BMC Medical Research Methodology最新文献

Purpose: Linear Mixed Model (LMM) is a common statistical approach to model the relation between exposure and outcome while capturing individual variability through random effects. However, this model assumes the homogeneity of the error term's variance. Breaking this assumption, known as homoscedasticity, can bias estimates and, consequently, may change a study's conclusions. If this assumption is unmet, the mixed-effect location-scale model (MELSM) offers a solution to account for within-individual variability.

Methods: Our work explores how LMMs and MELSMs behave when the homoscedasticity assumption is not met. Further, we study how misspecification affects inference for MELSM. To this aim, we propose a simulation study with longitudinal data and evaluate the estimates' bias and coverage.

Results: Our simulations show that neglecting heteroscedasticity in LMMs leads to loss of coverage for the estimated coefficients and biases the estimates of the standard deviations of the random effects. In MELSMs, scale misspecification does not bias the location model, but location misspecification alters the scale estimates.

Conclusion: Our simulation study illustrates the importance of modelling heteroscedasticity, with potential implications beyond mixed effect models, for generalised linear mixed models for non-normal outcomes and joint models with survival data.

Background: Missing values occur in almost all real-world medical data. Sometimes, more information is available for the missing values due to technical measurement limits. This was also the case for some sports medical data set where several laboratory measurements below a lower limit of quantification (LLOQ) were faced and supposed to be used in a multiple linear regression model. When studying the literature, the problem arises in several disciplines (environmental epidemiology, pharmacokinetic studies etc.) and different statistical methods are suggested. However, only very limited work on a method comparison is available, especially in the multivariable linear regression settting.

Methods: Therefore, we compare statistical methods for addressing values below a LLOQ in multiple linear regression modeling by a simulation study. We consider both the case that the variable below the LLOQ is among one of the independent variables and that it is the dependent variable in the regression model. We also vary different underlying assumptions, such as distributions, sample sizes, proportions of missing values, correlations, or linearity assumptions.

Results: Overall, the two compartment model showed the best performance in terms of bias and coverage when the LLOQ occurred in the independent variable and no big collinearity issue was present. When the variable subject to the LLOQ is the dependent variable, tobit showed the lowest bias and highest coverage for censoring proportions up to 0.8.

Conclusion: When facing a data set with values below a lower limit of quantification and a multiple linear regression model is chosen as analysis model, a conscious choice for dealing with those left-censored data should be made. In this article, we provide guidance on the performance of different established methods.

Background: Although there are numerous studies exploring predictors of clinical trial failure, no comprehensive review of their methodological specificities and findings exists. We performed a scoping review with the aim of exploring the methodological approaches and findings of studies analysing predictors of clinical trial failure.

Methods: The Ovid Medline and Embase databases were systematically searched from inception to December 13, 2024, for studies employing frequentist statistics or machine learning (ML) approaches to assess predictors of trial failure across multiple clinical trials. A generalized linear model (GLM) was employed to assess the impact of certain methodological factors (failure and non-failure definitions, study types included and trial phases included) on reported failure proportions. To estimate the effects of the predictors included in the model on failure proportions, odds ratios (OR) with 95% confidence interval (95% CI) were calculated from model coefficients.

Results: The literature search identified 17,961 records, 81 of which were included in the review. Most of the studies used Clinicaltrials.gov data (73 studies, 90.1%). Frequentist statistics were used to analyze predictors of trial failure in 73 studies (90.1%), and remaining 8 studies employed ML techniques (9.9%). The GLM showed a 27.5% deviance reduction, indicating that certain methodological factors substantially contribute to observed differences in failure proportions. Studies including trials with both completed and ongoing statuses when calculating failure proportions had lower odds of failure compared to those just including completed statuses (OR = 0.44, 95% CI: 0.29-0.67, p < 0.001).

Conclusions: There has been a recent expansion of ML approaches, potentially signaling the beginning of a paradigm shift. Methodological variations substantially influence reported failure proportions, implicating the need for adoption of standardized definitions of failure and calculation approach. We recommend categorizing terminated and withdrawn studies as failed and completed ones as non-failed.