Mohamad Adam Bujang

Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis

Sensitivity and specificity analysis is commonly used for screening and diagnostic tests. The main issue researchers face is to determine the sufficient sample sizes that are related with screening and diagnostic studies. Although the formula for sample size calculation is available but concerning majority of the researchers are not mathematicians or statisticians, hence, sample size calculation might not be easy for them. This review paper provides sample size tables with regards to sensitivity and specificity analysis. These tables were derived from formulation of sensitivity and specificity test using Power Analysis and Sample Size (PASS) software based on desired type I error, power and effect size. The approaches on how to use the tables were also discussed.

A comparison between convenience sampling versus systematic sampling in getting the true parameter in a population: Explore from a clinical database: The Audit Diabetes Control Management (ADCM) registry in 2009

Systematic sampling is an ideal sampling technique when sampling frame is not available. Dealing with patients recruitment make convenience sampling is a choice for most of clinicians. We compare results derived from analysis of eight subsamples with the true parameter from the population based on convenience sampling with first come and first serve basis and systematic sampling. This paper discussed at what extent convenience sampling with first come and first serve basis can be a valid sampling technique in comparison with conventional systematic sampling.

An audit of the statistics and the comparison with the parameter in the population

The sufficient sample size that is needed to closely estimate the statistics for particular parameters are use to be an issue. Although sample size might had been calculated referring to objective of the study, however, it is difficult to confirm whether the statistics are closed with the parameter for a particular population. All these while, guideline that uses a p-value less than 0.05 is widely used as inferential evidence. Therefore, this study had audited results that were analyzed from various sub sample and statistical analyses and had compared the results with the parameters in three different populations. Eight types of statistical analysis and eight sub samples for each statistical analysis were analyzed. Results found that the statistics were consistent and were closed to the parameters when the sample study covered at least 15% to 35% of population. Larger sample size is needed to estimate parameter that involve with categorical variables compared with numerical variables. Sample sizes with 30...

Forecasting the Incidence and Prevalence of Patients with End-Stage Renal Disease in Malaysia up to the Year 2040

Background. The incidence of patients with end-stage renal disease (ESRD) requiring dialysis has been growing rapidly in Malaysia from 18 per million population (pmp) in 1993 to 231 pmp in 2013. Objective. To forecast the incidence and prevalence of ESRD patients who will require dialysis treatment in Malaysia until 2040. Methodology. Univariate forecasting models using the number of new and current dialysis patients, by the Malaysian Dialysis and Transplant Registry from 1993 to 2013 were used. Four forecasting models were evaluated, and the model with the smallest error was selected for the prediction. Result. ARIMA (0, 2, 1) modeling with the lowest error was selected to predict both the incidence (RMSE = 135.50, MAPE = 2.85, and MAE = 87.71) and the prevalence (RMSE = 158.79, MAPE = 1.29, and MAE = 117.21) of dialysis patients. The estimated incidences of new dialysis patients in 2020 and 2040 are 10,208 and 19,418 cases, respectively, while the estimated prevalence is 51,269 and 106,249 cases. Conclusion. The growth of ESRD patients on dialysis in Malaysia can be expected to continue at an alarming rate. Effective steps to address and curb further increase in new patients requiring dialysis are urgently needed, in order to mitigate the expected financial and health catastrophes associated with the projected increase of such patients.

Validation of the Malay version of Diabetes Quality of Life (DQOL) Questionnaire for Adult Population with Type 2 Diabetes Mellitus

OBJECTIVE We aimed to validate the Malay version of Diabetes Quality of Life (DQOL) questionnaire for Malaysian adult population with type 2 diabetes mellitus (DM). METHODS This is a cross-sectional study to validate Malay version of DQOL among the adult diabetic patients. DQOL questionnaire has 46 items consist of three domains, namely Satisfaction Domain, Impact Domain and Worry Domain. Both forward and backward translations from the English version of DQOL into Malay version were performed. After the face validity of the Malay version was established, it was then pilot-tested. Finally, the validity and reliability of the final Malay version of DQOL questionnaire were evaluated. RESULTS There were 290 patients participated in this study with a mean (SD) age of 53.1 (10.0) years. The Cronbachs alpha coefficients of the overall items and the main domains were between 0.846 and 0.941. The Pearsons correlation coefficients for the three domains were between 0.228 and 0.451. HbA1C was found to be positively correlated with Impact Domain (P = 0.006). The Worry Domain was associated with diabetic retinopathy (P = 0.014) and nephropathy (P = 0.033). CONCLUSION The Malay version of diabetes quality of life (DQOL) questionnaire was found to be a valid and reliable survey instrument to be used for Malaysian adult patients with diabetes mellitus.

An Application of the Runs Test to Test for Randomness of Observations Obtained from a Clinical Survey in an Ordered Population

Runs test is a statistical procedure which determines whether a sequence of data within a given distribution have been derived with a random process or not. It may be applied to test the randomness of data in a survey that collect data from an ordered population. This article illustrates on method to perform a runs test and explains the rationale for performing it by providing some examples of how this test can be applied. The aim of this article was to describe on ways to use the runs test in a clinical survey from an ordered population to determine the degree of randomness in the sequence of subjects who are recruited within a sample obtained from the whole population. Clinical survey that involves an ordered population usually collects data from subjects who have been recruited by a consecutive sampling method. Therefore, this study recommends that the degree of randomness in the sequence of selected variable(s) obtained from consecutive sampling is necessary to be tested from a pilot study to ensure random data collection in the study.

Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data

Background Different study designs and population size may require different sample size for logistic regression. This study aims to propose sample size guidelines for logistic regression based on observational studies with large population. Methods We estimated the minimum sample size required based on evaluation from real clinical data to evaluate the accuracy between statistics derived and the actual parameters. Nagelkerke r-squared and coefficients derived were compared with their respective parameters. Results With a minimum sample size of 500, results showed that the differences between the sample estimates and the population was sufficiently small. Based on an audit from a medium size of population, the differences were within ± 0.5 for coefficients and ± 0.02 for Nagelkerke r-squared. Meanwhile for large population, the differences are within ± 1.0 for coefficients and ± 0.02 for Nagelkerke r-squared. Conclusions For observational studies with large population size that involve logistic regression in the analysis, taking a minimum sample size of 500 is necessary to derive the statistics that represent the parameters. The other recommended rules of thumb are EPV of 50 and formula; n = 100 + 50i where i refers to number of independent variables in the final model.

Validation of Malay Version of Body Self-Image Questionnaire-Short orm among Malaysian Young Adults

Background Body self-image questionnaire-short form (BSIQ-SF) is developed to measure body image perceptions. Due to the cultural, language and environmental differences between western and eastern population, the validity and reliability need to be established. The aim of this study was to determine validity and reliability of Malay version BSIQ-SF. Methods A cross-sectional study involved web-based survey was employed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) was performed using SPSS version 22 and Mplus 7.3. Results There were 688 young adults in Malaysia with mean age of 23.67 (SD = 0.188) and mean body mass index (BMI) of 23.34 (SD = 0.27) participated in the study. Exploratory factor analysis performed and the number domains reduced from nine to four, namely ‘Negative Affect’, ‘Attractiveness Evaluation’, ‘Physical Functionality Awareness’ and ‘Height Dissatisfaction’. CFA further confirmed the structure of the model with adequate goodness-of-fit values [CFI = 0.927, TLI = 0.913, SRMR = 0.075, RMSEA = 0.053 (95% CI: 0.047, 0.060)]. Conclusion The revised 21-item of the Malay version BSIQ-SF was a valid and reliable instrument to measure body image perceptions among Malaysian young adults.

Importance of Adopting BMI Classifications Using Public Health Action Points to Delineate Obstetric Risk Factors Resulting in Worsening Obstetric Outcomes Among Asian Population

Aims The aim of this study is to compare obstetric outcomes between overweight and class 1 obesity among pregnant women in their first pregnancy based on WHO’s BMI cut-offs and the potential public health action points identified by WHO expert consultations specific for high-risk population such as Asians.MethodsThis is a retrospective cohort review of data obtained from the Malaysian National Obstetrics and Gynaecology Registry between the year 2010 and year 2012. All women in their first pregnancy with a booking BMI in their first trimester were included in this study. The association between BMI classifications as defined by the WHO cut-offs and the potential public health action points identified by WHO expert consultations towards adverse obstetric outcomes was compared.ResultsA total of 88,837 pregnant women were included in this study. We noted that the risk of adverse obstetric outcomes was significantly higher using the public health action points identified by WHO expert consultations even among the overweight group as the risk of stillbirths was (OR 1.2; 95% CI 1.0,1.4), shoulder dystocia (OR 1.9; 95% CI 1.2,2.9), foetal macrosomia (OR 1.8; 95% CI 1.6,2.0), caesarean section (OR 1.9; 95% CI 1.8,2.0) and assisted conception (OR 1.9; 95% CI 1.6,2.1).ConclusionA specifically lower BMI references based on the potential public health action points for BMI classifications were a more sensitive predictor of adverse obstetric outcomes, and we recommend the use of these references in pregnancy especially among Asian population.