the sample size needed for a clinical study is the challenge most frequently put to statisticians, and it is one of the most relevant issues in the study design. The correct size of the study sample optimizes the number of patients needed to get the result, that is, to detect the minimum treatment effect that is clinically relevant. Minimizing the sample size of a study has the advantage of reducing costs, enhancing feasibility, and also has ethical implications. In this brief report, I will explore the main concepts on which the sample size calculation is based.

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