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Selection for Treatment

Selection for Treatment: The process of choosing the most appropriate medical intervention or course of action for a patient, based on factors such as disease severity, patient preferences, and potential benefits and risks.
This decision-making involves evaluating available treatment options and determining the optimal approach to achieve the desired therapeutic outcomes.
Effective treatment selection is crucial for enhancing patient care, improving clinical outcomes, and promoting personalized medicine.

Most cited protocols related to «Selection for Treatment»

1
Propensity Score Methods for Time-to-Event Outcomes
Stratification on the propensity score and covariate adjustment using the propensity score are the two other propensity score methods19 . With covariate adjustment using the propensity score, one regresses the outcome on the propensity score and an indicator variable denoting treatment selection. With time-to-event outcomes, a Cox proportional hazards model would be used to regress the hazard of the occurrence of the outcome on the propensity score and an indicator variable denoting treatment status. This approach has been shown to result in biased estimation of marginal hazard ratios30 . Furthermore, it has also been shown to result in a biased estimate of the conditional hazard ratio that would result from adjusting for all the prognostically important covariates in a multivariable Cox regression model46 (link).
Stratification on the propensity score involves stratifying subjects into mutually exclusive subsets based on their estimated propensity score. In practice, analysts often use five subclasses on the basis of the quintiles of the estimated propensity score. When estimating linear treatment effects, stratum-specific estimates of effect are obtained. These stratum-specific estimates are then pooled to obtain an overall estimate of treatment effect. There are three ways in which one could estimate a hazard ratio using stratification on the propensity score. First, one can estimate stratum-specific Cox regression models in which survival is regressed on treatment selection. The stratum-specific log-hazard ratios are then pooled or averaged to obtain a pooled hazard ratio. Second, one can regress survival on an indicator variable denoting treatment status and a categorical variable denoting the propensity score strata. Third, one can regress survival on an indicator variable denoting treatment status and stratify on the propensity score strata, thereby allowing the baseline hazard to vary across strata. While stratification performs well for estimating linear treatment effects19 , it results in biased estimation of marginal hazard ratios30 . Furthermore, one implementation of stratification has also been shown to result in a biased estimate of the conditional hazard ratio that would result from adjusting for all the prognostically important covariates in a multivariable Cox regression model46 (link). It appears that each of these approaches results in an estimate of a conditional hazard ratio, rather than a marginal hazard ratio. Further research is required to determine how these conditional hazard ratios differ from that obtained by adjusting for the prognostically important covariates in a conventional Cox regression model.
Because our focus is on methods that allow estimation of both marginal survival curves and marginal hazard ratios, we do not consider these two propensity score methods further in this study.
, & Austin P.C. (2013). The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Statistics in Medicine, 33(7), 1242-1258.
Publication 2013
Selection for Treatment
2
Algorithmic Approach to Propensity Score Adjustment
A wide range of databases of health care utilization data (“claims”) is available for use in pharmacoepidemiology.3 (link) Each database is arranged in specific ways using a variety of classifications to code diagnoses (e.g. International Classification of Diseases [ICD]-8 through ICD-10), procedures (e.g. Current Procedural Terminology, Canadian Classification of Proceddures, ICD-9-Clinical Modification), or medications (e.g. National Drug Codes, American Hospital Formulary Services, Anatomical Therapeutic Chemical Classification). Beyond these basic data dimensions and coding systems, many more data dimensions can be found in such databases. Some databases provide additional dimensions such as laboratory results, other electronic medical record information, and accident registries.
We propose an algorithm that is independent of the specific data source as long as the source’s data dimensions can be identified. In Figure 2 we provide a flow diagram using a typical example of data dimensions available in US Medicare claims data linked to medication use data. First, a temporal window must be defined in which baseline covariates will be identified. A frequent choice is 6 or 12 months preceding the initiation of the study or comparison drug.2 (link) The recording of diagnoses and procedures is correlated with the frequency of health care encounters. Therefore, longer baseline periods increase the number of encounters and therefore yield more covariate information.2 (link)The most basic patient information always available to typical databases is age, sex and calendar time. We assume that given their ubiquity, these demographic covariates will always be adjusted for.
Additional covariates can then be identified from the various data dimensions, but it is first necessary to identify variables that should not be part of covariate adjustment. While it is generally recommended to include many covariates in a propensity score regression model, in specific cases researchers may exclude variables from covariate adjustment.17 (link) Surrogates for the exposure (i.e. covariates that are strong correlates of the study exposure but not associated with the outcome) will not only increase standard errors but may also increase bias—and should therefore not be included in propensity score analyses.18 ,19 (link) Bias can also occur through the inclusion of so-called “collider” variables, although this bias is generally thought to be weak.20 (link),21 (link) In our example study comparing statin initiation with glaucoma drug initiation, diagnostic codes for glaucoma should not be included in a propensity score because of their close correlation with treatment choice. 22 (link),23 At this stage of the procedure, such codes can be identified and removed from the dimension data input to the algorithm. We have developed a screening tool for such covariates as part of the algorithm that will help investigators identify and remove such covariates (eAppendix 1, http://links.lww.com).
Schneeweiss S., Rassen J.A., Glynn R.J., Avorn J., Mogun H, & Brookhart M.A. (2009). High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology (Cambridge, Mass.), 20(4), 512-522.
Publication 2009
Accidents Debility Diagnosis Glaucoma Hydroxymethylglutaryl-CoA Reductase Inhibitors Patient Acceptance of Health Care Patients Pharmaceutical Preparations Selection for Treatment Therapeutics
3
Evaluating Propensity Score Matching Methods
In each simulated data set, we estimated the propensity score using a logistic regression model to regress treatment status on the 10 baseline covariates. Propensity-score matching was used to construct a matched sample consisting of pairs of treated and untreated subjects. We used greedy nearest neighbor matching on the logit of the propensity score using a caliper of width equal to , where is the variance of the logit of the propensity score in the ith treatment group. This caliper width was used as it has been shown to result in optimal estimation of risk differences in a variety of settings 10 .
In the propensity-score matched sample, the absolute risk reduction was estimated as the sample difference of the proportion of treated subjects in whom the outcome occurred and the proportion of untreated subjects in whom the outcome occurred in the propensity-score matched sample. When the true absolute risk reduction was 0 (the null hypothesis), the statistical significance of the estimated risk difference was assessed using two different methods. First, using methods for independent samples, the Pearson Chi-squared was used to assess the statistical significance of the difference in the probability of the outcome occurring between treatment groups 13 . Second, using methods for paired samples, McNemar's test was used for this comparison.
The variance of the difference in proportions was estimated in two different methods. First, using methods for independent samples, let pT and pC denote the observed probability of the outcome occurring in treated and untreated subjects, respectively, in the propensity-score matched sample. Furthermore, assume that there are N propensity score matched pairs. Then the standard error of the estimated risk difference is given by 13 . Second, using methods for paired samples, we assume that in the matched sample there were a pairs in which both the treated and untreated subjects experienced the event; b pairs in which the treated subject experienced the event while the untreated subject does not; and c pairs in which the untreated subject experienced the event while the treated subject did not. Then, the variance of the difference in proportions was estimated by ((b+ c)−(c−b)2/n)/n214 (link). In both cases, 95 per cent confidence intervals were estimated as pT−pC±1.96 × se(pT−pC), where se(pT−pC) denotes the estimated standard error of the risk difference.
For each of the 100 scenarios (2 treatment−selection models × 2 probabilities of outcomes × 5covariate scenarios × 5 absolute risk reductions), we simulated 1825 data sets. The above analyses were conducted using each of the 1825 simulated data sets. In the 20 scenarios in which the true risk difference was 0, we estimated the empirical type I error rate as the proportion of simulated data sets in which the null hypothesis of no-treatment effect was rejected with a significance level of less than 0.05. Owing to our use of 1825 simulated data sets, an empirical type I error rate that was less than 0.04 or greater than 0.06 would be classified as being statistically significantly different from 0.05. For each of the 100 scenarios, we determined the proportion of estimated 95 per cent confidence intervals that contained the true risk difference. As above, due to the use of 1825 simulated data sets, empirical coverage rates that are less than 0.94 or that exceed 0.96 are statistically significantly different from the advertised coverage rate of 0.95. We also determined the mean width of the estimated 95 per cent confidence intervals across the 1825 simulated data sets. Finally, we compared the standard deviation of the empirical sampling distribution of the estimated treatment effects (i.e. the standard deviation of the 1825 estimated risk differences across the simulated data sets) with the mean of the estimated standard errors of the estimated treatment effect.
, & Austin P.C. (2011). Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples. Statistics in Medicine, 30(11), 1292-1301.
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Publication 2011
NUP214 protein, human Selection for Treatment Specimen Handling
4
Propensity Score Adjustment for Survival
Rosenbaum and Rubin proposed covariate adjustment using the propensity score in the context of estimating linear treatment effects for continuous outcomes 2 . Using this approach, we regress the outcome on two covariates: an indicator variable denoting treatment status and the propensity score. The regression coefficient associated with the treatment selection indicator represents the effect of treatment. In the current study, we used Cox regression to regress survival time on these two variables. The regression coefficient for the treatment status indicator is the estimated log-hazard ratio.
, & Austin P.C. (2012). The performance of different propensity score methods for estimating marginal hazard ratios. Statistics in Medicine, 32(16), 2837-2849.
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Publication 2012
Selection for Treatment
5
Propensity Score Model for Smoking Cessation
An initial propensity score model was estimated using the 33 variables described in Table 1. To estimate the propensity score, a logistic regression model was used in which treatment status (receipt of smoking cessation counseling vs. no smoking cessation counseling) was regressed on the baseline characteristics listed in Table 1 (Rosenbaum & Rubin, 1984 ). The continuous baseline variables were linearly related to the log-odds of receipt of treatment in the initial specification of the propensity score model. Prior research on variable selection for the propensity score suggests that it is preferable to either include those variables that affect the outcome or include those variables that affect both treatment selection and the outcome (Austin, Grootendorst, & Anderson, 2007 (link)). The variables listed in Table 1 are plausible predictors of mortality in AMI patients. Because we want to induce balance on variables that are prognostic of mortality, we included these variables in our initial propensity score model.
, & Austin P.C. (2011). A Tutorial and Case Study in Propensity Score Analysis: An Application to Estimating the Effect of In-Hospital Smoking Cessation Counseling on Mortality. Multivariate Behavioral Research, 46(1), 119-151.
Publication 2011
austin Patients Selection for Treatment

Most recents protocols related to «Selection for Treatment»

1
Selective Treg Proliferation by OX40L-JAG1
Not available on PMC !

Example 7

To validate whether OX40L-JAG1-induced Treg proliferation differs from TCR-stimulation approach, T-cell proliferation induced by TCR-dependent anti-CD3/CD28 was compared with TCR-independent OX40L-JAG1 stimulation. As shown in FIG. 14A, robust proliferation of Tregs was observed upon both OX40L-JAG1 and anti-CD3/CD28 treatment. However, unlike anti-CD3/CD28 treatment which also induced very strong Teff cell proliferation, OX40L-JAG1 treatment induced selective proliferation of Tregs without significant Teff proliferation. Analyses of activation markers expression showed a significant (***p<0.001) increase in the percentage of Teff cells expressing CD25, CD44 and CD69 upon treatment with anti-CD3/CD28 compared to control cells (FIG. 14B-D). However, no significant difference was observed between the control and OX40L-JAG1 treated Teff cells. Moreover, Tregs from both OX40L-JAG and anti-CD3/CD28 treated cells had increased CD25, CD44 and CD69 expressing cells compared to control cells. These results suggested that soluble OX40L-JAG1 can cause selective proliferation of Tregs, without significantly affecting Teff cell activation and proliferation.

US11873510B2. T-reg cell expansion (2024-01-16). The Board of Trustees of the University of Illinois [US]. Inventors: Bellur Prabhakar [US].
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Patent 2024
Activation Analysis CD44 protein, human Cell Proliferation Cells Eragrostis IL2RA protein, human Muromonab-CD3 Receptor-CD3 Complex, Antigen, T-Cell Selection for Treatment TNFSF4 protein, human
2
Cardiovascular Prevention Protocol for Iran
The IraPEN's preventive actions are expected to reduce cardiovascular events. The relative risks (RRs) of these preventive actions and the medications that are used in the program were obtained from meta-analyses or randomized clinical trials (RCTs). By multiplying or adding up the RRs of different medications, there is a risk of effect overestimation, and a correction was made by using the formula below wherever multiple interventions were involved:
This equation has been developed based on a study that compared the effect of controlling the risk factors separately vs. controlling all of them simultaneously (15 (link)).
Based on the field interviews, it was clear which medications are used for each index cohort. Almost in all cases, angiotensin-converting enzyme (ACE) inhibitors are the first choice for hypertension treatment. Enalapril is the most prescribed one as monotherapy. Thiazides (diuretics) are the second choice followed by beta-blockers. In case the hypertension is not controlled by monotherapy instead of increasing the dose, the second drug is added. As recommended by guidelines, small doses of various classes of antihypertensive medications are more useful than a high dose of one (16 ). In general, the combination of ACE inhibitors and thiazide is the most common one. This pattern is aligned with Joint National Committee (JNC8) guidelines. Statins are prescribed for hyperlipidemia treatment. Among statins, Atorvastatin is the choice as it is one of the most potent ones. For diabetes, Metformin is started and increased to the maximum dose (2 g) and then the second medication that is Glibenclamide is added. Due to its potential harm and insufficient evidence of its efficacy, Aspirin was not recommended for primary prevention by PEN protocols. Therefore, Aspirin is not used in IraPEN as well. Here are the list of medications and their daily dosages which are used in IraPEN:
The unit price of each of these medications was derived from the Iranian Annual Pharma Statistics file. For the calculation of the intervention's effects, it is assumed that the adherence of individuals to the treatment is 100%. Table 3 lists the RRs of different interventions (medications) for CHD and stroke.
Jamshidi A., Daroudi R., Aas E, & Khalili D. (2023). A cost-effectiveness analysis of risk-based intervention for prevention of cardiovascular diseases in IraPEN program: A modeling study. Frontiers in Public Health, 11, 1075277.
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Publication 2023
Adrenergic beta-Antagonists Angiotensin-Converting Enzyme Inhibitors Antihypertensive Agents Aspirin Atorvastatin Cardiovascular System Cerebrovascular Accident Diabetes Mellitus Diuretics Enalapril Glyburide High Blood Pressures Hydroxymethylglutaryl-CoA Reductase Inhibitors Hyperlipidemia Joints Metformin PEN protocol Pharmaceutical Preparations Primary Prevention Selection for Treatment Thiazides
3
Assessing Virulence and Transmission Dynamics
For the 10 individual and 10 social lines, we determined the induced host mortality as a measure of virulence and the outgrowing spore number as transmission stage production under their matched and non-matched current host conditions. We exposed the workers as in the selection treatment, kept them either alone or with two untreated nestmates, and monitored their mortality daily for 8 d. Again, ants dying in the first 24 h after treatment and dying nestmates were excluded from the analysis. In total, we obtained survival data of 797 ants (19–20 ants exposed for each of the 10 replicates from each of 4 combinations of selection history and current host condition). Dead ants were treated as above and their outgrowing spores collected by a needle dipped in sterile 0.05% Triton X-100 directly from the carcass, and resuspended in 100 µl of sterile 0.05% Triton X-100. The number of spores per carcass was counted individually using the automated cell counter, as described above (n = 215; median of 5 per replicate). We excluded one outlier carcass(from replicate I5) where we expected a counting error as this single carcass showed approx. 100-fold higher spore count than the other carcasses of this replicate. Exclusion of this outlier did not affect the statistical outcome. The proportion of ants dying per replicate line for each combination of selection history and current host condition and the number of spores produced by all carcasses per replicate were respectively used as measures of virulence and transmission (mean carcass spore load per replicate plotted in Fig. 2).
Stock M., Milutinović B., Hoenigsberger M., Grasse A.V., Wiesenhofer F., Kampleitner N., Narasimhan M., Schmitt T, & Cremer S. (2023). Pathogen evasion of social immunity. Nature Ecology & Evolution, 7(3), 450-460.
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Publication 2023
Ants Cells DNA Replication Needles Selection for Treatment Spore Count Spores Sterility, Reproductive Transmission, Communicable Disease Triton X-100 Virulence Workers
4
Spore Chemical Composition Analysis
To analyse the differences in spore chemical composition of individual and social lines after passage 10, we first performed a permutational multivariate analysis of variance (PERMANOVA; Supplementary Table 1) using the adonis function in the vegan package and a Euclidean distance matrix67 . To identify the individual compounds that differed between the two selection treatments, we performed a conditional random forest classification (n trees = 500, n variables per split = 6) using the randomForest package68 . For the selection of important compounds, we used the absolute value of the lowest negative score of the mean decrease in accuracy as a threshold (Supplementary Fig. 2). The six compounds that were identified as important contributors to treatment differences were subsequently analysed with separate WRST for independent samples, and the P values corrected for multiple testing with the Benjamini-Hochberg method, as detailed above (Fig. 3b and Extended Data Fig. 1). Statistics are reported in Supplementary Table 1; for the subset of the R1-dominant lines only (highlighted symbols in Fig. 3b and Extended Data Fig. 1), see Supplementary Table 2.
Stock M., Milutinović B., Hoenigsberger M., Grasse A.V., Wiesenhofer F., Kampleitner N., Narasimhan M., Schmitt T, & Cremer S. (2023). Pathogen evasion of social immunity. Nature Ecology & Evolution, 7(3), 450-460.
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Publication 2023
Adonis chemical composition Selection for Treatment Spores Trees Vegan
5
Strain Diversity in Immune Selection
We determined whether strain diversity (that is, the number of strains prevailing per line) differed between the two selection treatments, both during (passage 5) and at the end of the experiment (passage 10), using non-parametric Wilcoxon Rank Sum tests (WRST) for independent samples. For the strains persisting at the end of the experiment (that is, strains R1, R3 and B2), we analysed whether their representation (presence or absence) differed between the individual and social lines using a Fisher exact test based on a 3 × 2 contingency table to determine whether final strain composition of the lines successful in the serial passage experiment differed between the individual and social immunity treatment.
Stock M., Milutinović B., Hoenigsberger M., Grasse A.V., Wiesenhofer F., Kampleitner N., Narasimhan M., Schmitt T, & Cremer S. (2023). Pathogen evasion of social immunity. Nature Ecology & Evolution, 7(3), 450-460.
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Publication 2023
Response, Immune Selection for Treatment Strains

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More about "Selection for Treatment"

Treatment Selection is the crucial process of choosing the most suitable medical intervention or course of action for a patient.
This decision-making involves evaluating available treatment options, considering factors like disease severity, patient preferences, and potential benefits and risks.
Effective treatment selection is vital for enhancing patient care, improving clinical outcomes, and promoting personalized medicine.
Key aspects of Treatment Selection include assessing disease severity, analyzing patient characteristics and preferences, and determining the optimal approach to achieve desired therapeutic outcomes.
Selecting the right treatment is a complex process that requires expertise and careful evaluation of multiple factors.
Researchers and clinicians often utilize statistical software like SAS 9.4, SPSS 20, R 3.6.1, Stata 12.0, and SPSS 28.0 to analyze data, model outcomes, and support treatment selection decisions.
Techniques like Lipofectamine 3000 transfection can also play a role in evaluating treatment options.
Optimizing the treatment selection process is crucial for delivering high-quality, personalized patient care.
AI-driven platforms like PubCompare.ai can help researchers and clinicians easily locate the best protocols from literature, preprints, and patents, enhancing reproducibility and accuracy in the treatment selection process.
By leveraging the power of data analysis and AI-driven insights, healthcare professionals can make more informed decisions and improve patient outcomes.