Estimation of the Coefficient of Variation with Minimum Risk: A Sequential Method for Minimizing Sampling Error and Study Cost.

The coefficient of variation is an effect size measure with many potential uses in psychology and related disciplines. We propose a general theory for a sequential estimation of the population coefficient of variation that considers both the sampling error and the study cost, importantly without specific distributional assumptions. Fixed sample size planning methods, commonly used in psychology and related fields, cannot simultaneously minimize both the sampling error and the study cost. The sequential procedure we develop is the first sequential sampling procedure developed for estimating the coefficient of variation. We first present a method of planning a pilot sample size after the research goals are specified by the researcher. Then, after collecting a sample size as large as the estimated pilot sample size, a check is performed to assess whether the conditions necessary to stop the data collection have been satisfied. If not an additional observation is collected and the check is performed again. This process continues, sequentially, until a stopping rule involving a risk function is satisfied. Our method ensures that the sampling error and the study costs are considered simultaneously so that the cost is not higher than necessary for the tolerable sampling error. We also demonstrate a variety of properties of the distribution of the final sample size for five different distributions under a variety of conditions with a Monte Carlo simulation study. In addition, we provide freely available functions via the MBESS package in R to implement the methods discussed.

Prespecified Homeopathic Medicines in the Prevention of Confirmed and Suspected Cases of COVID-19: A Community-Based, Double-Blind, Randomized, Placebo-Controlled Prophylaxis Trial.

INTRODUCTION: Homeopathic medicines have been used for decades in the prevention and treatment of infectious diseases. However, the preventive efficacy of specific homeopathic medicines in COVID-19 is not well characterized. This study aimed to evaluate the comparative efficacy of prespecified homeopathic medicines in preventing COVID-19. METHODS: A community-based, double-blind, randomized, placebo-controlled trial was conducted on 4,034 participants residing in Ward No. 27 of the Howrah Municipal Corporation in India. Participants were randomized to receive one of three prespecified homeopathic medicines [Influenzinum 30C, Arsenicum album 30C, Anas barbariae hepatis et cordis extractum 200K (Oscillococcinum®)], or placebo. The outcomes were the incidence of laboratory-confirmed and suspected cases of COVID-19 during a follow-up period of 1 month. RESULTS: During the follow-up period, a total of 13 new laboratory-confirmed COVID-19 cases were reported in the study population. Among these, 5 cases in Influenzinum group, 2 cases in Arsenicum album group, 1 case in Oscillococcinum® group, and 5 cases in Placebo group were reported. On the other hand, number of suspected COVID-19 cases was significantly less in all the three homeopathic medicine groups compared to placebo. The least number of suspected cases reported in the Oscillococcinum® group (aOR: 0.058; 95% confidence interval [CI]: 0.029, 0.114), followed by the Arsenicum album (aOR: 0.337; 95% CI: 0.238, 0.475) and Influenzinum (aOR: 0.539; 95% CI: 0.401, 0.726) groups. CONCLUSION: Prespecified homeopathic medicines, particularly Oscillococcinum® and Arsenicum album 30C, may have a role in preventing COVID-19, especially in reducing the incidence of suspected or COVID-19-like respiratory illnesses. However, the result failed to demonstrate a statistically significant difference in the occurrence of confirmed cases of COVID-19 between the study groups. Further research is needed to evaluate the efficacy of these medicines in different populations and settings.Homöopathische Arzneimittel werden seit Jahrzehnten zur Prävention und Behandlung von Infektionskrankheiten eingesetzt. Die Wirksamkeit spezifischer homöopathischer Arzneimittel zur Prophylaxe von COVID-19 ist jedoch nicht gut untersucht. Mit dieser Studie sollte die vergleichende Wirksamkeit spezifischer homöopathischer Arzneimittel bei der Prävention von COVID-19 untersucht werden.Es handelte sich um eine gemeindebasierte, doppelblinde, randomisierte, placebokontrollierte Studie mit 4.034 Teilnehmern, die im Bezirk Nr. 27 der Howrah Municipal Corporation in Indien lebten. Die Teilnehmer erhielten randomisiert eines von drei zuvor festgelegten homöopathischen Arzneimitteln [Influenzinum 30C, Arsenicum album 30C, Anas barbariae hepatis et cordis extractum 200K (Oscillococcinum®)] oder Placebo. Zielkriterien waren die Inzidenz von laborchemisch bestätigten und vermuteten COVID-19-Fällen während des Follow-up-Zeitraums von einem Monat.Während des Follow-up-Zeitraums wurden insgesamt 13 neue, laborchemisch bestätigte COVID-19-Fälle in der Studienpopulation berichtet, davon 5 Fälle in der Influenzinum-Gruppe, 2 Fälle in der Arsenicum album-Gruppe, 1 Fall in der Oscillococcinum®-Gruppe und 5 Fälle in der Placebo-Gruppe. Demgegenüber fiel Zahl der COVID-19-Verdachtsfälle in allen drei homöopathischen Arzneimittelgruppen signifikant geringer aus als in der Placebogruppe. Die wenigsten Verdachtsfälle wurden in der Oscillococcinum®-Gruppe berichtet (aOR: 0.058; 95%-KI: 0.029, 0.114), gefolgt von der Arsenicum album- (aOR: 0.337; 95%-KI: 0.238, 0.475) und der Influenzinum- (aOR: 0.539; 95%-KI: 0.401, 0.726) Gruppe.Spezifische homöopathische Arzneimittel, insbesondere Oscillococcinum® und Arsenicum album 30C, könnten bei der Prävention von COVID-19 eine Rolle spielen, vor allem bei der Senkung der Inzidenz von COVID-19-Verdachtsfällen oder COVID-19-ähnlichen Atemwegserkrankungen. Allerdings war kein statistisch signifikanter Unterschied im Auftreten von bestätigten COVID-19-Fällen zwischen den Studiengruppen nachweisbar. Weitere Untersuchungen sind erforderlich, um die Wirksamkeit dieser Arzneimittel in verschiedenen Populationen und Umgebungen zu bewerten.

A sequential approach for noninferiority or equivalence of a linear contrast under cost constraints.

Planning an appropriate sample size for a study involves considering several issues. Two important considerations are cost constraints and variability inherent in the population from which data will be sampled. Methodologists have developed sample size planning methods for two or more populations when testing for equivalence or noninferiority/superiority for a linear contrast of population means. Additionally, cost constraints and variance heterogeneity among populations have also been considered. We extend these methods by developing a theory for sequential procedures for testing the equivalence or noninferiority/superiority for a linear contrast of population means under cost constraints, which we prove to effectively utilize the allocated resources. Our method, due to the sequential framework, does not require prespecified values of unknown population variance(s), something that is historically an impediment to designing studies. Importantly, our method does not require an assumption of a specific type of distribution of the data in the relevant population from which the observations are sampled, as we make our developments in a data distribution-free context. We provide an illustrative example to show how the implementation of the proposed approach can be useful in applied research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Plant Extracts as Add-On Therapeutics in Homeopathy: An Open-Label, Randomized Trial Using Mother Tinctures in Prediabetes.

Objectives: Prediabetes is a major public health concern. Different plant extracts are used in homeopathy as mother tinctures (MTs) for the treatment of prediabetes as an adjunct to individualized homeopathic medicines (IHMs); however, their effectiveness remains under-researched. Design: Open-label, randomized (1:1), active-controlled, pragmatic, exploratory trial. Setting: Mahesh Bhattacharyya Homoeopathic Medical College and Hospital, Howrah, West Bengal, India. Subjects: Eighty-nine patients with prediabetes. Interventions: Group 1 (n = 45; IHMs plus any one of the following MTs: Cephalandra indica, Gymnema sylvestre, and Syzygium jambolanum; experimental/verum) versus Group 2 (n = 44; IHMs only; control). Outcome measures: Blood parameters, including-the fasting blood sugar (FBS) level, blood sugar level 2 h after ingestion of 75 g of glucose (oral glucose tolerance test [OGTT] result), and glycosylated hemoglobin percentage (HbA1c%), and symptoms, including the Diabetes Symptom Checklist-Revised (DSC-R) score; all of them were measured at baseline and after 3 and 6 months. Results: Although recruitment of 140 patients was initially planned, the target sample size could not be achieved because of coronavirus disease pandemic-related restrictions. Only 89 patients could be enrolled, and the trial had to be terminated prematurely owing to the time constraints of the project. The data of 82 patients (Group 1, n = 40; Group 2, n = 42) were analyzed using a modified intention-to-treat approach. Improvements in all outcomes were greater in Group 1 than in Group 2, but without a significant difference: FBS level (F1, 80 = 4.095, p = 0.046), OGTT result (F1, 80 = 2.399, p = 0.125), HbA1c% (F1, 80 = 1.612, p = 0.208), and DSC-R score (F1, 80 = 0.023, p = 0.880). Conclusions: A promising but nonsignificant trend favored the combination of MTs and IHMs compared with IHMs alone among the patients with prediabetes, especially in FBS. Therefore, further studies are required. Clinical Trial Registration Number: CTRI/2018/08/015319; secondary identifier (UTN): U1111-1218-6016.

Sequential accuracy in parameter estimation for population correlation coefficients.

Correlation coefficients are effect size measures that are widely used in psychology and related disciplines for quantifying the degree of relationship of two variables, where different correlation coefficients are used to describe different types of relationships for different types of data. We develop methods for constructing a sufficiently narrow confidence interval for 3 different population correlation coefficients with a specified upper bound on the confidence interval width (e.g., .10 units) at a specified level of confidence (e.g., 95%). In particular, we develop methods for Pearson's r, Kendall's tau, and Spearman's rho. Our methods solve an important problem because existing methods of study design for correlation coefficients generally require the use of supposed but typically unknowable population values as input parameters. We develop sequential estimation procedures and prove their desirable properties in order to obtain sufficiently narrow confidence interval for population correlation coefficients without using supposed values of population parameters, doing so in a distribution-free environment. In sequential estimation procedures, supposed values of population parameters for purposes of sample size planning are not needed, but instead stopping rules are developed and once satisfied, they provide a rule-based stop to the sampling of additional units. In particular, data in sequential estimation procedures are collected in stages, whereby at each stage the estimated population values are updated and the stopping rule evaluated. Correspondingly, the final sample size required to obtain a sufficiently narrow confidence interval is not known a priori, but is based on the outcome of the study. Additionally, we extend our methods to the squared multiple correlation coefficient under the assumption of multivariate normality. We demonstrate the effectiveness of our sequential procedure using a Monte Carlo simulation study. We provide freely available R code to implement the methods in the MBESS package. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Accuracy in parameter estimation for a general class of effect sizes: A sequential approach.

Sequential estimation is a well recognized approach to inference in statistical theory. In sequential estimation the sample size to use is not specified at the start of the study, and instead study outcomes are used to evaluate a predefined stopping rule, if sampling should continue or stop. In this article we develop a general theory for sequential estimation procedure for constructing a narrow confidence interval for a general class of effect sizes with a specified level of confidence (e.g., 95%) and a specified upper bound on the confidence interval width. Our method does not require prespecified, yet usually unknowable, population values of certain parameters for certain types of distributions, thus offering advantages compared to commonly used approaches to sample size planning. Importantly, we make our developments in a distribution-free environment and thus do not make untenable assumptions about the population from which observations are sampled. Our work is thus very general, timely due to the interest in effect sizes, and has wide applicability in the context of estimation of a general class of effect sizes. (PsycINFO Database Record

Estrogen receptor and progesterone receptor status of breast cancer patients of eastern India: A multi-institutional study.

CONTEXT: There is a paucity of any significant data on the estrogen receptor (ER) and progesterone receptor (PR) status of breast cancer patients from the eastern part of India. AIMS: This study aims to document the ER and PR status of breast cancer patients in the eastern Indian population, as catered by two premier tertiary care hospitals in Kolkata. SUBJECTS AND METHODS: All breast cancer patients registered between January 1, 2013 and December 31, 2015, in the Departments of Oncology, of IPGMER and SSKM Hospitals and R. G. Kar Medical College and Hospital, Kolkata, who had at least undergone a core biopsy or surgery, were analyzed retrospectively for documentation of their ER and PR status, using the 2010 American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) interpretation guidelines. RESULTS: Over a period of 3 years, a total of 927 patients were included for the study. A total of 825 (89%) patients had their ER and PR data available for evaluation. ER and PR positive was seen in 312 (37.82%) patients, ER and PR negative in 399 (48.36%) patients, ER positive and PR negative in 71 (8.6%) patients, and ER negative and PR positive results was found in 43 (5.21%) patients. CONCLUSIONS: This is the first multi-institutional documentation of ER and PR status from eastern India, having a modest number of patients and one of the earliest documentations using the latest ASCO/CAP interpretation guidelines. These findings resemble the data from the south and also reiterate the fact that majority of the Indian breast cancer patients are still ER and PR negative in spite of the changes in the interpretation guidelines.

Continuous measurement of elemental composition of ambient aerosol by induction-coupled plasma mass spectrometry.

There is presently no instrumentation that can provide (near) real time information on elemental composition of atmospheric aerosols. We describe an arrangement where air is sampled through a cyclone @30L/min with a 50% cutoff @ ~250nm. The particles deposit into a cup through which deionized water is continuously flowing. High purity HNO3 is added downstream and the mixed stream optionally flows through a quartz photo reactor (185nm, ~90°C, tR ~1.2min) and is aspirated by an induction coupled plasma mass spectrometer (ICP-MS). Comparative batch experiments in which samples were not photodigested at all or thermally digested off-line for an extended period indicated no statistically significant difference in the results. This observation agrees with early theoretical and experimental work. Some 22 elements were quantifiable (S/N > 10) at all times in the aerosol samples collected in our highly urban sampling location; an additional 4 elements were quantifiable at times of construction activity in the general area. Presently attained system limits of detection (LODs) are orders of magnitude higher than the instrumental LOD, both because of the purity of the acid and pump-induced contamination. These aspects can be vastly improved and will need to be improved to determine background concentrations.

Estimating the standardized mean difference with minimum risk: Maximizing accuracy and minimizing cost with sequential estimation.

The standardized mean difference is a widely used effect size measure. In this article, we develop a general theory for estimating the population standardized mean difference by minimizing both the mean square error of the estimator and the total sampling cost. Fixed sample size methods, when sample size is planned before the start of a study, cannot simultaneously minimize both the mean square error of the estimator and the total sampling cost. To overcome this limitation of the current state of affairs, this article develops a purely sequential sampling procedure, which provides an estimate of the sample size required to achieve a sufficiently accurate estimate with minimum expected sampling cost. Performance of the purely sequential procedure is examined via a simulation study to show that our analytic developments are highly accurate. Additionally, we provide freely available functions in R to implement the algorithm of the purely sequential procedure. (PsycINFO Database Record

Regional reporting of the incidence of Anaplastic Lymphoma Kinase mutation in 379 non-small-cell lung cancer patients from Kolkata: Using immunohistochemistry as the diagnostic modality in a significant subset.

CONTEXT: Regional epidemiology of anaplastic lymphoma kinase (ALK) mutation in non-small-cell lung cancer (NSCLC) is an unmet need in India, and so is the knowledge of its incidence based on immunohistochemistry (IHC). AIMS: Reporting the incidence of ALK mutation in NSCLC from Kolkata, incorporating IHC as the diagnostic modality in a considerable subset of patients. SUBJECTS AND METHODS: It is a retrospective observational study done on NSCLC patients with adenocarcinoma histology, unselected by epidermal growth factor receptor, whose samples were tested for ALK mutation status between March 1, 2013, and March 15, 2017. The study involved all cancer facilities in Kolkata, except Tata Medical Centre. Up to June 2015, the tests were done by fluorescence in situ hybridization (FISH) and from July 2015 to the end, tests were done using IHC, as per the standard testing guidelines existing during the respective time periods. Results were documented in a de-identified manner to analyze the incidence of ALK mutations. RESULTS: A total of 379 patients was tested for ALK mutations. March 2013 to June 2015, 200 (52.77%) patients were tested by FISH, 17 (8.5%) samples were unreportable and 4 patients [(2.19%) 4/183] tested positive for ALK mutations. From July 2015 to March 2017, 179 (47.22%) patients were tested by IHC, 9 (5.02%) samples were unreportable, and 10 patients [(5.88%) 10/170] tested positive for ALK mutations. Overall, 26 (6.8%) samples were unreportable and 14 [(3.9%) 14/353] patients tested positive for ALK mutations. CONCLUSIONS: The overall incidence of ALK mutation positive NSCLC in Kolkata is 3.9%. The incidence by IHC is 5.88% and by FISH is 2.19%, in the subset of patients tested by these two modalities respectively.