Number Of Time Points Research Articles

Appropriate Sampling and Longer Follow-Up Are Required to Rigorously Evaluate Longevity of Humoral Memory After Vaccination.

One of the goals of vaccination is to induce long-lived immunity against the infection and/or disease. Many studies have followed the generation of humoral immunity to SARS-CoV-2 after vaccination; however, such studies typically varied by the duration of the follow-up and the number of time points at which immune response measurements were done. How these parameters (the number of time points and the overall duration of the follow-up) impact estimates of immunity longevity remain largely unknown. Several studies, including one by Arunachalam etal. (2023. J. Clin. Invest. 133: e167955), evaluated the humoral immune response in individuals receiving either a third or fourth dose of mRNA COVID-19 vaccine; by measuring Ab levels at three time points (prior to vaccination and at 1 and 6 mo), Arunachalam etal. found similar half-life times for serum Abs in the two groups and thus suggested that additional boosting is unnecessary to prolong immunity to SARS-CoV-2. I demonstrate that measuring Ab levels at these three time points and only for 6 mo does not allow one to accurately evaluate the long-term half-life of vaccine-induced Abs. By using the data from a cohort of blood donors followed for several years, I show that after revaccination with vaccinia virus, vaccinia virus-specific Abs decay biphasically, and even the late decay rate exceeds the true slow loss rate of humoral memory observed years prior to the boosting. Mathematical models of Ab response kinetics, parameterized using preliminary data, should be used for power analysis to determine the most appropriate timing and duration of sampling to rigorously determine the duration of humoral immunity after vaccination.

Pooled Rate of Pseudoprogression, Patterns of Response, and Tumor Burden Analysis in Patients Undergoing Immunotherapy Oncologic Trials for Different Malignancies

AimsAssess rates of true pseudoprogression in unconfirmed progressive disease (iUPD) in a pool of immunotherapy clinical trials for different cancers, analyze tumor characteristics that drive iUPD classification, and investigate potentials predictors of pseudoprogression. Materials and methodsRetrospective interpretation of prospectively acquired data. Patients from 18 immunotherapy clinical trials with two arms (RECIST 1.1, iRECIST), of 10 cancer types were selected. Pooled rate of true pseudoprogression among iUPD was estimated using a common effect meta-analysis. Target, Non-target, and new lesions as the trigger of confirmed-vs pseudo-progression were compared using Chi-Square and Fisher exact tests. Conditional logistic regression was used to investigate the association between age, sex, tumor burden at baseline, and number of follow ups and pseudoprogression. Results60/287 (21%) patients (17 women) were classified as iUPD with at least one subsequent confirmatory timepoint. The overall pooled estimate of pseudoprogression was 15% (95%CI: 8%–-26%). Nontarget lesions were significantly more frequent the cause of iUPD than change in Target lesions size (p< 0.001). Most observations of true pseudoprogression occurred in the first follow-up (77%), whereas confirmed progression occurred in later time points during the trial. Pseudoprogression was not significantly associated with age, sex, tumor burden at baseline, or number of timepoints. ConclusionIn a pool of immunotherapy trials, the rate of true pseudoprogression was 15%, most often in the first timepoint after baseline than later in treatment. iUPD categorization was mostly driven by changes in NT lesions rather than objective changes in measurements of target lesions.

Participant recruitment and attrition in surgical randomised trials with placebo controls versus non-operative controls: a meta-epidemiological study and meta-analysis

ObjectiveTo compare differences in recruitment and attrition between placebo control randomised trials of surgery, and trials of the same surgical interventions and conditions that used non-operative (non-placebo) controls.DesignMeta-epidemiological study.Data sourcesRandomised...

An automated methodology for whole-body, multimodality tracking of individual cancer lesions.

Objective. Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion tracking, across an arbitrary number of scans, and acquired with various imaging modalities.Approach. This study introduces a lesion tracking methodology and benchmarked it using 2368Ga-DOTATATE PET/CT and PET/MR images of eight neuroendocrine tumor patients. The methodology consists of six steps: (1) alignment of multiple scans via image registration, (2) body-part labeling, (3) automatic lesion-wise dilation, (4) clustering of lesions based on local lesion shape metrics, (5) assignment of lesion tracks, and (6) output of a lesion graph. Registration performance was evaluated via landmark distance, lesion matching accuracy was evaluated between each image pair, and lesion tracking accuracy was evaluated via identical track ratio. Sensitivity studies were performed to evaluate the impact of lesion dilation (fixed versus automatic dilation), anatomic location, image modalities (inter- versus intra-modality), registration mode (direct versus indirect registration), and track size (number of time-points and lesions) on lesion matching and tracking performance.Main results. Manual contouring yielded 956 lesions, 1570 lesion-matching decisions, and 493 lesion tracks. The median residual registration error was 2.5 mm. The automatic lesion dilation led to 0.90 overall lesion matching accuracy, and an 88% identical track ratio. The methodology is robust regarding anatomic locations, image modalities, and registration modes. The number of scans had a moderate negative impact on the identical track ratio (94% for 2 scans, 91% for 3 scans, and 81% for 4 scans). The number of lesions substantially impacted the identical track ratio (93% for 2 nodes versus 54% for ≥5 nodes).Significance. The developed methodology resulted in high lesion-matching accuracy and enables automated lesion tracking in PET/CT and PET/MR.

Effect of blood oxygen-level-dependent signal data trimming on functional connectivity metrics

In the big data era, with a lack of comparable functional neuroimaging data, researchers try to combine heterogeneous data of different lengths, trimming those to the same number of timepoints (NTPs). However, the effects of trimming blood-oxygen-level dependent (BOLD) signal data on functional connectivity (FC) are still poorly understood.Resting-state functional magnetic resonance imaging data from thirty healthy subjects were pre-processed for five different NTPs, from which FC matrices were computed. These BOLD signal correlation matrices were binarized for several thresholds, excluding weak correlations. Graph metrics were computed to study FC differences between different NTPs. The study included node degree analysis for each brain region and assessment of small-worldness coefficients (σ and ω), whereas in small-world networks, characteristic values are σ > 1 and ω≈ 0, indicating a balance between high clustering coefficients and short characteristic path lengths.A tendency of decreasing global network degrees for higher NTPs was observed, translating the loss of stronger correlations with longer BOLD signals. Trimming such data affects brain regions differently, probably due to brain network dynamics. Regarding small-worldness, we observed that σ was greater than 1 for all the different NTPs, showing an increasing trend for higher NTPs (median value: σBRAIN= 3.05). In addition, ω consistently remained greater than 0 for all NTPs, gradually approaching 0 as the NTPs increased (median value ωBRAIN= 0.20). As such, the results suggest a tendency for an increase of small-world properties for increasing NTPs. Nonetheless, the overall properties of brain networks almost remain constant. In conclusion, trimming BOLD signal data leads to small differences in FC.

Time to Move Beyond a Binary Criterion for Gestational Diabetes?

Over the years, several international guidelines have been developed by specialist organizations for the diagnosis of gestational diabetes mellitus (GDM). However, these guidelines vary and lack consensus on what level of glycemia defines GDM and worryingly, there is now evidence of over- or- under-diagnosis of women with GDM by current criteria. Towards this end, the National Priorities Research Program (NPRP) funded a program of research aimed at elucidating the problem with GDM diagnosis. It was determined, on completion of the project, that the solution required diagnosis of graded levels of dysglycemia in pregnancy and not just a diagnosis of presence or absence of GDM. A new diagnostic criterion (called the NPRP criterion) was created based on a single numerical summary of the three readings from the oral glucose tolerance test (GTT) that diagnosed women in pregnancy into four levels: normal, impaired, GDM and high risk GDM. This paper now examines existing GDM criteria vis-à-vis the NPRP criterion. It is noted that no significant change has happened over the years for existing criteria except for a gradual reduction in the threshold values of individual time-points or the number of time points, bringing us towards over-diagnosis of GDM in pregnancy. The new criterion unifies all readings from the GTT into one numerical value and, because it results in four levels of glycemia, represents a new way forwards for GDM diagnosis and can potentially reduce the rates of under diagnosis and over diagnosis of GDM.

Dynamic Structural Equation Models with Missing Data: Data Requirements on N and T

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in Mplus to determine whether a data configuration is sufficient to obtain accurate and precise results from a specific DSEM.

A Network Study of Family Affect Systems in Daily Life

Adolescence is a time period characterized by extremes in affect and increasing prevalence of mental health problems. Prior studies have illustrated how affect states of adolescents are related to interactions with parents. However, it remains unclear how affect states among family triads, that is adolescents and their parents, are related in daily life. This study investigated affect state dynamics (happy, sad, relaxed, and irritated) of 60 family triads, including 60 adolescents (M age = 15.92, 63.3% females), fathers and mothers (M age = 49.16). The families participated in the RE-PAIR study, where they reported their affect states in four ecological momentary assessments per day for 14 days. First, we used multilevel vector-autoregressive network models to estimate affect dynamics across all families, and for each family individually. Resulting models elucidated how family affect states were related at the same moment, and over time. We identified relations from parents to adolescents and vice versa, while considering family variation in these relations. Second, we evaluated the statistical performance of the network model via a simulation study, varying the percentage missing data, the number of families, and the number of time points. We conclude with substantive and statistical recommendations for future research on family affect dynamics.

91 Agent Orange Exposure and Mild Cognitive Impairment in U.S. Vietnam Era Veterans

Objective:US forces used Agent Orange (AO) during the Vietnam War and continued to store/test it at other locations after the war. AO is a powerful herbicide including dioxin, a highly toxic ingredient classified as a human carcinogen. The National Academies of Sciences, Engineering, and Medicine periodically review the literature on the health effects of AO exposure (AOE) and concluded in 2018 that there is sufficient evidence linking AO with a wide range of adverse health outcomes, including neurologic disorders (e.g., Parkinson’s disease). The VA has a list of medical disorders considered presumptive conditions related to AOE. More recently, AOE has been linked to a nearly double risk compared to those without AOE for receiving a dementia diagnosis. To our knowledge, no one has investigated the association of AOE to mild cognitive impairment (MCI), a condition thought to precede dementia.Participants and Methods:We examined men in three waves of the Vietnam Era Twin Study of Aging (VETSA). In wave 3, participants self-reported yes/no to the question of whether they ever had prolonged or serious AOE. MCI was diagnosed by the Jak-Bondi approach. Impairment was defined as 2+ tests within a cognitive domain that were more than 1.5 standard deviations below normative means after adjusting for premorbid cognitive ability. In mixed effects models, we tested the effect of AOE on MCI status. Models were adjusted for age, ethnicity, and non-independence within twin pairs.Results:In wave 3, 12.6% (230) of 1167 participants reported AOE. Those with AOE data had mean ages of 51.1 (wave 1), 56.0 (wave 2), and 61.4 (wave 3). Those with data on both AOE and MCI numbered 861 (wave 1), 900 (wave 2), 1121 (wave 3), and 766 had AOE and MCI at all waves. AOE was significantly related to wave 2 MCI (p &lt; .001), but not to waves 1 and 3 MCI. AOE was significantly associated with the number of time points at which someone met criteria for MCI (p = .011). Analyses were conducted on six cognitive domains used to diagnose MCI, using available participants per wave. At all 3 waves, AOE was significantly associated with lower scores in processing speed (p = .003, p = .004, p = .005, respectively), working memory (p &lt; .001, p = .002, p = .008) and nearly significant at all waves for executive dysfunction (p &lt; .001, p &lt; .001, p = .050). There were two other significant associations [wave 2 memory (p = .038), wave 3 fluency (p = .024)]. The semantic fluency cognitive domain was unrelated to AOE in all waves.Conclusions:AOE was consistently associated with lower processing speed, working memory, and executive dysfunction in males ages 51-61. It was also associated with the number of time points at which one met criteria for MCI in that age range, and with MCI in the mid-fifties. Findings support the idea of a risk for greater cognitive decline in those exposed to AO earlier in their lives, and with a risk for developing MCI.

Investigating the feasibility of idiographic network models.

Recent times have seen a call for personalized psychotherapy and tailored communication during treatment, leading to the necessity to model the complex dynamics of mental disorders in a single subject. To this aim, time-series data in one patient can be collected through ecological momentary assessment and analyzed with the graphical vector autoregressive model, estimating temporal and contemporaneous idiographic networks. Idiographic networks graph interindividual processes that may be potentially used to tailor psychotherapy and provide personalized feedback to clients and are regarded as a promising tool for clinical practice. However, the question whether we can reliably estimate them in clinical settings remains unanswered. We conducted a large-scale simulation study in the context of psychopathology, testing the performance of personalized networks with different numbers of time points, percentages of missing data, and estimation methods. Results indicate that sensitivity is low with sample sizes feasible for clinical practice (75 and 100 time points). It seems possible to retrieve the global network structure but not to recover the full network. Estimating temporal networks appears particularly challenging; thus, with 75 and 100 observations, it is advisable to reduce the number of nodes to around six variables. With regard to missing data, full information maximum likelihood and the Kalman filter are effective in addressing random item-level missing data; consequently, planned missingness is a valid method to deal with missing data. We discuss possible methodological and clinical solutions to the challenges raised in this work. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Changes in abortion legislation and admissions to paediatric intensive care in Ireland

The Health (Regulation of Termination of Pregnancy) Act 2018 was commenced on 01/01/2019 in Ireland. The Act provides for legal termination of pregnancy under defined circumstances including for any reason at &lt; 12 weeks gestation; and where two doctors agree there is ‘a condition affecting the foetus that is likely to lead to the death of the foetus either before, or within 28 days of, birth’. As such, abortion for congenital anomaly (CA) can occur at a number of time points, depending on the adjudged severity. Infants born with CAs frequently require significant medical intervention and account for a high proportion of admissions to paediatric intensive care units (PICUs). The purpose of this paper was to evaluate the number of infants with CAs admitted to an Irish PICU in the period before and after the implementation of the Act. All PICU admissions &lt; 1 month of age to a single Irish paediatric hospital between 2012 and 2021 were analysed. CAs were recorded, and the periods before and after the commencement of the Act compared. We found a difference in admissions involving CAs, particularly those related to congenital heart disease involving single ventricle anatomy. It is plausible that this difference was as a result of improved access to abortion services following the implementation of the Act. This article explores the legal conditions related to the abortion of a foetus with a CA in Ireland, and the possible impact of the Independent Review of the Operation of the Health ( Regulation of Termination of Pregnancy) Act 2018.

A faster algorithm for converting simple temporal networks with uncertainty into dispatchable form

A Simple Temporal Network with Uncertainty (STNU) is a data structure for reasoning about time constraints on actions that may have uncertain durations. An STNU is dispatchable if it can be executed in real-time with minimal computation 1) satisfying all constraints no matter how the uncertain durations play out and 2) retaining maximum flexibility. The fastest known algorithm for converting STNUs into dispatchable form runs in O(n3) time, where n is the number of timepoints. This paper presents a faster algorithm that runs in O(mn+kn2+n2log⁡n) time, where m is the number of edges and k is the number of uncertain durations. This performance is particularly meaningful in fields like Business Process Management, where sparse STNUs can represent temporal processes or plans. For sparse STNUs, our algorithm generates dispatchable forms in time O(n2log⁡n), a significant improvement over the O(n3)-time previous fastest algorithm.

Resolving Deleterious and Near-Neutral Effects Requires Different Pooled Fitness Assay Designs.

Pooled sequencing-based fitness assays are a powerful and widely used approach to quantifying fitness of thousands of genetic variants in parallel. Despite the throughput of such assays, they are prone to biases in fitness estimates, and errors in measurements are typically larger for deleterious fitness effects, relative to neutral effects. In practice, designing pooled fitness assays involves tradeoffs between the number of timepoints, the sequencing depth, and other parameters to gain as much information as possible within a feasible experiment. Here, we combined simulations and reanalysis of an existing experimental dataset to explore how assay parameters impact measurements of near-neutral and deleterious fitness effects using a standard fitness estimator. We found that sequencing multiple timepoints at relatively modest depth improved estimates of near-neutral fitness effects, but systematically biased measurements of deleterious effects. We showed that a fixed total number of reads, deeper sequencing at fewer timepoints improved resolution of deleterious fitness effects. Our results highlight a tradeoff between measurement of deleterious and near-neutral effect sizes for a fixed amount of data and suggest that fitness assay design should be tuned for fitness effects that are relevant to the specific biological question.

P136 T-cell-Specific Biomarkers of Early Treatment Response in Patients with Rheumatoid Arthritis Treated with a Tumour Necrosis Factor Inhibitor

Abstract Background/Aims The use of tumour necrosis factor inhibitors (TNFi) has advanced the clinical management of rheumatoid arthritis (RA). However, only 30-40% of patients who receive initial treatment with a TNFi experience a beneficial response. The discovery of reliable biomarkers of TNFi response is, therefore, a priority. Whilst T-cell dysfunction is documented in RA pathophysiology, T-cell involvement in TNFi treatment response is unclear. We aimed to phenotype peripheral blood-derived T-cells in RA patients over a number of time-points through the first 12-weeks of TNFi treatment; and to apply a cluster-free association testing method developed on single-cell CITE-seq data—co-varying neighbourhood analysis (CNA)—to high-dimensional single-cell CyTOF data. Methods 16 TNFi-naïve RA patients commenced treatment with a TNFi (10 on Adalimumab, 6 on Etanercept) and peripheral blood samples were collected at 6-7 time-points between pre-treatment and 12-weeks. Peripheral blood mononuclear cells were extracted, and stained with a 35-marker CyTOF panel. Treatment response was classified using the EULAR response criteria, and patients were characterised as good, moderate or non-responders. Data were cleaned in FlowJo and analysed using both i) a cluster-based approach, where data were clustered into 12 populations, and linear mixed models were fitted per cluster to test for significant differences in population abundance and ii) a cluster-free approach called CNA where association testing is purely data-driven. Both analyses controlled for age, sex, type of TNFi and TNFi concentration. Results Cluster-based analysis showed at 12-weeks, 1 of the 12 clusters (CD4+CD39+HLA-DR+) significantly increased in abundance in the non-responder cohort compared to the good and moderate responders (adjusted p = 0.037). Interestingly, HLA-DR and CD39 genetic effects have previously been associated with TNFi response. No significant differences were observed with CNA. Conclusion These results demonstrate that significant differences in T-cell abundance can be seen as early as 12-weeks after commencing treatment with a TNFi. Validation of these findings is required before the predictive value of this T-cell population can be tested. Disclosure M.A. Sutcliffe: None. S. Ling: None. A. Banyard: None. L. Rumker: None. S. Viatte: None. A. Morris: None. A. Barton: None. S. Raychaudhuri: None. D. Plant: None.

Data fusion in a two-stage spatio-temporal model using the INLA-SPDE approach

This paper proposes a two-stage estimation approach for a spatial misalignment scenario that is motivated by the epidemiological problem of linking pollutant exposures and health outcomes. We use the integrated nested Laplace approximation method to estimate the parameters of a two-stage spatio-temporal model — the first stage models the exposures using data fusion while the second stage links the health outcomes to exposures. The first stage is based on the Bayesian melding model, which assumes a common latent field for the different data sources for the pollutants. The second stage fits a generalized linear mixed model using the spatial averages of the estimated latent field, and additional spatial and temporal random effects. Uncertainty from the first stage is accounted for by simulating repeatedly from the posterior predictive distribution of the latent field. A simulation study was carried out to assess the impact of the sparsity of the data on the monitors, number of time points, and the specification of the priors in terms of the biases, RMSEs, and coverage probabilities of the parameters and the block-level exposure estimates. The results show that the parameters are generally estimated correctly but there is difficulty in estimating the Matèrn field parameters. The effect of exposures on the health outcomes is the primary parameter of interest for spatial epidemiologists and health policy makers, and our results show that the proposed method estimates these very well. The proposed method is applied to measurements of NO2 concentration and respiratory hospitalizations for year 2007 in England. The results show that an increase in NO2 levels is significantly associated with an increase in the relative risks of the health outcome. Also, there is a strong spatial structure in the risks, a strong temporal autocorrelation, and a significant spatio-temporal interaction effect.

Evolution of Portable Sensors for In-Vivo Dose and Time-Activity Curve Monitoring as Tools for Personalized Dosimetry in Molecular Radiotherapy

Treatment personalization in Molecular Radiotherapy (MRT) relies on pre- and post-treatment SPECT/ PET-based images and measurements to obtain a patient-specific absorbed dose-rate distribution map and its evolution over time. Unfortunately, the number of time points that are available per patient to investigate individual pharmacokinetics is often reduced by limited patient compliance or SPECT or PET/CT scanner availability for dosimetry in busy departments. The adoption of portable sensors for in-vivo dose monitoring during the entire treatment could improve the assessment of individual biokinetics in MRT and, thus, the treatment personalization. The evolution of portable devices, non-SPECT/PET-based options, already used for monitoring radionuclide activity transit and accumulation during therapy with radionuclides (i.e., MRT or brachytherapy), is presented to identify valuable ones, which combined with conventional nuclear medicine imaging systems could be effective in MRT. External probes, integration dosimeters and active detecting systems were included in the study. The devices and their technology, the range of applications, the features and limitations are discussed. Our overview of the available technologies encourages research and development of portable devices and dedicated algorithms for MRT patient-specific biokinetics study. This would represent a crucial advancement towards personalized treatment in MRT.

Volume-guarantee vs. pressure-limited ventilation in evolving bronchopulmonary dysplasia.

Extremely premature infants are at high risk for developing bronchopulmonary dysplasia (BPD). While noninvasive support is preferred, they may require ventilator support. Although volume-targeted ventilation (VTV) has been shown to be beneficial in preventing BPD, no data exists to guide ventilator management of infants with evolving BPD. Thus, clinicians employ a host of ventilator strategies, traditionally time-cycled pressure-limited ventilation (PLV) and more recently volume-guarantee ventilation (VGV) (a form of VTV). In this study, we sought to test the hypothesis that use of VGV in evolving BPD is associated with improved clinical and pulmonary outcomes when compared with PLV. Single-center, retrospective cohort review of premature infants born less than 28 weeks inborn to a Level 4 NICU from January 2015 to December 2020. Data abstracted included demographics, maternal and birth data, and ventilator data until death or discharge. Exposure to either VGV or PLV was also examined, including ventilator "dose" (number of time points from DOL 14, 21 and 28 the patient was on that particular ventilator) during the period of evolving BPD. Of a total of 471 patients with ventilation data available on DOL 14, 268 were not ventilated and 203 were ventilated. PLV at DOL 21 and 28 was associated with significantly higher risk of BPD and the composite outcome of BPD or death before 36 weeks compared to VGV. Both increasing VGV and PLV doses were significantly associated with higher odds of BPD and the composite outcome. For each additional time point of VGV and PLV exposure, the predicted length of stay (LOS) increased by 15.3 days (p < 0.001) and 28.8 days (p < 0.001), respectively. Our study demonstrates the association of use of VGV at DOL 21 and 28 with decreased risk of BPD compared to use of PLV. Prospective trials are needed to further delineate the most effective ventilatory modality for this population with "evolving" BPD.

A class of cylindrically symmetric exact solutions of the wave equation

A class of analytically exact solutions is derived of the two-dimensional wave equation. The solutions are due to a point source (i.e. a uniform line source in 3D) at the origin r=0 with polynomial time dependence that starts at t=0. They are essentially similarity solutions in the variable c0t/r. Their explicit formulation is supported by analytical results for large t and small r behaviour. By taking advantage of linearity, sources of finite duration can be modelled by subtracting from a solution its delayed version. For illustration, some examples are given graphically as functions of r, of t, and of t−r/c0, for a number of points in time and location, respectively.

Statistical considerations for design and analysis of stability, comparability and formulation tests.

This article considers designed experiments for stability, comparability, and formulation testing that are analyzed with regression models in which the degradation rate is a fixed effect. In this setting, we investigate how the number of lots, the number of time points and their locations affect the precision of the entities of interest, leverages of the time points, detection of non-linearity and interim analyses. This investigation shows that modifying time point locations suggested by ICH for stability studies can significantly improve these objectives. In addition, we show that estimates of precision can be biased when a regression model that assumes independent measurements is used in the presence of within-assay session correlation. This bias can lead to longer shelf life estimates in stability studies and loss of power in comparability studies. Mixed-effect models that take into account within-assay session correlation are shown to reduce this bias. The findings in this article are obtained from well known statistical theory but provide valuable practical advice to scientists and statisticians designing and interpreting these types of experiments.

Power analysis for single-case designs: Computations for (AB)k designs.

Currently, the design standards for single-case experimental designs (SCEDs) are based on validity considerations as prescribed by the What Works Clearinghouse. However, there is a need for design considerations such as power based on statistical analyses. We compute and derive power using computations for (AB)k designs with multiple cases which are common in SCEDs. Our computations show that effect size has the maximum impact on power followed by the number of subjects and then the number of phase reversals. An effect size of 0.75 or higher, at least one set of phase reversals (i.e., where k > 1), and at least three subjects showed high power. The latter two conditions agree with current standards about either having at least an ABAB design or a multiple baseline design with three subjects to meet design standards. An effect size of 0.75 or higher is not uncommon in SCEDs either. Autocorrelations, the number of time-points per phase, and intraclass correlations had a smaller but non-negligible impact on power. In sum, power analyses in the present study show that conditions to meet power requirements are not unreasonable in SCEDs. The software code to compute power is available on GitHub for the use of the reader.