Dengue fever is a mosquito-borne viral infection that produces characteristic abnormalities in routine blood tests, yet these hematologic changes are typically analysed separately for each parameter rather than as a combined multivariate profile. This study investigated whether the joint hematologic profile of adult dengue patients in Bangladesh is systematically displaced from healthy adult reference values. We analysed a cohort of laboratory-confirmed adult dengue cases from a Bangladeshi hospital and focused on four core hematologic indices: haemoglobin, white blood cell count, platelet count, and platelet distribution width (PDW). External adult reference means were used to define a healthy location vector, and robust multivariate inference was carried out using the rank-based location test of Utts and Hettmansperger (1980). Sex-specific (male, female) and pooled (all adults) analyses were performed after careful data cleaning, outlier diagnostics, and checks of non-normality. Across all sex-specific and pooled analyses, the same multivariate profile emerged: haemoglobin, white-cell, and platelet levels were consistently lower than their healthy reference means, whereas PDW was higher, indicating greater platelet-size variability. The Utts–Hettmansperger test strongly rejected the null hypothesis of equality with the healthy reference vector in every analysis, documenting a large and coherent displacement of the dengue group in the four-dimensional hematologic space. Taken together, these results provide robust, distribution-free statistical evidence that adult dengue fever in Bangladesh is associated with a stable, biologically interpretable shift in core blood indices, integrating leukopenia, thrombocytopenia, and altered platelet morphology into a single multivariate summary. This study demonstrates that robust rank-based multivariate location tests can enhance traditional laboratory interpretation by quantifying the joint displacement of key blood indices in infectious-disease cohorts such as adult dengue.
Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.
In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.
The longitudinal balanced data profile was compiled into subgroup
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