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Statistical Methods for Time-Dependent Variables in Hematopoietic Cell Transplantation Studies.

ABSTRACT: The interaction of clinically important yet time-dependent events such as infection and acute graft-versus-host disease (GVHD) on hematopoietic cell transplant outcomes is of particular interest to transplant physicians. Clinically, the development of these events is unknown at the time of transplant, but both events place the patient at risk of morbidity and mortality. Furthermore, the occurrence of one may affect the risk for the development of the other (ie, GVHD results in increased immunosuppression, resulting in infection). While these risks can be determined using traditional Cox modeling, due to their time-varying effects on the outcome, it is challenging to graphically display the patient's expected clinical status over time. Landmark analysis is one of the commonly used methods to present time-dependent variables graphically. It can be a useful tool for describing an outcome of interest with time-dependent variables. In this article, we review the basic concepts of time-dependent variables and describe a landmark study with a single-landmark time point and a dynamic landmark study with multiple landmark time points. We illustrate these methods with a hematopoietic cell transplantation data set with infections.

SUBMITTER: Kim S

PROVIDER: S-EPMC8015678 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Statistical Methods for Time-Dependent Variables in Hematopoietic Cell Transplantation Studies.

Kim Soyoung S Logan Brent B Riches Marcie M Chen Min M Ahn Kwang Woo KW

Transplantation and cellular therapy 20201002 2

The interaction of clinically important yet time-dependent events such as infection and acute graft-versus-host disease (GVHD) on hematopoietic cell transplant outcomes is of particular interest to transplant physicians. Clinically, the development of these events is unknown at the time of transplant, but both events place the patient at risk of morbidity and mortality. Furthermore, the occurrence of one may affect the risk for the development of the other (ie, GVHD results in increased immunosu ...[more]

PMID: 33017663

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Project description:IntroductionHigh-dose chemotherapy followed by autologous hematopoietic stem cell transplantation (auto-HSCT) represents a standard treatment regime for multiple myeloma (MM) patients. Common and potentially fatal side effects after auto-HSCT are infections due to a severely compromised immune system with hampered humoral and cellular immunity. This study delineates in depth the quantitative and functional B cell defects and investigates underlying extrinsic or intrinsic drivers.MethodsPeripheral blood of MM patients undergoing high-dose chemotherapy and auto-HSCT (before high-dose chemotherapy and in early reconstitution after HSCT) was studied. Absolute numbers and distribution of B cell subsets were analyzed ex vivo using flow cytometry. Additionally, B cell function was assessed with T cell dependent (TD) and T cell independent (TI) stimulation assays, analyzing proliferation and differentiation of B cells by flow cytometry and numbers of immunoglobulin secreting cells in ELISpots.ResultsQuantitative B cell defects including a shift in the B cell subset distribution occurred after auto-HSCT. Functionally, these patients showed an impaired TD as well as TI B cell immune response. Individual functional responses correlated with quantitative alterations of CD19+, CD4+, memory B cells and marginal zone-like B cells. The TD B cell function could be partially restored upon stimulation with CD40L/IL-21, successfully inducing B cell proliferation and differentiation into plasmablasts and immunoglobulin secreting cells.ConclusionQuantitative and functional B cell defects contribute to the compromised immune defense in MM patients undergoing auto-HSCT. Functional recovery upon TD stimulation and correlation with CD4+ T cell numbers, indicate these as extrinsic drivers of the functional B cell defect. Observed correlations of CD4+, CD19+, memory B and MZ-like B cell numbers with the B cell function suggest that these markers should be tested as potential biomarkers in prospective studies.

Project description:BackgroundThe Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data.MethodsWe sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods.ResultsOf 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method.ConclusionsOur empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice.

Dataset Information

Statistical Methods for Time-Dependent Variables in Hematopoietic Cell Transplantation Studies.

Publications

Statistical Methods for Time-Dependent Variables in Hematopoietic Cell Transplantation Studies.

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