scholarly journals PowerLAPIM: An Application to Conduct Power Analysis for Longitudinal Actor-Partner Interdependence Models that Include Quadratic Effects

2021 ◽  
Author(s):  
Ginette Lafit ◽  
Laura Sels ◽  
Janne Adolf ◽  
Tom Loeys ◽  
Eva Ceulemans
Keyword(s):  
Power Analysis ◽  
Test Group ◽  
Repeated Measurements ◽  
Outcome Variable ◽  
Heterosexual Couples ◽  
Modeling Framework ◽  
Analysis Application ◽  
Quadratic Effects ◽  
Time Varying Covariates ◽  
Power Analyses

The longitudinal actor-partner interdependence modeling framework (L-APIM) is often used to study actor and partner effects in dyadic intensive longitudinal data. To capture curvilinear actor and partner patterns, the L-APIM can be extended to include quadratic actor and partner effects. A burning question is how to conduct power analyses for different L-APIM variants. In this paper, we introduce a power analysis application, called PowerLAPIM, and provide a hands-on tutorial for conducting simulation-based power analyses for 32 L-APIM variants, many of which include quadratic effects. With PowerLAPIM, we target the number of dyads needed, but not the number of repeated measurements for both partners, because this is usually fixed in many longitudinal dyadic studies. PowerLAPIM allows studying moderation of the linear and quadratic actor and partner effects by incorporating time-varying covariates or a categorical dyad-level predictor to test group differences. We also provide the functionality to account for serial dependency in the outcome variable by including autoregressive effects. We illustrate how to perform a power analysis for a longitudinal dyadic study using PowerLAPIM based on data from 94 heterosexual couples for which both partners simultaneously reported on their feelings and experiences several times a day for one week.

EpiPen: An R Package to Investigate Two-Locus Epistatic Models

2014 ◽  
Vol 17 (4) ◽  
Author(s):  
Raymond K. Walters ◽  
Charles Laurin ◽  
Gitta H. Lubke
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Model Interpretation ◽  
Genome Wide ◽  
Using Data ◽  
Power Analyses

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

scholarly journals Clinical efficacy of scaling and root planing with and without metronidazole on glycemic control: three-arm randomized controlled trial

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ambrina Qureshi ◽  
Syed Akhtar Hussain Bokhari ◽  
Zeba Haque ◽  
Akhtar Ali Baloch ◽  
Sidra Zaheer
Keyword(s):  
Randomized Controlled Trial ◽  
Glycemic Control ◽  
Clinical Efficacy ◽  
Controlled Trial ◽  
Test Group ◽  
Periodontal Pocket ◽  
Outcome Variable ◽  
Scaling And Root Planing ◽  
Randomized Controlled ◽  
Root Planing

Abstract Background Treating periodontitis through non-surgical periodontal therapy (NSPT) may improve glycemic control in type-2 Diabetes Mellitus (T2DM) patients. However, the evidence to maintain this improvement beyond four months is insufficient. Hence, this trial was conducted to assess clinical efficacy of NSPT on glycemic control in T2DM patients. Methods This three-arm randomized controlled trial recruited 150 known T2DM participants (35–65 years), suffering from moderate to severe periodontitis, having HbA1c level ≥ 6.5% at baseline. Participants were followed up at 3 and 6 months. Intervention for test group-1 included scaling and root planing (SRP) with metronidazole (MET) and oral hygiene instructions (OHI). Test group-2 was intervened with SRP + OHI and control group with OHI only. Stata v. 14 was used to observe inter and intragroup mean changes in glycemic [glycated hemoglobin (HbA1c), fasting blood glucose (FBG)] and periodontal variables [bleeding on probing (BOP), periodontal pocket depth (PPD), clinical attachment loss (CAL)] using ANOVA and RMANOVA. Proportion of change in outcome variable (HbA1c) was assessed between treatment groups using chi-square test. Change was considered significant at p-value ≤ 0.05. Results A significant reduction was observed in BOP, PPD, CAL, HbA1c and FBG over time [p < 0.05]. Significant reductions were observed in same variables in both test groups in comparison to control arm [p < 0.05]. No change between the two test groups was observed [p > 0.05]. Conclusion Scaling and root planing improves glycemic control of T2DM patients independently of the use of MET. Therefore, SRP after every 6 months may be suggested and included as a part of overall diabetes management for patients suffering from T2DM. Clinical trial registration NCT 03,343,366 [Date of Registration: 17/11/2017]

Abstract WP138: Effects of Intensive Medical Management (IMM) and Encephaloduroarteriosynangiosis (EDAS) On VEGFA165a/b Ratios in Patients With Intracranial Atherosclerosis (ICASi

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Nestor R Gonzalez ◽  
Raymond Liou ◽  
Yinn Ooi ◽  
Jason D Hinman ◽  
Neal Rao ◽  
...  
Keyword(s):  
Prospective Observational Study ◽  
Mixed Model ◽  
Sandwich Elisa ◽  
Repeated Measurements ◽  
Likelihood Method ◽  
Outcome Variable ◽  
Serum Samples ◽  
Persistent Symptoms ◽  
Early Elevation

Objective: VEGFA isoforms 165a and 165b are pro and antiangiogenic, respectively. We aimed to evaluate the effects of IMM and EDAS in the VEGFA165a/b ratio in patients with ICAS. Methods: This is a prospective observational study of VEGFA165a and b in patients with stenosis greater than 70% due to ICAS. All patients received IMM. Patients with persistent symptoms underwent EDAS while maintaining IMM. Serum samples were collected at baseline, 1 week, 1, 3, and 6 months. VEGFA isoforms were quantified using multiplex sandwich ELISA. All samples were run in duplicate and accepted as valid if the intersample variability was less than 20%. A mixed model was built for the outcome variable VEGFA165a/b ratio using the predictor variables timepoint, treatment, and the interaction of time and treatment. The restricted maximum likelihood method was used to fit the model with random effects to account for the repeated measurements and intersubject variability. Results: A total of 72 patients were enrolled, of which 58 had IMM alone and 14 had EDAS. Mean age was 61.8 ± 12.3, 53% were females. The regression model demonstrated that there were no significant differences in the VEGFA165a/b ratio at baseline and 1 month after enrollment. Significant differences in VEGFA165a/b ratio were found at one week with higher levels in the surgical group (EDAS: 0.46 ± 0.22, IMM: 0.24 ± 0.07, p=0.03) and at 3 and 6 months with higher levels in the IMM group (3m: EDAS: 0.29 ± 0.14, IMM: 0.45 ± 0.20, p=0.03, 6m: EDAS:0.19 ± 0.11, IMM 0.37 ± 0.19 p=0.01). Conclusion: While the surgical event may well explain the early elevation of the VEGFA165a/b ratio one week after surgery, the elevation of a proangiogenic profile by the 3rd and 6th month in the IMM group is relevant. None of the patients in the IMM or EDAS groups had strokes at the last 6 months follow-up, and the peak (early for EDAS and at 3 and 6 months for IMM) of the VEGFA165a/b ratio may indicate a protective effect, averting stroke.

scholarly journals A Multi-faceted Mess: A Review of Statistical Power Analysis in Psychology Journal Articles

2019 ◽  
Author(s):  
Rob Cribbie ◽  
Nataly Beribisky ◽  
Udi Alter
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Type I Error ◽  
A Priori ◽  
Type I ◽  
Specific Level ◽  
Maximum Sample Size ◽  
Power Analyses

Many bodies recommend that a sample planning procedure, such as traditional NHST a priori power analysis, is conducted during the planning stages of a study. Power analysis allows the researcher to estimate how many participants are required in order to detect a minimally meaningful effect size at a specific level of power and Type I error rate. However, there are several drawbacks to the procedure that render it “a mess.” Specifically, the identification of the minimally meaningful effect size is often difficult but unavoidable for conducting the procedure properly, the procedure is not precision oriented, and does not guide the researcher to collect as many participants as feasibly possible. In this study, we explore how these three theoretical issues are reflected in applied psychological research in order to better understand whether these issues are concerns in practice. To investigate how power analysis is currently used, this study reviewed the reporting of 443 power analyses in high impact psychology journals in 2016 and 2017. It was found that researchers rarely use the minimally meaningful effect size as a rationale for the chosen effect in a power analysis. Further, precision-based approaches and collecting the maximum sample size feasible are almost never used in tandem with power analyses. In light of these findings, we offer that researchers should focus on tools beyond traditional power analysis when sample planning, such as collecting the maximum sample size feasible.

scholarly journals Conditional Process Analysis in Two-Instance Repeated-Measures Designs

2019 ◽  
Author(s):  
Amanda Kay Montoya
Keyword(s):  
Mediation Analysis ◽  
Repeated Measures ◽  
Process Analysis ◽  
Repeated Measurements ◽  
Process Models ◽  
Outcome Variable ◽  
Moderation Analysis ◽  
Repeated Measures Design ◽  
Independent Variable ◽  
Conditional Process Analysis

Conditional process models are commonly used in many areas of psychology research as well as research in other academic fi?elds (e.g., marketing, communication, and education). Conditional process models combine mediation analysis and moderation analysis. Mediation analysis, sometimes called process analysis, investigates if an independent variable influences an outcome variable through a specific?c intermediary variable, sometimes called a mediator. Moderation analysis investigates if the relationship between two variables depends on another. Conditional process models are very popular because they allow us to better understand how the processes we are interested in might vary depending on characteristics of different individuals, situations, and other moderating variables. Methodological developments in conditional process analysis have primarily focused on the analysis of data collected using between-subjects experimental designs or cross-sectional designs. However, another very common design is the two-instance repeated-measures design. A two-instance repeated-measures design is one where each subject is measured twice; once in each of two instances. In the analysis discussed in this dissertation, the factor that differentiates the two repeated measurements is the independent variable of interest. Research on how to statistically test mediation, moderation, and conditional process models in these designs has been minimal. Judd, Kenny, and McClelland (2001) introduced a piece-wise method for testing for mediation, reminiscent of the Baron and Kenny causal steps approach for between-participant designs. Montoya and Hayes (2017) took thispiece-wise approach and translated it to a path-analytic approach, allowing for a quanti?cation of the indirect e?ect, more sophisticated methods of inference, and the extension to multiple mediator models. Moderation analysis in these designs has been described by Judd, McClelland, and Smith (1996), Judd et al. (2001), and Montoya (in press). However, the generalization to conditional process analysis, or moderated mediation, remains unknown. Describing this approach is the purpose of this dissertation.

Epi2Loc: An R Package to Investigate Two-Locus Epistatic Models

2014 ◽  
Vol 17 (4) ◽  
pp. 272-278 ◽  
Author(s):  
Raymond K. Walters ◽  
Charles Laurin ◽  
Gitta H. Lubke
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Model Interpretation ◽  
Genome Wide ◽  
Using Data ◽  
Power Analyses

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, Epi2Loc, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. Epi2Loc facilitates research on SNP–SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

scholarly journals Statistics in Experimental Cerebrovascular Research—Comparison of Two Groups with a Continuous Outcome Variable

2010 ◽  
Vol 30 (3) ◽  
pp. 474-479 ◽  
Author(s):  
Peter Schlattmann ◽  
Ulrich Dirnagl
Keyword(s):  
Statistical Analysis ◽  
Experimental Research ◽  
Power Analysis ◽  
A Priori ◽  
Statistical Testing ◽  
Outcome Variable ◽  
Continuous Outcome ◽  
Basic Concepts ◽  
Analysis Error ◽  
Definition Of

Part one of this mini-series on statistics in cerebrovascular research uses the simplest yet most common comparison in experimental research (two groups with a continuous outcome variable) to introduce the very basic concepts of statistical testing: a priori formulation of hypotheses and definition of planned statistical analysis, error considerations, and power analysis.

scholarly journals Measuring effects of medication adherence on time-varying health outcomes using Bayesian dynamic linear models

Biostatistics ◽  
2019 ◽  
Author(s):  
Luis F Campos ◽  
Mark E Glickman ◽  
Kristen B Hunter
Keyword(s):  
Medication Adherence ◽  
Linear Models ◽  
Time Varying ◽  
Dynamic Parameters ◽  
Posterior Density ◽  
Modeling Framework ◽  
Dynamic Linear Models ◽  
Health Measures ◽  
The Impact ◽  
Time Varying Covariates

Summary One of the most significant barriers to medication treatment is patients’ non-adherence to a prescribed medication regimen. The extent of the impact of poor adherence on resulting health measures is often unknown, and typical analyses ignore the time-varying nature of adherence. This article develops a modeling framework for longitudinally recorded health measures modeled as a function of time-varying medication adherence. Our framework, which relies on normal Bayesian dynamic linear models (DLMs), accounts for time-varying covariates such as adherence and non-dynamic covariates such as baseline health characteristics. Standard inferential procedures for DLMs are inefficient when faced with infrequent and irregularly recorded response data. We develop an approach that relies on factoring the posterior density into a product of two terms: a marginal posterior density for the non-dynamic parameters, and a multivariate normal posterior density of the dynamic parameters conditional on the non-dynamic ones. This factorization leads to a two-stage process for inference in which the non-dynamic parameters can be inferred separately from the time-varying parameters. We demonstrate the application of this model to the time-varying effect of antihypertensive medication on blood pressure levels for a cohort of patients diagnosed with hypertension. Our model results are compared to ones in which adherence is incorporated through non-dynamic summaries.

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