Target Population Of Interest Research Articles

When impact trials are not feasible: alternatives to study the impact of prediction models on clinical practice.

Patients with kidney disease have an uncertain future with prognosis varying greatly per patient. To get a better idea of what the future holds and tailor interventions to the individual patient, prediction models can be of great value. Before a prediction model can be applied in practice, its performance should be measured in target populations of interest (i.e., external validation) and whether it helps improve clinical practice (i.e., whether it impacts clinical practice) should be determined. The impact would ideally be determined using an impact trial, but such a trial is often not feasible, and the impact of prediction models is therefore rarely assessed. As a result, prediction models that may not be so impactful may end up in clinical practice and impactful models may not be implemented due to a lack of impact studies. Ultimately, many prediction models end up never being implemented, resulting in much research waste. To allow researchers to get an indication of a prediction model's impact on clinical practice, alternative methods to assess a prediction model's impact are important. In this paper, we discuss several alternatives, including interviews, case-based surveys, decision comparisons, outcome modelling, before-after analyses, and decision curve analyses. We discuss the general idea behind these approaches, including what information can be gathered from such studies and important pitfalls. Lastly, we provide examples of the different alternatives.

Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study.

Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations. We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs. We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient. The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs. Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.

Addressing Systematic Missing Data in the Context of Causally Interpretable Meta-analysis.

Evidence synthesis involves drawing conclusions from trial samples that may differ from the target population of interest, and there is often heterogeneity among trials in sample characteristics, treatment implementation, study design, and assessment of covariates. Stitching together this patchwork of evidence requires subject-matter knowledge, a clearly defined target population, and guidance on how to weigh evidence from different trials. Transportability analysis has provided formal identifiability conditions required to make unbiased causal inference in the target population. In this manuscript, we review these conditions along with an additional assumption required to address systematic missing data. The identifiability conditions highlight the importance of accounting for differences in treatment effect modifiers between the populations underlying the trials and the target population. We perform simulations to evaluate the bias of conventional random effect models and multiply imputed estimates using the pooled trials sample and describe causal estimators that explicitly address trial-to-target differences in key covariates in the context of systematic missing data. Results indicate that the causal transportability estimators are unbiased when treatment effect modifiers are accounted for in the analyses. Results also highlight the importance of carefully evaluating identifiability conditions for each trial to reduce bias due to differences in participant characteristics between trials and the target population. Bias can be limited by adjusting for covariates that are strongly correlated with missing treatment effect modifiers, including data from trials that do not differ from the target on treatment modifiers, and removing trials that do differ from the target and did not assess a modifier.

A Review of Noise Management Practice in Nigeria

Technological advancement, modern systems of transportation, and an expanding human population have brought in their wake a disturbing situation of exposure to environmental noise. This paper was therefore written to create awareness about the expository effects and impacts of environmental noise pollution on the human population. The target population of interest was the Nigerian population. The key findings were linked and related to existing regulations in Nigeria and other countries to enable effective intervention strategies to reduce the harmful impact of noise pollution. Noise pollution was identified as contributing to the poor health of citizens and as a barrier to development and growth at the national level. By focusing on adverse effects and risk factors, the paper highlighted methods to promote the best interests of the vulnerable population. The paper also acknowledged the issue of industrial and environmental noise pollution from the perspective of its contribution to the economic growth and development of the country. Noise pollution was, therefore, required to be measured and controlled with the help of effective strategic laws. It would help reduce the negative effects on the economy and the vulnerable Nigerian population. This paper will be useful in anticipating future reforms that can reduce the considerable noise pollution and its negative impact on the population group

Robust sample weighting to facilitate individualized treatment rule learning for a target population

Summary Learning individualized treatment rules is an important topic in precision medicine. Current literature mainly focuses on deriving individualized treatment rules from a single source population. We consider the observational data setting when the source population differs from a target population of interest. Compared with causal generalization for the average treatment effect that is a scalar quantity, individualized treatment rule generalization poses new challenges due to the need to model and generalize the rules based on a prespecified class of functions that may not contain the unrestricted true optimal individualized treatment rule. The aim of this paper is to develop a weighting framework to mitigate the impact of such misspecification, and thus facilitate the generalizability of optimal individualized treatment rules from a source population to a target population. Our method seeks covariate balance over a nonparametric function class characterized by a reproducing kernel Hilbert space and can improve many individualized treatment rule learning methods that rely on weights. We show that the proposed method encompasses importance weights and overlap weights as two extreme cases, allowing for a better bias-variance trade-off in between. Numerical examples demonstrate that the use of our weighting method can greatly improve individualized treatment rule estimation for the target population compared with other weighting methods.

Propensity-based standardization to enhance the validation and interpretation of prediction model discrimination for a target population.

External validation of the discriminative ability of prediction models is of key importance. However, the interpretation of such evaluations is challenging, as the ability to discriminate depends on both the sample characteristics (ie, case-mix) and the generalizability of predictor coefficients, but most discrimination indices do not provide any insight into their respective contributions. To disentangle differences in discriminative ability across external validation samples due to a lack of model generalizability from differences in sample characteristics, we propose propensity-weighted measures of discrimination. These weighted metrics, which are derived from propensity scores for sample membership, are standardized for case-mix differences between the model development and validation samples, allowing for a fair comparison of discriminative ability in terms of model characteristics in a target population of interest. We illustrate our methods with the validation of eight prediction models for deep vein thrombosis in 12 external validation data sets and assess our methods in a simulation study. In the illustrative example, propensity score standardization reduced between-study heterogeneity of discrimination, indicating that between-study variability was partially attributable to case-mix. The simulation study showed that only flexible propensity-score methods (allowing for non-linear effects) produced unbiased estimates of model discrimination in the target population, and only when the positivity assumption was met. Propensity score-based standardization may facilitate the interpretation of (heterogeneity in) discriminative ability of a prediction model as observed across multiple studies, and may guide model updating strategies for a particular target population. Careful propensity score modeling with attention for non-linear relations is recommended.

Conditional cross-design synthesis estimators for generalizability in Medicaid.

While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population that is not well-represented by a randomized study but is reflected when additionally incorporating observational data. To generalize to a target population represented by a union of these data, we propose a novel class of conditional cross-design synthesis estimators that combine randomized and observational data, while addressing their estimates' respective biases-lack of overlap and unmeasured confounding. These methods enable estimating the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City, which requires obtaining estimates for the 7% of beneficiaries randomized to a plan and 93% who choose a plan, who do not resemble randomized beneficiaries. Our new estimators include outcome regression, propensity weighting, and double robust approaches. All use the covariate overlap between the randomized and observational data to remove potential unmeasured confounding bias. Applying these methods, we find substantial heterogeneity in spending effects across managed care plans. This has major implications for our understanding of Medicaid, where this heterogeneity has previously been hidden. Additionally, we demonstrate that unmeasured confounding rather than lack of overlap poses a larger concern in thissetting.

COMMUTE: Communication-efficient transfer learning for multi-site risk prediction

Objectives:We propose a communication-efficient transfer learning approach (COMMUTE) that effectively incorporates multi-site healthcare data for training a risk prediction model in a target population of interest, accounting for challenges including population heterogeneity and data sharing constraints across sites. Methods:We first train population-specific source models locally within each site. Using data from a given target population, COMMUTE learns a calibration term for each source model, which adjusts for potential data heterogeneity through flexible distance-based regularizations. In a centralized setting where multi-site data can be directly pooled, all data are combined to train the target model after calibration. When individual-level data are not shareable in some sites, COMMUTE requests only the locally trained models from these sites, with which, COMMUTE generates heterogeneity-adjusted synthetic data for training the target model. We evaluate COMMUTE via extensive simulation studies and an application to multi-site data from the electronic Medical Records and Genomics (eMERGE) Network to predict extreme obesity. Results:Simulation studies show that COMMUTE outperforms methods without adjusting for population heterogeneity and methods trained in a single population over a broad spectrum of settings. Using eMERGE data, COMMUTE achieves an area under the receiver operating characteristic curve (AUC) around 0.80, which outperforms other benchmark methods with AUC ranging from 0.51 to 0.70. Conclusion:COMMUTE improves the risk prediction in a target population with limited samples and safeguards against negative transfer when some source populations are highly different from the target. In a federated setting, it is highly communication efficient as it only requires each site to share model parameter estimates once, and no iterative communication or higher-order terms are needed.

Some considerations on target estimands for health technology assessment.

Some considerations on target estimands for health technology assessment.

The Moderating Effect of Inter- Organization Systems on the Relationship between Supply Chain Relationship Management and Performance of Pharmaceutical Firms in Kenya

systems on the relationship between supply chain relationship management and performance of pharmaceutical firms in Kenya
 Methodology: This research adopted a descriptive research design. The target population of interest in this study were the 22 local pharmaceutical manufacturing and 149 importing firms that act as subsidiaries making a total of 171. The study sample was drawn from the list of 171 pharmaceutical firms, where various categories with the relevant information for the study were drawn. The study adopted a census sampling technique. The data was analyzed using SPSS 22 version by making use of multiple regressions analysis which helped to generate a weighted estimation equation (OLS) that was used to predict values for dependent variable from the values of the independent variable.
 Findings: The results showed that inter-organization systems significantly moderated the relationship between supply chain relationship management practices and organizational performance.
 Recommendations: Based on the study findings, it was recommended that management in pharmaceutical firms should integrate their systems with those of customers such as electronic inventory management, electronic customer feedback; integrate electronic sharing of market information among other IT systems. This is because the study confirmed that integration of IT systems by pharmaceutical companies would lead to improvement in terms of speed and quality services, waste elimination, increased inventory management, responsiveness, accuracy, customer feedback and reduced lead-times

The Effects of Process Alignment on Performance of Pharmaceutical Firms in Kenya

Purpose: The purpose of this study was to evaluate the effects of process alignment on performance of pharmaceutical firms in Kenya
 Methodology: This research adopted a descriptive research design. The target population of interest in this study were the 22 local pharmaceutical manufacturing and 149 importing firms that act as subsidiaries making a total of 171. The study sample was drawn from the list of 171 pharmaceutical firms where various categories with the relevant information for the study were drawn. The study adopted a census sampling technique. The data was analyzed using SPSS version 22 by making use of multiple regressions analysis which helped to generate a weighted estimation equation (OLS) that was used to predict values for dependent variable from the values of the independent variable.
 Findings: The correlation and regression results revealed that process alignment had a positive and significant relationship with performance of pharmaceutical firms. The null hypothesis was rejected which indicated that there was no significant relationship between resilience building and performance of pharmaceutical firms in Kenya.
 Recommendations: Based on the study findings, it was recommended that pharmaceutical firms should align their processes with those of the customers. The firms are encouraged to adopt management styles which are compatible with the clients in terms of mission and vision, improve or maintain their delivery standards such as on time and quality delivery, implement marketing strategies which are compatible with customers’ standards and or exceed their expectations, and finally the management should strive to meet the customer expectations by improving the characteristics of the products. According to the regression analysis results, these activities enhance the performance of the firm.

Meta-analysis with sample-standardization in multi-site studies.

To conceptualize a particular target population and estimand for multi-site pharmacoepidemiologic studies within data networks and to analytically examine sample-standardization as a meta-analytic method compared with inverse-variance weighted meta-analyses. The target population of interest is all and only all individuals from the data-contributing sites. Standardization, a general conditioning technique frequently employed for confounding control, was adopted to estimate the network-wide causal treatment effect. Specifically, the proposed sample-standardization yields a meta-analysis estimator, that is, a weighted summation of site-specific results, where the weight for a site is the proportion of its size in the entire network. This sample-standardization estimator was evaluated analytically in comparison to estimators from inverse-variance weighted fixed-effect and random-effects meta-analyses in terms of statistical consistency. A proof is reported to justify the consistency of the sample-standardization estimator with and without treatment effect heterogeneity by site. Both inverse-variance weighted fixed-effect and random-effects meta-analyses were found to generally result in inconsistent estimators in the presence of treatment effect heterogeneity by site for this particular target population and estimand. Sample-standardization is a valid approach to generate causal inference in multi-site studies when the target population comprises all and only all individuals within the network, even in the presence of heterogeneity of treatment effect by site. Multi-site studies should clearly specify the target population and estimand to help select the most appropriate meta-analytic methods.

Abstract 5305: Delta-like ligand 3 immunohistochemical expression landscape in high-grade lung neuroendocrine tumors

Abstract Background: High-grade lung neuroendocrine tumors are a heterogeneous group of cancers with aggressive common features and a lack of effective therapeutic options. Recently, efforts have been made to identify new targets and drugs to improve clinical outcomes in these patients. Because of its high expression in neuroendocrine tumors compared to normal tissue, delta-like ligand 3 (DLL3) has emerged as a new therapeutic target in this setting.1,2 Targeting DLL3 with the half-life extended bispecific T cell engager (HLE BiTE®) immune therapy, tarlatamab, in a phase 1 clinical trial has demonstrated promising anti-tumor activity in small cell lung cancer (SCLC) patients.3 In order to more precisely guide patient selection and clinical trial design, a better understanding and quantification of DLL3 expression is required. Thus, we investigated DLL3 immunohistochemical expression across SCLC, large cell neuroendocrine carcinoma (LCNEC), and neuroendocrine carcinoma with combined histology (CNEC), and its associations with clinicopathological characteristics. Design: Formalin-fixed and paraffin-embedded surgically resected lung neuroendocrine carcinomas (SCLC, n=17; LCNEC, n=20; CNEC, n=8) were selected based on tissue availability at MD Anderson Cancer Center and stained with a commercially available immunohistochemistry assay for DLL3 (clone SP347, Ventana). Total percentage and intensity of DLL3 expression of neuroendocrine carcinoma cells were scored and the results were presented as H-Score (0-300) and percentage (%) of DLL3 positive tumor cells. We correlated DLL3 expression with clinicopathological characteristics. Results: DLL3 expression was observed in 37/45 (82%) of all cases (SCLC: %, median=65%, 0-100%; H-score, median=95, 0-175; LCNEC: %, median=65%, 0-100%; H-score, median=100, 0-180; CNEC: %, median=55%, 0-95%; H-score, median=90, 0-175). In patients with LCNEC, DLL3 % was higher in male patients (p=0.0500) and former smokers (p=0.0298) compared to females and current smokers, respectively. No other clinicopathological associations were found with age, Tumor-Node-Metastasis (TNM) staging system, recurrence, neoadjuvant therapy or overall survival. Conclusion:Our work confirms that most high-grade neuroendocrine tumors express DLL3 across histology types and TNM stage. Our results suggest that a large subset of patients with high-grade lung neuroendocrine tumors may be a target population of interest for DLL3-targeted therapies.

Rejective Sampling, Rerandomization, and Regression Adjustment in Survey Experiments

Classical randomized experiments, equipped with randomization-based inference, provide assumption-free inference for treatment effects. They have been the gold standard for drawing causal inference and provide excellent internal validity. However, they have also been criticized for questionable external validity, in the sense that the conclusion may not generalize well to a larger population. The randomized survey experiment is a design tool that can help mitigate this concern, by randomly selecting the experimental units from the target population of interest. However, as pointed out by Morgan and Rubin, chance imbalances often exist in covariate distributions between different treatment groups even under completely randomized experiments. Not surprisingly, such covariate imbalances also occur in randomized survey experiments. Furthermore, the covariate imbalances happen not only between different treatment groups, but also between the sampled experimental units and the overall population of interest. In this article, we propose a two-stage rerandomization design that can actively avoid undesirable covariate imbalances at both the sampling and treatment assignment stages. We further develop asymptotic theory for rerandomized survey experiments, demonstrating that rerandomization provides better covariate balance, more precise treatment effect estimators, and shorter large-sample confidence intervals. We also propose covariate adjustment to deal with remaining covariate imbalances after rerandomization, showing that it can further improve both the sampling and estimated precision. Our work allows general relationship among covariates at the sampling, treatment assignment and analysis stages, and generalizes both rerandomization in classical randomized experiments and rejective sampling in survey sampling.

Target validity: Bringing treatment of external validity in line with internal validity.

"Target bias" is the difference between an estimate of association from a study sample and the causal effect in the target population of interest. It is the sum of internal and external bias. Given the extensive literature on internal validity, here, we review threats and methods to improve external validity. External bias may arise when the distribution of modifiers of the effect of treatment differs between the study sample and the target population. Methods including those based on modeling the outcome, modeling sample membership, and doubly robust methods are available, assuming data on the target population is available. The relevance of information for making policy decisions is dependent on both the actions that were studied and the sample in which they were evaluated. Combining methods for addressing internal and external validity can improve the policy relevance of study results.

Effects of Credit Risk Management on Performance of Banks in Kenya

The purpose of study was to analyze the effects of credit risk management on performance of banks in Kenya. The study was guided by the following specific objective; to determine the effects of on bank performance in Kenya. Lending is an investment and therefore as the bank constructs a portfolio it considers an “efficient frontier” of optimal portfolios, offering the maximum possible expected return for a given level of risk. The study was based on modern portfolio, capital asset pricing and liquidity preference theories. It considered the credit scoring model as statistical analysis used by banks to evaluate worthiness of a borrower. The target population of interest was 44 commercial banks categorized into 28 local and 16 foreign banks. They are also categorized into small, medium and large banks. The study used census survey because of the small population. The questionnaires were administered to all respondents of commercial banks in Kenya. Piloting was done to check reliability and validity of data collection and instruments. Data was coded, edited to bring meaning. Multiple regression was also used to test the significance of one variable to the others. Additional secondary data in form of annual reports and financial statements were obtained from Central Bank of Kenya for the period 2011 to 2016. Data collected was analyzed using SPSS version 21.The null hypothesis that liquidity do not have statistically significant influence on of banks performance in Kenya was rejected. Therefore, the independent variable had significant influence on banks performance in Kenya. The bank should take into consideration potential future changes in economic conditions when assessing individual credits and their credit portfolio. The study is significant especially to decision makers involved in the implementation of credit risk management for their banks. The credit managers are able to make strategic, tactical and operational decisions based on the results of a scientific study rather than relying on trial and error.

Numeric score-based conditional and overall change-in-status indices for ordered categorical data.

Planned interventions and/or natural conditions often effect change on an ordinal categorical outcome (e.g., symptom severity). In such scenarios, it is sometimes desirable to assign a priori scores to observed changes in status, typically giving higher weight to changes of greater magnitude. We define change indices for such data based upon a multinomial model for each row of a c × c table, where the rows represent the baseline status categories. We distinguish an index designed to assess conditional changes within each baseline category from two others designed to capture overall change. One of these overall indices measures expected change across a target population. The other is scaled to capture the proportion of total possible change in the direction indicated by the data, so that it ranges from -1 (when all subjects finish in the least favorable category) to +1 (when all finish in the most favorable category). The conditional assessment of change can be informative regardless of how subjects are sampled into the baseline categories. In contrast, the overall indices become relevant when subjects are randomly sampled at baseline from the target population of interest, or when the investigator is able to make certain assumptions about the baseline status distribution in that population. We use a Dirichlet-multinomial model to obtain Bayesian credible intervals for the conditional change index that exhibit favorable small-sample frequentist properties. Simulation studies illustrate the methods, and we apply them to examples involving changes in ordinal responses for studies of sleep deprivation and activities of daily living.

The Numbers Game in Older Adult Care

The Numbers Game in Older Adult Care

The trail making test as a screening instrument for driving performance in older drivers; a translational research.

BackgroundIn many countries, primary care physicians determine whether or not older drivers are fit to drive. Little, however, is known regarding the effects of cognitive decline on driving performance and the means to detect it. This study explores to what extent the trail making test (TMT) can provide indications to clinicians about their older patients’ on-road driving performance in the context of cognitive decline.MethodsThis translational study was nested within a cohort study and an exploratory psychophysics study. The target population of interest was constituted of older drivers in the absence of important cognitive or physical disorders. We therefore recruited and tested 404 home-dwelling drivers, aged 70 years or more and in possession of valid drivers’ licenses, who volunteered to participate in a driving refresher course. Forty-five drivers also agreed to undergo further testing at our lab. On-road driving performance was evaluated by instructors during a 45 minute validated open-road circuit. Drivers were classified as either being excellent, good, moderate, or poor depending on their score on a standardized evaluation of on-road driving performance.ResultsThe area under the receiver operator curve for detecting poorly performing drivers was 0.668 (CI95% 0.558 to 0.778) for the TMT-A, and 0.662 (CI95% 0.542 to 0.783) for the TMT-B. TMT was related to contrast sensitivity, motion direction, orientation discrimination, working memory, verbal fluency, and literacy. Older patients with a TMT-A ≥ 54 seconds or a TMT-B ≥ 150 seconds have a threefold (CI95% 1.3 to 7.0) increased risk of performing poorly during the on-road evaluation. TMT had a sensitivity of 63.6%, a specificity of 64.9%, a positive predictive value of 9.5%, and a negative predictive value of 96.9%.ConclusionIn screening settings, the TMT would have clinicians uselessly consider driving cessation in nine drivers out of ten. Given the important negative impact this could have on older drivers, this study confirms the TMT not to be specific enough for clinicians to justify driving cessation without complementary investigations on driving behaviors.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2318-14-123) contains supplementary material, which is available to authorized users.

Defining blog: A fool’s errand or a necessary undertaking

Weblogs (blogs) emerged as a grassroots phenomenon in the late 1990s and since then their growth has been staggering. There has also been a growing body of scholarship with an underlying assumption that there is consensus about the definition of the word ‘blog’, which there is not. This article reviews some of the varied ways the term is being used, addresses the distinction as to whether a blog should be considered a medium or a genre, and considers whether links and comment-threads are defining characteristics. This article argues that the real problem is not that the term blog is difficult to define (it is) but that most scholars are using it in vague, contradictory, ambiguous and imprecise ways. Definitions require descriptive qualifiers to reflect current usages, and scholars need to provide clear and precise definitions according to the particular research questions asked and the target populations of interest.